For this study, we took advantage of the ESA CCI soil moisture combined data set, as it offers a better option as referred in other soil moisture measuring systems (Entekhabi et al. 2014). Although data is available from November 1978, high uncertainty is reported until 1991 (Dorigo et al. 2015), thus we initially decided to download data from January 1995 to December 2015. In addition, validation data from the North America Soil Moisture Database (NASMD) over the region of interest is available from January 1996 to December 2012 (252 Montlhy information layers).
As original ESA CCI data was acquired for a global coverage, soil moisture daily values were cropped to the conterminous territory of the United States (CONUS). Monthly stacks were generated in order to calculate basic metrics (including mean and median values) for each month of the study period, obtaining single values to describe soil moisture monthly behavior over each pixel. Finally, data were cropped to the region of interest over Oklahoma and surrounding areas.
In order to explore relationships between soil moisture and some physical variables, ancillary layers were generated for Precipitation, Maximum Air Temperature, Minimum Air Temperature, Soil Texture, and Topographic Wetness index. These selected variables are known to work as drivers for water input in soil, thus its importance in soil moisture inference (Koster and Suarez 2001; Seneviratne et al. 2010; Entekhabi et al. 2014).
Meteorological data was acquired at 1-km spatial resolution monthly layers produced by the Daily Surface Weather and Climatological Summaries (DAYMET) (Thornton et al. 2018). Total monthly precipitation, as well as monthly average air temperature raster layers from January 1996 to December 2012 were cropped to the region of interest, projected to the WGS84 Lat-Long coordinate system and resampled to 0.25 degrees by means of nearest neighbor method (ngb) (J. A. Parker, Kenyon, and Troxel 1983).
Soil texture data was obtained from the US soil survey geographic database (USDA 2016) at state-level for Arkansas, Colorado, Kansas, Missouri, New Mexico, Oklahoma and Texas. Original texture classes from each state classification were grouped in four major categories (Coarse, Medium, Medium-fine and Fine) regarding the texture triangle from US Department of Agriculture (USDA) and its modification proposed by (Bertermann et al. 2013).
Soil texture aggregation in four general classes a resampling of data to 0.25 degrees over the region of interest
Besides the more explicit topographic features (primary attributes) , the influence of topography over the soil properties and water distribution in the landscape can be expressed by means of wetness indexes derived from compound topographic attributes (Wilson and Gallant 2000). The most widely used index to describe flow and concentration of water in soil is the topographic wetness index (TWI) (Beven and Kirkby 1979).
Soil texture aggregation in four general classes a resampling of data to 0.25 degrees over the region of interest
To calculate TWI, we used a digital elevation model at 250m pixel size, generated by (Hengl, Heuvelink, and Stein 2004) as input and we applied a basic terrain analysis function from SAGA GIS tools, which generates a set of topographic parameters, including TWI (Conrad et al. 2015). The output was then resampled using nearest neighbor method (J. A. Parker, Kenyon, and Troxel 1983), to 0.25 degrees pixel size to match with soil moisture monthly layers as well as the other ancillary layers generated previously.
Topographic Index over region of interest. 0.25x0.25 degrees, dimensionless units
In order to define the covariates used to model soil moisture, correlation analysis, both temporal and spatial were performed regarding ancillary layer of information generated in the previous step. As shown in Figure below, each valid pixel from in every monthly layer of soil moisture information in the region of interest was compared against the same pixel in every covariate layer. Monthly mean and median values from soil moisture layers were used for correlation analysis.
Conceptual temporal and correlation analysis between soil moisture and covariates (e.g. precipitation)
For temporal correlation analysis, the set of data was extracted from each valid pixel along the 252 monthly layers of soil moisture (both Mean and Median monthly values), as well as the correspondent pixels from precipitation, minimum temperature, maximum temperature, this way, generating time series of all data for each pixel. This means, each pixel might have a different number of valid values in the time series, no greater than 252. Correlation values were calculated for each time series of 252 monthly values, in each of the 741 pixels with the region of interest. Soil texture and topographic wetness index analysis were not performed as their values are static along time period.
TWI and Soil Texture are not used in temporal correlation analysis, as these values are static in every month across the study period.
setwd("e:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
SoilMoisture_MEAN <- read.csv("SoilMoisture_region_interest_MEAN_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
SoilMoisture_MEAN[1:4] <- NULL
SoilMoisture_MEAN <- replace(SoilMoisture_MEAN, SoilMoisture_MEAN == -9999, NA)
SoilMoisture_MEAN <- t(SoilMoisture_MEAN)
SoilMoisture_MEAN <- as.data.frame(SoilMoisture_MEAN)
names(SoilMoisture_MEAN) <- paste(c(1:741))
Precipitation <- read.csv("Daymet_prcp_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Precipitation[1:4] <- NULL
Precipitation <- replace(Precipitation, Precipitation == -9999, NA)
Precipitation <- t(Precipitation)
Precipitation <- as.data.frame(Precipitation)
names(Precipitation) <- paste(c(1:741))
base_matrix <- read.csv("pixels.csv", header = TRUE, sep = ',', dec = '.')
final_temporal_correlation <- base_matrix
names(final_temporal_correlation)[4] <- paste('Corr_Precipitation')
for (i in 1:741) {
correlation <- cor(SoilMoisture_MEAN[i], Precipitation[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEAN[i]))
number_values <- 252 - number_values
final_temporal_correlation[i,4] <- correlation
final_temporal_correlation[i,5] <- number_values
}
mean_temp_corr_meanSM_Prcp <- round(mean(final_temporal_correlation$Corr_Precipitation), digits = 3)
kable(final_temporal_correlation, caption = 'Temporal Correlation Mean Soil Moistre and Precipitation', digits = 3)
Pixel | X | Y | Corr_Precipitation | Number_of_pairs |
---|---|---|---|---|
1 | -103.375 | 37.625 | 0.375 | 252 |
2 | -103.125 | 37.625 | 0.398 | 232 |
3 | -102.875 | 37.625 | 0.473 | 211 |
4 | -102.625 | 37.625 | 0.375 | 234 |
5 | -102.375 | 37.625 | 0.355 | 251 |
6 | -102.125 | 37.625 | 0.442 | 221 |
7 | -101.875 | 37.625 | 0.434 | 236 |
8 | -101.625 | 37.625 | 0.388 | 252 |
9 | -101.375 | 37.625 | 0.407 | 252 |
10 | -101.125 | 37.625 | 0.362 | 252 |
11 | -100.875 | 37.625 | 0.384 | 251 |
12 | -100.625 | 37.625 | 0.364 | 249 |
13 | -100.375 | 37.625 | 0.347 | 248 |
14 | -100.125 | 37.625 | 0.410 | 225 |
15 | -99.875 | 37.625 | 0.352 | 247 |
16 | -99.625 | 37.625 | 0.369 | 247 |
17 | -99.375 | 37.625 | 0.379 | 247 |
18 | -99.125 | 37.625 | 0.427 | 234 |
19 | -98.875 | 37.625 | 0.370 | 232 |
20 | -98.625 | 37.625 | 0.278 | 249 |
21 | -98.375 | 37.625 | 0.235 | 251 |
22 | -98.125 | 37.625 | 0.204 | 251 |
23 | -97.875 | 37.625 | 0.119 | 236 |
24 | -97.625 | 37.625 | 0.142 | 250 |
25 | -97.375 | 37.625 | 0.090 | 249 |
26 | -97.125 | 37.625 | 0.139 | 247 |
27 | -96.875 | 37.625 | 0.224 | 247 |
28 | -96.625 | 37.625 | 0.186 | 243 |
29 | -96.375 | 37.625 | 0.231 | 241 |
30 | -96.125 | 37.625 | 0.174 | 241 |
31 | -95.875 | 37.625 | 0.110 | 243 |
32 | -95.625 | 37.625 | 0.107 | 244 |
33 | -95.375 | 37.625 | 0.127 | 242 |
34 | -95.125 | 37.625 | 0.165 | 241 |
35 | -94.875 | 37.625 | 0.217 | 245 |
36 | -94.625 | 37.625 | 0.189 | 241 |
37 | -94.375 | 37.625 | 0.222 | 238 |
38 | -94.125 | 37.625 | 0.270 | 233 |
39 | -93.875 | 37.625 | 0.318 | 233 |
40 | -103.375 | 37.375 | 0.381 | 251 |
41 | -103.125 | 37.375 | 0.361 | 251 |
42 | -102.875 | 37.375 | 0.478 | 233 |
43 | -102.625 | 37.375 | 0.488 | 235 |
44 | -102.375 | 37.375 | 0.427 | 251 |
45 | -102.125 | 37.375 | 0.424 | 250 |
46 | -101.875 | 37.375 | 0.408 | 251 |
47 | -101.625 | 37.375 | 0.502 | 241 |
48 | -101.375 | 37.375 | 0.273 | 242 |
49 | -101.125 | 37.375 | 0.285 | 241 |
50 | -100.875 | 37.375 | 0.240 | 240 |
51 | -100.625 | 37.375 | 0.227 | 234 |
52 | -100.375 | 37.375 | 0.269 | 227 |
53 | -100.125 | 37.375 | 0.411 | 244 |
54 | -99.875 | 37.375 | 0.244 | 203 |
55 | -99.625 | 37.375 | 0.435 | 231 |
56 | -99.375 | 37.375 | 0.437 | 231 |
57 | -99.125 | 37.375 | 0.367 | 248 |
58 | -98.875 | 37.375 | 0.304 | 245 |
59 | -98.625 | 37.375 | 0.315 | 248 |
60 | -98.375 | 37.375 | 0.273 | 249 |
61 | -98.125 | 37.375 | 0.197 | 221 |
62 | -97.875 | 37.375 | 0.206 | 231 |
63 | -97.625 | 37.375 | 0.186 | 249 |
64 | -97.375 | 37.375 | 0.154 | 251 |
65 | -97.125 | 37.375 | 0.138 | 248 |
66 | -96.875 | 37.375 | 0.164 | 245 |
67 | -96.625 | 37.375 | 0.223 | 243 |
68 | -96.375 | 37.375 | 0.242 | 241 |
69 | -96.125 | 37.375 | 0.195 | 241 |
70 | -95.875 | 37.375 | 0.136 | 243 |
71 | -95.625 | 37.375 | 0.144 | 243 |
72 | -95.375 | 37.375 | 0.226 | 228 |
73 | -95.125 | 37.375 | 0.230 | 236 |
74 | -94.875 | 37.375 | 0.227 | 241 |
75 | -94.625 | 37.375 | 0.197 | 237 |
76 | -94.375 | 37.375 | 0.215 | 236 |
77 | -94.125 | 37.375 | 0.236 | 233 |
78 | -93.875 | 37.375 | 0.274 | 237 |
79 | -103.375 | 37.125 | 0.464 | 237 |
80 | -103.125 | 37.125 | 0.500 | 237 |
81 | -102.875 | 37.125 | 0.485 | 251 |
82 | -102.625 | 37.125 | 0.486 | 251 |
83 | -102.375 | 37.125 | 0.467 | 251 |
84 | -102.125 | 37.125 | 0.468 | 251 |
85 | -101.875 | 37.125 | 0.391 | 251 |
86 | -101.625 | 37.125 | 0.472 | 242 |
87 | -101.375 | 37.125 | 0.368 | 251 |
88 | -101.125 | 37.125 | 0.377 | 252 |
89 | -100.875 | 37.125 | 0.429 | 250 |
90 | -100.625 | 37.125 | 0.376 | 252 |
91 | -100.375 | 37.125 | 0.395 | 250 |
92 | -100.125 | 37.125 | 0.396 | 225 |
93 | -99.875 | 37.125 | 0.380 | 242 |
94 | -99.625 | 37.125 | 0.391 | 244 |
95 | -99.375 | 37.125 | 0.381 | 243 |
96 | -99.125 | 37.125 | 0.340 | 247 |
97 | -98.875 | 37.125 | 0.334 | 245 |
98 | -98.625 | 37.125 | 0.315 | 248 |
99 | -98.375 | 37.125 | 0.299 | 221 |
100 | -98.125 | 37.125 | 0.278 | 228 |
101 | -97.875 | 37.125 | 0.277 | 250 |
102 | -97.625 | 37.125 | 0.211 | 250 |
103 | -97.375 | 37.125 | 0.179 | 249 |
104 | -97.125 | 37.125 | 0.140 | 251 |
105 | -96.875 | 37.125 | 0.111 | 247 |
106 | -96.625 | 37.125 | 0.240 | 242 |
107 | -96.375 | 37.125 | 0.257 | 240 |
108 | -96.125 | 37.125 | 0.254 | 241 |
109 | -95.875 | 37.125 | 0.205 | 243 |
110 | -95.625 | 37.125 | 0.222 | 247 |
111 | -95.375 | 37.125 | 0.191 | 239 |
112 | -95.125 | 37.125 | 0.186 | 244 |
113 | -94.875 | 37.125 | 0.299 | 226 |
114 | -94.625 | 37.125 | 0.221 | 241 |
115 | -94.375 | 37.125 | 0.214 | 236 |
116 | -94.125 | 37.125 | 0.224 | 243 |
117 | -93.875 | 37.125 | 0.196 | 242 |
118 | -103.375 | 36.875 | 0.392 | 252 |
119 | -103.125 | 36.875 | 0.395 | 251 |
120 | -102.875 | 36.875 | 0.419 | 251 |
121 | -102.625 | 36.875 | 0.400 | 250 |
122 | -102.375 | 36.875 | 0.382 | 251 |
123 | -102.125 | 36.875 | 0.396 | 251 |
124 | -101.875 | 36.875 | 0.432 | 252 |
125 | -101.625 | 36.875 | 0.353 | 252 |
126 | -101.375 | 36.875 | 0.304 | 252 |
127 | -101.125 | 36.875 | 0.397 | 252 |
128 | -100.875 | 36.875 | 0.394 | 252 |
129 | -100.625 | 36.875 | 0.369 | 250 |
130 | -100.375 | 36.875 | 0.355 | 249 |
131 | -100.125 | 36.875 | 0.322 | 250 |
132 | -99.875 | 36.875 | 0.346 | 246 |
133 | -99.625 | 36.875 | 0.297 | 249 |
134 | -99.375 | 36.875 | 0.309 | 246 |
135 | -99.125 | 36.875 | 0.286 | 248 |
136 | -98.875 | 36.875 | 0.301 | 250 |
137 | -98.625 | 36.875 | 0.289 | 241 |
138 | -98.375 | 36.875 | 0.373 | 247 |
139 | -98.125 | 36.875 | 0.337 | 250 |
140 | -97.875 | 36.875 | 0.312 | 249 |
141 | -97.625 | 36.875 | 0.240 | 250 |
142 | -97.375 | 36.875 | 0.175 | 251 |
143 | -97.125 | 36.875 | 0.115 | 250 |
144 | -96.875 | 36.875 | 0.062 | 250 |
145 | -96.625 | 36.875 | 0.130 | 246 |
146 | -96.375 | 36.875 | 0.159 | 246 |
147 | -96.125 | 36.875 | 0.207 | 246 |
148 | -95.875 | 36.875 | 0.190 | 246 |
149 | -95.625 | 36.875 | 0.181 | 247 |
150 | -95.375 | 36.875 | 0.140 | 245 |
151 | -95.125 | 36.875 | 0.129 | 247 |
152 | -94.875 | 36.875 | 0.159 | 244 |
153 | -94.625 | 36.875 | 0.133 | 244 |
154 | -94.375 | 36.875 | 0.145 | 244 |
155 | -94.125 | 36.875 | 0.194 | 238 |
156 | -93.875 | 36.875 | 0.182 | 241 |
157 | -103.375 | 36.625 | 0.392 | 252 |
158 | -103.125 | 36.625 | 0.406 | 252 |
159 | -102.875 | 36.625 | 0.398 | 251 |
160 | -102.625 | 36.625 | 0.391 | 250 |
161 | -102.375 | 36.625 | 0.408 | 251 |
162 | -102.125 | 36.625 | 0.441 | 251 |
163 | -101.875 | 36.625 | 0.403 | 252 |
164 | -101.625 | 36.625 | 0.403 | 252 |
165 | -101.375 | 36.625 | 0.370 | 251 |
166 | -101.125 | 36.625 | 0.399 | 249 |
167 | -100.875 | 36.625 | 0.368 | 251 |
168 | -100.625 | 36.625 | 0.360 | 251 |
169 | -100.375 | 36.625 | 0.324 | 251 |
170 | -100.125 | 36.625 | 0.328 | 249 |
171 | -99.875 | 36.625 | 0.361 | 249 |
172 | -99.625 | 36.625 | 0.306 | 250 |
173 | -99.375 | 36.625 | 0.287 | 251 |
174 | -99.125 | 36.625 | 0.293 | 247 |
175 | -98.875 | 36.625 | 0.278 | 250 |
176 | -98.625 | 36.625 | 0.345 | 250 |
177 | -98.375 | 36.625 | 0.388 | 250 |
178 | -98.125 | 36.625 | 0.332 | 251 |
179 | -97.875 | 36.625 | 0.311 | 250 |
180 | -97.625 | 36.625 | 0.272 | 250 |
181 | -97.375 | 36.625 | 0.229 | 250 |
182 | -97.125 | 36.625 | 0.129 | 249 |
183 | -96.875 | 36.625 | 0.133 | 250 |
184 | -96.625 | 36.625 | 0.169 | 247 |
185 | -96.375 | 36.625 | 0.186 | 247 |
186 | -96.125 | 36.625 | 0.130 | 239 |
187 | -95.875 | 36.625 | 0.119 | 246 |
188 | -95.625 | 36.625 | 0.107 | 245 |
189 | -95.375 | 36.625 | 0.125 | 246 |
190 | -95.125 | 36.625 | 0.139 | 245 |
191 | -94.875 | 36.625 | 0.118 | 245 |
192 | -94.625 | 36.625 | 0.127 | 245 |
193 | -94.375 | 36.625 | 0.125 | 245 |
194 | -94.125 | 36.625 | 0.200 | 234 |
195 | -93.875 | 36.625 | 0.230 | 242 |
196 | -103.375 | 36.375 | 0.434 | 251 |
197 | -103.125 | 36.375 | 0.409 | 251 |
198 | -102.875 | 36.375 | 0.438 | 251 |
199 | -102.625 | 36.375 | 0.424 | 252 |
200 | -102.375 | 36.375 | 0.429 | 252 |
201 | -102.125 | 36.375 | 0.455 | 252 |
202 | -101.875 | 36.375 | 0.424 | 252 |
203 | -101.625 | 36.375 | 0.438 | 251 |
204 | -101.375 | 36.375 | 0.422 | 252 |
205 | -101.125 | 36.375 | 0.380 | 250 |
206 | -100.875 | 36.375 | 0.307 | 247 |
207 | -100.625 | 36.375 | 0.330 | 250 |
208 | -100.375 | 36.375 | 0.326 | 251 |
209 | -100.125 | 36.375 | 0.326 | 252 |
210 | -99.875 | 36.375 | 0.375 | 249 |
211 | -99.625 | 36.375 | 0.332 | 250 |
212 | -99.375 | 36.375 | 0.339 | 250 |
213 | -99.125 | 36.375 | 0.318 | 248 |
214 | -98.875 | 36.375 | 0.345 | 249 |
215 | -98.625 | 36.375 | 0.351 | 249 |
216 | -98.375 | 36.375 | 0.391 | 249 |
217 | -98.125 | 36.375 | 0.313 | 242 |
218 | -97.875 | 36.375 | 0.300 | 250 |
219 | -97.625 | 36.375 | 0.280 | 249 |
220 | -97.375 | 36.375 | 0.209 | 249 |
221 | -97.125 | 36.375 | 0.207 | 247 |
222 | -96.875 | 36.375 | 0.171 | 248 |
223 | -96.625 | 36.375 | 0.183 | 247 |
224 | -96.375 | 36.375 | 0.165 | 247 |
225 | -96.125 | 36.375 | 0.158 | 246 |
226 | -95.875 | 36.375 | 0.110 | 247 |
227 | -95.625 | 36.375 | 0.128 | 246 |
228 | -95.375 | 36.375 | 0.149 | 247 |
229 | -95.125 | 36.375 | 0.140 | 247 |
230 | -94.875 | 36.375 | 0.208 | 236 |
231 | -94.625 | 36.375 | 0.114 | 244 |
232 | -94.375 | 36.375 | 0.094 | 244 |
233 | -94.125 | 36.375 | 0.121 | 244 |
234 | -93.875 | 36.375 | 0.143 | 240 |
235 | -103.375 | 36.125 | 0.496 | 247 |
236 | -103.125 | 36.125 | 0.445 | 252 |
237 | -102.875 | 36.125 | 0.434 | 252 |
238 | -102.625 | 36.125 | 0.409 | 252 |
239 | -102.375 | 36.125 | 0.432 | 252 |
240 | -102.125 | 36.125 | 0.452 | 252 |
241 | -101.875 | 36.125 | 0.380 | 252 |
242 | -101.625 | 36.125 | 0.382 | 251 |
243 | -101.375 | 36.125 | 0.358 | 252 |
244 | -101.125 | 36.125 | 0.354 | 251 |
245 | -100.875 | 36.125 | 0.360 | 251 |
246 | -100.625 | 36.125 | 0.356 | 251 |
247 | -100.375 | 36.125 | 0.219 | 234 |
248 | -100.125 | 36.125 | 0.354 | 247 |
249 | -99.875 | 36.125 | 0.380 | 247 |
250 | -99.625 | 36.125 | 0.366 | 248 |
251 | -99.375 | 36.125 | 0.392 | 250 |
252 | -99.125 | 36.125 | 0.309 | 249 |
253 | -98.875 | 36.125 | 0.357 | 249 |
254 | -98.625 | 36.125 | 0.392 | 249 |
255 | -98.375 | 36.125 | 0.335 | 239 |
256 | -98.125 | 36.125 | 0.345 | 242 |
257 | -97.875 | 36.125 | 0.285 | 240 |
258 | -97.625 | 36.125 | 0.210 | 238 |
259 | -97.375 | 36.125 | 0.126 | 232 |
260 | -97.125 | 36.125 | 0.148 | 248 |
261 | -96.875 | 36.125 | 0.223 | 248 |
262 | -96.625 | 36.125 | 0.201 | 248 |
263 | -96.375 | 36.125 | 0.180 | 248 |
264 | -96.125 | 36.125 | 0.136 | 248 |
265 | -95.875 | 36.125 | 0.085 | 247 |
266 | -95.625 | 36.125 | 0.105 | 248 |
267 | -95.375 | 36.125 | 0.134 | 243 |
268 | -95.125 | 36.125 | 0.184 | 248 |
269 | -94.875 | 36.125 | 0.164 | 246 |
270 | -94.625 | 36.125 | 0.135 | 244 |
271 | -94.375 | 36.125 | 0.106 | 243 |
272 | -94.125 | 36.125 | 0.106 | 236 |
273 | -93.875 | 36.125 | 0.156 | 239 |
274 | -103.375 | 35.875 | 0.432 | 252 |
275 | -103.125 | 35.875 | 0.489 | 240 |
276 | -102.875 | 35.875 | 0.452 | 252 |
277 | -102.625 | 35.875 | 0.462 | 252 |
278 | -102.375 | 35.875 | 0.462 | 252 |
279 | -102.125 | 35.875 | 0.430 | 252 |
280 | -101.875 | 35.875 | 0.382 | 251 |
281 | -101.625 | 35.875 | 0.383 | 252 |
282 | -101.375 | 35.875 | 0.373 | 252 |
283 | -101.125 | 35.875 | 0.378 | 251 |
284 | -100.875 | 35.875 | 0.352 | 250 |
285 | -100.625 | 35.875 | 0.297 | 233 |
286 | -100.375 | 35.875 | 0.310 | 229 |
287 | -100.125 | 35.875 | 0.340 | 251 |
288 | -99.875 | 35.875 | 0.330 | 248 |
289 | -99.625 | 35.875 | 0.385 | 251 |
290 | -99.375 | 35.875 | 0.398 | 252 |
291 | -99.125 | 35.875 | 0.369 | 249 |
292 | -98.875 | 35.875 | 0.398 | 249 |
293 | -98.625 | 35.875 | 0.346 | 236 |
294 | -98.375 | 35.875 | 0.346 | 238 |
295 | -98.125 | 35.875 | 0.351 | 250 |
296 | -97.875 | 35.875 | 0.290 | 249 |
297 | -97.625 | 35.875 | 0.176 | 249 |
298 | -97.375 | 35.875 | 0.133 | 249 |
299 | -97.125 | 35.875 | 0.108 | 248 |
300 | -96.875 | 35.875 | 0.135 | 248 |
301 | -96.625 | 35.875 | 0.145 | 248 |
302 | -96.375 | 35.875 | 0.271 | 248 |
303 | -96.125 | 35.875 | 0.183 | 247 |
304 | -95.875 | 35.875 | 0.147 | 247 |
305 | -95.625 | 35.875 | 0.159 | 247 |
306 | -95.375 | 35.875 | 0.158 | 248 |
307 | -95.125 | 35.875 | 0.109 | 247 |
308 | -94.875 | 35.875 | 0.153 | 247 |
309 | -94.625 | 35.875 | 0.210 | 246 |
310 | -94.375 | 35.875 | 0.140 | 240 |
311 | -94.125 | 35.875 | 0.363 | 232 |
312 | -93.875 | 35.875 | 0.371 | 231 |
313 | -103.375 | 35.625 | 0.403 | 252 |
314 | -103.125 | 35.625 | 0.487 | 246 |
315 | -102.875 | 35.625 | 0.489 | 246 |
316 | -102.625 | 35.625 | 0.452 | 248 |
317 | -102.375 | 35.625 | 0.484 | 252 |
318 | -102.125 | 35.625 | 0.375 | 252 |
319 | -101.875 | 35.625 | 0.396 | 252 |
320 | -101.625 | 35.625 | 0.356 | 252 |
321 | -101.375 | 35.625 | 0.416 | 252 |
322 | -101.125 | 35.625 | 0.377 | 252 |
323 | -100.875 | 35.625 | 0.382 | 252 |
324 | -100.625 | 35.625 | 0.370 | 252 |
325 | -100.375 | 35.625 | 0.340 | 252 |
326 | -100.125 | 35.625 | 0.241 | 230 |
327 | -99.875 | 35.625 | 0.349 | 252 |
328 | -99.625 | 35.625 | 0.379 | 249 |
329 | -99.375 | 35.625 | 0.367 | 251 |
330 | -99.125 | 35.625 | 0.364 | 251 |
331 | -98.875 | 35.625 | 0.335 | 243 |
332 | -98.625 | 35.625 | 0.360 | 238 |
333 | -98.375 | 35.625 | 0.361 | 237 |
334 | -98.125 | 35.625 | 0.367 | 250 |
335 | -97.875 | 35.625 | 0.197 | 239 |
336 | -97.625 | 35.625 | 0.110 | 251 |
337 | -97.375 | 35.625 | 0.081 | 251 |
338 | -97.125 | 35.625 | 0.108 | 248 |
339 | -96.875 | 35.625 | 0.150 | 248 |
340 | -96.625 | 35.625 | 0.162 | 249 |
341 | -96.375 | 35.625 | 0.243 | 249 |
342 | -96.125 | 35.625 | 0.218 | 248 |
343 | -95.875 | 35.625 | 0.129 | 251 |
344 | -95.625 | 35.625 | 0.148 | 252 |
345 | -95.375 | 35.625 | 0.164 | 251 |
346 | -95.125 | 35.625 | 0.175 | 250 |
347 | -94.875 | 35.625 | 0.132 | 248 |
348 | -94.625 | 35.625 | 0.127 | 247 |
349 | -94.375 | 35.625 | 0.184 | 248 |
350 | -94.125 | 35.625 | 0.361 | 236 |
351 | -93.875 | 35.625 | 0.347 | 238 |
352 | -103.375 | 35.375 | 0.396 | 252 |
353 | -103.125 | 35.375 | 0.424 | 244 |
354 | -102.875 | 35.375 | 0.452 | 241 |
355 | -102.625 | 35.375 | 0.439 | 244 |
356 | -102.375 | 35.375 | 0.433 | 252 |
357 | -102.125 | 35.375 | 0.340 | 252 |
358 | -101.875 | 35.375 | 0.346 | 252 |
359 | -101.625 | 35.375 | 0.318 | 250 |
360 | -101.375 | 35.375 | 0.432 | 252 |
361 | -101.125 | 35.375 | 0.406 | 252 |
362 | -100.875 | 35.375 | 0.404 | 252 |
363 | -100.625 | 35.375 | 0.386 | 251 |
364 | -100.375 | 35.375 | 0.334 | 234 |
365 | -100.125 | 35.375 | 0.381 | 252 |
366 | -99.875 | 35.375 | 0.412 | 252 |
367 | -99.625 | 35.375 | 0.371 | 251 |
368 | -99.375 | 35.375 | 0.361 | 252 |
369 | -99.125 | 35.375 | 0.373 | 251 |
370 | -98.875 | 35.375 | 0.325 | 248 |
371 | -98.625 | 35.375 | 0.374 | 242 |
372 | -98.375 | 35.375 | 0.358 | 250 |
373 | -98.125 | 35.375 | 0.330 | 249 |
374 | -97.875 | 35.375 | 0.168 | 235 |
375 | -97.625 | 35.375 | 0.119 | 251 |
376 | -97.375 | 35.375 | 0.078 | 250 |
377 | -97.125 | 35.375 | 0.116 | 248 |
378 | -96.875 | 35.375 | 0.160 | 248 |
379 | -96.625 | 35.375 | 0.183 | 248 |
380 | -96.375 | 35.375 | 0.192 | 249 |
381 | -96.125 | 35.375 | 0.212 | 248 |
382 | -95.875 | 35.375 | 0.177 | 248 |
383 | -95.625 | 35.375 | 0.157 | 238 |
384 | -95.375 | 35.375 | 0.193 | 247 |
385 | -95.125 | 35.375 | 0.155 | 248 |
386 | -94.875 | 35.375 | 0.167 | 248 |
387 | -94.625 | 35.375 | 0.179 | 249 |
388 | -94.375 | 35.375 | 0.165 | 240 |
389 | -94.125 | 35.375 | 0.217 | 248 |
390 | -93.875 | 35.375 | 0.393 | 234 |
391 | -103.375 | 35.125 | 0.497 | 246 |
392 | -103.125 | 35.125 | 0.471 | 243 |
393 | -102.875 | 35.125 | 0.445 | 243 |
394 | -102.625 | 35.125 | 0.503 | 245 |
395 | -102.375 | 35.125 | 0.480 | 252 |
396 | -102.125 | 35.125 | 0.389 | 252 |
397 | -101.875 | 35.125 | 0.375 | 250 |
398 | -101.625 | 35.125 | 0.387 | 250 |
399 | -101.375 | 35.125 | 0.473 | 252 |
400 | -101.125 | 35.125 | 0.422 | 252 |
401 | -100.875 | 35.125 | 0.414 | 252 |
402 | -100.625 | 35.125 | 0.438 | 252 |
403 | -100.375 | 35.125 | 0.421 | 252 |
404 | -100.125 | 35.125 | 0.427 | 252 |
405 | -99.875 | 35.125 | 0.419 | 252 |
406 | -99.625 | 35.125 | 0.388 | 252 |
407 | -99.375 | 35.125 | 0.377 | 251 |
408 | -99.125 | 35.125 | 0.401 | 252 |
409 | -98.875 | 35.125 | 0.389 | 249 |
410 | -98.625 | 35.125 | 0.330 | 238 |
411 | -98.375 | 35.125 | 0.330 | 235 |
412 | -98.125 | 35.125 | 0.328 | 251 |
413 | -97.875 | 35.125 | 0.295 | 250 |
414 | -97.625 | 35.125 | 0.218 | 250 |
415 | -97.375 | 35.125 | 0.182 | 250 |
416 | -97.125 | 35.125 | 0.215 | 252 |
417 | -96.875 | 35.125 | 0.216 | 251 |
418 | -96.625 | 35.125 | 0.174 | 252 |
419 | -96.375 | 35.125 | 0.188 | 251 |
420 | -96.125 | 35.125 | 0.272 | 251 |
421 | -95.875 | 35.125 | 0.209 | 252 |
422 | -95.625 | 35.125 | 0.175 | 243 |
423 | -95.375 | 35.125 | 0.246 | 249 |
424 | -95.125 | 35.125 | 0.260 | 245 |
425 | -94.875 | 35.125 | 0.266 | 250 |
426 | -94.625 | 35.125 | 0.241 | 248 |
427 | -94.375 | 35.125 | 0.228 | 247 |
428 | -94.125 | 35.125 | 0.370 | 241 |
429 | -93.875 | 35.125 | 0.355 | 240 |
430 | -103.375 | 34.875 | 0.457 | 252 |
431 | -103.125 | 34.875 | 0.475 | 250 |
432 | -102.875 | 34.875 | 0.501 | 252 |
433 | -102.625 | 34.875 | 0.531 | 244 |
434 | -102.375 | 34.875 | 0.522 | 252 |
435 | -102.125 | 34.875 | 0.450 | 252 |
436 | -101.875 | 34.875 | 0.423 | 252 |
437 | -101.625 | 34.875 | 0.451 | 251 |
438 | -101.375 | 34.875 | 0.454 | 251 |
439 | -101.125 | 34.875 | 0.447 | 251 |
440 | -100.875 | 34.875 | 0.465 | 252 |
441 | -100.625 | 34.875 | 0.463 | 252 |
442 | -100.375 | 34.875 | 0.430 | 252 |
443 | -100.125 | 34.875 | 0.430 | 252 |
444 | -99.875 | 34.875 | 0.421 | 252 |
445 | -99.625 | 34.875 | 0.401 | 249 |
446 | -99.375 | 34.875 | 0.414 | 252 |
447 | -99.125 | 34.875 | 0.401 | 252 |
448 | -98.875 | 34.875 | 0.371 | 252 |
449 | -98.625 | 34.875 | 0.371 | 238 |
450 | -98.375 | 34.875 | 0.289 | 233 |
451 | -98.125 | 34.875 | 0.336 | 250 |
452 | -97.875 | 34.875 | 0.309 | 250 |
453 | -97.625 | 34.875 | 0.277 | 249 |
454 | -97.375 | 34.875 | 0.267 | 251 |
455 | -97.125 | 34.875 | 0.259 | 252 |
456 | -96.875 | 34.875 | 0.263 | 252 |
457 | -96.625 | 34.875 | 0.255 | 252 |
458 | -96.375 | 34.875 | 0.263 | 251 |
459 | -96.125 | 34.875 | 0.275 | 248 |
460 | -95.875 | 34.875 | 0.276 | 247 |
461 | -95.625 | 34.875 | 0.280 | 238 |
462 | -95.375 | 34.875 | 0.270 | 242 |
463 | -95.125 | 34.875 | 0.257 | 241 |
464 | -94.875 | 34.875 | 0.225 | 244 |
465 | -94.625 | 34.875 | 0.395 | 240 |
466 | -94.375 | 34.875 | 0.401 | 239 |
467 | -94.125 | 34.875 | 0.372 | 239 |
468 | -93.875 | 34.875 | 0.313 | 239 |
469 | -103.375 | 34.625 | 0.533 | 251 |
470 | -103.125 | 34.625 | 0.538 | 252 |
471 | -102.875 | 34.625 | 0.543 | 252 |
472 | -102.625 | 34.625 | 0.558 | 252 |
473 | -102.375 | 34.625 | 0.557 | 252 |
474 | -102.125 | 34.625 | 0.529 | 252 |
475 | -101.875 | 34.625 | 0.533 | 252 |
476 | -101.625 | 34.625 | 0.524 | 252 |
477 | -101.375 | 34.625 | 0.497 | 251 |
478 | -101.125 | 34.625 | 0.478 | 251 |
479 | -100.875 | 34.625 | 0.509 | 251 |
480 | -100.625 | 34.625 | 0.462 | 252 |
481 | -100.375 | 34.625 | 0.409 | 252 |
482 | -100.125 | 34.625 | 0.423 | 252 |
483 | -99.875 | 34.625 | 0.444 | 252 |
484 | -99.625 | 34.625 | 0.478 | 252 |
485 | -99.375 | 34.625 | 0.426 | 252 |
486 | -99.125 | 34.625 | 0.398 | 251 |
487 | -98.875 | 34.625 | 0.387 | 252 |
488 | -98.625 | 34.625 | 0.343 | 252 |
489 | -98.375 | 34.625 | 0.323 | 240 |
490 | -98.125 | 34.625 | 0.339 | 250 |
491 | -97.875 | 34.625 | 0.317 | 250 |
492 | -97.625 | 34.625 | 0.331 | 251 |
493 | -97.375 | 34.625 | 0.282 | 251 |
494 | -97.125 | 34.625 | 0.303 | 250 |
495 | -96.875 | 34.625 | 0.321 | 251 |
496 | -96.625 | 34.625 | 0.337 | 252 |
497 | -96.375 | 34.625 | 0.313 | 251 |
498 | -96.125 | 34.625 | 0.307 | 250 |
499 | -95.875 | 34.625 | 0.342 | 241 |
500 | -95.625 | 34.625 | 0.260 | 205 |
501 | -95.375 | 34.625 | 0.269 | 239 |
502 | -95.125 | 34.625 | 0.455 | 242 |
503 | -94.875 | 34.625 | 0.440 | 241 |
504 | -94.625 | 34.625 | 0.441 | 238 |
505 | -94.375 | 34.625 | 0.401 | 239 |
506 | -94.125 | 34.625 | 0.339 | 239 |
507 | -93.875 | 34.625 | 0.305 | 238 |
508 | -103.375 | 34.375 | 0.575 | 252 |
509 | -103.125 | 34.375 | 0.579 | 252 |
510 | -102.875 | 34.375 | 0.578 | 252 |
511 | -102.625 | 34.375 | 0.594 | 252 |
512 | -102.375 | 34.375 | 0.554 | 252 |
513 | -102.125 | 34.375 | 0.519 | 252 |
514 | -101.875 | 34.375 | 0.525 | 252 |
515 | -101.625 | 34.375 | 0.540 | 252 |
516 | -101.375 | 34.375 | 0.519 | 252 |
517 | -101.125 | 34.375 | 0.516 | 252 |
518 | -100.875 | 34.375 | 0.526 | 252 |
519 | -100.625 | 34.375 | 0.498 | 252 |
520 | -100.375 | 34.375 | 0.469 | 252 |
521 | -100.125 | 34.375 | 0.423 | 252 |
522 | -99.875 | 34.375 | 0.395 | 252 |
523 | -99.625 | 34.375 | 0.427 | 252 |
524 | -99.375 | 34.375 | 0.484 | 252 |
525 | -99.125 | 34.375 | 0.426 | 251 |
526 | -98.875 | 34.375 | 0.389 | 252 |
527 | -98.625 | 34.375 | 0.365 | 249 |
528 | -98.375 | 34.375 | 0.368 | 252 |
529 | -98.125 | 34.375 | 0.358 | 252 |
530 | -97.875 | 34.375 | 0.342 | 251 |
531 | -97.625 | 34.375 | 0.315 | 251 |
532 | -97.375 | 34.375 | 0.270 | 251 |
533 | -97.125 | 34.375 | 0.242 | 251 |
534 | -96.875 | 34.375 | 0.328 | 251 |
535 | -96.625 | 34.375 | 0.294 | 251 |
536 | -96.375 | 34.375 | 0.313 | 250 |
537 | -96.125 | 34.375 | 0.306 | 221 |
538 | -95.875 | 34.375 | 0.333 | 240 |
539 | -95.625 | 34.375 | 0.326 | 232 |
540 | -95.375 | 34.375 | 0.330 | 243 |
541 | -95.125 | 34.375 | 0.460 | 240 |
542 | -94.875 | 34.375 | 0.406 | 238 |
543 | -94.625 | 34.375 | 0.373 | 236 |
544 | -94.375 | 34.375 | 0.353 | 236 |
545 | -94.125 | 34.375 | 0.305 | 236 |
546 | -93.875 | 34.375 | 0.265 | 236 |
547 | -103.375 | 34.125 | 0.604 | 248 |
548 | -103.125 | 34.125 | 0.609 | 251 |
549 | -102.875 | 34.125 | 0.642 | 251 |
550 | -102.625 | 34.125 | 0.616 | 252 |
551 | -102.375 | 34.125 | 0.586 | 252 |
552 | -102.125 | 34.125 | 0.549 | 252 |
553 | -101.875 | 34.125 | 0.596 | 252 |
554 | -101.625 | 34.125 | 0.590 | 250 |
555 | -101.375 | 34.125 | 0.564 | 252 |
556 | -101.125 | 34.125 | 0.534 | 252 |
557 | -100.875 | 34.125 | 0.526 | 252 |
558 | -100.625 | 34.125 | 0.506 | 252 |
559 | -100.375 | 34.125 | 0.511 | 252 |
560 | -100.125 | 34.125 | 0.469 | 252 |
561 | -99.875 | 34.125 | 0.446 | 252 |
562 | -99.625 | 34.125 | 0.415 | 252 |
563 | -99.375 | 34.125 | 0.443 | 252 |
564 | -99.125 | 34.125 | 0.454 | 252 |
565 | -98.875 | 34.125 | 0.425 | 248 |
566 | -98.625 | 34.125 | 0.393 | 252 |
567 | -98.375 | 34.125 | 0.369 | 252 |
568 | -98.125 | 34.125 | 0.353 | 251 |
569 | -97.875 | 34.125 | 0.339 | 252 |
570 | -97.625 | 34.125 | 0.345 | 251 |
571 | -97.375 | 34.125 | 0.306 | 251 |
572 | -97.125 | 34.125 | 0.243 | 252 |
573 | -96.875 | 34.125 | 0.260 | 245 |
574 | -96.625 | 34.125 | 0.304 | 252 |
575 | -96.375 | 34.125 | 0.300 | 251 |
576 | -96.125 | 34.125 | 0.349 | 251 |
577 | -95.875 | 34.125 | 0.346 | 251 |
578 | -95.625 | 34.125 | 0.344 | 246 |
579 | -95.375 | 34.125 | 0.353 | 243 |
580 | -95.125 | 34.125 | 0.318 | 251 |
581 | -94.875 | 34.125 | 0.406 | 242 |
582 | -94.625 | 34.125 | 0.342 | 243 |
583 | -94.375 | 34.125 | 0.317 | 242 |
584 | -94.125 | 34.125 | 0.254 | 242 |
585 | -93.875 | 34.125 | 0.274 | 243 |
586 | -103.375 | 33.875 | 0.501 | 252 |
587 | -103.125 | 33.875 | 0.560 | 252 |
588 | -102.875 | 33.875 | 0.622 | 252 |
589 | -102.625 | 33.875 | 0.597 | 252 |
590 | -102.375 | 33.875 | 0.588 | 252 |
591 | -102.125 | 33.875 | 0.558 | 252 |
592 | -101.875 | 33.875 | 0.611 | 252 |
593 | -101.625 | 33.875 | 0.616 | 252 |
594 | -101.375 | 33.875 | 0.556 | 252 |
595 | -101.125 | 33.875 | 0.526 | 252 |
596 | -100.875 | 33.875 | 0.512 | 252 |
597 | -100.625 | 33.875 | 0.473 | 252 |
598 | -100.375 | 33.875 | 0.485 | 252 |
599 | -100.125 | 33.875 | 0.474 | 252 |
600 | -99.875 | 33.875 | 0.450 | 252 |
601 | -99.625 | 33.875 | 0.438 | 252 |
602 | -99.375 | 33.875 | 0.432 | 252 |
603 | -99.125 | 33.875 | 0.406 | 252 |
604 | -98.875 | 33.875 | 0.405 | 252 |
605 | -98.625 | 33.875 | 0.379 | 252 |
606 | -98.375 | 33.875 | 0.341 | 252 |
607 | -98.125 | 33.875 | 0.339 | 251 |
608 | -97.875 | 33.875 | 0.343 | 251 |
609 | -97.625 | 33.875 | 0.331 | 251 |
610 | -97.375 | 33.875 | 0.313 | 251 |
611 | -97.125 | 33.875 | 0.290 | 252 |
612 | -96.875 | 33.875 | 0.438 | 248 |
613 | -96.625 | 33.875 | 0.459 | 243 |
614 | -96.375 | 33.875 | 0.341 | 252 |
615 | -96.125 | 33.875 | 0.349 | 251 |
616 | -95.875 | 33.875 | 0.414 | 251 |
617 | -95.625 | 33.875 | 0.418 | 246 |
618 | -95.375 | 33.875 | 0.422 | 245 |
619 | -95.125 | 33.875 | 0.424 | 244 |
620 | -94.875 | 33.875 | 0.378 | 243 |
621 | -94.625 | 33.875 | 0.320 | 245 |
622 | -94.375 | 33.875 | 0.278 | 251 |
623 | -94.125 | 33.875 | 0.312 | 248 |
624 | -93.875 | 33.875 | 0.336 | 246 |
625 | -103.375 | 33.625 | 0.522 | 252 |
626 | -103.125 | 33.625 | 0.572 | 252 |
627 | -102.875 | 33.625 | 0.649 | 252 |
628 | -102.625 | 33.625 | 0.636 | 252 |
629 | -102.375 | 33.625 | 0.551 | 252 |
630 | -102.125 | 33.625 | 0.584 | 252 |
631 | -101.875 | 33.625 | 0.598 | 252 |
632 | -101.625 | 33.625 | 0.611 | 252 |
633 | -101.375 | 33.625 | 0.575 | 252 |
634 | -101.125 | 33.625 | 0.556 | 252 |
635 | -100.875 | 33.625 | 0.526 | 252 |
636 | -100.625 | 33.625 | 0.447 | 252 |
637 | -100.375 | 33.625 | 0.466 | 252 |
638 | -100.125 | 33.625 | 0.403 | 238 |
639 | -99.875 | 33.625 | 0.417 | 252 |
640 | -99.625 | 33.625 | 0.429 | 252 |
641 | -99.375 | 33.625 | 0.378 | 252 |
642 | -99.125 | 33.625 | 0.356 | 252 |
643 | -98.875 | 33.625 | 0.357 | 252 |
644 | -98.625 | 33.625 | 0.354 | 252 |
645 | -98.375 | 33.625 | 0.348 | 252 |
646 | -98.125 | 33.625 | 0.307 | 251 |
647 | -97.875 | 33.625 | 0.308 | 252 |
648 | -97.625 | 33.625 | 0.343 | 252 |
649 | -97.375 | 33.625 | 0.370 | 252 |
650 | -97.125 | 33.625 | 0.345 | 252 |
651 | -96.875 | 33.625 | 0.309 | 252 |
652 | -96.625 | 33.625 | 0.363 | 252 |
653 | -96.375 | 33.625 | 0.354 | 252 |
654 | -96.125 | 33.625 | 0.416 | 251 |
655 | -95.875 | 33.625 | 0.446 | 251 |
656 | -95.625 | 33.625 | 0.435 | 246 |
657 | -95.375 | 33.625 | 0.443 | 245 |
658 | -95.125 | 33.625 | 0.415 | 251 |
659 | -94.875 | 33.625 | 0.390 | 250 |
660 | -94.625 | 33.625 | 0.364 | 247 |
661 | -94.375 | 33.625 | 0.371 | 252 |
662 | -94.125 | 33.625 | 0.357 | 246 |
663 | -93.875 | 33.625 | 0.349 | 244 |
664 | -103.375 | 33.375 | 0.503 | 252 |
665 | -103.125 | 33.375 | 0.558 | 252 |
666 | -102.875 | 33.375 | 0.602 | 252 |
667 | -102.625 | 33.375 | 0.635 | 252 |
668 | -102.375 | 33.375 | 0.569 | 252 |
669 | -102.125 | 33.375 | 0.658 | 252 |
670 | -101.875 | 33.375 | 0.639 | 252 |
671 | -101.625 | 33.375 | 0.607 | 252 |
672 | -101.375 | 33.375 | 0.582 | 252 |
673 | -101.125 | 33.375 | 0.566 | 252 |
674 | -100.875 | 33.375 | 0.499 | 252 |
675 | -100.625 | 33.375 | 0.464 | 252 |
676 | -100.375 | 33.375 | 0.422 | 252 |
677 | -100.125 | 33.375 | 0.405 | 252 |
678 | -99.875 | 33.375 | 0.423 | 252 |
679 | -99.625 | 33.375 | 0.399 | 252 |
680 | -99.375 | 33.375 | 0.380 | 252 |
681 | -99.125 | 33.375 | 0.348 | 252 |
682 | -98.875 | 33.375 | 0.331 | 252 |
683 | -98.625 | 33.375 | 0.332 | 252 |
684 | -98.375 | 33.375 | 0.324 | 251 |
685 | -98.125 | 33.375 | 0.313 | 251 |
686 | -97.875 | 33.375 | 0.345 | 251 |
687 | -97.625 | 33.375 | 0.356 | 252 |
688 | -97.375 | 33.375 | 0.404 | 251 |
689 | -97.125 | 33.375 | 0.360 | 252 |
690 | -96.875 | 33.375 | 0.405 | 252 |
691 | -96.625 | 33.375 | 0.428 | 244 |
692 | -96.375 | 33.375 | 0.456 | 252 |
693 | -96.125 | 33.375 | 0.471 | 252 |
694 | -95.875 | 33.375 | 0.410 | 239 |
695 | -95.625 | 33.375 | 0.423 | 247 |
696 | -95.375 | 33.375 | 0.447 | 245 |
697 | -95.125 | 33.375 | 0.394 | 244 |
698 | -94.875 | 33.375 | 0.385 | 251 |
699 | -94.625 | 33.375 | 0.407 | 243 |
700 | -94.375 | 33.375 | 0.344 | 246 |
701 | -94.125 | 33.375 | 0.366 | 252 |
702 | -93.875 | 33.375 | 0.382 | 239 |
703 | -103.375 | 33.125 | 0.565 | 252 |
704 | -103.125 | 33.125 | 0.594 | 252 |
705 | -102.875 | 33.125 | 0.632 | 252 |
706 | -102.625 | 33.125 | 0.628 | 252 |
707 | -102.375 | 33.125 | 0.641 | 252 |
708 | -102.125 | 33.125 | 0.625 | 252 |
709 | -101.875 | 33.125 | 0.588 | 252 |
710 | -101.625 | 33.125 | 0.564 | 252 |
711 | -101.375 | 33.125 | 0.558 | 252 |
712 | -101.125 | 33.125 | 0.515 | 252 |
713 | -100.875 | 33.125 | 0.492 | 252 |
714 | -100.625 | 33.125 | 0.402 | 252 |
715 | -100.375 | 33.125 | 0.373 | 252 |
716 | -100.125 | 33.125 | 0.395 | 252 |
717 | -99.875 | 33.125 | 0.407 | 252 |
718 | -99.625 | 33.125 | 0.383 | 252 |
719 | -99.375 | 33.125 | 0.406 | 252 |
720 | -99.125 | 33.125 | 0.368 | 252 |
721 | -98.875 | 33.125 | 0.328 | 252 |
722 | -98.625 | 33.125 | 0.299 | 251 |
723 | -98.375 | 33.125 | 0.321 | 251 |
724 | -98.125 | 33.125 | 0.355 | 251 |
725 | -97.875 | 33.125 | 0.366 | 251 |
726 | -97.625 | 33.125 | 0.368 | 251 |
727 | -97.375 | 33.125 | 0.298 | 252 |
728 | -97.125 | 33.125 | 0.361 | 252 |
729 | -96.875 | 33.125 | 0.404 | 252 |
730 | -96.625 | 33.125 | 0.420 | 252 |
731 | -96.375 | 33.125 | 0.482 | 252 |
732 | -96.125 | 33.125 | 0.394 | 252 |
733 | -95.875 | 33.125 | 0.407 | 252 |
734 | -95.625 | 33.125 | 0.428 | 251 |
735 | -95.375 | 33.125 | 0.411 | 251 |
736 | -95.125 | 33.125 | 0.351 | 252 |
737 | -94.875 | 33.125 | 0.371 | 250 |
738 | -94.625 | 33.125 | 0.337 | 240 |
739 | -94.375 | 33.125 | 0.336 | 250 |
740 | -94.125 | 33.125 | 0.332 | 248 |
741 | -93.875 | 33.125 | 0.416 | 249 |
setwd("e:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
Temperature_Max <- read.csv("Daymet_tmax_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Temperature_Max[1:4] <- NULL
Temperature_Max <- replace(Temperature_Max, Temperature_Max == -9999, NA)
Temperature_Max <- t(Temperature_Max)
Temperature_Max <- as.data.frame(Temperature_Max)
names(Temperature_Max) <- paste(c(1:741))
base_matrix <- read.csv("pixels.csv", header = TRUE, sep = ',', dec = '.')
final_temporal_correlation <- base_matrix
names(final_temporal_correlation)[4] <- paste('Corr_Temperature_Max')
for (i in 1:741) {
correlation <- cor(SoilMoisture_MEAN[i], Temperature_Max[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEAN[i]))
number_values <- 252 - number_values
final_temporal_correlation[i,4] <- correlation
final_temporal_correlation[i,5] <- number_values
}
mean_temp_corr_meanSM_MaxTemp <- round(mean(final_temporal_correlation$Corr_Temperature_Max), digits = 3)
kable(final_temporal_correlation, caption = 'Temporal Correlation Mean Soil Moistre and Max Temperature', digits = 3)
Pixel | X | Y | Corr_Temperature_Max | Number_of_pairs |
---|---|---|---|---|
1 | -103.375 | 37.625 | -0.385 | 252 |
2 | -103.125 | 37.625 | -0.316 | 232 |
3 | -102.875 | 37.625 | -0.105 | 211 |
4 | -102.625 | 37.625 | -0.188 | 234 |
5 | -102.375 | 37.625 | -0.214 | 251 |
6 | -102.125 | 37.625 | -0.084 | 221 |
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411 | -98.375 | 35.125 | -0.496 | 235 |
412 | -98.125 | 35.125 | -0.441 | 251 |
413 | -97.875 | 35.125 | -0.537 | 250 |
414 | -97.625 | 35.125 | -0.598 | 250 |
415 | -97.375 | 35.125 | -0.623 | 250 |
416 | -97.125 | 35.125 | -0.603 | 252 |
417 | -96.875 | 35.125 | -0.655 | 251 |
418 | -96.625 | 35.125 | -0.675 | 252 |
419 | -96.375 | 35.125 | -0.700 | 251 |
420 | -96.125 | 35.125 | -0.640 | 251 |
421 | -95.875 | 35.125 | -0.687 | 252 |
422 | -95.625 | 35.125 | -0.661 | 243 |
423 | -95.375 | 35.125 | -0.630 | 249 |
424 | -95.125 | 35.125 | -0.574 | 245 |
425 | -94.875 | 35.125 | -0.613 | 250 |
426 | -94.625 | 35.125 | -0.664 | 248 |
427 | -94.375 | 35.125 | -0.606 | 247 |
428 | -94.125 | 35.125 | -0.269 | 241 |
429 | -93.875 | 35.125 | -0.173 | 240 |
430 | -103.375 | 34.875 | -0.337 | 252 |
431 | -103.125 | 34.875 | -0.319 | 250 |
432 | -102.875 | 34.875 | -0.286 | 252 |
433 | -102.625 | 34.875 | -0.163 | 244 |
434 | -102.375 | 34.875 | -0.232 | 252 |
435 | -102.125 | 34.875 | -0.329 | 252 |
436 | -101.875 | 34.875 | -0.362 | 252 |
437 | -101.625 | 34.875 | -0.348 | 251 |
438 | -101.375 | 34.875 | -0.401 | 251 |
439 | -101.125 | 34.875 | -0.418 | 251 |
440 | -100.875 | 34.875 | -0.420 | 252 |
441 | -100.625 | 34.875 | -0.438 | 252 |
442 | -100.375 | 34.875 | -0.451 | 252 |
443 | -100.125 | 34.875 | -0.456 | 252 |
444 | -99.875 | 34.875 | -0.479 | 252 |
445 | -99.625 | 34.875 | -0.500 | 249 |
446 | -99.375 | 34.875 | -0.449 | 252 |
447 | -99.125 | 34.875 | -0.475 | 252 |
448 | -98.875 | 34.875 | -0.547 | 252 |
449 | -98.625 | 34.875 | -0.616 | 238 |
450 | -98.375 | 34.875 | -0.588 | 233 |
451 | -98.125 | 34.875 | -0.505 | 250 |
452 | -97.875 | 34.875 | -0.548 | 250 |
453 | -97.625 | 34.875 | -0.647 | 249 |
454 | -97.375 | 34.875 | -0.669 | 251 |
455 | -97.125 | 34.875 | -0.675 | 252 |
456 | -96.875 | 34.875 | -0.640 | 252 |
457 | -96.625 | 34.875 | -0.663 | 252 |
458 | -96.375 | 34.875 | -0.645 | 251 |
459 | -96.125 | 34.875 | -0.660 | 248 |
460 | -95.875 | 34.875 | -0.651 | 247 |
461 | -95.625 | 34.875 | -0.594 | 238 |
462 | -95.375 | 34.875 | -0.579 | 242 |
463 | -95.125 | 34.875 | -0.578 | 241 |
464 | -94.875 | 34.875 | -0.547 | 244 |
465 | -94.625 | 34.875 | -0.138 | 240 |
466 | -94.375 | 34.875 | -0.052 | 239 |
467 | -94.125 | 34.875 | -0.002 | 239 |
468 | -93.875 | 34.875 | 0.077 | 239 |
469 | -103.375 | 34.625 | -0.214 | 251 |
470 | -103.125 | 34.625 | -0.212 | 252 |
471 | -102.875 | 34.625 | -0.219 | 252 |
472 | -102.625 | 34.625 | -0.186 | 252 |
473 | -102.375 | 34.625 | -0.198 | 252 |
474 | -102.125 | 34.625 | -0.236 | 252 |
475 | -101.875 | 34.625 | -0.261 | 252 |
476 | -101.625 | 34.625 | -0.288 | 252 |
477 | -101.375 | 34.625 | -0.377 | 251 |
478 | -101.125 | 34.625 | -0.421 | 251 |
479 | -100.875 | 34.625 | -0.418 | 251 |
480 | -100.625 | 34.625 | -0.430 | 252 |
481 | -100.375 | 34.625 | -0.452 | 252 |
482 | -100.125 | 34.625 | -0.455 | 252 |
483 | -99.875 | 34.625 | -0.437 | 252 |
484 | -99.625 | 34.625 | -0.418 | 252 |
485 | -99.375 | 34.625 | -0.447 | 252 |
486 | -99.125 | 34.625 | -0.461 | 251 |
487 | -98.875 | 34.625 | -0.528 | 252 |
488 | -98.625 | 34.625 | -0.591 | 252 |
489 | -98.375 | 34.625 | -0.598 | 240 |
490 | -98.125 | 34.625 | -0.532 | 250 |
491 | -97.875 | 34.625 | -0.580 | 250 |
492 | -97.625 | 34.625 | -0.624 | 251 |
493 | -97.375 | 34.625 | -0.656 | 251 |
494 | -97.125 | 34.625 | -0.654 | 250 |
495 | -96.875 | 34.625 | -0.639 | 251 |
496 | -96.625 | 34.625 | -0.647 | 252 |
497 | -96.375 | 34.625 | -0.663 | 251 |
498 | -96.125 | 34.625 | -0.674 | 250 |
499 | -95.875 | 34.625 | -0.672 | 241 |
500 | -95.625 | 34.625 | -0.685 | 205 |
501 | -95.375 | 34.625 | -0.537 | 239 |
502 | -95.125 | 34.625 | -0.192 | 242 |
503 | -94.875 | 34.625 | -0.068 | 241 |
504 | -94.625 | 34.625 | 0.028 | 238 |
505 | -94.375 | 34.625 | 0.054 | 239 |
506 | -94.125 | 34.625 | 0.073 | 239 |
507 | -93.875 | 34.625 | 0.107 | 238 |
508 | -103.375 | 34.375 | -0.146 | 252 |
509 | -103.125 | 34.375 | -0.137 | 252 |
510 | -102.875 | 34.375 | -0.168 | 252 |
511 | -102.625 | 34.375 | -0.161 | 252 |
512 | -102.375 | 34.375 | -0.222 | 252 |
513 | -102.125 | 34.375 | -0.262 | 252 |
514 | -101.875 | 34.375 | -0.280 | 252 |
515 | -101.625 | 34.375 | -0.280 | 252 |
516 | -101.375 | 34.375 | -0.348 | 252 |
517 | -101.125 | 34.375 | -0.366 | 252 |
518 | -100.875 | 34.375 | -0.400 | 252 |
519 | -100.625 | 34.375 | -0.430 | 252 |
520 | -100.375 | 34.375 | -0.440 | 252 |
521 | -100.125 | 34.375 | -0.463 | 252 |
522 | -99.875 | 34.375 | -0.472 | 252 |
523 | -99.625 | 34.375 | -0.477 | 252 |
524 | -99.375 | 34.375 | -0.475 | 252 |
525 | -99.125 | 34.375 | -0.509 | 251 |
526 | -98.875 | 34.375 | -0.517 | 252 |
527 | -98.625 | 34.375 | -0.575 | 249 |
528 | -98.375 | 34.375 | -0.586 | 252 |
529 | -98.125 | 34.375 | -0.621 | 252 |
530 | -97.875 | 34.375 | -0.601 | 251 |
531 | -97.625 | 34.375 | -0.631 | 251 |
532 | -97.375 | 34.375 | -0.706 | 251 |
533 | -97.125 | 34.375 | -0.697 | 251 |
534 | -96.875 | 34.375 | -0.663 | 251 |
535 | -96.625 | 34.375 | -0.726 | 251 |
536 | -96.375 | 34.375 | -0.688 | 250 |
537 | -96.125 | 34.375 | -0.737 | 221 |
538 | -95.875 | 34.375 | -0.666 | 240 |
539 | -95.625 | 34.375 | -0.592 | 232 |
540 | -95.375 | 34.375 | -0.527 | 243 |
541 | -95.125 | 34.375 | -0.048 | 240 |
542 | -94.875 | 34.375 | 0.024 | 238 |
543 | -94.625 | 34.375 | 0.076 | 236 |
544 | -94.375 | 34.375 | 0.096 | 236 |
545 | -94.125 | 34.375 | 0.134 | 236 |
546 | -93.875 | 34.375 | 0.166 | 236 |
547 | -103.375 | 34.125 | -0.009 | 248 |
548 | -103.125 | 34.125 | -0.089 | 251 |
549 | -102.875 | 34.125 | -0.048 | 251 |
550 | -102.625 | 34.125 | -0.130 | 252 |
551 | -102.375 | 34.125 | -0.173 | 252 |
552 | -102.125 | 34.125 | -0.180 | 252 |
553 | -101.875 | 34.125 | -0.209 | 252 |
554 | -101.625 | 34.125 | -0.234 | 250 |
555 | -101.375 | 34.125 | -0.283 | 252 |
556 | -101.125 | 34.125 | -0.320 | 252 |
557 | -100.875 | 34.125 | -0.384 | 252 |
558 | -100.625 | 34.125 | -0.453 | 252 |
559 | -100.375 | 34.125 | -0.455 | 252 |
560 | -100.125 | 34.125 | -0.496 | 252 |
561 | -99.875 | 34.125 | -0.501 | 252 |
562 | -99.625 | 34.125 | -0.541 | 252 |
563 | -99.375 | 34.125 | -0.559 | 252 |
564 | -99.125 | 34.125 | -0.541 | 252 |
565 | -98.875 | 34.125 | -0.569 | 248 |
566 | -98.625 | 34.125 | -0.557 | 252 |
567 | -98.375 | 34.125 | -0.614 | 252 |
568 | -98.125 | 34.125 | -0.656 | 251 |
569 | -97.875 | 34.125 | -0.621 | 252 |
570 | -97.625 | 34.125 | -0.638 | 251 |
571 | -97.375 | 34.125 | -0.702 | 251 |
572 | -97.125 | 34.125 | -0.740 | 252 |
573 | -96.875 | 34.125 | -0.744 | 245 |
574 | -96.625 | 34.125 | -0.753 | 252 |
575 | -96.375 | 34.125 | -0.745 | 251 |
576 | -96.125 | 34.125 | -0.726 | 251 |
577 | -95.875 | 34.125 | -0.729 | 251 |
578 | -95.625 | 34.125 | -0.668 | 246 |
579 | -95.375 | 34.125 | -0.650 | 243 |
580 | -95.125 | 34.125 | -0.627 | 251 |
581 | -94.875 | 34.125 | -0.152 | 242 |
582 | -94.625 | 34.125 | -0.086 | 243 |
583 | -94.375 | 34.125 | 0.023 | 242 |
584 | -94.125 | 34.125 | 0.070 | 242 |
585 | -93.875 | 34.125 | 0.012 | 243 |
586 | -103.375 | 33.875 | -0.262 | 252 |
587 | -103.125 | 33.875 | -0.217 | 252 |
588 | -102.875 | 33.875 | -0.180 | 252 |
589 | -102.625 | 33.875 | -0.223 | 252 |
590 | -102.375 | 33.875 | -0.212 | 252 |
591 | -102.125 | 33.875 | -0.198 | 252 |
592 | -101.875 | 33.875 | -0.190 | 252 |
593 | -101.625 | 33.875 | -0.260 | 252 |
594 | -101.375 | 33.875 | -0.303 | 252 |
595 | -101.125 | 33.875 | -0.329 | 252 |
596 | -100.875 | 33.875 | -0.423 | 252 |
597 | -100.625 | 33.875 | -0.470 | 252 |
598 | -100.375 | 33.875 | -0.480 | 252 |
599 | -100.125 | 33.875 | -0.508 | 252 |
600 | -99.875 | 33.875 | -0.532 | 252 |
601 | -99.625 | 33.875 | -0.558 | 252 |
602 | -99.375 | 33.875 | -0.591 | 252 |
603 | -99.125 | 33.875 | -0.587 | 252 |
604 | -98.875 | 33.875 | -0.603 | 252 |
605 | -98.625 | 33.875 | -0.625 | 252 |
606 | -98.375 | 33.875 | -0.653 | 252 |
607 | -98.125 | 33.875 | -0.672 | 251 |
608 | -97.875 | 33.875 | -0.701 | 251 |
609 | -97.625 | 33.875 | -0.715 | 251 |
610 | -97.375 | 33.875 | -0.756 | 251 |
611 | -97.125 | 33.875 | -0.755 | 252 |
612 | -96.875 | 33.875 | -0.611 | 248 |
613 | -96.625 | 33.875 | -0.536 | 243 |
614 | -96.375 | 33.875 | -0.785 | 252 |
615 | -96.125 | 33.875 | -0.785 | 251 |
616 | -95.875 | 33.875 | -0.751 | 251 |
617 | -95.625 | 33.875 | -0.729 | 246 |
618 | -95.375 | 33.875 | -0.695 | 245 |
619 | -95.125 | 33.875 | -0.682 | 244 |
620 | -94.875 | 33.875 | -0.642 | 243 |
621 | -94.625 | 33.875 | -0.536 | 245 |
622 | -94.375 | 33.875 | -0.593 | 251 |
623 | -94.125 | 33.875 | -0.567 | 248 |
624 | -93.875 | 33.875 | -0.716 | 246 |
625 | -103.375 | 33.625 | -0.328 | 252 |
626 | -103.125 | 33.625 | -0.259 | 252 |
627 | -102.875 | 33.625 | -0.182 | 252 |
628 | -102.625 | 33.625 | -0.163 | 252 |
629 | -102.375 | 33.625 | -0.183 | 252 |
630 | -102.125 | 33.625 | -0.170 | 252 |
631 | -101.875 | 33.625 | -0.203 | 252 |
632 | -101.625 | 33.625 | -0.260 | 252 |
633 | -101.375 | 33.625 | -0.324 | 252 |
634 | -101.125 | 33.625 | -0.329 | 252 |
635 | -100.875 | 33.625 | -0.421 | 252 |
636 | -100.625 | 33.625 | -0.468 | 252 |
637 | -100.375 | 33.625 | -0.467 | 252 |
638 | -100.125 | 33.625 | -0.545 | 238 |
639 | -99.875 | 33.625 | -0.533 | 252 |
640 | -99.625 | 33.625 | -0.553 | 252 |
641 | -99.375 | 33.625 | -0.609 | 252 |
642 | -99.125 | 33.625 | -0.633 | 252 |
643 | -98.875 | 33.625 | -0.632 | 252 |
644 | -98.625 | 33.625 | -0.658 | 252 |
645 | -98.375 | 33.625 | -0.674 | 252 |
646 | -98.125 | 33.625 | -0.695 | 251 |
647 | -97.875 | 33.625 | -0.712 | 252 |
648 | -97.625 | 33.625 | -0.745 | 252 |
649 | -97.375 | 33.625 | -0.743 | 252 |
650 | -97.125 | 33.625 | -0.743 | 252 |
651 | -96.875 | 33.625 | -0.718 | 252 |
652 | -96.625 | 33.625 | -0.730 | 252 |
653 | -96.375 | 33.625 | -0.724 | 252 |
654 | -96.125 | 33.625 | -0.749 | 251 |
655 | -95.875 | 33.625 | -0.779 | 251 |
656 | -95.625 | 33.625 | -0.750 | 246 |
657 | -95.375 | 33.625 | -0.733 | 245 |
658 | -95.125 | 33.625 | -0.697 | 251 |
659 | -94.875 | 33.625 | -0.663 | 250 |
660 | -94.625 | 33.625 | -0.673 | 247 |
661 | -94.375 | 33.625 | -0.690 | 252 |
662 | -94.125 | 33.625 | -0.652 | 246 |
663 | -93.875 | 33.625 | -0.674 | 244 |
664 | -103.375 | 33.375 | -0.316 | 252 |
665 | -103.125 | 33.375 | -0.265 | 252 |
666 | -102.875 | 33.375 | -0.219 | 252 |
667 | -102.625 | 33.375 | -0.151 | 252 |
668 | -102.375 | 33.375 | -0.144 | 252 |
669 | -102.125 | 33.375 | -0.115 | 252 |
670 | -101.875 | 33.375 | -0.165 | 252 |
671 | -101.625 | 33.375 | -0.246 | 252 |
672 | -101.375 | 33.375 | -0.312 | 252 |
673 | -101.125 | 33.375 | -0.387 | 252 |
674 | -100.875 | 33.375 | -0.459 | 252 |
675 | -100.625 | 33.375 | -0.470 | 252 |
676 | -100.375 | 33.375 | -0.484 | 252 |
677 | -100.125 | 33.375 | -0.527 | 252 |
678 | -99.875 | 33.375 | -0.558 | 252 |
679 | -99.625 | 33.375 | -0.581 | 252 |
680 | -99.375 | 33.375 | -0.584 | 252 |
681 | -99.125 | 33.375 | -0.617 | 252 |
682 | -98.875 | 33.375 | -0.643 | 252 |
683 | -98.625 | 33.375 | -0.685 | 252 |
684 | -98.375 | 33.375 | -0.696 | 251 |
685 | -98.125 | 33.375 | -0.717 | 251 |
686 | -97.875 | 33.375 | -0.702 | 251 |
687 | -97.625 | 33.375 | -0.742 | 252 |
688 | -97.375 | 33.375 | -0.729 | 251 |
689 | -97.125 | 33.375 | -0.668 | 252 |
690 | -96.875 | 33.375 | -0.683 | 252 |
691 | -96.625 | 33.375 | -0.722 | 244 |
692 | -96.375 | 33.375 | -0.727 | 252 |
693 | -96.125 | 33.375 | -0.724 | 252 |
694 | -95.875 | 33.375 | -0.697 | 239 |
695 | -95.625 | 33.375 | -0.720 | 247 |
696 | -95.375 | 33.375 | -0.759 | 245 |
697 | -95.125 | 33.375 | -0.687 | 244 |
698 | -94.875 | 33.375 | -0.688 | 251 |
699 | -94.625 | 33.375 | -0.633 | 243 |
700 | -94.375 | 33.375 | -0.614 | 246 |
701 | -94.125 | 33.375 | -0.743 | 252 |
702 | -93.875 | 33.375 | -0.552 | 239 |
703 | -103.375 | 33.125 | -0.285 | 252 |
704 | -103.125 | 33.125 | -0.289 | 252 |
705 | -102.875 | 33.125 | -0.234 | 252 |
706 | -102.625 | 33.125 | -0.167 | 252 |
707 | -102.375 | 33.125 | -0.181 | 252 |
708 | -102.125 | 33.125 | -0.210 | 252 |
709 | -101.875 | 33.125 | -0.277 | 252 |
710 | -101.625 | 33.125 | -0.346 | 252 |
711 | -101.375 | 33.125 | -0.383 | 252 |
712 | -101.125 | 33.125 | -0.464 | 252 |
713 | -100.875 | 33.125 | -0.484 | 252 |
714 | -100.625 | 33.125 | -0.494 | 252 |
715 | -100.375 | 33.125 | -0.564 | 252 |
716 | -100.125 | 33.125 | -0.573 | 252 |
717 | -99.875 | 33.125 | -0.568 | 252 |
718 | -99.625 | 33.125 | -0.606 | 252 |
719 | -99.375 | 33.125 | -0.609 | 252 |
720 | -99.125 | 33.125 | -0.617 | 252 |
721 | -98.875 | 33.125 | -0.669 | 252 |
722 | -98.625 | 33.125 | -0.726 | 251 |
723 | -98.375 | 33.125 | -0.696 | 251 |
724 | -98.125 | 33.125 | -0.681 | 251 |
725 | -97.875 | 33.125 | -0.705 | 251 |
726 | -97.625 | 33.125 | -0.710 | 251 |
727 | -97.375 | 33.125 | -0.719 | 252 |
728 | -97.125 | 33.125 | -0.724 | 252 |
729 | -96.875 | 33.125 | -0.743 | 252 |
730 | -96.625 | 33.125 | -0.743 | 252 |
731 | -96.375 | 33.125 | -0.722 | 252 |
732 | -96.125 | 33.125 | -0.690 | 252 |
733 | -95.875 | 33.125 | -0.743 | 252 |
734 | -95.625 | 33.125 | -0.744 | 251 |
735 | -95.375 | 33.125 | -0.734 | 251 |
736 | -95.125 | 33.125 | -0.672 | 252 |
737 | -94.875 | 33.125 | -0.677 | 250 |
738 | -94.625 | 33.125 | -0.608 | 240 |
739 | -94.375 | 33.125 | -0.551 | 250 |
740 | -94.125 | 33.125 | -0.528 | 248 |
741 | -93.875 | 33.125 | -0.634 | 249 |
setwd("e:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
Temperature_Min <- read.csv("Daymet_tmin_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Temperature_Min[1:4] <- NULL
Temperature_Min <- replace(Temperature_Min, Temperature_Min == -9999, NA)
Temperature_Min <- t(Temperature_Min)
Temperature_Min <- as.data.frame(Temperature_Min)
names(Temperature_Min) <- paste(c(1:741))
base_matrix <- read.csv("pixels.csv", header = TRUE, sep = ',', dec = '.')
final_temporal_correlation <- base_matrix
names(final_temporal_correlation)[4] <- paste('Corr_Temperature_Min')
for (i in 1:741) {
correlation <- cor(SoilMoisture_MEAN[i], Temperature_Min[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEAN[i]))
number_values <- 252 - number_values
final_temporal_correlation[i,4] <- correlation
final_temporal_correlation[i,5] <- number_values
}
mean_temp_corr_meanSM_MinTemp <- round(mean(final_temporal_correlation$Corr_Temperature_Min), digits = 3)
kable(final_temporal_correlation, caption = 'Temporal Correlation Mean Soil Moistre and Min Temperature', digits = 3)
Pixel | X | Y | Corr_Temperature_Min | Number_of_pairs |
---|---|---|---|---|
1 | -103.375 | 37.625 | -0.281 | 252 |
2 | -103.125 | 37.625 | -0.204 | 232 |
3 | -102.875 | 37.625 | 0.023 | 211 |
4 | -102.625 | 37.625 | -0.075 | 234 |
5 | -102.375 | 37.625 | -0.104 | 251 |
6 | -102.125 | 37.625 | 0.024 | 221 |
7 | -101.875 | 37.625 | -0.026 | 236 |
8 | -101.625 | 37.625 | -0.109 | 252 |
9 | -101.375 | 37.625 | -0.067 | 252 |
10 | -101.125 | 37.625 | -0.030 | 252 |
11 | -100.875 | 37.625 | -0.086 | 251 |
12 | -100.625 | 37.625 | -0.120 | 249 |
13 | -100.375 | 37.625 | -0.178 | 248 |
14 | -100.125 | 37.625 | -0.055 | 225 |
15 | -99.875 | 37.625 | -0.176 | 247 |
16 | -99.625 | 37.625 | -0.165 | 247 |
17 | -99.375 | 37.625 | -0.130 | 247 |
18 | -99.125 | 37.625 | -0.042 | 234 |
19 | -98.875 | 37.625 | -0.039 | 232 |
20 | -98.625 | 37.625 | -0.133 | 249 |
21 | -98.375 | 37.625 | -0.185 | 251 |
22 | -98.125 | 37.625 | -0.233 | 251 |
23 | -97.875 | 37.625 | -0.307 | 236 |
24 | -97.625 | 37.625 | -0.254 | 250 |
25 | -97.375 | 37.625 | -0.392 | 249 |
26 | -97.125 | 37.625 | -0.356 | 247 |
27 | -96.875 | 37.625 | -0.375 | 247 |
28 | -96.625 | 37.625 | -0.475 | 243 |
29 | -96.375 | 37.625 | -0.440 | 241 |
30 | -96.125 | 37.625 | -0.496 | 241 |
31 | -95.875 | 37.625 | -0.522 | 243 |
32 | -95.625 | 37.625 | -0.499 | 244 |
33 | -95.375 | 37.625 | -0.465 | 242 |
34 | -95.125 | 37.625 | -0.463 | 241 |
35 | -94.875 | 37.625 | -0.438 | 245 |
36 | -94.625 | 37.625 | -0.467 | 241 |
37 | -94.375 | 37.625 | -0.444 | 238 |
38 | -94.125 | 37.625 | -0.405 | 233 |
39 | -93.875 | 37.625 | -0.358 | 233 |
40 | -103.375 | 37.375 | -0.208 | 251 |
41 | -103.125 | 37.375 | -0.154 | 251 |
42 | -102.875 | 37.375 | -0.001 | 233 |
43 | -102.625 | 37.375 | 0.042 | 235 |
44 | -102.375 | 37.375 | -0.027 | 251 |
45 | -102.125 | 37.375 | -0.029 | 250 |
46 | -101.875 | 37.375 | -0.053 | 251 |
47 | -101.625 | 37.375 | 0.058 | 241 |
48 | -101.375 | 37.375 | -0.159 | 242 |
49 | -101.125 | 37.375 | -0.211 | 241 |
50 | -100.875 | 37.375 | -0.282 | 240 |
51 | -100.625 | 37.375 | -0.338 | 234 |
52 | -100.375 | 37.375 | -0.345 | 227 |
53 | -100.125 | 37.375 | -0.172 | 244 |
54 | -99.875 | 37.375 | -0.240 | 203 |
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460 | -95.875 | 34.875 | -0.588 | 247 |
461 | -95.625 | 34.875 | -0.526 | 238 |
462 | -95.375 | 34.875 | -0.510 | 242 |
463 | -95.125 | 34.875 | -0.525 | 241 |
464 | -94.875 | 34.875 | -0.503 | 244 |
465 | -94.625 | 34.875 | -0.060 | 240 |
466 | -94.375 | 34.875 | 0.025 | 239 |
467 | -94.125 | 34.875 | 0.073 | 239 |
468 | -93.875 | 34.875 | 0.135 | 239 |
469 | -103.375 | 34.625 | -0.030 | 251 |
470 | -103.125 | 34.625 | -0.035 | 252 |
471 | -102.875 | 34.625 | -0.046 | 252 |
472 | -102.625 | 34.625 | -0.010 | 252 |
473 | -102.375 | 34.625 | -0.021 | 252 |
474 | -102.125 | 34.625 | -0.065 | 252 |
475 | -101.875 | 34.625 | -0.087 | 252 |
476 | -101.625 | 34.625 | -0.115 | 252 |
477 | -101.375 | 34.625 | -0.219 | 251 |
478 | -101.125 | 34.625 | -0.274 | 251 |
479 | -100.875 | 34.625 | -0.272 | 251 |
480 | -100.625 | 34.625 | -0.291 | 252 |
481 | -100.375 | 34.625 | -0.320 | 252 |
482 | -100.125 | 34.625 | -0.324 | 252 |
483 | -99.875 | 34.625 | -0.301 | 252 |
484 | -99.625 | 34.625 | -0.285 | 252 |
485 | -99.375 | 34.625 | -0.328 | 252 |
486 | -99.125 | 34.625 | -0.348 | 251 |
487 | -98.875 | 34.625 | -0.416 | 252 |
488 | -98.625 | 34.625 | -0.485 | 252 |
489 | -98.375 | 34.625 | -0.505 | 240 |
490 | -98.125 | 34.625 | -0.434 | 250 |
491 | -97.875 | 34.625 | -0.488 | 250 |
492 | -97.625 | 34.625 | -0.532 | 251 |
493 | -97.375 | 34.625 | -0.572 | 251 |
494 | -97.125 | 34.625 | -0.572 | 250 |
495 | -96.875 | 34.625 | -0.557 | 251 |
496 | -96.625 | 34.625 | -0.565 | 252 |
497 | -96.375 | 34.625 | -0.588 | 251 |
498 | -96.125 | 34.625 | -0.609 | 250 |
499 | -95.875 | 34.625 | -0.614 | 241 |
500 | -95.625 | 34.625 | -0.633 | 205 |
501 | -95.375 | 34.625 | -0.476 | 239 |
502 | -95.125 | 34.625 | -0.106 | 242 |
503 | -94.875 | 34.625 | 0.016 | 241 |
504 | -94.625 | 34.625 | 0.108 | 238 |
505 | -94.375 | 34.625 | 0.139 | 239 |
506 | -94.125 | 34.625 | 0.137 | 239 |
507 | -93.875 | 34.625 | 0.173 | 238 |
508 | -103.375 | 34.375 | 0.035 | 252 |
509 | -103.125 | 34.375 | 0.035 | 252 |
510 | -102.875 | 34.375 | 0.008 | 252 |
511 | -102.625 | 34.375 | 0.017 | 252 |
512 | -102.375 | 34.375 | -0.049 | 252 |
513 | -102.125 | 34.375 | -0.105 | 252 |
514 | -101.875 | 34.375 | -0.118 | 252 |
515 | -101.625 | 34.375 | -0.116 | 252 |
516 | -101.375 | 34.375 | -0.191 | 252 |
517 | -101.125 | 34.375 | -0.215 | 252 |
518 | -100.875 | 34.375 | -0.252 | 252 |
519 | -100.625 | 34.375 | -0.288 | 252 |
520 | -100.375 | 34.375 | -0.303 | 252 |
521 | -100.125 | 34.375 | -0.332 | 252 |
522 | -99.875 | 34.375 | -0.340 | 252 |
523 | -99.625 | 34.375 | -0.346 | 252 |
524 | -99.375 | 34.375 | -0.339 | 252 |
525 | -99.125 | 34.375 | -0.388 | 251 |
526 | -98.875 | 34.375 | -0.398 | 252 |
527 | -98.625 | 34.375 | -0.464 | 249 |
528 | -98.375 | 34.375 | -0.474 | 252 |
529 | -98.125 | 34.375 | -0.516 | 252 |
530 | -97.875 | 34.375 | -0.500 | 251 |
531 | -97.625 | 34.375 | -0.541 | 251 |
532 | -97.375 | 34.375 | -0.622 | 251 |
533 | -97.125 | 34.375 | -0.620 | 251 |
534 | -96.875 | 34.375 | -0.582 | 251 |
535 | -96.625 | 34.375 | -0.652 | 251 |
536 | -96.375 | 34.375 | -0.612 | 250 |
537 | -96.125 | 34.375 | -0.674 | 221 |
538 | -95.875 | 34.375 | -0.594 | 240 |
539 | -95.625 | 34.375 | -0.526 | 232 |
540 | -95.375 | 34.375 | -0.467 | 243 |
541 | -95.125 | 34.375 | 0.042 | 240 |
542 | -94.875 | 34.375 | 0.105 | 238 |
543 | -94.625 | 34.375 | 0.150 | 236 |
544 | -94.375 | 34.375 | 0.165 | 236 |
545 | -94.125 | 34.375 | 0.183 | 236 |
546 | -93.875 | 34.375 | 0.234 | 236 |
547 | -103.375 | 34.125 | 0.171 | 248 |
548 | -103.125 | 34.125 | 0.097 | 251 |
549 | -102.875 | 34.125 | 0.144 | 251 |
550 | -102.625 | 34.125 | 0.057 | 252 |
551 | -102.375 | 34.125 | 0.004 | 252 |
552 | -102.125 | 34.125 | -0.019 | 252 |
553 | -101.875 | 34.125 | -0.052 | 252 |
554 | -101.625 | 34.125 | -0.071 | 250 |
555 | -101.375 | 34.125 | -0.125 | 252 |
556 | -101.125 | 34.125 | -0.165 | 252 |
557 | -100.875 | 34.125 | -0.237 | 252 |
558 | -100.625 | 34.125 | -0.311 | 252 |
559 | -100.375 | 34.125 | -0.316 | 252 |
560 | -100.125 | 34.125 | -0.363 | 252 |
561 | -99.875 | 34.125 | -0.377 | 252 |
562 | -99.625 | 34.125 | -0.417 | 252 |
563 | -99.375 | 34.125 | -0.432 | 252 |
564 | -99.125 | 34.125 | -0.419 | 252 |
565 | -98.875 | 34.125 | -0.457 | 248 |
566 | -98.625 | 34.125 | -0.441 | 252 |
567 | -98.375 | 34.125 | -0.500 | 252 |
568 | -98.125 | 34.125 | -0.548 | 251 |
569 | -97.875 | 34.125 | -0.517 | 252 |
570 | -97.625 | 34.125 | -0.540 | 251 |
571 | -97.375 | 34.125 | -0.617 | 251 |
572 | -97.125 | 34.125 | -0.666 | 252 |
573 | -96.875 | 34.125 | -0.680 | 245 |
574 | -96.625 | 34.125 | -0.690 | 252 |
575 | -96.375 | 34.125 | -0.679 | 251 |
576 | -96.125 | 34.125 | -0.655 | 251 |
577 | -95.875 | 34.125 | -0.660 | 251 |
578 | -95.625 | 34.125 | -0.595 | 246 |
579 | -95.375 | 34.125 | -0.578 | 243 |
580 | -95.125 | 34.125 | -0.565 | 251 |
581 | -94.875 | 34.125 | -0.062 | 242 |
582 | -94.625 | 34.125 | -0.006 | 243 |
583 | -94.375 | 34.125 | 0.101 | 242 |
584 | -94.125 | 34.125 | 0.136 | 242 |
585 | -93.875 | 34.125 | 0.093 | 243 |
586 | -103.375 | 33.875 | -0.081 | 252 |
587 | -103.125 | 33.875 | -0.029 | 252 |
588 | -102.875 | 33.875 | 0.015 | 252 |
589 | -102.625 | 33.875 | -0.040 | 252 |
590 | -102.375 | 33.875 | -0.039 | 252 |
591 | -102.125 | 33.875 | -0.040 | 252 |
592 | -101.875 | 33.875 | -0.031 | 252 |
593 | -101.625 | 33.875 | -0.104 | 252 |
594 | -101.375 | 33.875 | -0.148 | 252 |
595 | -101.125 | 33.875 | -0.176 | 252 |
596 | -100.875 | 33.875 | -0.276 | 252 |
597 | -100.625 | 33.875 | -0.326 | 252 |
598 | -100.375 | 33.875 | -0.344 | 252 |
599 | -100.125 | 33.875 | -0.375 | 252 |
600 | -99.875 | 33.875 | -0.405 | 252 |
601 | -99.625 | 33.875 | -0.434 | 252 |
602 | -99.375 | 33.875 | -0.474 | 252 |
603 | -99.125 | 33.875 | -0.467 | 252 |
604 | -98.875 | 33.875 | -0.482 | 252 |
605 | -98.625 | 33.875 | -0.510 | 252 |
606 | -98.375 | 33.875 | -0.542 | 252 |
607 | -98.125 | 33.875 | -0.563 | 251 |
608 | -97.875 | 33.875 | -0.604 | 251 |
609 | -97.625 | 33.875 | -0.622 | 251 |
610 | -97.375 | 33.875 | -0.675 | 251 |
611 | -97.125 | 33.875 | -0.686 | 252 |
612 | -96.875 | 33.875 | -0.530 | 248 |
613 | -96.625 | 33.875 | -0.454 | 243 |
614 | -96.375 | 33.875 | -0.723 | 252 |
615 | -96.125 | 33.875 | -0.717 | 251 |
616 | -95.875 | 33.875 | -0.676 | 251 |
617 | -95.625 | 33.875 | -0.657 | 246 |
618 | -95.375 | 33.875 | -0.620 | 245 |
619 | -95.125 | 33.875 | -0.612 | 244 |
620 | -94.875 | 33.875 | -0.574 | 243 |
621 | -94.625 | 33.875 | -0.457 | 245 |
622 | -94.375 | 33.875 | -0.531 | 251 |
623 | -94.125 | 33.875 | -0.510 | 248 |
624 | -93.875 | 33.875 | -0.648 | 246 |
625 | -103.375 | 33.625 | -0.147 | 252 |
626 | -103.125 | 33.625 | -0.074 | 252 |
627 | -102.875 | 33.625 | 0.007 | 252 |
628 | -102.625 | 33.625 | 0.017 | 252 |
629 | -102.375 | 33.625 | -0.014 | 252 |
630 | -102.125 | 33.625 | -0.009 | 252 |
631 | -101.875 | 33.625 | -0.053 | 252 |
632 | -101.625 | 33.625 | -0.107 | 252 |
633 | -101.375 | 33.625 | -0.168 | 252 |
634 | -101.125 | 33.625 | -0.177 | 252 |
635 | -100.875 | 33.625 | -0.265 | 252 |
636 | -100.625 | 33.625 | -0.323 | 252 |
637 | -100.375 | 33.625 | -0.325 | 252 |
638 | -100.125 | 33.625 | -0.413 | 238 |
639 | -99.875 | 33.625 | -0.402 | 252 |
640 | -99.625 | 33.625 | -0.425 | 252 |
641 | -99.375 | 33.625 | -0.490 | 252 |
642 | -99.125 | 33.625 | -0.518 | 252 |
643 | -98.875 | 33.625 | -0.519 | 252 |
644 | -98.625 | 33.625 | -0.548 | 252 |
645 | -98.375 | 33.625 | -0.568 | 252 |
646 | -98.125 | 33.625 | -0.598 | 251 |
647 | -97.875 | 33.625 | -0.624 | 252 |
648 | -97.625 | 33.625 | -0.664 | 252 |
649 | -97.375 | 33.625 | -0.660 | 252 |
650 | -97.125 | 33.625 | -0.664 | 252 |
651 | -96.875 | 33.625 | -0.655 | 252 |
652 | -96.625 | 33.625 | -0.669 | 252 |
653 | -96.375 | 33.625 | -0.663 | 252 |
654 | -96.125 | 33.625 | -0.674 | 251 |
655 | -95.875 | 33.625 | -0.704 | 251 |
656 | -95.625 | 33.625 | -0.681 | 246 |
657 | -95.375 | 33.625 | -0.663 | 245 |
658 | -95.125 | 33.625 | -0.621 | 251 |
659 | -94.875 | 33.625 | -0.579 | 250 |
660 | -94.625 | 33.625 | -0.595 | 247 |
661 | -94.375 | 33.625 | -0.610 | 252 |
662 | -94.125 | 33.625 | -0.579 | 246 |
663 | -93.875 | 33.625 | -0.602 | 244 |
664 | -103.375 | 33.375 | -0.129 | 252 |
665 | -103.125 | 33.375 | -0.075 | 252 |
666 | -102.875 | 33.375 | -0.033 | 252 |
667 | -102.625 | 33.375 | 0.029 | 252 |
668 | -102.375 | 33.375 | 0.023 | 252 |
669 | -102.125 | 33.375 | 0.043 | 252 |
670 | -101.875 | 33.375 | -0.012 | 252 |
671 | -101.625 | 33.375 | -0.095 | 252 |
672 | -101.375 | 33.375 | -0.163 | 252 |
673 | -101.125 | 33.375 | -0.232 | 252 |
674 | -100.875 | 33.375 | -0.307 | 252 |
675 | -100.625 | 33.375 | -0.325 | 252 |
676 | -100.375 | 33.375 | -0.348 | 252 |
677 | -100.125 | 33.375 | -0.391 | 252 |
678 | -99.875 | 33.375 | -0.427 | 252 |
679 | -99.625 | 33.375 | -0.459 | 252 |
680 | -99.375 | 33.375 | -0.466 | 252 |
681 | -99.125 | 33.375 | -0.505 | 252 |
682 | -98.875 | 33.375 | -0.535 | 252 |
683 | -98.625 | 33.375 | -0.580 | 252 |
684 | -98.375 | 33.375 | -0.598 | 251 |
685 | -98.125 | 33.375 | -0.624 | 251 |
686 | -97.875 | 33.375 | -0.601 | 251 |
687 | -97.625 | 33.375 | -0.653 | 252 |
688 | -97.375 | 33.375 | -0.635 | 251 |
689 | -97.125 | 33.375 | -0.589 | 252 |
690 | -96.875 | 33.375 | -0.618 | 252 |
691 | -96.625 | 33.375 | -0.661 | 244 |
692 | -96.375 | 33.375 | -0.656 | 252 |
693 | -96.125 | 33.375 | -0.647 | 252 |
694 | -95.875 | 33.375 | -0.631 | 239 |
695 | -95.625 | 33.375 | -0.653 | 247 |
696 | -95.375 | 33.375 | -0.683 | 245 |
697 | -95.125 | 33.375 | -0.607 | 244 |
698 | -94.875 | 33.375 | -0.599 | 251 |
699 | -94.625 | 33.375 | -0.536 | 243 |
700 | -94.375 | 33.375 | -0.539 | 246 |
701 | -94.125 | 33.375 | -0.675 | 252 |
702 | -93.875 | 33.375 | -0.464 | 239 |
703 | -103.375 | 33.125 | -0.093 | 252 |
704 | -103.125 | 33.125 | -0.101 | 252 |
705 | -102.875 | 33.125 | -0.048 | 252 |
706 | -102.625 | 33.125 | 0.013 | 252 |
707 | -102.375 | 33.125 | -0.008 | 252 |
708 | -102.125 | 33.125 | -0.046 | 252 |
709 | -101.875 | 33.125 | -0.116 | 252 |
710 | -101.625 | 33.125 | -0.187 | 252 |
711 | -101.375 | 33.125 | -0.230 | 252 |
712 | -101.125 | 33.125 | -0.313 | 252 |
713 | -100.875 | 33.125 | -0.339 | 252 |
714 | -100.625 | 33.125 | -0.356 | 252 |
715 | -100.375 | 33.125 | -0.432 | 252 |
716 | -100.125 | 33.125 | -0.440 | 252 |
717 | -99.875 | 33.125 | -0.438 | 252 |
718 | -99.625 | 33.125 | -0.480 | 252 |
719 | -99.375 | 33.125 | -0.490 | 252 |
720 | -99.125 | 33.125 | -0.503 | 252 |
721 | -98.875 | 33.125 | -0.565 | 252 |
722 | -98.625 | 33.125 | -0.629 | 251 |
723 | -98.375 | 33.125 | -0.597 | 251 |
724 | -98.125 | 33.125 | -0.584 | 251 |
725 | -97.875 | 33.125 | -0.610 | 251 |
726 | -97.625 | 33.125 | -0.614 | 251 |
727 | -97.375 | 33.125 | -0.633 | 252 |
728 | -97.125 | 33.125 | -0.656 | 252 |
729 | -96.875 | 33.125 | -0.683 | 252 |
730 | -96.625 | 33.125 | -0.676 | 252 |
731 | -96.375 | 33.125 | -0.641 | 252 |
732 | -96.125 | 33.125 | -0.615 | 252 |
733 | -95.875 | 33.125 | -0.672 | 252 |
734 | -95.625 | 33.125 | -0.668 | 251 |
735 | -95.375 | 33.125 | -0.655 | 251 |
736 | -95.125 | 33.125 | -0.591 | 252 |
737 | -94.875 | 33.125 | -0.588 | 250 |
738 | -94.625 | 33.125 | -0.535 | 240 |
739 | -94.375 | 33.125 | -0.491 | 250 |
740 | -94.125 | 33.125 | -0.474 | 248 |
741 | -93.875 | 33.125 | -0.559 | 249 |
setwd("e:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
SoilMoisture_MEDIAN <- read.csv("SoilMoisture_region_interest_MEDIAN_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
SoilMoisture_MEDIAN[1:4] <- NULL
SoilMoisture_MEDIAN <- replace(SoilMoisture_MEDIAN, SoilMoisture_MEDIAN == -9999, NA)
SoilMoisture_MEDIAN <- t(SoilMoisture_MEDIAN)
SoilMoisture_MEDIAN <- as.data.frame(SoilMoisture_MEDIAN)
names(SoilMoisture_MEDIAN) <- paste(c(1:741))
Precipitation <- read.csv("Daymet_prcp_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Precipitation[1:4] <- NULL
Precipitation <- replace(Precipitation, Precipitation == -9999, NA)
Precipitation <- t(Precipitation)
Precipitation <- as.data.frame(Precipitation)
names(Precipitation) <- paste(c(1:741))
base_matrix <- read.csv("pixels.csv", header = TRUE, sep = ',', dec = '.')
final_temporal_correlation <- base_matrix
names(final_temporal_correlation)[4] <- paste('Corr_Precipitation')
for (i in 1:741) {
correlation <- cor(SoilMoisture_MEDIAN[i], Precipitation[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEDIAN[i]))
number_values <- 252 - number_values
final_temporal_correlation[i,4] <- correlation
final_temporal_correlation[i,5] <- number_values
}
mean_temp_corr_medianSM_Prcp <- round(mean(final_temporal_correlation$Corr_Precipitation), digits = 3)
kable(final_temporal_correlation, caption = 'Temporal Correlation Median Soil Moistre and Precipitation', digits = 3)
Pixel | X | Y | Corr_Precipitation | Number_of_pairs |
---|---|---|---|---|
1 | -103.375 | 37.625 | 0.241 | 252 |
2 | -103.125 | 37.625 | 0.248 | 232 |
3 | -102.875 | 37.625 | 0.367 | 211 |
4 | -102.625 | 37.625 | 0.255 | 234 |
5 | -102.375 | 37.625 | 0.284 | 251 |
6 | -102.125 | 37.625 | 0.412 | 221 |
7 | -101.875 | 37.625 | 0.353 | 236 |
8 | -101.625 | 37.625 | 0.278 | 252 |
9 | -101.375 | 37.625 | 0.284 | 252 |
10 | -101.125 | 37.625 | 0.275 | 252 |
11 | -100.875 | 37.625 | 0.296 | 251 |
12 | -100.625 | 37.625 | 0.302 | 249 |
13 | -100.375 | 37.625 | 0.279 | 248 |
14 | -100.125 | 37.625 | 0.388 | 225 |
15 | -99.875 | 37.625 | 0.305 | 247 |
16 | -99.625 | 37.625 | 0.264 | 247 |
17 | -99.375 | 37.625 | 0.281 | 247 |
18 | -99.125 | 37.625 | 0.354 | 234 |
19 | -98.875 | 37.625 | 0.326 | 232 |
20 | -98.625 | 37.625 | 0.268 | 249 |
21 | -98.375 | 37.625 | 0.209 | 251 |
22 | -98.125 | 37.625 | 0.214 | 251 |
23 | -97.875 | 37.625 | 0.136 | 236 |
24 | -97.625 | 37.625 | 0.114 | 250 |
25 | -97.375 | 37.625 | 0.065 | 249 |
26 | -97.125 | 37.625 | 0.126 | 247 |
27 | -96.875 | 37.625 | 0.169 | 247 |
28 | -96.625 | 37.625 | 0.169 | 243 |
29 | -96.375 | 37.625 | 0.219 | 241 |
30 | -96.125 | 37.625 | 0.146 | 241 |
31 | -95.875 | 37.625 | 0.098 | 243 |
32 | -95.625 | 37.625 | 0.099 | 244 |
33 | -95.375 | 37.625 | 0.123 | 242 |
34 | -95.125 | 37.625 | 0.154 | 241 |
35 | -94.875 | 37.625 | 0.228 | 245 |
36 | -94.625 | 37.625 | 0.210 | 241 |
37 | -94.375 | 37.625 | 0.209 | 238 |
38 | -94.125 | 37.625 | 0.252 | 233 |
39 | -93.875 | 37.625 | 0.283 | 233 |
40 | -103.375 | 37.375 | 0.250 | 251 |
41 | -103.125 | 37.375 | 0.255 | 251 |
42 | -102.875 | 37.375 | 0.404 | 233 |
43 | -102.625 | 37.375 | 0.424 | 235 |
44 | -102.375 | 37.375 | 0.341 | 251 |
45 | -102.125 | 37.375 | 0.355 | 250 |
46 | -101.875 | 37.375 | 0.304 | 251 |
47 | -101.625 | 37.375 | 0.378 | 241 |
48 | -101.375 | 37.375 | 0.240 | 242 |
49 | -101.125 | 37.375 | 0.227 | 241 |
50 | -100.875 | 37.375 | 0.199 | 240 |
51 | -100.625 | 37.375 | 0.179 | 234 |
52 | -100.375 | 37.375 | 0.256 | 227 |
53 | -100.125 | 37.375 | 0.359 | 244 |
54 | -99.875 | 37.375 | 0.243 | 203 |
55 | -99.625 | 37.375 | 0.375 | 231 |
56 | -99.375 | 37.375 | 0.337 | 231 |
57 | -99.125 | 37.375 | 0.308 | 248 |
58 | -98.875 | 37.375 | 0.263 | 245 |
59 | -98.625 | 37.375 | 0.294 | 248 |
60 | -98.375 | 37.375 | 0.280 | 249 |
61 | -98.125 | 37.375 | 0.209 | 221 |
62 | -97.875 | 37.375 | 0.201 | 231 |
63 | -97.625 | 37.375 | 0.169 | 249 |
64 | -97.375 | 37.375 | 0.127 | 251 |
65 | -97.125 | 37.375 | 0.084 | 248 |
66 | -96.875 | 37.375 | 0.131 | 245 |
67 | -96.625 | 37.375 | 0.218 | 243 |
68 | -96.375 | 37.375 | 0.232 | 241 |
69 | -96.125 | 37.375 | 0.168 | 241 |
70 | -95.875 | 37.375 | 0.107 | 243 |
71 | -95.625 | 37.375 | 0.132 | 243 |
72 | -95.375 | 37.375 | 0.216 | 228 |
73 | -95.125 | 37.375 | 0.235 | 236 |
74 | -94.875 | 37.375 | 0.226 | 241 |
75 | -94.625 | 37.375 | 0.169 | 237 |
76 | -94.375 | 37.375 | 0.226 | 236 |
77 | -94.125 | 37.375 | 0.214 | 233 |
78 | -93.875 | 37.375 | 0.246 | 237 |
79 | -103.375 | 37.125 | 0.403 | 237 |
80 | -103.125 | 37.125 | 0.433 | 237 |
81 | -102.875 | 37.125 | 0.403 | 251 |
82 | -102.625 | 37.125 | 0.400 | 251 |
83 | -102.375 | 37.125 | 0.343 | 251 |
84 | -102.125 | 37.125 | 0.361 | 251 |
85 | -101.875 | 37.125 | 0.272 | 251 |
86 | -101.625 | 37.125 | 0.369 | 242 |
87 | -101.375 | 37.125 | 0.262 | 251 |
88 | -101.125 | 37.125 | 0.259 | 252 |
89 | -100.875 | 37.125 | 0.328 | 250 |
90 | -100.625 | 37.125 | 0.281 | 252 |
91 | -100.375 | 37.125 | 0.320 | 250 |
92 | -100.125 | 37.125 | 0.342 | 225 |
93 | -99.875 | 37.125 | 0.320 | 242 |
94 | -99.625 | 37.125 | 0.342 | 244 |
95 | -99.375 | 37.125 | 0.291 | 243 |
96 | -99.125 | 37.125 | 0.272 | 247 |
97 | -98.875 | 37.125 | 0.281 | 245 |
98 | -98.625 | 37.125 | 0.299 | 248 |
99 | -98.375 | 37.125 | 0.299 | 221 |
100 | -98.125 | 37.125 | 0.256 | 228 |
101 | -97.875 | 37.125 | 0.290 | 250 |
102 | -97.625 | 37.125 | 0.226 | 250 |
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111 | -95.375 | 37.125 | 0.184 | 239 |
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113 | -94.875 | 37.125 | 0.281 | 226 |
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255 | -98.375 | 36.125 | 0.299 | 239 |
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313 | -103.375 | 35.625 | 0.299 | 252 |
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527 | -98.625 | 34.375 | 0.344 | 249 |
528 | -98.375 | 34.375 | 0.344 | 252 |
529 | -98.125 | 34.375 | 0.337 | 252 |
530 | -97.875 | 34.375 | 0.331 | 251 |
531 | -97.625 | 34.375 | 0.287 | 251 |
532 | -97.375 | 34.375 | 0.220 | 251 |
533 | -97.125 | 34.375 | 0.207 | 251 |
534 | -96.875 | 34.375 | 0.312 | 251 |
535 | -96.625 | 34.375 | 0.281 | 251 |
536 | -96.375 | 34.375 | 0.293 | 250 |
537 | -96.125 | 34.375 | 0.300 | 221 |
538 | -95.875 | 34.375 | 0.318 | 240 |
539 | -95.625 | 34.375 | 0.314 | 232 |
540 | -95.375 | 34.375 | 0.295 | 243 |
541 | -95.125 | 34.375 | 0.418 | 240 |
542 | -94.875 | 34.375 | 0.375 | 238 |
543 | -94.625 | 34.375 | 0.360 | 236 |
544 | -94.375 | 34.375 | 0.322 | 236 |
545 | -94.125 | 34.375 | 0.253 | 236 |
546 | -93.875 | 34.375 | 0.230 | 236 |
547 | -103.375 | 34.125 | 0.527 | 248 |
548 | -103.125 | 34.125 | 0.562 | 251 |
549 | -102.875 | 34.125 | 0.592 | 251 |
550 | -102.625 | 34.125 | 0.569 | 252 |
551 | -102.375 | 34.125 | 0.546 | 252 |
552 | -102.125 | 34.125 | 0.497 | 252 |
553 | -101.875 | 34.125 | 0.536 | 252 |
554 | -101.625 | 34.125 | 0.535 | 250 |
555 | -101.375 | 34.125 | 0.505 | 252 |
556 | -101.125 | 34.125 | 0.486 | 252 |
557 | -100.875 | 34.125 | 0.465 | 252 |
558 | -100.625 | 34.125 | 0.448 | 252 |
559 | -100.375 | 34.125 | 0.453 | 252 |
560 | -100.125 | 34.125 | 0.446 | 252 |
561 | -99.875 | 34.125 | 0.412 | 252 |
562 | -99.625 | 34.125 | 0.386 | 252 |
563 | -99.375 | 34.125 | 0.417 | 252 |
564 | -99.125 | 34.125 | 0.437 | 252 |
565 | -98.875 | 34.125 | 0.397 | 248 |
566 | -98.625 | 34.125 | 0.387 | 252 |
567 | -98.375 | 34.125 | 0.360 | 252 |
568 | -98.125 | 34.125 | 0.338 | 251 |
569 | -97.875 | 34.125 | 0.322 | 252 |
570 | -97.625 | 34.125 | 0.313 | 251 |
571 | -97.375 | 34.125 | 0.253 | 251 |
572 | -97.125 | 34.125 | 0.224 | 252 |
573 | -96.875 | 34.125 | 0.265 | 245 |
574 | -96.625 | 34.125 | 0.273 | 252 |
575 | -96.375 | 34.125 | 0.279 | 251 |
576 | -96.125 | 34.125 | 0.323 | 251 |
577 | -95.875 | 34.125 | 0.315 | 251 |
578 | -95.625 | 34.125 | 0.316 | 246 |
579 | -95.375 | 34.125 | 0.349 | 243 |
580 | -95.125 | 34.125 | 0.294 | 251 |
581 | -94.875 | 34.125 | 0.390 | 242 |
582 | -94.625 | 34.125 | 0.316 | 243 |
583 | -94.375 | 34.125 | 0.283 | 242 |
584 | -94.125 | 34.125 | 0.230 | 242 |
585 | -93.875 | 34.125 | 0.236 | 243 |
586 | -103.375 | 33.875 | 0.411 | 252 |
587 | -103.125 | 33.875 | 0.461 | 252 |
588 | -102.875 | 33.875 | 0.533 | 252 |
589 | -102.625 | 33.875 | 0.527 | 252 |
590 | -102.375 | 33.875 | 0.535 | 252 |
591 | -102.125 | 33.875 | 0.503 | 252 |
592 | -101.875 | 33.875 | 0.555 | 252 |
593 | -101.625 | 33.875 | 0.555 | 252 |
594 | -101.375 | 33.875 | 0.498 | 252 |
595 | -101.125 | 33.875 | 0.475 | 252 |
596 | -100.875 | 33.875 | 0.469 | 252 |
597 | -100.625 | 33.875 | 0.410 | 252 |
598 | -100.375 | 33.875 | 0.437 | 252 |
599 | -100.125 | 33.875 | 0.427 | 252 |
600 | -99.875 | 33.875 | 0.408 | 252 |
601 | -99.625 | 33.875 | 0.405 | 252 |
602 | -99.375 | 33.875 | 0.399 | 252 |
603 | -99.125 | 33.875 | 0.379 | 252 |
604 | -98.875 | 33.875 | 0.381 | 252 |
605 | -98.625 | 33.875 | 0.366 | 252 |
606 | -98.375 | 33.875 | 0.330 | 252 |
607 | -98.125 | 33.875 | 0.307 | 251 |
608 | -97.875 | 33.875 | 0.293 | 251 |
609 | -97.625 | 33.875 | 0.279 | 251 |
610 | -97.375 | 33.875 | 0.278 | 251 |
611 | -97.125 | 33.875 | 0.267 | 252 |
612 | -96.875 | 33.875 | 0.393 | 248 |
613 | -96.625 | 33.875 | 0.424 | 243 |
614 | -96.375 | 33.875 | 0.319 | 252 |
615 | -96.125 | 33.875 | 0.337 | 251 |
616 | -95.875 | 33.875 | 0.395 | 251 |
617 | -95.625 | 33.875 | 0.407 | 246 |
618 | -95.375 | 33.875 | 0.436 | 245 |
619 | -95.125 | 33.875 | 0.409 | 244 |
620 | -94.875 | 33.875 | 0.363 | 243 |
621 | -94.625 | 33.875 | 0.302 | 245 |
622 | -94.375 | 33.875 | 0.270 | 251 |
623 | -94.125 | 33.875 | 0.291 | 248 |
624 | -93.875 | 33.875 | 0.321 | 246 |
625 | -103.375 | 33.625 | 0.451 | 252 |
626 | -103.125 | 33.625 | 0.492 | 252 |
627 | -102.875 | 33.625 | 0.593 | 252 |
628 | -102.625 | 33.625 | 0.559 | 252 |
629 | -102.375 | 33.625 | 0.482 | 252 |
630 | -102.125 | 33.625 | 0.533 | 252 |
631 | -101.875 | 33.625 | 0.545 | 252 |
632 | -101.625 | 33.625 | 0.538 | 252 |
633 | -101.375 | 33.625 | 0.529 | 252 |
634 | -101.125 | 33.625 | 0.519 | 252 |
635 | -100.875 | 33.625 | 0.481 | 252 |
636 | -100.625 | 33.625 | 0.406 | 252 |
637 | -100.375 | 33.625 | 0.415 | 252 |
638 | -100.125 | 33.625 | 0.363 | 238 |
639 | -99.875 | 33.625 | 0.369 | 252 |
640 | -99.625 | 33.625 | 0.388 | 252 |
641 | -99.375 | 33.625 | 0.343 | 252 |
642 | -99.125 | 33.625 | 0.330 | 252 |
643 | -98.875 | 33.625 | 0.331 | 252 |
644 | -98.625 | 33.625 | 0.329 | 252 |
645 | -98.375 | 33.625 | 0.314 | 252 |
646 | -98.125 | 33.625 | 0.265 | 251 |
647 | -97.875 | 33.625 | 0.261 | 252 |
648 | -97.625 | 33.625 | 0.294 | 252 |
649 | -97.375 | 33.625 | 0.336 | 252 |
650 | -97.125 | 33.625 | 0.323 | 252 |
651 | -96.875 | 33.625 | 0.282 | 252 |
652 | -96.625 | 33.625 | 0.341 | 252 |
653 | -96.375 | 33.625 | 0.333 | 252 |
654 | -96.125 | 33.625 | 0.402 | 251 |
655 | -95.875 | 33.625 | 0.427 | 251 |
656 | -95.625 | 33.625 | 0.416 | 246 |
657 | -95.375 | 33.625 | 0.432 | 245 |
658 | -95.125 | 33.625 | 0.390 | 251 |
659 | -94.875 | 33.625 | 0.363 | 250 |
660 | -94.625 | 33.625 | 0.358 | 247 |
661 | -94.375 | 33.625 | 0.347 | 252 |
662 | -94.125 | 33.625 | 0.330 | 246 |
663 | -93.875 | 33.625 | 0.325 | 244 |
664 | -103.375 | 33.375 | 0.449 | 252 |
665 | -103.125 | 33.375 | 0.502 | 252 |
666 | -102.875 | 33.375 | 0.530 | 252 |
667 | -102.625 | 33.375 | 0.588 | 252 |
668 | -102.375 | 33.375 | 0.496 | 252 |
669 | -102.125 | 33.375 | 0.634 | 252 |
670 | -101.875 | 33.375 | 0.583 | 252 |
671 | -101.625 | 33.375 | 0.550 | 252 |
672 | -101.375 | 33.375 | 0.541 | 252 |
673 | -101.125 | 33.375 | 0.545 | 252 |
674 | -100.875 | 33.375 | 0.473 | 252 |
675 | -100.625 | 33.375 | 0.434 | 252 |
676 | -100.375 | 33.375 | 0.379 | 252 |
677 | -100.125 | 33.375 | 0.352 | 252 |
678 | -99.875 | 33.375 | 0.397 | 252 |
679 | -99.625 | 33.375 | 0.360 | 252 |
680 | -99.375 | 33.375 | 0.351 | 252 |
681 | -99.125 | 33.375 | 0.322 | 252 |
682 | -98.875 | 33.375 | 0.317 | 252 |
683 | -98.625 | 33.375 | 0.303 | 252 |
684 | -98.375 | 33.375 | 0.263 | 251 |
685 | -98.125 | 33.375 | 0.249 | 251 |
686 | -97.875 | 33.375 | 0.285 | 251 |
687 | -97.625 | 33.375 | 0.309 | 252 |
688 | -97.375 | 33.375 | 0.363 | 251 |
689 | -97.125 | 33.375 | 0.344 | 252 |
690 | -96.875 | 33.375 | 0.387 | 252 |
691 | -96.625 | 33.375 | 0.419 | 244 |
692 | -96.375 | 33.375 | 0.451 | 252 |
693 | -96.125 | 33.375 | 0.448 | 252 |
694 | -95.875 | 33.375 | 0.392 | 239 |
695 | -95.625 | 33.375 | 0.400 | 247 |
696 | -95.375 | 33.375 | 0.426 | 245 |
697 | -95.125 | 33.375 | 0.383 | 244 |
698 | -94.875 | 33.375 | 0.380 | 251 |
699 | -94.625 | 33.375 | 0.384 | 243 |
700 | -94.375 | 33.375 | 0.331 | 246 |
701 | -94.125 | 33.375 | 0.341 | 252 |
702 | -93.875 | 33.375 | 0.364 | 239 |
703 | -103.375 | 33.125 | 0.524 | 252 |
704 | -103.125 | 33.125 | 0.561 | 252 |
705 | -102.875 | 33.125 | 0.588 | 252 |
706 | -102.625 | 33.125 | 0.583 | 252 |
707 | -102.375 | 33.125 | 0.616 | 252 |
708 | -102.125 | 33.125 | 0.605 | 252 |
709 | -101.875 | 33.125 | 0.570 | 252 |
710 | -101.625 | 33.125 | 0.507 | 252 |
711 | -101.375 | 33.125 | 0.517 | 252 |
712 | -101.125 | 33.125 | 0.476 | 252 |
713 | -100.875 | 33.125 | 0.470 | 252 |
714 | -100.625 | 33.125 | 0.384 | 252 |
715 | -100.375 | 33.125 | 0.325 | 252 |
716 | -100.125 | 33.125 | 0.352 | 252 |
717 | -99.875 | 33.125 | 0.373 | 252 |
718 | -99.625 | 33.125 | 0.338 | 252 |
719 | -99.375 | 33.125 | 0.379 | 252 |
720 | -99.125 | 33.125 | 0.341 | 252 |
721 | -98.875 | 33.125 | 0.294 | 252 |
722 | -98.625 | 33.125 | 0.253 | 251 |
723 | -98.375 | 33.125 | 0.248 | 251 |
724 | -98.125 | 33.125 | 0.267 | 251 |
725 | -97.875 | 33.125 | 0.306 | 251 |
726 | -97.625 | 33.125 | 0.332 | 251 |
727 | -97.375 | 33.125 | 0.284 | 252 |
728 | -97.125 | 33.125 | 0.362 | 252 |
729 | -96.875 | 33.125 | 0.382 | 252 |
730 | -96.625 | 33.125 | 0.399 | 252 |
731 | -96.375 | 33.125 | 0.453 | 252 |
732 | -96.125 | 33.125 | 0.365 | 252 |
733 | -95.875 | 33.125 | 0.371 | 252 |
734 | -95.625 | 33.125 | 0.413 | 251 |
735 | -95.375 | 33.125 | 0.393 | 251 |
736 | -95.125 | 33.125 | 0.342 | 252 |
737 | -94.875 | 33.125 | 0.354 | 250 |
738 | -94.625 | 33.125 | 0.344 | 240 |
739 | -94.375 | 33.125 | 0.319 | 250 |
740 | -94.125 | 33.125 | 0.324 | 248 |
741 | -93.875 | 33.125 | 0.395 | 249 |
setwd("e:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
Temperature_Max <- read.csv("Daymet_tmax_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Temperature_Max[1:4] <- NULL
Temperature_Max <- replace(Temperature_Max, Temperature_Max == -9999, NA)
Temperature_Max <- t(Temperature_Max)
Temperature_Max <- as.data.frame(Temperature_Max)
names(Temperature_Max) <- paste(c(1:741))
base_matrix <- read.csv("pixels.csv", header = TRUE, sep = ',', dec = '.')
final_temporal_correlation <- base_matrix
names(final_temporal_correlation)[4] <- paste('Corr_Temperature_Max')
for (i in 1:741) {
correlation <- cor(SoilMoisture_MEDIAN[i], Temperature_Max[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEDIAN[i]))
number_values <- 252 - number_values
final_temporal_correlation[i,4] <- correlation
final_temporal_correlation[i,5] <- number_values
}
mean_temp_corr_medianSM_MaxTemp <- round(mean(final_temporal_correlation$Corr_Temperature_Max), digits = 3)
kable(final_temporal_correlation, caption = 'Temporal Correlation Median Soil Moistre and Max Temperature', digits = 3)
Pixel | X | Y | Corr_Temperature_Max | Number_of_pairs |
---|---|---|---|---|
1 | -103.375 | 37.625 | -0.486 | 252 |
2 | -103.125 | 37.625 | -0.418 | 232 |
3 | -102.875 | 37.625 | -0.201 | 211 |
4 | -102.625 | 37.625 | -0.279 | 234 |
5 | -102.375 | 37.625 | -0.277 | 251 |
6 | -102.125 | 37.625 | -0.131 | 221 |
7 | -101.875 | 37.625 | -0.228 | 236 |
8 | -101.625 | 37.625 | -0.313 | 252 |
9 | -101.375 | 37.625 | -0.249 | 252 |
10 | -101.125 | 37.625 | -0.179 | 252 |
11 | -100.875 | 37.625 | -0.223 | 251 |
12 | -100.625 | 37.625 | -0.260 | 249 |
13 | -100.375 | 37.625 | -0.371 | 248 |
14 | -100.125 | 37.625 | -0.236 | 225 |
15 | -99.875 | 37.625 | -0.366 | 247 |
16 | -99.625 | 37.625 | -0.357 | 247 |
17 | -99.375 | 37.625 | -0.320 | 247 |
18 | -99.125 | 37.625 | -0.230 | 234 |
19 | -98.875 | 37.625 | -0.213 | 232 |
20 | -98.625 | 37.625 | -0.313 | 249 |
21 | -98.375 | 37.625 | -0.333 | 251 |
22 | -98.125 | 37.625 | -0.353 | 251 |
23 | -97.875 | 37.625 | -0.380 | 236 |
24 | -97.625 | 37.625 | -0.364 | 250 |
25 | -97.375 | 37.625 | -0.482 | 249 |
26 | -97.125 | 37.625 | -0.446 | 247 |
27 | -96.875 | 37.625 | -0.510 | 247 |
28 | -96.625 | 37.625 | -0.573 | 243 |
29 | -96.375 | 37.625 | -0.538 | 241 |
30 | -96.125 | 37.625 | -0.597 | 241 |
31 | -95.875 | 37.625 | -0.605 | 243 |
32 | -95.625 | 37.625 | -0.585 | 244 |
33 | -95.375 | 37.625 | -0.541 | 242 |
34 | -95.125 | 37.625 | -0.541 | 241 |
35 | -94.875 | 37.625 | -0.500 | 245 |
36 | -94.625 | 37.625 | -0.531 | 241 |
37 | -94.375 | 37.625 | -0.515 | 238 |
38 | -94.125 | 37.625 | -0.483 | 233 |
39 | -93.875 | 37.625 | -0.434 | 233 |
40 | -103.375 | 37.375 | -0.434 | 251 |
41 | -103.125 | 37.375 | -0.375 | 251 |
42 | -102.875 | 37.375 | -0.207 | 233 |
43 | -102.625 | 37.375 | -0.126 | 235 |
44 | -102.375 | 37.375 | -0.207 | 251 |
45 | -102.125 | 37.375 | -0.198 | 250 |
46 | -101.875 | 37.375 | -0.231 | 251 |
47 | -101.625 | 37.375 | -0.118 | 241 |
48 | -101.375 | 37.375 | -0.253 | 242 |
49 | -101.125 | 37.375 | -0.336 | 241 |
50 | -100.875 | 37.375 | -0.395 | 240 |
51 | -100.625 | 37.375 | -0.450 | 234 |
52 | -100.375 | 37.375 | -0.447 | 227 |
53 | -100.125 | 37.375 | -0.356 | 244 |
54 | -99.875 | 37.375 | -0.350 | 203 |
55 | -99.625 | 37.375 | -0.358 | 231 |
56 | -99.375 | 37.375 | -0.394 | 231 |
57 | -99.125 | 37.375 | -0.369 | 248 |
58 | -98.875 | 37.375 | -0.432 | 245 |
59 | -98.625 | 37.375 | -0.378 | 248 |
60 | -98.375 | 37.375 | -0.339 | 249 |
61 | -98.125 | 37.375 | -0.448 | 221 |
62 | -97.875 | 37.375 | -0.436 | 231 |
63 | -97.625 | 37.375 | -0.414 | 249 |
64 | -97.375 | 37.375 | -0.439 | 251 |
65 | -97.125 | 37.375 | -0.546 | 248 |
66 | -96.875 | 37.375 | -0.554 | 245 |
67 | -96.625 | 37.375 | -0.563 | 243 |
68 | -96.375 | 37.375 | -0.565 | 241 |
69 | -96.125 | 37.375 | -0.606 | 241 |
70 | -95.875 | 37.375 | -0.633 | 243 |
71 | -95.625 | 37.375 | -0.600 | 243 |
72 | -95.375 | 37.375 | -0.417 | 228 |
73 | -95.125 | 37.375 | -0.422 | 236 |
74 | -94.875 | 37.375 | -0.479 | 241 |
75 | -94.625 | 37.375 | -0.476 | 237 |
76 | -94.375 | 37.375 | -0.496 | 236 |
77 | -94.125 | 37.375 | -0.528 | 233 |
78 | -93.875 | 37.375 | -0.482 | 237 |
79 | -103.375 | 37.125 | -0.222 | 237 |
80 | -103.125 | 37.125 | -0.203 | 237 |
81 | -102.875 | 37.125 | -0.211 | 251 |
82 | -102.625 | 37.125 | -0.209 | 251 |
83 | -102.375 | 37.125 | -0.268 | 251 |
84 | -102.125 | 37.125 | -0.228 | 251 |
85 | -101.875 | 37.125 | -0.236 | 251 |
86 | -101.625 | 37.125 | -0.131 | 242 |
87 | -101.375 | 37.125 | -0.216 | 251 |
88 | -101.125 | 37.125 | -0.197 | 252 |
89 | -100.875 | 37.125 | -0.234 | 250 |
90 | -100.625 | 37.125 | -0.349 | 252 |
91 | -100.375 | 37.125 | -0.395 | 250 |
92 | -100.125 | 37.125 | -0.373 | 225 |
93 | -99.875 | 37.125 | -0.415 | 242 |
94 | -99.625 | 37.125 | -0.347 | 244 |
95 | -99.375 | 37.125 | -0.439 | 243 |
96 | -99.125 | 37.125 | -0.479 | 247 |
97 | -98.875 | 37.125 | -0.466 | 245 |
98 | -98.625 | 37.125 | -0.428 | 248 |
99 | -98.375 | 37.125 | -0.394 | 221 |
100 | -98.125 | 37.125 | -0.385 | 228 |
101 | -97.875 | 37.125 | -0.400 | 250 |
102 | -97.625 | 37.125 | -0.416 | 250 |
103 | -97.375 | 37.125 | -0.437 | 249 |
104 | -97.125 | 37.125 | -0.501 | 251 |
105 | -96.875 | 37.125 | -0.608 | 247 |
106 | -96.625 | 37.125 | -0.574 | 242 |
107 | -96.375 | 37.125 | -0.555 | 240 |
108 | -96.125 | 37.125 | -0.537 | 241 |
109 | -95.875 | 37.125 | -0.588 | 243 |
110 | -95.625 | 37.125 | -0.563 | 247 |
111 | -95.375 | 37.125 | -0.589 | 239 |
112 | -95.125 | 37.125 | -0.617 | 244 |
113 | -94.875 | 37.125 | -0.389 | 226 |
114 | -94.625 | 37.125 | -0.495 | 241 |
115 | -94.375 | 37.125 | -0.510 | 236 |
116 | -94.125 | 37.125 | -0.564 | 243 |
117 | -93.875 | 37.125 | -0.574 | 242 |
118 | -103.375 | 36.875 | -0.377 | 252 |
119 | -103.125 | 36.875 | -0.378 | 251 |
120 | -102.875 | 36.875 | -0.372 | 251 |
121 | -102.625 | 36.875 | -0.404 | 250 |
122 | -102.375 | 36.875 | -0.375 | 251 |
123 | -102.125 | 36.875 | -0.348 | 251 |
124 | -101.875 | 36.875 | -0.310 | 252 |
125 | -101.625 | 36.875 | -0.370 | 252 |
126 | -101.375 | 36.875 | -0.447 | 252 |
127 | -101.125 | 36.875 | -0.356 | 252 |
128 | -100.875 | 36.875 | -0.403 | 252 |
129 | -100.625 | 36.875 | -0.482 | 250 |
130 | -100.375 | 36.875 | -0.496 | 249 |
131 | -100.125 | 36.875 | -0.537 | 250 |
132 | -99.875 | 36.875 | -0.480 | 246 |
133 | -99.625 | 36.875 | -0.528 | 249 |
134 | -99.375 | 36.875 | -0.527 | 246 |
135 | -99.125 | 36.875 | -0.576 | 248 |
136 | -98.875 | 36.875 | -0.551 | 250 |
137 | -98.625 | 36.875 | -0.494 | 241 |
138 | -98.375 | 36.875 | -0.364 | 247 |
139 | -98.125 | 36.875 | -0.414 | 250 |
140 | -97.875 | 36.875 | -0.439 | 249 |
141 | -97.625 | 36.875 | -0.456 | 250 |
142 | -97.375 | 36.875 | -0.478 | 251 |
143 | -97.125 | 36.875 | -0.599 | 250 |
144 | -96.875 | 36.875 | -0.706 | 250 |
145 | -96.625 | 36.875 | -0.668 | 246 |
146 | -96.375 | 36.875 | -0.655 | 246 |
147 | -96.125 | 36.875 | -0.644 | 246 |
148 | -95.875 | 36.875 | -0.659 | 246 |
149 | -95.625 | 36.875 | -0.649 | 247 |
150 | -95.375 | 36.875 | -0.646 | 245 |
151 | -95.125 | 36.875 | -0.668 | 247 |
152 | -94.875 | 36.875 | -0.644 | 244 |
153 | -94.625 | 36.875 | -0.628 | 244 |
154 | -94.375 | 36.875 | -0.578 | 244 |
155 | -94.125 | 36.875 | -0.602 | 238 |
156 | -93.875 | 36.875 | -0.569 | 241 |
157 | -103.375 | 36.625 | -0.363 | 252 |
158 | -103.125 | 36.625 | -0.358 | 252 |
159 | -102.875 | 36.625 | -0.369 | 251 |
160 | -102.625 | 36.625 | -0.377 | 250 |
161 | -102.375 | 36.625 | -0.374 | 251 |
162 | -102.125 | 36.625 | -0.352 | 251 |
163 | -101.875 | 36.625 | -0.348 | 252 |
164 | -101.625 | 36.625 | -0.365 | 252 |
165 | -101.375 | 36.625 | -0.412 | 251 |
166 | -101.125 | 36.625 | -0.403 | 249 |
167 | -100.875 | 36.625 | -0.437 | 251 |
168 | -100.625 | 36.625 | -0.497 | 251 |
169 | -100.375 | 36.625 | -0.533 | 251 |
170 | -100.125 | 36.625 | -0.540 | 249 |
171 | -99.875 | 36.625 | -0.535 | 249 |
172 | -99.625 | 36.625 | -0.547 | 250 |
173 | -99.375 | 36.625 | -0.559 | 251 |
174 | -99.125 | 36.625 | -0.548 | 247 |
175 | -98.875 | 36.625 | -0.545 | 250 |
176 | -98.625 | 36.625 | -0.472 | 250 |
177 | -98.375 | 36.625 | -0.416 | 250 |
178 | -98.125 | 36.625 | -0.464 | 251 |
179 | -97.875 | 36.625 | -0.450 | 250 |
180 | -97.625 | 36.625 | -0.460 | 250 |
181 | -97.375 | 36.625 | -0.515 | 250 |
182 | -97.125 | 36.625 | -0.626 | 249 |
183 | -96.875 | 36.625 | -0.682 | 250 |
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588 | -102.875 | 33.875 | -0.220 | 252 |
589 | -102.625 | 33.875 | -0.257 | 252 |
590 | -102.375 | 33.875 | -0.244 | 252 |
591 | -102.125 | 33.875 | -0.212 | 252 |
592 | -101.875 | 33.875 | -0.196 | 252 |
593 | -101.625 | 33.875 | -0.284 | 252 |
594 | -101.375 | 33.875 | -0.328 | 252 |
595 | -101.125 | 33.875 | -0.332 | 252 |
596 | -100.875 | 33.875 | -0.425 | 252 |
597 | -100.625 | 33.875 | -0.473 | 252 |
598 | -100.375 | 33.875 | -0.504 | 252 |
599 | -100.125 | 33.875 | -0.534 | 252 |
600 | -99.875 | 33.875 | -0.550 | 252 |
601 | -99.625 | 33.875 | -0.559 | 252 |
602 | -99.375 | 33.875 | -0.598 | 252 |
603 | -99.125 | 33.875 | -0.592 | 252 |
604 | -98.875 | 33.875 | -0.602 | 252 |
605 | -98.625 | 33.875 | -0.626 | 252 |
606 | -98.375 | 33.875 | -0.653 | 252 |
607 | -98.125 | 33.875 | -0.686 | 251 |
608 | -97.875 | 33.875 | -0.705 | 251 |
609 | -97.625 | 33.875 | -0.728 | 251 |
610 | -97.375 | 33.875 | -0.751 | 251 |
611 | -97.125 | 33.875 | -0.761 | 252 |
612 | -96.875 | 33.875 | -0.616 | 248 |
613 | -96.625 | 33.875 | -0.556 | 243 |
614 | -96.375 | 33.875 | -0.777 | 252 |
615 | -96.125 | 33.875 | -0.787 | 251 |
616 | -95.875 | 33.875 | -0.760 | 251 |
617 | -95.625 | 33.875 | -0.728 | 246 |
618 | -95.375 | 33.875 | -0.697 | 245 |
619 | -95.125 | 33.875 | -0.684 | 244 |
620 | -94.875 | 33.875 | -0.648 | 243 |
621 | -94.625 | 33.875 | -0.522 | 245 |
622 | -94.375 | 33.875 | -0.598 | 251 |
623 | -94.125 | 33.875 | -0.572 | 248 |
624 | -93.875 | 33.875 | -0.703 | 246 |
625 | -103.375 | 33.625 | -0.348 | 252 |
626 | -103.125 | 33.625 | -0.299 | 252 |
627 | -102.875 | 33.625 | -0.215 | 252 |
628 | -102.625 | 33.625 | -0.200 | 252 |
629 | -102.375 | 33.625 | -0.215 | 252 |
630 | -102.125 | 33.625 | -0.163 | 252 |
631 | -101.875 | 33.625 | -0.215 | 252 |
632 | -101.625 | 33.625 | -0.286 | 252 |
633 | -101.375 | 33.625 | -0.354 | 252 |
634 | -101.125 | 33.625 | -0.332 | 252 |
635 | -100.875 | 33.625 | -0.417 | 252 |
636 | -100.625 | 33.625 | -0.463 | 252 |
637 | -100.375 | 33.625 | -0.478 | 252 |
638 | -100.125 | 33.625 | -0.584 | 238 |
639 | -99.875 | 33.625 | -0.535 | 252 |
640 | -99.625 | 33.625 | -0.555 | 252 |
641 | -99.375 | 33.625 | -0.606 | 252 |
642 | -99.125 | 33.625 | -0.625 | 252 |
643 | -98.875 | 33.625 | -0.631 | 252 |
644 | -98.625 | 33.625 | -0.666 | 252 |
645 | -98.375 | 33.625 | -0.688 | 252 |
646 | -98.125 | 33.625 | -0.698 | 251 |
647 | -97.875 | 33.625 | -0.723 | 252 |
648 | -97.625 | 33.625 | -0.760 | 252 |
649 | -97.375 | 33.625 | -0.751 | 252 |
650 | -97.125 | 33.625 | -0.737 | 252 |
651 | -96.875 | 33.625 | -0.700 | 252 |
652 | -96.625 | 33.625 | -0.724 | 252 |
653 | -96.375 | 33.625 | -0.725 | 252 |
654 | -96.125 | 33.625 | -0.731 | 251 |
655 | -95.875 | 33.625 | -0.772 | 251 |
656 | -95.625 | 33.625 | -0.719 | 246 |
657 | -95.375 | 33.625 | -0.716 | 245 |
658 | -95.125 | 33.625 | -0.704 | 251 |
659 | -94.875 | 33.625 | -0.673 | 250 |
660 | -94.625 | 33.625 | -0.667 | 247 |
661 | -94.375 | 33.625 | -0.675 | 252 |
662 | -94.125 | 33.625 | -0.647 | 246 |
663 | -93.875 | 33.625 | -0.666 | 244 |
664 | -103.375 | 33.375 | -0.314 | 252 |
665 | -103.125 | 33.375 | -0.275 | 252 |
666 | -102.875 | 33.375 | -0.241 | 252 |
667 | -102.625 | 33.375 | -0.161 | 252 |
668 | -102.375 | 33.375 | -0.175 | 252 |
669 | -102.125 | 33.375 | -0.130 | 252 |
670 | -101.875 | 33.375 | -0.202 | 252 |
671 | -101.625 | 33.375 | -0.265 | 252 |
672 | -101.375 | 33.375 | -0.340 | 252 |
673 | -101.125 | 33.375 | -0.383 | 252 |
674 | -100.875 | 33.375 | -0.459 | 252 |
675 | -100.625 | 33.375 | -0.473 | 252 |
676 | -100.375 | 33.375 | -0.481 | 252 |
677 | -100.125 | 33.375 | -0.522 | 252 |
678 | -99.875 | 33.375 | -0.551 | 252 |
679 | -99.625 | 33.375 | -0.581 | 252 |
680 | -99.375 | 33.375 | -0.592 | 252 |
681 | -99.125 | 33.375 | -0.604 | 252 |
682 | -98.875 | 33.375 | -0.633 | 252 |
683 | -98.625 | 33.375 | -0.691 | 252 |
684 | -98.375 | 33.375 | -0.704 | 251 |
685 | -98.125 | 33.375 | -0.725 | 251 |
686 | -97.875 | 33.375 | -0.709 | 251 |
687 | -97.625 | 33.375 | -0.750 | 252 |
688 | -97.375 | 33.375 | -0.722 | 251 |
689 | -97.125 | 33.375 | -0.644 | 252 |
690 | -96.875 | 33.375 | -0.673 | 252 |
691 | -96.625 | 33.375 | -0.717 | 244 |
692 | -96.375 | 33.375 | -0.723 | 252 |
693 | -96.125 | 33.375 | -0.714 | 252 |
694 | -95.875 | 33.375 | -0.690 | 239 |
695 | -95.625 | 33.375 | -0.702 | 247 |
696 | -95.375 | 33.375 | -0.746 | 245 |
697 | -95.125 | 33.375 | -0.667 | 244 |
698 | -94.875 | 33.375 | -0.674 | 251 |
699 | -94.625 | 33.375 | -0.618 | 243 |
700 | -94.375 | 33.375 | -0.616 | 246 |
701 | -94.125 | 33.375 | -0.753 | 252 |
702 | -93.875 | 33.375 | -0.560 | 239 |
703 | -103.375 | 33.125 | -0.282 | 252 |
704 | -103.125 | 33.125 | -0.267 | 252 |
705 | -102.875 | 33.125 | -0.230 | 252 |
706 | -102.625 | 33.125 | -0.182 | 252 |
707 | -102.375 | 33.125 | -0.157 | 252 |
708 | -102.125 | 33.125 | -0.219 | 252 |
709 | -101.875 | 33.125 | -0.293 | 252 |
710 | -101.625 | 33.125 | -0.376 | 252 |
711 | -101.375 | 33.125 | -0.395 | 252 |
712 | -101.125 | 33.125 | -0.484 | 252 |
713 | -100.875 | 33.125 | -0.483 | 252 |
714 | -100.625 | 33.125 | -0.485 | 252 |
715 | -100.375 | 33.125 | -0.569 | 252 |
716 | -100.125 | 33.125 | -0.584 | 252 |
717 | -99.875 | 33.125 | -0.560 | 252 |
718 | -99.625 | 33.125 | -0.597 | 252 |
719 | -99.375 | 33.125 | -0.602 | 252 |
720 | -99.125 | 33.125 | -0.607 | 252 |
721 | -98.875 | 33.125 | -0.664 | 252 |
722 | -98.625 | 33.125 | -0.727 | 251 |
723 | -98.375 | 33.125 | -0.712 | 251 |
724 | -98.125 | 33.125 | -0.704 | 251 |
725 | -97.875 | 33.125 | -0.719 | 251 |
726 | -97.625 | 33.125 | -0.699 | 251 |
727 | -97.375 | 33.125 | -0.716 | 252 |
728 | -97.125 | 33.125 | -0.710 | 252 |
729 | -96.875 | 33.125 | -0.732 | 252 |
730 | -96.625 | 33.125 | -0.730 | 252 |
731 | -96.375 | 33.125 | -0.733 | 252 |
732 | -96.125 | 33.125 | -0.685 | 252 |
733 | -95.875 | 33.125 | -0.731 | 252 |
734 | -95.625 | 33.125 | -0.719 | 251 |
735 | -95.375 | 33.125 | -0.742 | 251 |
736 | -95.125 | 33.125 | -0.644 | 252 |
737 | -94.875 | 33.125 | -0.660 | 250 |
738 | -94.625 | 33.125 | -0.583 | 240 |
739 | -94.375 | 33.125 | -0.570 | 250 |
740 | -94.125 | 33.125 | -0.552 | 248 |
741 | -93.875 | 33.125 | -0.638 | 249 |
setwd("e:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
Temperature_Min <- read.csv("Daymet_tmin_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Temperature_Min[1:4] <- NULL
Temperature_Min <- replace(Temperature_Min, Temperature_Min == -9999, NA)
Temperature_Min <- t(Temperature_Min)
Temperature_Min <- as.data.frame(Temperature_Min)
names(Temperature_Min) <- paste(c(1:741))
base_matrix <- read.csv("pixels.csv", header = TRUE, sep = ',', dec = '.')
final_temporal_correlation <- base_matrix
names(final_temporal_correlation)[4] <- paste('Corr_Temperature_Min')
for (i in 1:741) {
correlation <- cor(SoilMoisture_MEDIAN[i], Temperature_Min[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEDIAN[i]))
number_values <- 252 - number_values
final_temporal_correlation[i,4] <- correlation
final_temporal_correlation[i,5] <- number_values
}
mean_temp_corr_medianSM_MinTemp <- round(mean(final_temporal_correlation$Corr_Temperature_Min), digits = 3)
kable(final_temporal_correlation, caption = 'Temporal Correlation Median Soil Moistre and Min Temperature', digits = 3)
Pixel | X | Y | Corr_Temperature_Min | Number_of_pairs |
---|---|---|---|---|
1 | -103.375 | 37.625 | -0.390 | 252 |
2 | -103.125 | 37.625 | -0.319 | 232 |
3 | -102.875 | 37.625 | -0.083 | 211 |
4 | -102.625 | 37.625 | -0.176 | 234 |
5 | -102.375 | 37.625 | -0.175 | 251 |
6 | -102.125 | 37.625 | -0.030 | 221 |
7 | -101.875 | 37.625 | -0.125 | 236 |
8 | -101.625 | 37.625 | -0.206 | 252 |
9 | -101.375 | 37.625 | -0.147 | 252 |
10 | -101.125 | 37.625 | -0.095 | 252 |
11 | -100.875 | 37.625 | -0.137 | 251 |
12 | -100.625 | 37.625 | -0.164 | 249 |
13 | -100.375 | 37.625 | -0.256 | 248 |
14 | -100.125 | 37.625 | -0.109 | 225 |
15 | -99.875 | 37.625 | -0.254 | 247 |
16 | -99.625 | 37.625 | -0.244 | 247 |
17 | -99.375 | 37.625 | -0.210 | 247 |
18 | -99.125 | 37.625 | -0.111 | 234 |
19 | -98.875 | 37.625 | -0.093 | 232 |
20 | -98.625 | 37.625 | -0.203 | 249 |
21 | -98.375 | 37.625 | -0.230 | 251 |
22 | -98.125 | 37.625 | -0.250 | 251 |
23 | -97.875 | 37.625 | -0.307 | 236 |
24 | -97.625 | 37.625 | -0.284 | 250 |
25 | -97.375 | 37.625 | -0.412 | 249 |
26 | -97.125 | 37.625 | -0.381 | 247 |
27 | -96.875 | 37.625 | -0.431 | 247 |
28 | -96.625 | 37.625 | -0.495 | 243 |
29 | -96.375 | 37.625 | -0.457 | 241 |
30 | -96.125 | 37.625 | -0.522 | 241 |
31 | -95.875 | 37.625 | -0.531 | 243 |
32 | -95.625 | 37.625 | -0.516 | 244 |
33 | -95.375 | 37.625 | -0.474 | 242 |
34 | -95.125 | 37.625 | -0.477 | 241 |
35 | -94.875 | 37.625 | -0.435 | 245 |
36 | -94.625 | 37.625 | -0.467 | 241 |
37 | -94.375 | 37.625 | -0.460 | 238 |
38 | -94.125 | 37.625 | -0.424 | 233 |
39 | -93.875 | 37.625 | -0.378 | 233 |
40 | -103.375 | 37.375 | -0.337 | 251 |
41 | -103.125 | 37.375 | -0.276 | 251 |
42 | -102.875 | 37.375 | -0.083 | 233 |
43 | -102.625 | 37.375 | -0.010 | 235 |
44 | -102.375 | 37.375 | -0.105 | 251 |
45 | -102.125 | 37.375 | -0.098 | 250 |
46 | -101.875 | 37.375 | -0.132 | 251 |
47 | -101.625 | 37.375 | -0.005 | 241 |
48 | -101.375 | 37.375 | -0.173 | 242 |
49 | -101.125 | 37.375 | -0.253 | 241 |
50 | -100.875 | 37.375 | -0.315 | 240 |
51 | -100.625 | 37.375 | -0.364 | 234 |
52 | -100.375 | 37.375 | -0.349 | 227 |
53 | -100.125 | 37.375 | -0.236 | 244 |
54 | -99.875 | 37.375 | -0.260 | 203 |
55 | -99.625 | 37.375 | -0.233 | 231 |
56 | -99.375 | 37.375 | -0.270 | 231 |
57 | -99.125 | 37.375 | -0.266 | 248 |
58 | -98.875 | 37.375 | -0.322 | 245 |
59 | -98.625 | 37.375 | -0.266 | 248 |
60 | -98.375 | 37.375 | -0.233 | 249 |
61 | -98.125 | 37.375 | -0.369 | 221 |
62 | -97.875 | 37.375 | -0.354 | 231 |
63 | -97.625 | 37.375 | -0.330 | 249 |
64 | -97.375 | 37.375 | -0.358 | 251 |
65 | -97.125 | 37.375 | -0.473 | 248 |
66 | -96.875 | 37.375 | -0.473 | 245 |
67 | -96.625 | 37.375 | -0.484 | 243 |
68 | -96.375 | 37.375 | -0.481 | 241 |
69 | -96.125 | 37.375 | -0.525 | 241 |
70 | -95.875 | 37.375 | -0.557 | 243 |
71 | -95.625 | 37.375 | -0.525 | 243 |
72 | -95.375 | 37.375 | -0.344 | 228 |
73 | -95.125 | 37.375 | -0.347 | 236 |
74 | -94.875 | 37.375 | -0.407 | 241 |
75 | -94.625 | 37.375 | -0.418 | 237 |
76 | -94.375 | 37.375 | -0.424 | 236 |
77 | -94.125 | 37.375 | -0.469 | 233 |
78 | -93.875 | 37.375 | -0.419 | 237 |
79 | -103.375 | 37.125 | -0.108 | 237 |
80 | -103.125 | 37.125 | -0.077 | 237 |
81 | -102.875 | 37.125 | -0.102 | 251 |
82 | -102.625 | 37.125 | -0.098 | 251 |
83 | -102.375 | 37.125 | -0.152 | 251 |
84 | -102.125 | 37.125 | -0.120 | 251 |
85 | -101.875 | 37.125 | -0.151 | 251 |
86 | -101.625 | 37.125 | -0.009 | 242 |
87 | -101.375 | 37.125 | -0.121 | 251 |
88 | -101.125 | 37.125 | -0.102 | 252 |
89 | -100.875 | 37.125 | -0.120 | 250 |
90 | -100.625 | 37.125 | -0.236 | 252 |
91 | -100.375 | 37.125 | -0.285 | 250 |
92 | -100.125 | 37.125 | -0.246 | 225 |
93 | -99.875 | 37.125 | -0.292 | 242 |
94 | -99.625 | 37.125 | -0.222 | 244 |
95 | -99.375 | 37.125 | -0.320 | 243 |
96 | -99.125 | 37.125 | -0.369 | 247 |
97 | -98.875 | 37.125 | -0.354 | 245 |
98 | -98.625 | 37.125 | -0.314 | 248 |
99 | -98.375 | 37.125 | -0.289 | 221 |
100 | -98.125 | 37.125 | -0.298 | 228 |
101 | -97.875 | 37.125 | -0.304 | 250 |
102 | -97.625 | 37.125 | -0.329 | 250 |
103 | -97.375 | 37.125 | -0.352 | 249 |
104 | -97.125 | 37.125 | -0.428 | 251 |
105 | -96.875 | 37.125 | -0.526 | 247 |
106 | -96.625 | 37.125 | -0.496 | 242 |
107 | -96.375 | 37.125 | -0.470 | 240 |
108 | -96.125 | 37.125 | -0.458 | 241 |
109 | -95.875 | 37.125 | -0.508 | 243 |
110 | -95.625 | 37.125 | -0.486 | 247 |
111 | -95.375 | 37.125 | -0.518 | 239 |
112 | -95.125 | 37.125 | -0.550 | 244 |
113 | -94.875 | 37.125 | -0.309 | 226 |
114 | -94.625 | 37.125 | -0.430 | 241 |
115 | -94.375 | 37.125 | -0.445 | 236 |
116 | -94.125 | 37.125 | -0.502 | 243 |
117 | -93.875 | 37.125 | -0.519 | 242 |
118 | -103.375 | 36.875 | -0.283 | 252 |
119 | -103.125 | 36.875 | -0.279 | 251 |
120 | -102.875 | 36.875 | -0.263 | 251 |
121 | -102.625 | 36.875 | -0.291 | 250 |
122 | -102.375 | 36.875 | -0.261 | 251 |
123 | -102.125 | 36.875 | -0.229 | 251 |
124 | -101.875 | 36.875 | -0.201 | 252 |
125 | -101.625 | 36.875 | -0.262 | 252 |
126 | -101.375 | 36.875 | -0.340 | 252 |
127 | -101.125 | 36.875 | -0.238 | 252 |
128 | -100.875 | 36.875 | -0.284 | 252 |
129 | -100.625 | 36.875 | -0.363 | 250 |
130 | -100.375 | 36.875 | -0.377 | 249 |
131 | -100.125 | 36.875 | -0.425 | 250 |
132 | -99.875 | 36.875 | -0.362 | 246 |
133 | -99.625 | 36.875 | -0.417 | 249 |
134 | -99.375 | 36.875 | -0.417 | 246 |
135 | -99.125 | 36.875 | -0.470 | 248 |
136 | -98.875 | 36.875 | -0.445 | 250 |
137 | -98.625 | 36.875 | -0.406 | 241 |
138 | -98.375 | 36.875 | -0.264 | 247 |
139 | -98.125 | 36.875 | -0.311 | 250 |
140 | -97.875 | 36.875 | -0.335 | 249 |
141 | -97.625 | 36.875 | -0.358 | 250 |
142 | -97.375 | 36.875 | -0.385 | 251 |
143 | -97.125 | 36.875 | -0.515 | 250 |
144 | -96.875 | 36.875 | -0.632 | 250 |
145 | -96.625 | 36.875 | -0.594 | 246 |
146 | -96.375 | 36.875 | -0.579 | 246 |
147 | -96.125 | 36.875 | -0.571 | 246 |
148 | -95.875 | 36.875 | -0.587 | 246 |
149 | -95.625 | 36.875 | -0.580 | 247 |
150 | -95.375 | 36.875 | -0.583 | 245 |
151 | -95.125 | 36.875 | -0.605 | 247 |
152 | -94.875 | 36.875 | -0.576 | 244 |
153 | -94.625 | 36.875 | -0.571 | 244 |
154 | -94.375 | 36.875 | -0.517 | 244 |
155 | -94.125 | 36.875 | -0.542 | 238 |
156 | -93.875 | 36.875 | -0.516 | 241 |
157 | -103.375 | 36.625 | -0.252 | 252 |
158 | -103.125 | 36.625 | -0.246 | 252 |
159 | -102.875 | 36.625 | -0.257 | 251 |
160 | -102.625 | 36.625 | -0.255 | 250 |
161 | -102.375 | 36.625 | -0.254 | 251 |
162 | -102.125 | 36.625 | -0.229 | 251 |
163 | -101.875 | 36.625 | -0.223 | 252 |
164 | -101.625 | 36.625 | -0.245 | 252 |
165 | -101.375 | 36.625 | -0.289 | 251 |
166 | -101.125 | 36.625 | -0.277 | 249 |
167 | -100.875 | 36.625 | -0.313 | 251 |
168 | -100.625 | 36.625 | -0.374 | 251 |
169 | -100.375 | 36.625 | -0.414 | 251 |
170 | -100.125 | 36.625 | -0.422 | 249 |
171 | -99.875 | 36.625 | -0.417 | 249 |
172 | -99.625 | 36.625 | -0.437 | 250 |
173 | -99.375 | 36.625 | -0.453 | 251 |
174 | -99.125 | 36.625 | -0.442 | 247 |
175 | -98.875 | 36.625 | -0.444 | 250 |
176 | -98.625 | 36.625 | -0.370 | 250 |
177 | -98.375 | 36.625 | -0.308 | 250 |
178 | -98.125 | 36.625 | -0.367 | 251 |
179 | -97.875 | 36.625 | -0.350 | 250 |
180 | -97.625 | 36.625 | -0.362 | 250 |
181 | -97.375 | 36.625 | -0.422 | 250 |
182 | -97.125 | 36.625 | -0.540 | 249 |
183 | -96.875 | 36.625 | -0.609 | 250 |
184 | -96.625 | 36.625 | -0.627 | 247 |
185 | -96.375 | 36.625 | -0.589 | 247 |
186 | -96.125 | 36.625 | -0.631 | 239 |
187 | -95.875 | 36.625 | -0.634 | 246 |
188 | -95.625 | 36.625 | -0.643 | 245 |
189 | -95.375 | 36.625 | -0.634 | 246 |
190 | -95.125 | 36.625 | -0.607 | 245 |
191 | -94.875 | 36.625 | -0.658 | 245 |
192 | -94.625 | 36.625 | -0.622 | 245 |
193 | -94.375 | 36.625 | -0.536 | 245 |
194 | -94.125 | 36.625 | -0.491 | 234 |
195 | -93.875 | 36.625 | -0.452 | 242 |
196 | -103.375 | 36.375 | -0.205 | 251 |
197 | -103.125 | 36.375 | -0.236 | 251 |
198 | -102.875 | 36.375 | -0.188 | 251 |
199 | -102.625 | 36.375 | -0.232 | 252 |
200 | -102.375 | 36.375 | -0.219 | 252 |
201 | -102.125 | 36.375 | -0.231 | 252 |
202 | -101.875 | 36.375 | -0.237 | 252 |
203 | -101.625 | 36.375 | -0.221 | 251 |
204 | -101.375 | 36.375 | -0.248 | 252 |
205 | -101.125 | 36.375 | -0.334 | 250 |
206 | -100.875 | 36.375 | -0.386 | 247 |
207 | -100.625 | 36.375 | -0.391 | 250 |
208 | -100.375 | 36.375 | -0.412 | 251 |
209 | -100.125 | 36.375 | -0.420 | 252 |
210 | -99.875 | 36.375 | -0.411 | 249 |
211 | -99.625 | 36.375 | -0.417 | 250 |
212 | -99.375 | 36.375 | -0.386 | 250 |
213 | -99.125 | 36.375 | -0.391 | 248 |
214 | -98.875 | 36.375 | -0.418 | 249 |
215 | -98.625 | 36.375 | -0.421 | 249 |
216 | -98.375 | 36.375 | -0.330 | 249 |
217 | -98.125 | 36.375 | -0.374 | 242 |
218 | -97.875 | 36.375 | -0.347 | 250 |
219 | -97.625 | 36.375 | -0.401 | 249 |
220 | -97.375 | 36.375 | -0.455 | 249 |
221 | -97.125 | 36.375 | -0.495 | 247 |
222 | -96.875 | 36.375 | -0.585 | 248 |
223 | -96.625 | 36.375 | -0.548 | 247 |
224 | -96.375 | 36.375 | -0.576 | 247 |
225 | -96.125 | 36.375 | -0.568 | 246 |
226 | -95.875 | 36.375 | -0.638 | 247 |
227 | -95.625 | 36.375 | -0.665 | 246 |
228 | -95.375 | 36.375 | -0.572 | 247 |
229 | -95.125 | 36.375 | -0.574 | 247 |
230 | -94.875 | 36.375 | -0.510 | 236 |
231 | -94.625 | 36.375 | -0.601 | 244 |
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637 | -100.375 | 33.625 | -0.347 | 252 |
638 | -100.125 | 33.625 | -0.458 | 238 |
639 | -99.875 | 33.625 | -0.412 | 252 |
640 | -99.625 | 33.625 | -0.433 | 252 |
641 | -99.375 | 33.625 | -0.492 | 252 |
642 | -99.125 | 33.625 | -0.515 | 252 |
643 | -98.875 | 33.625 | -0.527 | 252 |
644 | -98.625 | 33.625 | -0.563 | 252 |
645 | -98.375 | 33.625 | -0.589 | 252 |
646 | -98.125 | 33.625 | -0.606 | 251 |
647 | -97.875 | 33.625 | -0.640 | 252 |
648 | -97.625 | 33.625 | -0.684 | 252 |
649 | -97.375 | 33.625 | -0.671 | 252 |
650 | -97.125 | 33.625 | -0.662 | 252 |
651 | -96.875 | 33.625 | -0.638 | 252 |
652 | -96.625 | 33.625 | -0.665 | 252 |
653 | -96.375 | 33.625 | -0.667 | 252 |
654 | -96.125 | 33.625 | -0.655 | 251 |
655 | -95.875 | 33.625 | -0.695 | 251 |
656 | -95.625 | 33.625 | -0.648 | 246 |
657 | -95.375 | 33.625 | -0.643 | 245 |
658 | -95.125 | 33.625 | -0.627 | 251 |
659 | -94.875 | 33.625 | -0.590 | 250 |
660 | -94.625 | 33.625 | -0.592 | 247 |
661 | -94.375 | 33.625 | -0.596 | 252 |
662 | -94.125 | 33.625 | -0.579 | 246 |
663 | -93.875 | 33.625 | -0.600 | 244 |
664 | -103.375 | 33.375 | -0.134 | 252 |
665 | -103.125 | 33.375 | -0.091 | 252 |
666 | -102.875 | 33.375 | -0.059 | 252 |
667 | -102.625 | 33.375 | 0.013 | 252 |
668 | -102.375 | 33.375 | -0.016 | 252 |
669 | -102.125 | 33.375 | 0.025 | 252 |
670 | -101.875 | 33.375 | -0.058 | 252 |
671 | -101.625 | 33.375 | -0.123 | 252 |
672 | -101.375 | 33.375 | -0.200 | 252 |
673 | -101.125 | 33.375 | -0.238 | 252 |
674 | -100.875 | 33.375 | -0.312 | 252 |
675 | -100.625 | 33.375 | -0.333 | 252 |
676 | -100.375 | 33.375 | -0.352 | 252 |
677 | -100.125 | 33.375 | -0.391 | 252 |
678 | -99.875 | 33.375 | -0.426 | 252 |
679 | -99.625 | 33.375 | -0.463 | 252 |
680 | -99.375 | 33.375 | -0.478 | 252 |
681 | -99.125 | 33.375 | -0.497 | 252 |
682 | -98.875 | 33.375 | -0.529 | 252 |
683 | -98.625 | 33.375 | -0.593 | 252 |
684 | -98.375 | 33.375 | -0.617 | 251 |
685 | -98.125 | 33.375 | -0.640 | 251 |
686 | -97.875 | 33.375 | -0.616 | 251 |
687 | -97.625 | 33.375 | -0.668 | 252 |
688 | -97.375 | 33.375 | -0.632 | 251 |
689 | -97.125 | 33.375 | -0.565 | 252 |
690 | -96.875 | 33.375 | -0.608 | 252 |
691 | -96.625 | 33.375 | -0.657 | 244 |
692 | -96.375 | 33.375 | -0.651 | 252 |
693 | -96.125 | 33.375 | -0.638 | 252 |
694 | -95.875 | 33.375 | -0.626 | 239 |
695 | -95.625 | 33.375 | -0.637 | 247 |
696 | -95.375 | 33.375 | -0.669 | 245 |
697 | -95.125 | 33.375 | -0.583 | 244 |
698 | -94.875 | 33.375 | -0.579 | 251 |
699 | -94.625 | 33.375 | -0.521 | 243 |
700 | -94.375 | 33.375 | -0.544 | 246 |
701 | -94.125 | 33.375 | -0.690 | 252 |
702 | -93.875 | 33.375 | -0.476 | 239 |
703 | -103.375 | 33.125 | -0.093 | 252 |
704 | -103.125 | 33.125 | -0.081 | 252 |
705 | -102.875 | 33.125 | -0.046 | 252 |
706 | -102.625 | 33.125 | -0.004 | 252 |
707 | -102.375 | 33.125 | 0.008 | 252 |
708 | -102.125 | 33.125 | -0.066 | 252 |
709 | -101.875 | 33.125 | -0.141 | 252 |
710 | -101.625 | 33.125 | -0.229 | 252 |
711 | -101.375 | 33.125 | -0.250 | 252 |
712 | -101.125 | 33.125 | -0.342 | 252 |
713 | -100.875 | 33.125 | -0.343 | 252 |
714 | -100.625 | 33.125 | -0.352 | 252 |
715 | -100.375 | 33.125 | -0.440 | 252 |
716 | -100.125 | 33.125 | -0.457 | 252 |
717 | -99.875 | 33.125 | -0.434 | 252 |
718 | -99.625 | 33.125 | -0.477 | 252 |
719 | -99.375 | 33.125 | -0.489 | 252 |
720 | -99.125 | 33.125 | -0.499 | 252 |
721 | -98.875 | 33.125 | -0.567 | 252 |
722 | -98.625 | 33.125 | -0.637 | 251 |
723 | -98.375 | 33.125 | -0.622 | 251 |
724 | -98.125 | 33.125 | -0.617 | 251 |
725 | -97.875 | 33.125 | -0.632 | 251 |
726 | -97.625 | 33.125 | -0.607 | 251 |
727 | -97.375 | 33.125 | -0.632 | 252 |
728 | -97.125 | 33.125 | -0.641 | 252 |
729 | -96.875 | 33.125 | -0.674 | 252 |
730 | -96.625 | 33.125 | -0.662 | 252 |
731 | -96.375 | 33.125 | -0.655 | 252 |
732 | -96.125 | 33.125 | -0.612 | 252 |
733 | -95.875 | 33.125 | -0.666 | 252 |
734 | -95.625 | 33.125 | -0.645 | 251 |
735 | -95.375 | 33.125 | -0.665 | 251 |
736 | -95.125 | 33.125 | -0.564 | 252 |
737 | -94.875 | 33.125 | -0.573 | 250 |
738 | -94.625 | 33.125 | -0.509 | 240 |
739 | -94.375 | 33.125 | -0.515 | 250 |
740 | -94.125 | 33.125 | -0.500 | 248 |
741 | -93.875 | 33.125 | -0.562 | 249 |
X axis show the number of pixels, starting in the upper left corner and finishing in n the lower right corner.
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation/temporal_correlation")
#MEAN Soil Moisture vs Precipitation
data <- read.csv('Temporal_Correlation_region_interest_MEAN_montlhy_SoilMoist_daymet_Precipitation.csv')
data$X.1 <- NULL
data$X <- NULL
data$Y <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Pixel, y = Corr_Precipitation, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Pixel, y = Corr_Precipitation)) +
labs (title = paste0('Temporal correlation, Mean Soil Moisture and Daymet Precipitation.
', x), x = 'Number of Pixel', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEAN Soil Moisture vs Max Temperature
data <- read.csv('Temporal_Correlation_region_interest_MEAN_montlhy_SoilMoist_daymet_Temperature_Max.csv')
data$X.1 <- NULL
data$X <- NULL
data$Y <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Pixel, y = Corr_Temperature_Max, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Pixel, y = Corr_Temperature_Max)) +
labs (title = paste0('Temporal correlation, Mean Soil Moisture and Daymet Max Temperature.
', x), x = 'Number of Pixel', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEAN Soil Moisture vs Min Temperature
data <- read.csv('Temporal_Correlation_region_interest_MEAN_montlhy_SoilMoist_daymet_Temperature_Min.csv')
data$X.1 <- NULL
data$X <- NULL
data$Y <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Pixel, y = Corr_Temperature_Min, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Pixel, y = Corr_Temperature_Min)) +
labs (title = paste0('Temporal correlation, Mean Soil Moisture and daymet Min Temperature.
', x), x = 'Number of Pixel', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEDIAN Soil Moisture vs Precipitation
data <- read.csv('Temporal_Correlation_region_interest_MEDIAN_montlhy_SoilMoist_daymet_Precipitation.csv')
data$X.1 <- NULL
data$X <- NULL
data$Y <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Pixel, y = Corr_Precipitation, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Pixel, y = Corr_Precipitation)) +
labs (title = paste0('Temporal correlation, Median Soil Moisture and Daymet Precipitation.
', x), x = 'Number of Pixel', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEDIAN Soil Moisture vs Max Temperature
data <- read.csv('Temporal_Correlation_region_interest_MEDIAN_montlhy_SoilMoist_daymet_Temperature_Max.csv')
data$X.1 <- NULL
data$X <- NULL
data$Y <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Median Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Pixel, y = Corr_Temperature_Max, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Pixel, y = Corr_Temperature_Max)) +
labs (title = paste0('Temporal correlation, Median Soil Moisture and Daymet Max Temperature.
', x), x = 'Number of Pixel', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEDIAN Soil Moisture vs Min Temperature#
data <- read.csv('Temporal_Correlation_region_interest_MEDIAN_montlhy_SoilMoist_daymet_Temperature_Min.csv')
data$X.1 <- NULL
data$X <- NULL
data$Y <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Median Correlation ', x)
jpeg('Temporal_Corr_MEDIAN_SM_daymet_Temperature_Min_.jpg', res = 300, units = 'cm', width = 22, height = 18, pointsize = 12)
ggplot(data = data) +
geom_point(mapping = aes(x = Pixel, y = Corr_Temperature_Min, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Pixel, y = Corr_Temperature_Min)) +
labs (title = paste0('Temporal correlation, Median Soil Moisture and Daymet Min Temperature.
', x), x = 'Number of Pixel', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
dev.off()
## png
## 2
Spatial correlation was calculated regarding the values of all valid pixels available in every soil moisture monthly layer, mean and median values were used as well as in the temporal analysis. All values were also compared to their correspondent values from the ancillary layers. Soil texture and Topographic Wetness Index were included in spatial correlation analysis as the values across space are not static values as in the case of temporal analysis. Which means, the data to be correlated are derived from the mean value of all available valid pixels in each monthly layer of soil moisture (mean and median layers), as well as their correspondent valid pixels in each ancillary layer (precipitation, maximum and minimum temperature, soil texture and TWI). Thus, 252 correlation values are obtained regarding 252 monthly layers in study period.
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
SoilMoisture_MEAN <- read.csv("SoilMoisture_region_interest_MEAN_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
SoilMoisture_MEAN[1:4] <- NULL
SoilMoisture_MEAN <- replace(SoilMoisture_MEAN, SoilMoisture_MEAN == -9999, NA)
#Precipitation <- read.csv("Precipitation_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Precipitation <- read.csv("Daymet_prcp_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Precipitation[1:4] <- NULL
Precipitation <- replace(Precipitation, Precipitation == -9999, NA)
base_matrix <- read.csv("dates.csv", header = TRUE, sep = ',', dec = '.')
final_spatial_correlation <- base_matrix
names(final_spatial_correlation)[2] <- paste('Corr_Precipitation')
for (i in 1:252) {
correlation <- cor(SoilMoisture_MEAN[i], Precipitation[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEAN[i]))
number_values <- 741 - number_values
final_spatial_correlation[i,2] <- correlation
final_spatial_correlation[i,3] <- number_values
}
mean_spatial_corr_meanSM_Prcp <- round(mean(final_spatial_correlation$Corr_Precipitation), digits = 3)
kable(final_spatial_correlation, caption = 'Spatial Correlation Mean Soil Moistre and Precipitation', digits = 3)
Month | Corr_Precipitation | Number_of_pairs |
---|---|---|
1995_01 | 0.658 | 466 |
1995_02 | 0.575 | 597 |
1995_03 | 0.731 | 724 |
1995_04 | 0.794 | 709 |
1995_05 | 0.402 | 676 |
1995_06 | 0.519 | 731 |
1995_07 | 0.298 | 728 |
1995_08 | 0.313 | 731 |
1995_09 | 0.570 | 730 |
1995_10 | -0.060 | 732 |
1995_11 | 0.039 | 720 |
1995_12 | 0.372 | 496 |
1996_01 | 0.573 | 544 |
1996_02 | 0.585 | 720 |
1996_03 | 0.696 | 728 |
1996_04 | 0.840 | 733 |
1996_05 | 0.644 | 717 |
1996_06 | 0.510 | 740 |
1996_07 | 0.132 | 712 |
1996_08 | 0.344 | 737 |
1996_09 | 0.132 | 738 |
1996_10 | 0.578 | 732 |
1996_11 | 0.723 | 737 |
1996_12 | 0.476 | 587 |
1997_01 | 0.506 | 532 |
1997_02 | 0.638 | 617 |
1997_03 | 0.825 | 731 |
1997_04 | 0.415 | 716 |
1997_05 | 0.477 | 714 |
1997_06 | 0.447 | 725 |
1997_07 | 0.156 | 715 |
1997_08 | 0.337 | 729 |
1997_09 | 0.438 | 718 |
1997_10 | 0.655 | 730 |
1997_11 | 0.638 | 716 |
1997_12 | 0.663 | 702 |
1998_01 | 0.788 | 702 |
1998_02 | 0.508 | 721 |
1998_03 | 0.788 | 729 |
1998_04 | 0.643 | 733 |
1998_05 | 0.691 | 732 |
1998_06 | 0.628 | 731 |
1998_07 | 0.578 | 737 |
1998_08 | 0.391 | 732 |
1998_09 | 0.813 | 734 |
1998_10 | 0.452 | 739 |
1998_11 | 0.714 | 740 |
1998_12 | 0.699 | 721 |
1999_01 | 0.539 | 690 |
1999_02 | 0.762 | 727 |
1999_03 | 0.694 | 740 |
1999_04 | 0.567 | 737 |
1999_05 | 0.729 | 735 |
1999_06 | 0.552 | 738 |
1999_07 | 0.046 | 735 |
1999_08 | 0.209 | 730 |
1999_09 | 0.547 | 736 |
1999_10 | 0.229 | 720 |
1999_11 | 0.664 | 730 |
1999_12 | 0.769 | 708 |
2000_01 | 0.714 | 695 |
2000_02 | 0.840 | 727 |
2000_03 | 0.299 | 741 |
2000_04 | 0.483 | 736 |
2000_05 | 0.774 | 734 |
2000_06 | 0.704 | 736 |
2000_07 | 0.484 | 738 |
2000_08 | -0.032 | 734 |
2000_09 | 0.696 | 733 |
2000_10 | 0.147 | 739 |
2000_11 | 0.586 | 741 |
2000_12 | 0.712 | 705 |
2001_01 | 0.695 | 696 |
2001_02 | 0.717 | 677 |
2001_03 | 0.282 | 689 |
2001_04 | 0.765 | 732 |
2001_05 | 0.251 | 711 |
2001_06 | 0.789 | 735 |
2001_07 | 0.382 | 730 |
2001_08 | 0.430 | 734 |
2001_09 | 0.623 | 739 |
2001_10 | 0.855 | 738 |
2001_11 | 0.461 | 735 |
2001_12 | 0.772 | 719 |
2002_01 | 0.686 | 683 |
2002_02 | 0.664 | 706 |
2002_03 | 0.793 | 714 |
2002_04 | 0.804 | 735 |
2002_05 | 0.735 | 726 |
2002_06 | 0.456 | 740 |
2002_07 | 0.269 | 741 |
2002_08 | 0.240 | 741 |
2002_09 | 0.383 | 741 |
2002_10 | 0.360 | 741 |
2002_11 | 0.466 | 740 |
2002_12 | 0.644 | 740 |
2003_01 | 0.309 | 726 |
2003_02 | 0.754 | 729 |
2003_03 | 0.685 | 700 |
2003_04 | 0.578 | 701 |
2003_05 | 0.707 | 700 |
2003_06 | 0.041 | 700 |
2003_07 | 0.485 | 701 |
2003_08 | 0.374 | 740 |
2003_09 | 0.607 | 741 |
2003_10 | 0.669 | 741 |
2003_11 | 0.751 | 741 |
2003_12 | 0.760 | 738 |
2004_01 | 0.714 | 731 |
2004_02 | 0.425 | 738 |
2004_03 | 0.586 | 741 |
2004_04 | 0.618 | 741 |
2004_05 | 0.782 | 741 |
2004_06 | 0.534 | 741 |
2004_07 | 0.395 | 741 |
2004_08 | -0.195 | 741 |
2004_09 | 0.028 | 741 |
2004_10 | 0.430 | 741 |
2004_11 | 0.399 | 741 |
2004_12 | 0.677 | 736 |
2005_01 | 0.773 | 725 |
2005_02 | 0.713 | 739 |
2005_03 | 0.600 | 741 |
2005_04 | 0.701 | 741 |
2005_05 | 0.451 | 741 |
2005_06 | 0.398 | 741 |
2005_07 | 0.226 | 741 |
2005_08 | 0.316 | 741 |
2005_09 | 0.402 | 741 |
2005_10 | 0.403 | 741 |
2005_11 | 0.445 | 741 |
2005_12 | 0.739 | 707 |
2006_01 | 0.647 | 728 |
2006_02 | 0.545 | 737 |
2006_03 | 0.753 | 741 |
2006_04 | 0.805 | 741 |
2006_05 | 0.615 | 741 |
2006_06 | 0.564 | 741 |
2006_07 | 0.473 | 741 |
2006_08 | 0.594 | 741 |
2006_09 | 0.423 | 741 |
2006_10 | 0.360 | 741 |
2006_11 | 0.832 | 741 |
2006_12 | 0.184 | 737 |
2007_01 | 0.684 | 714 |
2007_02 | 0.714 | 741 |
2007_03 | 0.012 | 741 |
2007_04 | 0.676 | 741 |
2007_05 | 0.598 | 741 |
2007_06 | 0.745 | 741 |
2007_07 | 0.677 | 741 |
2007_08 | 0.197 | 741 |
2007_09 | 0.620 | 741 |
2007_10 | 0.812 | 741 |
2007_11 | 0.606 | 741 |
2007_12 | 0.669 | 740 |
2008_01 | 0.664 | 740 |
2008_02 | 0.883 | 741 |
2008_03 | 0.846 | 741 |
2008_04 | 0.905 | 741 |
2008_05 | 0.670 | 741 |
2008_06 | 0.812 | 741 |
2008_07 | 0.426 | 741 |
2008_08 | 0.286 | 741 |
2008_09 | 0.697 | 741 |
2008_10 | 0.571 | 741 |
2008_11 | 0.705 | 741 |
2008_12 | 0.734 | 741 |
2009_01 | 0.684 | 740 |
2009_02 | 0.849 | 741 |
2009_03 | 0.874 | 741 |
2009_04 | 0.579 | 741 |
2009_05 | 0.779 | 741 |
2009_06 | 0.329 | 741 |
2009_07 | 0.426 | 741 |
2009_08 | 0.582 | 741 |
2009_09 | 0.806 | 741 |
2009_10 | 0.638 | 741 |
2009_11 | 0.728 | 741 |
2009_12 | 0.690 | 741 |
2010_01 | 0.634 | 741 |
2010_02 | 0.526 | 738 |
2010_03 | 0.746 | 738 |
2010_04 | 0.194 | 738 |
2010_05 | 0.669 | 741 |
2010_06 | 0.525 | 741 |
2010_07 | 0.455 | 741 |
2010_08 | 0.373 | 741 |
2010_09 | 0.777 | 741 |
2010_10 | 0.257 | 739 |
2010_11 | 0.769 | 741 |
2010_12 | 0.444 | 741 |
2011_01 | 0.540 | 738 |
2011_02 | 0.737 | 739 |
2011_03 | 0.729 | 741 |
2011_04 | 0.864 | 741 |
2011_05 | 0.888 | 741 |
2011_06 | 0.446 | 741 |
2011_07 | 0.313 | 741 |
2011_08 | 0.696 | 741 |
2011_09 | 0.647 | 741 |
2011_10 | 0.504 | 741 |
2011_11 | 0.728 | 741 |
2011_12 | 0.507 | 741 |
2012_01 | 0.659 | 739 |
2012_02 | 0.745 | 707 |
2012_03 | 0.892 | 717 |
2012_04 | 0.551 | 741 |
2012_05 | 0.483 | 741 |
2012_06 | 0.349 | 741 |
2012_07 | 0.598 | 741 |
2012_08 | 0.385 | 741 |
2012_09 | 0.517 | 741 |
2012_10 | 0.546 | 733 |
2012_11 | 0.671 | 740 |
2012_12 | 0.448 | 741 |
2013_01 | 0.714 | 738 |
2013_02 | 0.638 | 711 |
2013_03 | 0.830 | 720 |
2013_04 | 0.855 | 724 |
2013_05 | 0.871 | 730 |
2013_06 | 0.253 | 735 |
2013_07 | 0.479 | 739 |
2013_08 | 0.735 | 739 |
2013_09 | 0.311 | 739 |
2013_10 | 0.783 | 724 |
2013_11 | 0.537 | 739 |
2013_12 | 0.754 | 735 |
2014_01 | 0.648 | 738 |
2014_02 | 0.551 | 711 |
2014_03 | 0.847 | 741 |
2014_04 | 0.847 | 741 |
2014_05 | 0.673 | 741 |
2014_06 | 0.454 | 741 |
2014_07 | 0.514 | 741 |
2014_08 | 0.421 | 741 |
2014_09 | 0.619 | 741 |
2014_10 | 0.709 | 737 |
2014_11 | 0.385 | 740 |
2014_12 | 0.714 | 741 |
2015_01 | 0.454 | 734 |
2015_02 | 0.678 | 721 |
2015_03 | 0.808 | 724 |
2015_04 | 0.593 | 732 |
2015_05 | 0.739 | 741 |
2015_06 | 0.612 | 741 |
2015_07 | 0.311 | 741 |
2015_08 | 0.631 | 741 |
2015_09 | 0.444 | 741 |
2015_10 | 0.162 | 734 |
2015_11 | 0.764 | 741 |
2015_12 | 0.665 | 741 |
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
Temperature_Max <- read.csv("Daymet_tmax_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Temperature_Max[1:4] <- NULL
Temperature_Max <- replace(Temperature_Max, Temperature_Max == -9999, NA)
base_matrix <- read.csv("dates.csv", header = TRUE, sep = ',', dec = '.')
final_spatial_correlation <- base_matrix
names(final_spatial_correlation)[2] <- paste('Corr_Temperature_Max')
for (i in 1:252) {
correlation <- cor(SoilMoisture_MEAN[i], Temperature_Max[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEAN[i]))
number_values <- 741 - number_values
final_spatial_correlation[i,2] <- correlation
final_spatial_correlation[i,3] <- number_values
}
mean_spatial_corr_meanSM_MaxTemp <- round(mean(final_spatial_correlation$Corr_Temperature_Max), digits = 3)
kable(final_spatial_correlation, caption = 'Spatial Correlation Mean Soil Moistre and Max Temperature', digits = 3)
Month | Corr_Temperature_Max | Number_of_pairs |
---|---|---|
1995_01 | 0.170 | 466 |
1995_02 | -0.015 | 597 |
1995_03 | 0.124 | 724 |
1995_04 | 0.091 | 709 |
1995_05 | -0.104 | 676 |
1995_06 | -0.289 | 731 |
1995_07 | -0.606 | 728 |
1995_08 | 0.070 | 731 |
1995_09 | 0.239 | 730 |
1995_10 | 0.169 | 732 |
1995_11 | 0.104 | 720 |
1995_12 | 0.203 | 496 |
1996_01 | -0.382 | 544 |
1996_02 | -0.274 | 720 |
1996_03 | -0.170 | 728 |
1996_04 | -0.264 | 733 |
1996_05 | -0.749 | 717 |
1996_06 | -0.464 | 740 |
1996_07 | -0.342 | 712 |
1996_08 | -0.271 | 737 |
1996_09 | 0.151 | 738 |
1996_10 | 0.054 | 732 |
1996_11 | -0.344 | 737 |
1996_12 | -0.117 | 587 |
1997_01 | 0.029 | 532 |
1997_02 | 0.429 | 617 |
1997_03 | -0.196 | 731 |
1997_04 | 0.289 | 716 |
1997_05 | 0.251 | 714 |
1997_06 | -0.150 | 725 |
1997_07 | -0.335 | 715 |
1997_08 | -0.413 | 729 |
1997_09 | -0.014 | 718 |
1997_10 | 0.107 | 730 |
1997_11 | -0.077 | 716 |
1997_12 | 0.312 | 702 |
1998_01 | -0.211 | 702 |
1998_02 | 0.387 | 721 |
1998_03 | 0.139 | 729 |
1998_04 | -0.107 | 733 |
1998_05 | -0.449 | 732 |
1998_06 | -0.525 | 731 |
1998_07 | -0.609 | 737 |
1998_08 | -0.480 | 732 |
1998_09 | -0.424 | 734 |
1998_10 | -0.239 | 739 |
1998_11 | -0.388 | 740 |
1998_12 | 0.034 | 721 |
1999_01 | -0.279 | 690 |
1999_02 | -0.339 | 727 |
1999_03 | -0.121 | 740 |
1999_04 | 0.054 | 737 |
1999_05 | -0.118 | 735 |
1999_06 | -0.458 | 738 |
1999_07 | -0.373 | 735 |
1999_08 | -0.360 | 730 |
1999_09 | -0.358 | 736 |
1999_10 | 0.188 | 720 |
1999_11 | 0.073 | 730 |
1999_12 | 0.136 | 708 |
2000_01 | -0.251 | 695 |
2000_02 | -0.435 | 727 |
2000_03 | -0.271 | 741 |
2000_04 | -0.452 | 736 |
2000_05 | -0.686 | 734 |
2000_06 | -0.514 | 736 |
2000_07 | -0.624 | 738 |
2000_08 | -0.192 | 734 |
2000_09 | -0.335 | 733 |
2000_10 | 0.306 | 739 |
2000_11 | 0.331 | 741 |
2000_12 | -0.224 | 705 |
2001_01 | 0.352 | 696 |
2001_02 | 0.016 | 677 |
2001_03 | 0.216 | 689 |
2001_04 | 0.012 | 732 |
2001_05 | -0.215 | 711 |
2001_06 | -0.736 | 735 |
2001_07 | -0.663 | 730 |
2001_08 | -0.216 | 734 |
2001_09 | -0.666 | 739 |
2001_10 | -0.549 | 738 |
2001_11 | 0.473 | 735 |
2001_12 | 0.191 | 719 |
2002_01 | 0.061 | 683 |
2002_02 | -0.018 | 706 |
2002_03 | -0.140 | 714 |
2002_04 | -0.093 | 735 |
2002_05 | -0.678 | 726 |
2002_06 | -0.793 | 740 |
2002_07 | -0.288 | 741 |
2002_08 | -0.301 | 741 |
2002_09 | 0.034 | 741 |
2002_10 | 0.100 | 741 |
2002_11 | 0.220 | 740 |
2002_12 | 0.481 | 740 |
2003_01 | -0.521 | 726 |
2003_02 | -0.252 | 729 |
2003_03 | -0.338 | 700 |
2003_04 | -0.307 | 701 |
2003_05 | -0.474 | 700 |
2003_06 | -0.198 | 700 |
2003_07 | -0.383 | 701 |
2003_08 | -0.208 | 740 |
2003_09 | -0.457 | 741 |
2003_10 | -0.411 | 741 |
2003_11 | 0.281 | 741 |
2003_12 | -0.397 | 738 |
2004_01 | -0.271 | 731 |
2004_02 | -0.160 | 738 |
2004_03 | 0.022 | 741 |
2004_04 | -0.130 | 741 |
2004_05 | -0.708 | 741 |
2004_06 | -0.466 | 741 |
2004_07 | -0.453 | 741 |
2004_08 | -0.090 | 741 |
2004_09 | -0.088 | 741 |
2004_10 | 0.330 | 741 |
2004_11 | 0.357 | 741 |
2004_12 | -0.022 | 736 |
2005_01 | -0.091 | 725 |
2005_02 | 0.308 | 739 |
2005_03 | 0.201 | 741 |
2005_04 | -0.114 | 741 |
2005_05 | -0.039 | 741 |
2005_06 | -0.417 | 741 |
2005_07 | -0.222 | 741 |
2005_08 | 0.107 | 741 |
2005_09 | 0.011 | 741 |
2005_10 | 0.170 | 741 |
2005_11 | -0.238 | 741 |
2005_12 | -0.296 | 707 |
2006_01 | -0.224 | 728 |
2006_02 | -0.348 | 737 |
2006_03 | 0.237 | 741 |
2006_04 | -0.203 | 741 |
2006_05 | -0.541 | 741 |
2006_06 | -0.773 | 741 |
2006_07 | -0.530 | 741 |
2006_08 | -0.608 | 741 |
2006_09 | -0.170 | 741 |
2006_10 | -0.037 | 741 |
2006_11 | -0.063 | 741 |
2006_12 | 0.397 | 737 |
2007_01 | 0.495 | 714 |
2007_02 | -0.311 | 741 |
2007_03 | 0.289 | 741 |
2007_04 | -0.068 | 741 |
2007_05 | 0.377 | 741 |
2007_06 | -0.239 | 741 |
2007_07 | -0.535 | 741 |
2007_08 | 0.142 | 741 |
2007_09 | -0.204 | 741 |
2007_10 | -0.397 | 741 |
2007_11 | 0.077 | 741 |
2007_12 | 0.167 | 740 |
2008_01 | -0.486 | 740 |
2008_02 | -0.424 | 741 |
2008_03 | -0.154 | 741 |
2008_04 | -0.348 | 741 |
2008_05 | -0.483 | 741 |
2008_06 | -0.792 | 741 |
2008_07 | -0.403 | 741 |
2008_08 | -0.446 | 741 |
2008_09 | -0.312 | 741 |
2008_10 | -0.539 | 741 |
2008_11 | -0.583 | 741 |
2008_12 | -0.352 | 741 |
2009_01 | -0.603 | 740 |
2009_02 | -0.458 | 741 |
2009_03 | -0.405 | 741 |
2009_04 | -0.556 | 741 |
2009_05 | -0.431 | 741 |
2009_06 | -0.343 | 741 |
2009_07 | -0.599 | 741 |
2009_08 | -0.671 | 741 |
2009_09 | -0.401 | 741 |
2009_10 | -0.369 | 741 |
2009_11 | -0.279 | 741 |
2009_12 | 0.159 | 741 |
2010_01 | -0.404 | 741 |
2010_02 | 0.220 | 738 |
2010_03 | -0.176 | 738 |
2010_04 | 0.462 | 738 |
2010_05 | 0.040 | 741 |
2010_06 | -0.538 | 741 |
2010_07 | -0.106 | 741 |
2010_08 | -0.213 | 741 |
2010_09 | -0.656 | 741 |
2010_10 | 0.056 | 739 |
2010_11 | -0.105 | 741 |
2010_12 | -0.550 | 741 |
2011_01 | -0.470 | 738 |
2011_02 | -0.197 | 739 |
2011_03 | -0.544 | 741 |
2011_04 | -0.429 | 741 |
2011_05 | -0.684 | 741 |
2011_06 | -0.635 | 741 |
2011_07 | -0.330 | 741 |
2011_08 | -0.535 | 741 |
2011_09 | -0.502 | 741 |
2011_10 | -0.025 | 741 |
2011_11 | -0.196 | 741 |
2011_12 | 0.607 | 741 |
2012_01 | -0.125 | 739 |
2012_02 | 0.051 | 707 |
2012_03 | 0.007 | 717 |
2012_04 | -0.532 | 741 |
2012_05 | -0.330 | 741 |
2012_06 | -0.471 | 741 |
2012_07 | -0.224 | 741 |
2012_08 | -0.112 | 741 |
2012_09 | -0.274 | 741 |
2012_10 | -0.242 | 733 |
2012_11 | -0.546 | 740 |
2012_12 | -0.129 | 741 |
2013_01 | 0.119 | 738 |
2013_02 | -0.050 | 711 |
2013_03 | -0.712 | 720 |
2013_04 | -0.299 | 724 |
2013_05 | -0.738 | 730 |
2013_06 | -0.849 | 735 |
2013_07 | -0.361 | 739 |
2013_08 | -0.785 | 739 |
2013_09 | -0.481 | 739 |
2013_10 | -0.333 | 724 |
2013_11 | -0.370 | 739 |
2013_12 | -0.005 | 735 |
2014_01 | -0.560 | 738 |
2014_02 | -0.344 | 711 |
2014_03 | -0.505 | 741 |
2014_04 | -0.373 | 741 |
2014_05 | -0.612 | 741 |
2014_06 | -0.726 | 741 |
2014_07 | -0.605 | 741 |
2014_08 | -0.479 | 741 |
2014_09 | -0.578 | 741 |
2014_10 | -0.496 | 737 |
2014_11 | -0.213 | 740 |
2014_12 | -0.354 | 741 |
2015_01 | 0.262 | 734 |
2015_02 | -0.440 | 721 |
2015_03 | -0.653 | 724 |
2015_04 | -0.150 | 732 |
2015_05 | 0.385 | 741 |
2015_06 | -0.267 | 741 |
2015_07 | -0.378 | 741 |
2015_08 | -0.622 | 741 |
2015_09 | -0.627 | 741 |
2015_10 | -0.385 | 734 |
2015_11 | 0.290 | 741 |
2015_12 | 0.359 | 741 |
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
Temperature_Min <- read.csv("Daymet_tmin_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Temperature_Min[1:4] <- NULL
Temperature_Min <- replace(Temperature_Min, Temperature_Min == -9999, NA)
base_matrix <- read.csv("dates.csv", header = TRUE, sep = ',', dec = '.')
final_spatial_correlation <- base_matrix
names(final_spatial_correlation)[2] <- paste('Corr_Temperature_Min')
for (i in 1:252) {
correlation <- cor(SoilMoisture_MEAN[i], Temperature_Min[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEAN[i]))
number_values <- 741 - number_values
final_spatial_correlation[i,2] <- correlation
final_spatial_correlation[i,3] <- number_values
}
mean_spatial_corr_meanSM_MinTemp <- round(mean(final_spatial_correlation$Corr_Temperature_Min), digits = 3)
kable(final_spatial_correlation, caption = 'Spatial Correlation Mean Soil Moistre and Min Temperature', digits = 3)
Month | Corr_Temperature_Min | Number_of_pairs |
---|---|---|
1995_01 | 0.550 | 466 |
1995_02 | 0.530 | 597 |
1995_03 | 0.669 | 724 |
1995_04 | 0.568 | 709 |
1995_05 | 0.297 | 676 |
1995_06 | 0.479 | 731 |
1995_07 | 0.218 | 728 |
1995_08 | 0.432 | 731 |
1995_09 | 0.569 | 730 |
1995_10 | 0.091 | 732 |
1995_11 | 0.177 | 720 |
1995_12 | 0.432 | 496 |
1996_01 | 0.287 | 544 |
1996_02 | 0.077 | 720 |
1996_03 | 0.407 | 728 |
1996_04 | 0.498 | 733 |
1996_05 | 0.254 | 717 |
1996_06 | 0.229 | 740 |
1996_07 | -0.037 | 712 |
1996_08 | -0.019 | 737 |
1996_09 | 0.324 | 738 |
1996_10 | 0.521 | 732 |
1996_11 | 0.526 | 737 |
1996_12 | 0.450 | 587 |
1997_01 | 0.259 | 532 |
1997_02 | 0.648 | 617 |
1997_03 | 0.691 | 731 |
1997_04 | 0.310 | 716 |
1997_05 | 0.631 | 714 |
1997_06 | 0.526 | 725 |
1997_07 | 0.248 | 715 |
1997_08 | 0.040 | 729 |
1997_09 | 0.223 | 718 |
1997_10 | 0.676 | 730 |
1997_11 | 0.587 | 716 |
1997_12 | 0.753 | 702 |
1998_01 | 0.692 | 702 |
1998_02 | 0.723 | 721 |
1998_03 | 0.775 | 729 |
1998_04 | 0.584 | 733 |
1998_05 | 0.470 | 732 |
1998_06 | 0.443 | 731 |
1998_07 | -0.187 | 737 |
1998_08 | -0.327 | 732 |
1998_09 | 0.320 | 734 |
1998_10 | 0.184 | 739 |
1998_11 | 0.266 | 740 |
1998_12 | 0.680 | 721 |
1999_01 | 0.422 | 690 |
1999_02 | 0.655 | 727 |
1999_03 | 0.453 | 740 |
1999_04 | 0.459 | 737 |
1999_05 | 0.341 | 735 |
1999_06 | 0.411 | 738 |
1999_07 | 0.276 | 735 |
1999_08 | -0.322 | 730 |
1999_09 | -0.122 | 736 |
1999_10 | 0.346 | 720 |
1999_11 | 0.710 | 730 |
1999_12 | 0.633 | 708 |
2000_01 | 0.558 | 695 |
2000_02 | 0.492 | 727 |
2000_03 | 0.403 | 741 |
2000_04 | 0.335 | 736 |
2000_05 | 0.438 | 734 |
2000_06 | 0.359 | 736 |
2000_07 | -0.065 | 738 |
2000_08 | 0.078 | 734 |
2000_09 | 0.218 | 733 |
2000_10 | 0.375 | 739 |
2000_11 | 0.588 | 741 |
2000_12 | 0.435 | 705 |
2001_01 | 0.609 | 696 |
2001_02 | 0.418 | 677 |
2001_03 | 0.417 | 689 |
2001_04 | 0.696 | 732 |
2001_05 | 0.104 | 711 |
2001_06 | 0.152 | 735 |
2001_07 | 0.060 | 730 |
2001_08 | 0.161 | 734 |
2001_09 | 0.442 | 739 |
2001_10 | 0.431 | 738 |
2001_11 | 0.576 | 735 |
2001_12 | 0.745 | 719 |
2002_01 | 0.556 | 683 |
2002_02 | 0.718 | 706 |
2002_03 | 0.814 | 714 |
2002_04 | 0.732 | 735 |
2002_05 | 0.526 | 726 |
2002_06 | 0.338 | 740 |
2002_07 | 0.550 | 741 |
2002_08 | 0.243 | 741 |
2002_09 | 0.275 | 741 |
2002_10 | 0.333 | 741 |
2002_11 | 0.448 | 740 |
2002_12 | 0.714 | 740 |
2003_01 | 0.192 | 726 |
2003_02 | 0.464 | 729 |
2003_03 | 0.521 | 700 |
2003_04 | 0.503 | 701 |
2003_05 | 0.296 | 700 |
2003_06 | -0.010 | 700 |
2003_07 | 0.334 | 701 |
2003_08 | 0.239 | 740 |
2003_09 | 0.207 | 741 |
2003_10 | 0.181 | 741 |
2003_11 | 0.706 | 741 |
2003_12 | 0.533 | 738 |
2004_01 | 0.399 | 731 |
2004_02 | 0.602 | 738 |
2004_03 | 0.556 | 741 |
2004_04 | 0.115 | 741 |
2004_05 | 0.595 | 741 |
2004_06 | 0.463 | 741 |
2004_07 | 0.117 | 741 |
2004_08 | 0.151 | 741 |
2004_09 | -0.037 | 741 |
2004_10 | 0.515 | 741 |
2004_11 | 0.451 | 741 |
2004_12 | 0.619 | 736 |
2005_01 | 0.395 | 725 |
2005_02 | 0.517 | 739 |
2005_03 | 0.595 | 741 |
2005_04 | 0.532 | 741 |
2005_05 | 0.220 | 741 |
2005_06 | 0.211 | 741 |
2005_07 | 0.422 | 741 |
2005_08 | 0.285 | 741 |
2005_09 | 0.429 | 741 |
2005_10 | 0.420 | 741 |
2005_11 | 0.525 | 741 |
2005_12 | 0.272 | 707 |
2006_01 | 0.493 | 728 |
2006_02 | 0.529 | 737 |
2006_03 | 0.614 | 741 |
2006_04 | 0.636 | 741 |
2006_05 | 0.509 | 741 |
2006_06 | 0.019 | 741 |
2006_07 | -0.178 | 741 |
2006_08 | -0.540 | 741 |
2006_09 | 0.044 | 741 |
2006_10 | 0.245 | 741 |
2006_11 | 0.679 | 741 |
2006_12 | 0.678 | 737 |
2007_01 | 0.681 | 714 |
2007_02 | 0.283 | 741 |
2007_03 | 0.640 | 741 |
2007_04 | 0.388 | 741 |
2007_05 | 0.726 | 741 |
2007_06 | 0.796 | 741 |
2007_07 | 0.803 | 741 |
2007_08 | 0.701 | 741 |
2007_09 | 0.631 | 741 |
2007_10 | 0.686 | 741 |
2007_11 | 0.667 | 741 |
2007_12 | 0.575 | 740 |
2008_01 | 0.412 | 740 |
2008_02 | 0.386 | 741 |
2008_03 | 0.662 | 741 |
2008_04 | 0.615 | 741 |
2008_05 | 0.571 | 741 |
2008_06 | 0.399 | 741 |
2008_07 | 0.089 | 741 |
2008_08 | 0.168 | 741 |
2008_09 | 0.465 | 741 |
2008_10 | -0.216 | 741 |
2008_11 | 0.301 | 741 |
2008_12 | 0.214 | 741 |
2009_01 | 0.306 | 740 |
2009_02 | 0.509 | 741 |
2009_03 | 0.525 | 741 |
2009_04 | 0.278 | 741 |
2009_05 | 0.634 | 741 |
2009_06 | 0.383 | 741 |
2009_07 | -0.123 | 741 |
2009_08 | -0.037 | 741 |
2009_09 | 0.576 | 741 |
2009_10 | 0.437 | 741 |
2009_11 | 0.598 | 741 |
2009_12 | 0.754 | 741 |
2010_01 | 0.562 | 741 |
2010_02 | 0.646 | 738 |
2010_03 | 0.593 | 738 |
2010_04 | 0.669 | 738 |
2010_05 | 0.627 | 741 |
2010_06 | 0.379 | 741 |
2010_07 | 0.491 | 741 |
2010_08 | -0.039 | 741 |
2010_09 | 0.496 | 741 |
2010_10 | 0.446 | 739 |
2010_11 | 0.539 | 741 |
2010_12 | 0.111 | 741 |
2011_01 | 0.402 | 738 |
2011_02 | 0.561 | 739 |
2011_03 | 0.438 | 741 |
2011_04 | 0.511 | 741 |
2011_05 | 0.569 | 741 |
2011_06 | 0.244 | 741 |
2011_07 | -0.021 | 741 |
2011_08 | -0.271 | 741 |
2011_09 | -0.273 | 741 |
2011_10 | 0.374 | 741 |
2011_11 | 0.503 | 741 |
2011_12 | 0.556 | 741 |
2012_01 | 0.546 | 739 |
2012_02 | 0.584 | 707 |
2012_03 | 0.751 | 717 |
2012_04 | 0.433 | 741 |
2012_05 | 0.416 | 741 |
2012_06 | 0.303 | 741 |
2012_07 | 0.053 | 741 |
2012_08 | 0.198 | 741 |
2012_09 | 0.223 | 741 |
2012_10 | 0.376 | 733 |
2012_11 | 0.073 | 740 |
2012_12 | 0.405 | 741 |
2013_01 | 0.471 | 738 |
2013_02 | 0.513 | 711 |
2013_03 | 0.192 | 720 |
2013_04 | 0.684 | 724 |
2013_05 | 0.493 | 730 |
2013_06 | 0.282 | 735 |
2013_07 | 0.116 | 739 |
2013_08 | -0.195 | 739 |
2013_09 | -0.239 | 739 |
2013_10 | 0.474 | 724 |
2013_11 | 0.340 | 739 |
2013_12 | 0.589 | 735 |
2014_01 | 0.094 | 738 |
2014_02 | 0.204 | 711 |
2014_03 | 0.364 | 741 |
2014_04 | 0.491 | 741 |
2014_05 | 0.501 | 741 |
2014_06 | 0.308 | 741 |
2014_07 | -0.158 | 741 |
2014_08 | 0.044 | 741 |
2014_09 | -0.181 | 741 |
2014_10 | 0.107 | 737 |
2014_11 | 0.418 | 740 |
2014_12 | 0.496 | 741 |
2015_01 | 0.378 | 734 |
2015_02 | 0.120 | 721 |
2015_03 | 0.660 | 724 |
2015_04 | 0.755 | 732 |
2015_05 | 0.727 | 741 |
2015_06 | 0.616 | 741 |
2015_07 | 0.341 | 741 |
2015_08 | -0.253 | 741 |
2015_09 | 0.043 | 741 |
2015_10 | -0.268 | 734 |
2015_11 | 0.674 | 741 |
2015_12 | 0.637 | 741 |
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
Soil_Texture <- read.csv("Soil_Texture_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Soil_Texture[1:4] <- NULL
Soil_Texture <- replace(Soil_Texture, Soil_Texture == -9999, NA)
base_matrix <- read.csv("dates.csv", header = TRUE, sep = ',', dec = '.')
final_spatial_correlation <- base_matrix
names(final_spatial_correlation)[2] <- paste('Corr_Soil_Texture')
for (i in 1:252) {
correlation <- cor(SoilMoisture_MEAN[i], Soil_Texture[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEAN[i]))
number_values <- 741 - number_values
final_spatial_correlation[i,2] <- correlation
final_spatial_correlation[i,3] <- number_values
}
mean_spatial_corr_meanSM_SoilText <- round(mean(final_spatial_correlation$Corr_Soil_Texture), digits = 3)
kable(final_spatial_correlation, caption = 'Spatial Correlation Mean Soil Moistre and Soil Texture', digits = 3)
Month | Corr_Soil_Texture | Number_of_pairs |
---|---|---|
1995_01 | -0.245 | 466 |
1995_02 | -0.294 | 597 |
1995_03 | -0.301 | 724 |
1995_04 | -0.313 | 709 |
1995_05 | -0.188 | 676 |
1995_06 | -0.312 | 731 |
1995_07 | -0.233 | 728 |
1995_08 | -0.134 | 731 |
1995_09 | -0.130 | 730 |
1995_10 | 0.066 | 732 |
1995_11 | -0.093 | 720 |
1995_12 | -0.147 | 496 |
1996_01 | -0.256 | 544 |
1996_02 | -0.243 | 720 |
1996_03 | -0.275 | 728 |
1996_04 | -0.300 | 733 |
1996_05 | -0.272 | 717 |
1996_06 | -0.219 | 740 |
1996_07 | -0.135 | 712 |
1996_08 | 0.016 | 737 |
1996_09 | -0.120 | 738 |
1996_10 | -0.183 | 732 |
1996_11 | -0.292 | 737 |
1996_12 | -0.263 | 587 |
1997_01 | -0.170 | 532 |
1997_02 | -0.227 | 617 |
1997_03 | -0.348 | 731 |
1997_04 | -0.161 | 716 |
1997_05 | -0.311 | 714 |
1997_06 | -0.292 | 725 |
1997_07 | -0.176 | 715 |
1997_08 | -0.029 | 729 |
1997_09 | -0.034 | 718 |
1997_10 | -0.253 | 730 |
1997_11 | -0.327 | 716 |
1997_12 | -0.352 | 702 |
1998_01 | -0.344 | 702 |
1998_02 | -0.337 | 721 |
1998_03 | -0.358 | 729 |
1998_04 | -0.296 | 733 |
1998_05 | -0.290 | 732 |
1998_06 | -0.324 | 731 |
1998_07 | -0.103 | 737 |
1998_08 | 0.056 | 732 |
1998_09 | -0.256 | 734 |
1998_10 | -0.184 | 739 |
1998_11 | -0.175 | 740 |
1998_12 | -0.270 | 721 |
1999_01 | -0.257 | 690 |
1999_02 | -0.324 | 727 |
1999_03 | -0.307 | 740 |
1999_04 | -0.277 | 737 |
1999_05 | -0.293 | 735 |
1999_06 | -0.249 | 738 |
1999_07 | -0.222 | 735 |
1999_08 | -0.020 | 730 |
1999_09 | -0.124 | 736 |
1999_10 | -0.146 | 720 |
1999_11 | -0.336 | 730 |
1999_12 | -0.253 | 708 |
2000_01 | -0.275 | 695 |
2000_02 | -0.315 | 727 |
2000_03 | -0.245 | 741 |
2000_04 | -0.296 | 736 |
2000_05 | -0.305 | 734 |
2000_06 | -0.259 | 736 |
2000_07 | -0.092 | 738 |
2000_08 | -0.153 | 734 |
2000_09 | -0.243 | 733 |
2000_10 | -0.196 | 739 |
2000_11 | -0.292 | 741 |
2000_12 | -0.322 | 705 |
2001_01 | -0.335 | 696 |
2001_02 | -0.272 | 677 |
2001_03 | -0.282 | 689 |
2001_04 | -0.333 | 732 |
2001_05 | -0.136 | 711 |
2001_06 | -0.228 | 735 |
2001_07 | -0.212 | 730 |
2001_08 | -0.215 | 734 |
2001_09 | -0.284 | 739 |
2001_10 | -0.298 | 738 |
2001_11 | -0.301 | 735 |
2001_12 | -0.354 | 719 |
2002_01 | -0.272 | 683 |
2002_02 | -0.310 | 706 |
2002_03 | -0.358 | 714 |
2002_04 | -0.367 | 735 |
2002_05 | -0.352 | 726 |
2002_06 | -0.340 | 740 |
2002_07 | -0.332 | 741 |
2002_08 | -0.230 | 741 |
2002_09 | -0.184 | 741 |
2002_10 | -0.228 | 741 |
2002_11 | -0.288 | 740 |
2002_12 | -0.313 | 740 |
2003_01 | -0.321 | 726 |
2003_02 | -0.342 | 729 |
2003_03 | -0.305 | 700 |
2003_04 | -0.272 | 701 |
2003_05 | -0.232 | 700 |
2003_06 | -0.016 | 700 |
2003_07 | -0.205 | 701 |
2003_08 | -0.231 | 740 |
2003_09 | -0.258 | 741 |
2003_10 | -0.185 | 741 |
2003_11 | -0.373 | 741 |
2003_12 | -0.283 | 738 |
2004_01 | -0.323 | 731 |
2004_02 | -0.357 | 738 |
2004_03 | -0.323 | 741 |
2004_04 | -0.216 | 741 |
2004_05 | -0.339 | 741 |
2004_06 | -0.311 | 741 |
2004_07 | -0.216 | 741 |
2004_08 | -0.187 | 741 |
2004_09 | -0.062 | 741 |
2004_10 | -0.179 | 741 |
2004_11 | -0.273 | 741 |
2004_12 | -0.351 | 736 |
2005_01 | -0.317 | 725 |
2005_02 | -0.317 | 739 |
2005_03 | -0.350 | 741 |
2005_04 | -0.312 | 741 |
2005_05 | -0.260 | 741 |
2005_06 | -0.221 | 741 |
2005_07 | -0.208 | 741 |
2005_08 | -0.062 | 741 |
2005_09 | -0.171 | 741 |
2005_10 | -0.177 | 741 |
2005_11 | -0.290 | 741 |
2005_12 | -0.242 | 707 |
2006_01 | -0.262 | 728 |
2006_02 | -0.308 | 737 |
2006_03 | -0.353 | 741 |
2006_04 | -0.341 | 741 |
2006_05 | -0.327 | 741 |
2006_06 | -0.268 | 741 |
2006_07 | -0.100 | 741 |
2006_08 | 0.139 | 741 |
2006_09 | -0.080 | 741 |
2006_10 | -0.189 | 741 |
2006_11 | -0.370 | 741 |
2006_12 | -0.286 | 737 |
2007_01 | -0.364 | 714 |
2007_02 | -0.264 | 741 |
2007_03 | -0.277 | 741 |
2007_04 | -0.256 | 741 |
2007_05 | -0.335 | 741 |
2007_06 | -0.309 | 741 |
2007_07 | -0.348 | 741 |
2007_08 | -0.291 | 741 |
2007_09 | -0.271 | 741 |
2007_10 | -0.285 | 741 |
2007_11 | -0.307 | 741 |
2007_12 | -0.304 | 740 |
2008_01 | -0.249 | 740 |
2008_02 | -0.285 | 741 |
2008_03 | -0.343 | 741 |
2008_04 | -0.333 | 741 |
2008_05 | -0.288 | 741 |
2008_06 | -0.261 | 741 |
2008_07 | -0.122 | 741 |
2008_08 | -0.169 | 741 |
2008_09 | -0.191 | 741 |
2008_10 | 0.128 | 741 |
2008_11 | -0.172 | 741 |
2008_12 | -0.223 | 741 |
2009_01 | -0.291 | 740 |
2009_02 | -0.302 | 741 |
2009_03 | -0.301 | 741 |
2009_04 | -0.245 | 741 |
2009_05 | -0.348 | 741 |
2009_06 | -0.263 | 741 |
2009_07 | -0.158 | 741 |
2009_08 | -0.162 | 741 |
2009_09 | -0.310 | 741 |
2009_10 | -0.246 | 741 |
2009_11 | -0.263 | 741 |
2009_12 | -0.310 | 741 |
2010_01 | -0.318 | 741 |
2010_02 | -0.344 | 738 |
2010_03 | -0.324 | 738 |
2010_04 | -0.345 | 738 |
2010_05 | -0.349 | 741 |
2010_06 | -0.259 | 741 |
2010_07 | -0.271 | 741 |
2010_08 | -0.076 | 741 |
2010_09 | -0.298 | 741 |
2010_10 | -0.218 | 739 |
2010_11 | -0.186 | 741 |
2010_12 | -0.212 | 741 |
2011_01 | -0.268 | 738 |
2011_02 | -0.316 | 739 |
2011_03 | -0.255 | 741 |
2011_04 | -0.311 | 741 |
2011_05 | -0.353 | 741 |
2011_06 | -0.327 | 741 |
2011_07 | -0.220 | 741 |
2011_08 | -0.146 | 741 |
2011_09 | -0.138 | 741 |
2011_10 | -0.192 | 741 |
2011_11 | -0.290 | 741 |
2011_12 | -0.292 | 741 |
2012_01 | -0.319 | 739 |
2012_02 | -0.289 | 707 |
2012_03 | -0.324 | 717 |
2012_04 | -0.252 | 741 |
2012_05 | -0.278 | 741 |
2012_06 | -0.260 | 741 |
2012_07 | -0.185 | 741 |
2012_08 | -0.180 | 741 |
2012_09 | -0.181 | 741 |
2012_10 | -0.281 | 733 |
2012_11 | -0.206 | 740 |
2012_12 | -0.232 | 741 |
2013_01 | -0.249 | 738 |
2013_02 | -0.311 | 711 |
2013_03 | -0.256 | 720 |
2013_04 | -0.318 | 724 |
2013_05 | -0.321 | 730 |
2013_06 | -0.283 | 735 |
2013_07 | -0.212 | 739 |
2013_08 | -0.052 | 739 |
2013_09 | -0.037 | 739 |
2013_10 | -0.232 | 724 |
2013_11 | -0.209 | 739 |
2013_12 | -0.303 | 735 |
2014_01 | -0.268 | 738 |
2014_02 | -0.310 | 711 |
2014_03 | -0.307 | 741 |
2014_04 | -0.337 | 741 |
2014_05 | -0.313 | 741 |
2014_06 | -0.228 | 741 |
2014_07 | -0.196 | 741 |
2014_08 | -0.149 | 741 |
2014_09 | -0.036 | 741 |
2014_10 | -0.165 | 737 |
2014_11 | -0.212 | 740 |
2014_12 | -0.278 | 741 |
2015_01 | -0.279 | 734 |
2015_02 | -0.264 | 721 |
2015_03 | -0.362 | 724 |
2015_04 | -0.349 | 732 |
2015_05 | -0.358 | 741 |
2015_06 | -0.331 | 741 |
2015_07 | -0.242 | 741 |
2015_08 | -0.052 | 741 |
2015_09 | -0.146 | 741 |
2015_10 | 0.150 | 734 |
2015_11 | -0.315 | 741 |
2015_12 | -0.300 | 741 |
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
TWI <- read.csv("TWI_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
TWI[1:4] <- NULL
TWI <- replace(TWI, TWI == -9999, NA)
base_matrix <- read.csv("dates.csv", header = TRUE, sep = ',', dec = '.')
final_spatial_correlation <- base_matrix
names(final_spatial_correlation)[2] <- paste('Corr_TWI')
for (i in 1:252) {
correlation <- cor(SoilMoisture_MEAN[i], TWI[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEAN[i]))
number_values <- 741 - number_values
final_spatial_correlation[i,2] <- correlation
final_spatial_correlation[i,3] <- number_values
}
mean_spatial_corr_meanSM_TWI <- round(mean(final_spatial_correlation$Corr_TWI), digits = 3)
kable(final_spatial_correlation, caption = 'Spatial Correlation Mean Soil Moistre and TWI', digits = 3)
Month | Corr_TWI | Number_of_pairs |
---|---|---|
1995_01 | -0.014 | 466 |
1995_02 | -0.059 | 597 |
1995_03 | -0.142 | 724 |
1995_04 | -0.091 | 709 |
1995_05 | -0.076 | 676 |
1995_06 | -0.137 | 731 |
1995_07 | -0.079 | 728 |
1995_08 | -0.113 | 731 |
1995_09 | 0.010 | 730 |
1995_10 | 0.075 | 732 |
1995_11 | 0.076 | 720 |
1995_12 | -0.028 | 496 |
1996_01 | -0.087 | 544 |
1996_02 | -0.097 | 720 |
1996_03 | -0.056 | 728 |
1996_04 | -0.071 | 733 |
1996_05 | -0.096 | 717 |
1996_06 | -0.068 | 740 |
1996_07 | 0.006 | 712 |
1996_08 | 0.018 | 737 |
1996_09 | 0.061 | 738 |
1996_10 | 0.009 | 732 |
1996_11 | -0.087 | 737 |
1996_12 | -0.115 | 587 |
1997_01 | -0.011 | 532 |
1997_02 | -0.149 | 617 |
1997_03 | -0.088 | 731 |
1997_04 | -0.032 | 716 |
1997_05 | -0.089 | 714 |
1997_06 | -0.100 | 725 |
1997_07 | -0.106 | 715 |
1997_08 | -0.087 | 729 |
1997_09 | -0.035 | 718 |
1997_10 | -0.109 | 730 |
1997_11 | -0.136 | 716 |
1997_12 | -0.109 | 702 |
1998_01 | -0.124 | 702 |
1998_02 | -0.129 | 721 |
1998_03 | -0.128 | 729 |
1998_04 | -0.138 | 733 |
1998_05 | -0.129 | 732 |
1998_06 | -0.105 | 731 |
1998_07 | -0.096 | 737 |
1998_08 | 0.014 | 732 |
1998_09 | -0.069 | 734 |
1998_10 | -0.081 | 739 |
1998_11 | -0.113 | 740 |
1998_12 | -0.102 | 721 |
1999_01 | -0.125 | 690 |
1999_02 | -0.133 | 727 |
1999_03 | -0.146 | 740 |
1999_04 | -0.120 | 737 |
1999_05 | -0.094 | 735 |
1999_06 | -0.111 | 738 |
1999_07 | -0.047 | 735 |
1999_08 | 0.001 | 730 |
1999_09 | -0.077 | 736 |
1999_10 | -0.011 | 720 |
1999_11 | -0.123 | 730 |
1999_12 | -0.118 | 708 |
2000_01 | -0.115 | 695 |
2000_02 | -0.141 | 727 |
2000_03 | -0.126 | 741 |
2000_04 | -0.096 | 736 |
2000_05 | -0.111 | 734 |
2000_06 | -0.084 | 736 |
2000_07 | -0.025 | 738 |
2000_08 | -0.081 | 734 |
2000_09 | -0.072 | 733 |
2000_10 | -0.085 | 739 |
2000_11 | -0.065 | 741 |
2000_12 | -0.090 | 705 |
2001_01 | -0.105 | 696 |
2001_02 | -0.112 | 677 |
2001_03 | -0.103 | 689 |
2001_04 | -0.084 | 732 |
2001_05 | -0.028 | 711 |
2001_06 | -0.095 | 735 |
2001_07 | -0.058 | 730 |
2001_08 | -0.047 | 734 |
2001_09 | -0.069 | 739 |
2001_10 | -0.074 | 738 |
2001_11 | -0.080 | 735 |
2001_12 | -0.086 | 719 |
2002_01 | -0.109 | 683 |
2002_02 | -0.124 | 706 |
2002_03 | -0.070 | 714 |
2002_04 | -0.100 | 735 |
2002_05 | -0.101 | 726 |
2002_06 | -0.124 | 740 |
2002_07 | -0.084 | 741 |
2002_08 | -0.065 | 741 |
2002_09 | -0.067 | 741 |
2002_10 | -0.036 | 741 |
2002_11 | -0.085 | 740 |
2002_12 | -0.103 | 740 |
2003_01 | -0.108 | 726 |
2003_02 | -0.102 | 729 |
2003_03 | -0.100 | 700 |
2003_04 | -0.097 | 701 |
2003_05 | -0.061 | 700 |
2003_06 | -0.002 | 700 |
2003_07 | -0.044 | 701 |
2003_08 | -0.055 | 740 |
2003_09 | -0.081 | 741 |
2003_10 | -0.097 | 741 |
2003_11 | -0.091 | 741 |
2003_12 | -0.105 | 738 |
2004_01 | -0.106 | 731 |
2004_02 | -0.112 | 738 |
2004_03 | -0.108 | 741 |
2004_04 | -0.096 | 741 |
2004_05 | -0.098 | 741 |
2004_06 | -0.076 | 741 |
2004_07 | -0.073 | 741 |
2004_08 | -0.050 | 741 |
2004_09 | 0.030 | 741 |
2004_10 | -0.025 | 741 |
2004_11 | -0.068 | 741 |
2004_12 | -0.111 | 736 |
2005_01 | -0.123 | 725 |
2005_02 | -0.124 | 739 |
2005_03 | -0.126 | 741 |
2005_04 | -0.128 | 741 |
2005_05 | -0.079 | 741 |
2005_06 | -0.118 | 741 |
2005_07 | -0.075 | 741 |
2005_08 | -0.053 | 741 |
2005_09 | -0.089 | 741 |
2005_10 | -0.044 | 741 |
2005_11 | -0.111 | 741 |
2005_12 | -0.106 | 707 |
2006_01 | -0.116 | 728 |
2006_02 | -0.094 | 737 |
2006_03 | -0.119 | 741 |
2006_04 | -0.115 | 741 |
2006_05 | -0.143 | 741 |
2006_06 | -0.109 | 741 |
2006_07 | -0.094 | 741 |
2006_08 | 0.013 | 741 |
2006_09 | 0.016 | 741 |
2006_10 | -0.014 | 741 |
2006_11 | -0.110 | 741 |
2006_12 | -0.091 | 737 |
2007_01 | -0.071 | 714 |
2007_02 | -0.112 | 741 |
2007_03 | -0.138 | 741 |
2007_04 | -0.164 | 741 |
2007_05 | -0.133 | 741 |
2007_06 | -0.127 | 741 |
2007_07 | -0.106 | 741 |
2007_08 | -0.062 | 741 |
2007_09 | -0.075 | 741 |
2007_10 | -0.106 | 741 |
2007_11 | -0.088 | 741 |
2007_12 | -0.092 | 740 |
2008_01 | -0.101 | 740 |
2008_02 | -0.121 | 741 |
2008_03 | -0.124 | 741 |
2008_04 | -0.135 | 741 |
2008_05 | -0.126 | 741 |
2008_06 | -0.134 | 741 |
2008_07 | -0.078 | 741 |
2008_08 | -0.060 | 741 |
2008_09 | -0.072 | 741 |
2008_10 | 0.029 | 741 |
2008_11 | -0.090 | 741 |
2008_12 | -0.093 | 741 |
2009_01 | -0.107 | 740 |
2009_02 | -0.127 | 741 |
2009_03 | -0.105 | 741 |
2009_04 | -0.151 | 741 |
2009_05 | -0.131 | 741 |
2009_06 | -0.086 | 741 |
2009_07 | -0.009 | 741 |
2009_08 | -0.047 | 741 |
2009_09 | -0.088 | 741 |
2009_10 | -0.064 | 741 |
2009_11 | -0.103 | 741 |
2009_12 | -0.070 | 741 |
2010_01 | -0.106 | 741 |
2010_02 | -0.090 | 738 |
2010_03 | -0.141 | 738 |
2010_04 | -0.145 | 738 |
2010_05 | -0.142 | 741 |
2010_06 | -0.134 | 741 |
2010_07 | -0.083 | 741 |
2010_08 | -0.042 | 741 |
2010_09 | -0.105 | 741 |
2010_10 | -0.076 | 739 |
2010_11 | -0.078 | 741 |
2010_12 | -0.094 | 741 |
2011_01 | -0.094 | 738 |
2011_02 | -0.111 | 739 |
2011_03 | -0.120 | 741 |
2011_04 | -0.125 | 741 |
2011_05 | -0.127 | 741 |
2011_06 | -0.119 | 741 |
2011_07 | -0.066 | 741 |
2011_08 | -0.101 | 741 |
2011_09 | -0.056 | 741 |
2011_10 | -0.057 | 741 |
2011_11 | -0.136 | 741 |
2011_12 | -0.118 | 741 |
2012_01 | -0.106 | 739 |
2012_02 | -0.129 | 707 |
2012_03 | -0.143 | 717 |
2012_04 | -0.145 | 741 |
2012_05 | -0.134 | 741 |
2012_06 | -0.091 | 741 |
2012_07 | -0.018 | 741 |
2012_08 | -0.010 | 741 |
2012_09 | -0.013 | 741 |
2012_10 | -0.028 | 733 |
2012_11 | -0.083 | 740 |
2012_12 | -0.099 | 741 |
2013_01 | -0.079 | 738 |
2013_02 | -0.125 | 711 |
2013_03 | -0.122 | 720 |
2013_04 | -0.148 | 724 |
2013_05 | -0.141 | 730 |
2013_06 | -0.120 | 735 |
2013_07 | -0.103 | 739 |
2013_08 | -0.130 | 739 |
2013_09 | -0.084 | 739 |
2013_10 | -0.096 | 724 |
2013_11 | -0.075 | 739 |
2013_12 | -0.065 | 735 |
2014_01 | -0.121 | 738 |
2014_02 | -0.127 | 711 |
2014_03 | -0.131 | 741 |
2014_04 | -0.118 | 741 |
2014_05 | -0.104 | 741 |
2014_06 | -0.123 | 741 |
2014_07 | -0.095 | 741 |
2014_08 | -0.076 | 741 |
2014_09 | 0.042 | 741 |
2014_10 | -0.062 | 737 |
2014_11 | -0.062 | 740 |
2014_12 | -0.103 | 741 |
2015_01 | -0.068 | 734 |
2015_02 | -0.086 | 721 |
2015_03 | -0.111 | 724 |
2015_04 | -0.133 | 732 |
2015_05 | -0.120 | 741 |
2015_06 | -0.101 | 741 |
2015_07 | -0.086 | 741 |
2015_08 | -0.070 | 741 |
2015_09 | -0.101 | 741 |
2015_10 | 0.055 | 734 |
2015_11 | -0.092 | 741 |
2015_12 | -0.116 | 741 |
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
SoilMoisture_MEDIAN <- read.csv("SoilMoisture_region_interest_MEDIAN_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
SoilMoisture_MEDIAN[1:4] <- NULL
SoilMoisture_MEDIAN <- replace(SoilMoisture_MEDIAN, SoilMoisture_MEDIAN == -9999, NA)
Precipitation <- read.csv("Daymet_prcp_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Precipitation[1:4] <- NULL
Precipitation <- replace(Precipitation, Precipitation == -9999, NA)
base_matrix <- read.csv("dates.csv", header = TRUE, sep = ',', dec = '.')
final_spatial_correlation <- base_matrix
names(final_spatial_correlation)[2] <- paste('Corr_Precipitation')
for (i in 1:252) {
correlation <- cor(SoilMoisture_MEDIAN[i], Precipitation[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEDIAN[i]))
number_values <- 741 - number_values
final_spatial_correlation[i,2] <- correlation
final_spatial_correlation[i,3] <- number_values
}
mean_spatial_corr_medianSM_Prcp <- round(mean(final_spatial_correlation$Corr_Precipitation), digits = 3)
kable(final_spatial_correlation, caption = 'Spatial Correlation Median Soil Moistre and Precipitation', digits = 3)
Month | Corr_Precipitation | Number_of_pairs |
---|---|---|
1995_01 | 0.649 | 466 |
1995_02 | 0.592 | 597 |
1995_03 | 0.704 | 724 |
1995_04 | 0.760 | 709 |
1995_05 | 0.383 | 676 |
1995_06 | 0.535 | 731 |
1995_07 | 0.279 | 728 |
1995_08 | 0.225 | 731 |
1995_09 | 0.638 | 730 |
1995_10 | -0.085 | 732 |
1995_11 | 0.027 | 720 |
1995_12 | 0.395 | 496 |
1996_01 | 0.533 | 544 |
1996_02 | 0.577 | 720 |
1996_03 | 0.700 | 728 |
1996_04 | 0.817 | 733 |
1996_05 | 0.614 | 717 |
1996_06 | 0.482 | 740 |
1996_07 | 0.153 | 712 |
1996_08 | 0.339 | 737 |
1996_09 | 0.035 | 738 |
1996_10 | 0.561 | 732 |
1996_11 | 0.697 | 737 |
1996_12 | 0.459 | 587 |
1997_01 | 0.512 | 532 |
1997_02 | 0.625 | 617 |
1997_03 | 0.805 | 731 |
1997_04 | 0.442 | 716 |
1997_05 | 0.441 | 714 |
1997_06 | 0.462 | 725 |
1997_07 | 0.121 | 715 |
1997_08 | 0.376 | 729 |
1997_09 | 0.431 | 718 |
1997_10 | 0.661 | 730 |
1997_11 | 0.612 | 716 |
1997_12 | 0.658 | 702 |
1998_01 | 0.773 | 702 |
1998_02 | 0.517 | 721 |
1998_03 | 0.794 | 729 |
1998_04 | 0.650 | 733 |
1998_05 | 0.694 | 732 |
1998_06 | 0.629 | 731 |
1998_07 | 0.484 | 737 |
1998_08 | 0.347 | 732 |
1998_09 | 0.797 | 734 |
1998_10 | 0.474 | 739 |
1998_11 | 0.742 | 740 |
1998_12 | 0.702 | 721 |
1999_01 | 0.531 | 690 |
1999_02 | 0.749 | 727 |
1999_03 | 0.678 | 740 |
1999_04 | 0.554 | 737 |
1999_05 | 0.706 | 735 |
1999_06 | 0.547 | 738 |
1999_07 | 0.059 | 735 |
1999_08 | 0.179 | 730 |
1999_09 | 0.567 | 736 |
1999_10 | 0.216 | 720 |
1999_11 | 0.680 | 730 |
1999_12 | 0.785 | 708 |
2000_01 | 0.716 | 695 |
2000_02 | 0.842 | 727 |
2000_03 | 0.314 | 741 |
2000_04 | 0.514 | 736 |
2000_05 | 0.780 | 734 |
2000_06 | 0.722 | 736 |
2000_07 | 0.485 | 738 |
2000_08 | -0.090 | 734 |
2000_09 | 0.646 | 733 |
2000_10 | 0.134 | 739 |
2000_11 | 0.586 | 741 |
2000_12 | 0.697 | 705 |
2001_01 | 0.712 | 696 |
2001_02 | 0.685 | 677 |
2001_03 | 0.274 | 689 |
2001_04 | 0.761 | 732 |
2001_05 | 0.291 | 711 |
2001_06 | 0.781 | 735 |
2001_07 | 0.377 | 730 |
2001_08 | 0.396 | 734 |
2001_09 | 0.617 | 739 |
2001_10 | 0.831 | 738 |
2001_11 | 0.381 | 735 |
2001_12 | 0.776 | 719 |
2002_01 | 0.676 | 683 |
2002_02 | 0.650 | 706 |
2002_03 | 0.785 | 714 |
2002_04 | 0.804 | 735 |
2002_05 | 0.743 | 726 |
2002_06 | 0.447 | 740 |
2002_07 | 0.238 | 741 |
2002_08 | 0.199 | 741 |
2002_09 | 0.324 | 741 |
2002_10 | 0.499 | 741 |
2002_11 | 0.472 | 740 |
2002_12 | 0.619 | 740 |
2003_01 | 0.312 | 726 |
2003_02 | 0.745 | 729 |
2003_03 | 0.675 | 700 |
2003_04 | 0.562 | 701 |
2003_05 | 0.712 | 700 |
2003_06 | 0.081 | 700 |
2003_07 | 0.493 | 701 |
2003_08 | 0.402 | 740 |
2003_09 | 0.626 | 741 |
2003_10 | 0.647 | 741 |
2003_11 | 0.739 | 741 |
2003_12 | 0.763 | 738 |
2004_01 | 0.685 | 731 |
2004_02 | 0.404 | 738 |
2004_03 | 0.611 | 741 |
2004_04 | 0.638 | 741 |
2004_05 | 0.773 | 741 |
2004_06 | 0.497 | 741 |
2004_07 | 0.408 | 741 |
2004_08 | -0.230 | 741 |
2004_09 | -0.232 | 741 |
2004_10 | 0.498 | 741 |
2004_11 | 0.340 | 741 |
2004_12 | 0.688 | 736 |
2005_01 | 0.750 | 725 |
2005_02 | 0.682 | 739 |
2005_03 | 0.599 | 741 |
2005_04 | 0.674 | 741 |
2005_05 | 0.425 | 741 |
2005_06 | 0.396 | 741 |
2005_07 | 0.252 | 741 |
2005_08 | 0.319 | 741 |
2005_09 | 0.375 | 741 |
2005_10 | 0.332 | 741 |
2005_11 | 0.488 | 741 |
2005_12 | 0.722 | 707 |
2006_01 | 0.623 | 728 |
2006_02 | 0.518 | 737 |
2006_03 | 0.771 | 741 |
2006_04 | 0.818 | 741 |
2006_05 | 0.596 | 741 |
2006_06 | 0.545 | 741 |
2006_07 | 0.370 | 741 |
2006_08 | 0.651 | 741 |
2006_09 | 0.403 | 741 |
2006_10 | 0.422 | 741 |
2006_11 | 0.832 | 741 |
2006_12 | 0.159 | 737 |
2007_01 | 0.673 | 714 |
2007_02 | 0.725 | 741 |
2007_03 | 0.008 | 741 |
2007_04 | 0.687 | 741 |
2007_05 | 0.594 | 741 |
2007_06 | 0.714 | 741 |
2007_07 | 0.682 | 741 |
2007_08 | 0.186 | 741 |
2007_09 | 0.604 | 741 |
2007_10 | 0.817 | 741 |
2007_11 | 0.580 | 741 |
2007_12 | 0.678 | 740 |
2008_01 | 0.654 | 740 |
2008_02 | 0.869 | 741 |
2008_03 | 0.843 | 741 |
2008_04 | 0.906 | 741 |
2008_05 | 0.679 | 741 |
2008_06 | 0.810 | 741 |
2008_07 | 0.346 | 741 |
2008_08 | 0.317 | 741 |
2008_09 | 0.660 | 741 |
2008_10 | 0.562 | 741 |
2008_11 | 0.709 | 741 |
2008_12 | 0.727 | 741 |
2009_01 | 0.671 | 740 |
2009_02 | 0.837 | 741 |
2009_03 | 0.874 | 741 |
2009_04 | 0.599 | 741 |
2009_05 | 0.775 | 741 |
2009_06 | 0.306 | 741 |
2009_07 | 0.395 | 741 |
2009_08 | 0.546 | 741 |
2009_09 | 0.815 | 741 |
2009_10 | 0.629 | 741 |
2009_11 | 0.728 | 741 |
2009_12 | 0.667 | 741 |
2010_01 | 0.632 | 741 |
2010_02 | 0.531 | 738 |
2010_03 | 0.738 | 738 |
2010_04 | 0.132 | 738 |
2010_05 | 0.705 | 741 |
2010_06 | 0.494 | 741 |
2010_07 | 0.441 | 741 |
2010_08 | 0.277 | 741 |
2010_09 | 0.781 | 741 |
2010_10 | 0.211 | 739 |
2010_11 | 0.764 | 741 |
2010_12 | 0.418 | 741 |
2011_01 | 0.539 | 738 |
2011_02 | 0.750 | 739 |
2011_03 | 0.708 | 741 |
2011_04 | 0.855 | 741 |
2011_05 | 0.880 | 741 |
2011_06 | 0.410 | 741 |
2011_07 | 0.257 | 741 |
2011_08 | 0.677 | 741 |
2011_09 | 0.640 | 741 |
2011_10 | 0.512 | 741 |
2011_11 | 0.731 | 741 |
2011_12 | 0.488 | 741 |
2012_01 | 0.644 | 739 |
2012_02 | 0.745 | 707 |
2012_03 | 0.906 | 717 |
2012_04 | 0.529 | 741 |
2012_05 | 0.446 | 741 |
2012_06 | 0.305 | 741 |
2012_07 | 0.561 | 741 |
2012_08 | 0.368 | 741 |
2012_09 | 0.485 | 741 |
2012_10 | 0.549 | 733 |
2012_11 | 0.664 | 740 |
2012_12 | 0.460 | 741 |
2013_01 | 0.714 | 738 |
2013_02 | 0.624 | 711 |
2013_03 | 0.836 | 720 |
2013_04 | 0.860 | 724 |
2013_05 | 0.873 | 730 |
2013_06 | 0.254 | 735 |
2013_07 | 0.387 | 739 |
2013_08 | 0.720 | 739 |
2013_09 | 0.231 | 739 |
2013_10 | 0.781 | 724 |
2013_11 | 0.535 | 739 |
2013_12 | 0.744 | 735 |
2014_01 | 0.644 | 738 |
2014_02 | 0.525 | 711 |
2014_03 | 0.846 | 741 |
2014_04 | 0.838 | 741 |
2014_05 | 0.600 | 741 |
2014_06 | 0.507 | 741 |
2014_07 | 0.491 | 741 |
2014_08 | 0.392 | 741 |
2014_09 | 0.665 | 741 |
2014_10 | 0.718 | 737 |
2014_11 | 0.385 | 740 |
2014_12 | 0.714 | 741 |
2015_01 | 0.454 | 734 |
2015_02 | 0.671 | 721 |
2015_03 | 0.802 | 724 |
2015_04 | 0.631 | 732 |
2015_05 | 0.725 | 741 |
2015_06 | 0.623 | 741 |
2015_07 | 0.293 | 741 |
2015_08 | 0.619 | 741 |
2015_09 | 0.405 | 741 |
2015_10 | 0.066 | 734 |
2015_11 | 0.755 | 741 |
2015_12 | 0.629 | 741 |
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
Temperature_Max <- read.csv("Daymet_tmax_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Temperature_Max[1:4] <- NULL
Temperature_Max <- replace(Temperature_Max, Temperature_Max == -9999, NA)
base_matrix <- read.csv("dates.csv", header = TRUE, sep = ',', dec = '.')
final_spatial_correlation <- base_matrix
names(final_spatial_correlation)[2] <- paste('Corr_Temperature_Max')
for (i in 1:252) {
correlation <- cor(SoilMoisture_MEDIAN[i], Temperature_Max[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEDIAN[i]))
number_values <- 741 - number_values
final_spatial_correlation[i,2] <- correlation
final_spatial_correlation[i,3] <- number_values
}
mean_spatial_corr_medianSM_MaxTemp <- round(mean(final_spatial_correlation$Corr_Temperature_Max), digits = 3)
kable(final_spatial_correlation, caption = 'Spatial Correlation Median Soil Moistre and Max Temperature', digits = 3)
Month | Corr_Temperature_Max | Number_of_pairs |
---|---|---|
1995_01 | 0.165 | 466 |
1995_02 | -0.030 | 597 |
1995_03 | 0.098 | 724 |
1995_04 | 0.173 | 709 |
1995_05 | -0.134 | 676 |
1995_06 | -0.218 | 731 |
1995_07 | -0.571 | 728 |
1995_08 | 0.203 | 731 |
1995_09 | 0.230 | 730 |
1995_10 | 0.156 | 732 |
1995_11 | 0.082 | 720 |
1995_12 | 0.183 | 496 |
1996_01 | -0.399 | 544 |
1996_02 | -0.256 | 720 |
1996_03 | -0.176 | 728 |
1996_04 | -0.201 | 733 |
1996_05 | -0.716 | 717 |
1996_06 | -0.417 | 740 |
1996_07 | -0.294 | 712 |
1996_08 | -0.341 | 737 |
1996_09 | 0.148 | 738 |
1996_10 | 0.066 | 732 |
1996_11 | -0.359 | 737 |
1996_12 | -0.103 | 587 |
1997_01 | 0.028 | 532 |
1997_02 | 0.441 | 617 |
1997_03 | -0.194 | 731 |
1997_04 | 0.266 | 716 |
1997_05 | 0.262 | 714 |
1997_06 | -0.138 | 725 |
1997_07 | -0.275 | 715 |
1997_08 | -0.389 | 729 |
1997_09 | 0.040 | 718 |
1997_10 | 0.104 | 730 |
1997_11 | -0.072 | 716 |
1997_12 | 0.303 | 702 |
1998_01 | -0.226 | 702 |
1998_02 | 0.381 | 721 |
1998_03 | 0.119 | 729 |
1998_04 | -0.091 | 733 |
1998_05 | -0.451 | 732 |
1998_06 | -0.568 | 731 |
1998_07 | -0.542 | 737 |
1998_08 | -0.391 | 732 |
1998_09 | -0.457 | 734 |
1998_10 | -0.173 | 739 |
1998_11 | -0.367 | 740 |
1998_12 | 0.007 | 721 |
1999_01 | -0.282 | 690 |
1999_02 | -0.349 | 727 |
1999_03 | -0.195 | 740 |
1999_04 | 0.078 | 737 |
1999_05 | -0.105 | 735 |
1999_06 | -0.437 | 738 |
1999_07 | -0.359 | 735 |
1999_08 | -0.311 | 730 |
1999_09 | -0.300 | 736 |
1999_10 | 0.175 | 720 |
1999_11 | 0.074 | 730 |
1999_12 | 0.107 | 708 |
2000_01 | -0.253 | 695 |
2000_02 | -0.436 | 727 |
2000_03 | -0.279 | 741 |
2000_04 | -0.449 | 736 |
2000_05 | -0.677 | 734 |
2000_06 | -0.562 | 736 |
2000_07 | -0.614 | 738 |
2000_08 | -0.135 | 734 |
2000_09 | -0.301 | 733 |
2000_10 | 0.259 | 739 |
2000_11 | 0.344 | 741 |
2000_12 | -0.254 | 705 |
2001_01 | 0.345 | 696 |
2001_02 | -0.008 | 677 |
2001_03 | 0.195 | 689 |
2001_04 | 0.029 | 732 |
2001_05 | -0.193 | 711 |
2001_06 | -0.719 | 735 |
2001_07 | -0.667 | 730 |
2001_08 | -0.221 | 734 |
2001_09 | -0.674 | 739 |
2001_10 | -0.535 | 738 |
2001_11 | 0.486 | 735 |
2001_12 | 0.172 | 719 |
2002_01 | 0.054 | 683 |
2002_02 | -0.077 | 706 |
2002_03 | -0.199 | 714 |
2002_04 | -0.103 | 735 |
2002_05 | -0.675 | 726 |
2002_06 | -0.767 | 740 |
2002_07 | -0.297 | 741 |
2002_08 | -0.262 | 741 |
2002_09 | 0.151 | 741 |
2002_10 | 0.236 | 741 |
2002_11 | 0.197 | 740 |
2002_12 | 0.462 | 740 |
2003_01 | -0.538 | 726 |
2003_02 | -0.278 | 729 |
2003_03 | -0.335 | 700 |
2003_04 | -0.291 | 701 |
2003_05 | -0.493 | 700 |
2003_06 | -0.205 | 700 |
2003_07 | -0.392 | 701 |
2003_08 | -0.125 | 740 |
2003_09 | -0.465 | 741 |
2003_10 | -0.393 | 741 |
2003_11 | 0.245 | 741 |
2003_12 | -0.386 | 738 |
2004_01 | -0.289 | 731 |
2004_02 | -0.199 | 738 |
2004_03 | 0.002 | 741 |
2004_04 | -0.075 | 741 |
2004_05 | -0.706 | 741 |
2004_06 | -0.481 | 741 |
2004_07 | -0.510 | 741 |
2004_08 | -0.095 | 741 |
2004_09 | 0.206 | 741 |
2004_10 | 0.343 | 741 |
2004_11 | 0.309 | 741 |
2004_12 | -0.038 | 736 |
2005_01 | -0.128 | 725 |
2005_02 | 0.312 | 739 |
2005_03 | 0.177 | 741 |
2005_04 | -0.093 | 741 |
2005_05 | -0.032 | 741 |
2005_06 | -0.402 | 741 |
2005_07 | -0.213 | 741 |
2005_08 | 0.041 | 741 |
2005_09 | -0.002 | 741 |
2005_10 | 0.161 | 741 |
2005_11 | -0.190 | 741 |
2005_12 | -0.324 | 707 |
2006_01 | -0.248 | 728 |
2006_02 | -0.360 | 737 |
2006_03 | 0.235 | 741 |
2006_04 | -0.229 | 741 |
2006_05 | -0.596 | 741 |
2006_06 | -0.761 | 741 |
2006_07 | -0.429 | 741 |
2006_08 | -0.678 | 741 |
2006_09 | -0.155 | 741 |
2006_10 | 0.052 | 741 |
2006_11 | -0.067 | 741 |
2006_12 | 0.397 | 737 |
2007_01 | 0.489 | 714 |
2007_02 | -0.316 | 741 |
2007_03 | 0.325 | 741 |
2007_04 | -0.047 | 741 |
2007_05 | 0.382 | 741 |
2007_06 | -0.217 | 741 |
2007_07 | -0.551 | 741 |
2007_08 | 0.156 | 741 |
2007_09 | -0.176 | 741 |
2007_10 | -0.404 | 741 |
2007_11 | 0.052 | 741 |
2007_12 | 0.167 | 740 |
2008_01 | -0.467 | 740 |
2008_02 | -0.441 | 741 |
2008_03 | -0.152 | 741 |
2008_04 | -0.334 | 741 |
2008_05 | -0.510 | 741 |
2008_06 | -0.804 | 741 |
2008_07 | -0.320 | 741 |
2008_08 | -0.405 | 741 |
2008_09 | -0.357 | 741 |
2008_10 | -0.547 | 741 |
2008_11 | -0.564 | 741 |
2008_12 | -0.328 | 741 |
2009_01 | -0.606 | 740 |
2009_02 | -0.425 | 741 |
2009_03 | -0.393 | 741 |
2009_04 | -0.542 | 741 |
2009_05 | -0.416 | 741 |
2009_06 | -0.336 | 741 |
2009_07 | -0.606 | 741 |
2009_08 | -0.658 | 741 |
2009_09 | -0.418 | 741 |
2009_10 | -0.373 | 741 |
2009_11 | -0.250 | 741 |
2009_12 | 0.129 | 741 |
2010_01 | -0.404 | 741 |
2010_02 | 0.209 | 738 |
2010_03 | -0.178 | 738 |
2010_04 | 0.444 | 738 |
2010_05 | 0.005 | 741 |
2010_06 | -0.547 | 741 |
2010_07 | -0.171 | 741 |
2010_08 | -0.132 | 741 |
2010_09 | -0.651 | 741 |
2010_10 | 0.023 | 739 |
2010_11 | -0.138 | 741 |
2010_12 | -0.553 | 741 |
2011_01 | -0.482 | 738 |
2011_02 | -0.204 | 739 |
2011_03 | -0.519 | 741 |
2011_04 | -0.402 | 741 |
2011_05 | -0.687 | 741 |
2011_06 | -0.617 | 741 |
2011_07 | -0.262 | 741 |
2011_08 | -0.491 | 741 |
2011_09 | -0.463 | 741 |
2011_10 | 0.032 | 741 |
2011_11 | -0.190 | 741 |
2011_12 | 0.602 | 741 |
2012_01 | -0.139 | 739 |
2012_02 | 0.034 | 707 |
2012_03 | 0.059 | 717 |
2012_04 | -0.467 | 741 |
2012_05 | -0.367 | 741 |
2012_06 | -0.482 | 741 |
2012_07 | -0.168 | 741 |
2012_08 | -0.111 | 741 |
2012_09 | -0.275 | 741 |
2012_10 | -0.213 | 733 |
2012_11 | -0.547 | 740 |
2012_12 | -0.138 | 741 |
2013_01 | 0.102 | 738 |
2013_02 | -0.086 | 711 |
2013_03 | -0.720 | 720 |
2013_04 | -0.282 | 724 |
2013_05 | -0.735 | 730 |
2013_06 | -0.846 | 735 |
2013_07 | -0.339 | 739 |
2013_08 | -0.764 | 739 |
2013_09 | -0.475 | 739 |
2013_10 | -0.377 | 724 |
2013_11 | -0.395 | 739 |
2013_12 | -0.027 | 735 |
2014_01 | -0.576 | 738 |
2014_02 | -0.370 | 711 |
2014_03 | -0.495 | 741 |
2014_04 | -0.382 | 741 |
2014_05 | -0.615 | 741 |
2014_06 | -0.728 | 741 |
2014_07 | -0.614 | 741 |
2014_08 | -0.521 | 741 |
2014_09 | -0.571 | 741 |
2014_10 | -0.464 | 737 |
2014_11 | -0.238 | 740 |
2014_12 | -0.373 | 741 |
2015_01 | 0.229 | 734 |
2015_02 | -0.510 | 721 |
2015_03 | -0.657 | 724 |
2015_04 | -0.166 | 732 |
2015_05 | 0.401 | 741 |
2015_06 | -0.250 | 741 |
2015_07 | -0.333 | 741 |
2015_08 | -0.597 | 741 |
2015_09 | -0.598 | 741 |
2015_10 | -0.518 | 734 |
2015_11 | 0.254 | 741 |
2015_12 | 0.353 | 741 |
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
Temperature_Min <- read.csv("Daymet_tmin_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Temperature_Min[1:4] <- NULL
Temperature_Min <- replace(Temperature_Min, Temperature_Min == -9999, NA)
base_matrix <- read.csv("dates.csv", header = TRUE, sep = ',', dec = '.')
final_spatial_correlation <- base_matrix
names(final_spatial_correlation)[2] <- paste('Corr_Temperature_Min')
for (i in 1:252) {
correlation <- cor(SoilMoisture_MEDIAN[i], Temperature_Min[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEDIAN[i]))
number_values <- 741 - number_values
final_spatial_correlation[i,2] <- correlation
final_spatial_correlation[i,3] <- number_values
}
mean_spatial_corr_medianSM_MinTemp <- round(mean(final_spatial_correlation$Corr_Temperature_Min), digits = 3)
kable(final_spatial_correlation, caption = 'Spatial Correlation Median Soil Moistre and Min Temperature', digits = 3)
Month | Corr_Temperature_Min | Number_of_pairs |
---|---|---|
1995_01 | 0.543 | 466 |
1995_02 | 0.525 | 597 |
1995_03 | 0.631 | 724 |
1995_04 | 0.601 | 709 |
1995_05 | 0.273 | 676 |
1995_06 | 0.546 | 731 |
1995_07 | 0.248 | 728 |
1995_08 | 0.460 | 731 |
1995_09 | 0.571 | 730 |
1995_10 | 0.025 | 732 |
1995_11 | 0.127 | 720 |
1995_12 | 0.438 | 496 |
1996_01 | 0.240 | 544 |
1996_02 | 0.102 | 720 |
1996_03 | 0.407 | 728 |
1996_04 | 0.516 | 733 |
1996_05 | 0.305 | 717 |
1996_06 | 0.269 | 740 |
1996_07 | 0.037 | 712 |
1996_08 | -0.108 | 737 |
1996_09 | 0.287 | 738 |
1996_10 | 0.529 | 732 |
1996_11 | 0.489 | 737 |
1996_12 | 0.455 | 587 |
1997_01 | 0.262 | 532 |
1997_02 | 0.642 | 617 |
1997_03 | 0.688 | 731 |
1997_04 | 0.286 | 716 |
1997_05 | 0.626 | 714 |
1997_06 | 0.520 | 725 |
1997_07 | 0.308 | 715 |
1997_08 | 0.109 | 729 |
1997_09 | 0.266 | 718 |
1997_10 | 0.676 | 730 |
1997_11 | 0.569 | 716 |
1997_12 | 0.741 | 702 |
1998_01 | 0.676 | 702 |
1998_02 | 0.700 | 721 |
1998_03 | 0.774 | 729 |
1998_04 | 0.596 | 733 |
1998_05 | 0.473 | 732 |
1998_06 | 0.394 | 731 |
1998_07 | -0.121 | 737 |
1998_08 | -0.214 | 732 |
1998_09 | 0.260 | 734 |
1998_10 | 0.267 | 739 |
1998_11 | 0.305 | 740 |
1998_12 | 0.661 | 721 |
1999_01 | 0.417 | 690 |
1999_02 | 0.630 | 727 |
1999_03 | 0.391 | 740 |
1999_04 | 0.478 | 737 |
1999_05 | 0.358 | 735 |
1999_06 | 0.438 | 738 |
1999_07 | 0.247 | 735 |
1999_08 | -0.292 | 730 |
1999_09 | -0.062 | 736 |
1999_10 | 0.348 | 720 |
1999_11 | 0.694 | 730 |
1999_12 | 0.621 | 708 |
2000_01 | 0.549 | 695 |
2000_02 | 0.492 | 727 |
2000_03 | 0.407 | 741 |
2000_04 | 0.391 | 736 |
2000_05 | 0.461 | 734 |
2000_06 | 0.334 | 736 |
2000_07 | 0.008 | 738 |
2000_08 | 0.127 | 734 |
2000_09 | 0.209 | 733 |
2000_10 | 0.360 | 739 |
2000_11 | 0.587 | 741 |
2000_12 | 0.401 | 705 |
2001_01 | 0.591 | 696 |
2001_02 | 0.388 | 677 |
2001_03 | 0.388 | 689 |
2001_04 | 0.696 | 732 |
2001_05 | 0.164 | 711 |
2001_06 | 0.180 | 735 |
2001_07 | 0.050 | 730 |
2001_08 | 0.159 | 734 |
2001_09 | 0.431 | 739 |
2001_10 | 0.426 | 738 |
2001_11 | 0.574 | 735 |
2001_12 | 0.725 | 719 |
2002_01 | 0.542 | 683 |
2002_02 | 0.672 | 706 |
2002_03 | 0.793 | 714 |
2002_04 | 0.718 | 735 |
2002_05 | 0.533 | 726 |
2002_06 | 0.380 | 740 |
2002_07 | 0.546 | 741 |
2002_08 | 0.292 | 741 |
2002_09 | 0.380 | 741 |
2002_10 | 0.534 | 741 |
2002_11 | 0.432 | 740 |
2002_12 | 0.698 | 740 |
2003_01 | 0.175 | 726 |
2003_02 | 0.439 | 729 |
2003_03 | 0.525 | 700 |
2003_04 | 0.517 | 701 |
2003_05 | 0.319 | 700 |
2003_06 | -0.002 | 700 |
2003_07 | 0.357 | 701 |
2003_08 | 0.345 | 740 |
2003_09 | 0.216 | 741 |
2003_10 | 0.182 | 741 |
2003_11 | 0.675 | 741 |
2003_12 | 0.542 | 738 |
2004_01 | 0.375 | 731 |
2004_02 | 0.565 | 738 |
2004_03 | 0.536 | 741 |
2004_04 | 0.181 | 741 |
2004_05 | 0.578 | 741 |
2004_06 | 0.437 | 741 |
2004_07 | 0.086 | 741 |
2004_08 | 0.169 | 741 |
2004_09 | 0.259 | 741 |
2004_10 | 0.557 | 741 |
2004_11 | 0.397 | 741 |
2004_12 | 0.600 | 736 |
2005_01 | 0.358 | 725 |
2005_02 | 0.524 | 739 |
2005_03 | 0.578 | 741 |
2005_04 | 0.550 | 741 |
2005_05 | 0.244 | 741 |
2005_06 | 0.248 | 741 |
2005_07 | 0.457 | 741 |
2005_08 | 0.219 | 741 |
2005_09 | 0.409 | 741 |
2005_10 | 0.422 | 741 |
2005_11 | 0.561 | 741 |
2005_12 | 0.228 | 707 |
2006_01 | 0.457 | 728 |
2006_02 | 0.489 | 737 |
2006_03 | 0.607 | 741 |
2006_04 | 0.621 | 741 |
2006_05 | 0.491 | 741 |
2006_06 | 0.044 | 741 |
2006_07 | -0.054 | 741 |
2006_08 | -0.619 | 741 |
2006_09 | 0.053 | 741 |
2006_10 | 0.366 | 741 |
2006_11 | 0.661 | 741 |
2006_12 | 0.703 | 737 |
2007_01 | 0.673 | 714 |
2007_02 | 0.273 | 741 |
2007_03 | 0.660 | 741 |
2007_04 | 0.406 | 741 |
2007_05 | 0.728 | 741 |
2007_06 | 0.798 | 741 |
2007_07 | 0.787 | 741 |
2007_08 | 0.709 | 741 |
2007_09 | 0.648 | 741 |
2007_10 | 0.690 | 741 |
2007_11 | 0.648 | 741 |
2007_12 | 0.569 | 740 |
2008_01 | 0.426 | 740 |
2008_02 | 0.360 | 741 |
2008_03 | 0.662 | 741 |
2008_04 | 0.628 | 741 |
2008_05 | 0.573 | 741 |
2008_06 | 0.420 | 741 |
2008_07 | 0.160 | 741 |
2008_08 | 0.231 | 741 |
2008_09 | 0.401 | 741 |
2008_10 | -0.192 | 741 |
2008_11 | 0.318 | 741 |
2008_12 | 0.229 | 741 |
2009_01 | 0.295 | 740 |
2009_02 | 0.539 | 741 |
2009_03 | 0.540 | 741 |
2009_04 | 0.302 | 741 |
2009_05 | 0.648 | 741 |
2009_06 | 0.388 | 741 |
2009_07 | -0.085 | 741 |
2009_08 | -0.014 | 741 |
2009_09 | 0.561 | 741 |
2009_10 | 0.438 | 741 |
2009_11 | 0.625 | 741 |
2009_12 | 0.726 | 741 |
2010_01 | 0.560 | 741 |
2010_02 | 0.621 | 738 |
2010_03 | 0.598 | 738 |
2010_04 | 0.679 | 738 |
2010_05 | 0.619 | 741 |
2010_06 | 0.402 | 741 |
2010_07 | 0.448 | 741 |
2010_08 | 0.051 | 741 |
2010_09 | 0.489 | 741 |
2010_10 | 0.413 | 739 |
2010_11 | 0.532 | 741 |
2010_12 | 0.113 | 741 |
2011_01 | 0.384 | 738 |
2011_02 | 0.572 | 739 |
2011_03 | 0.466 | 741 |
2011_04 | 0.526 | 741 |
2011_05 | 0.553 | 741 |
2011_06 | 0.277 | 741 |
2011_07 | 0.051 | 741 |
2011_08 | -0.214 | 741 |
2011_09 | -0.234 | 741 |
2011_10 | 0.396 | 741 |
2011_11 | 0.506 | 741 |
2011_12 | 0.551 | 741 |
2012_01 | 0.527 | 739 |
2012_02 | 0.575 | 707 |
2012_03 | 0.789 | 717 |
2012_04 | 0.521 | 741 |
2012_05 | 0.388 | 741 |
2012_06 | 0.263 | 741 |
2012_07 | 0.128 | 741 |
2012_08 | 0.198 | 741 |
2012_09 | 0.244 | 741 |
2012_10 | 0.409 | 733 |
2012_11 | 0.068 | 740 |
2012_12 | 0.379 | 741 |
2013_01 | 0.456 | 738 |
2013_02 | 0.470 | 711 |
2013_03 | 0.188 | 720 |
2013_04 | 0.694 | 724 |
2013_05 | 0.504 | 730 |
2013_06 | 0.315 | 735 |
2013_07 | 0.107 | 739 |
2013_08 | -0.135 | 739 |
2013_09 | -0.214 | 739 |
2013_10 | 0.442 | 724 |
2013_11 | 0.338 | 739 |
2013_12 | 0.569 | 735 |
2014_01 | 0.075 | 738 |
2014_02 | 0.172 | 711 |
2014_03 | 0.358 | 741 |
2014_04 | 0.484 | 741 |
2014_05 | 0.526 | 741 |
2014_06 | 0.350 | 741 |
2014_07 | -0.181 | 741 |
2014_08 | 0.051 | 741 |
2014_09 | -0.129 | 741 |
2014_10 | 0.155 | 737 |
2014_11 | 0.402 | 740 |
2014_12 | 0.466 | 741 |
2015_01 | 0.339 | 734 |
2015_02 | 0.068 | 721 |
2015_03 | 0.665 | 724 |
2015_04 | 0.778 | 732 |
2015_05 | 0.735 | 741 |
2015_06 | 0.625 | 741 |
2015_07 | 0.337 | 741 |
2015_08 | -0.198 | 741 |
2015_09 | 0.117 | 741 |
2015_10 | -0.387 | 734 |
2015_11 | 0.636 | 741 |
2015_12 | 0.627 | 741 |
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
Soil_Texture <- read.csv("Soil_Texture_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
Soil_Texture[1:4] <- NULL
Soil_Texture <- replace(Soil_Texture, Soil_Texture == -9999, NA)
base_matrix <- read.csv("dates.csv", header = TRUE, sep = ',', dec = '.')
final_spatial_correlation <- base_matrix
names(final_spatial_correlation)[2] <- paste('Corr_Soil_Texture')
for (i in 1:252) {
correlation <- cor(SoilMoisture_MEDIAN[i], Soil_Texture[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEDIAN[i]))
number_values <- 741 - number_values
final_spatial_correlation[i,2] <- correlation
final_spatial_correlation[i,3] <- number_values
}
mean_spatial_corr_medianSM_SoilText <- round(mean(final_spatial_correlation$Corr_Soil_Texture), digits = 3)
kable(final_spatial_correlation, caption = 'Spatial Correlation Median Soil Moistre and Soil Texture', digits = 3)
Month | Corr_Soil_Texture | Number_of_pairs |
---|---|---|
1995_01 | -0.251 | 466 |
1995_02 | -0.293 | 597 |
1995_03 | -0.284 | 724 |
1995_04 | -0.325 | 709 |
1995_05 | -0.183 | 676 |
1995_06 | -0.305 | 731 |
1995_07 | -0.239 | 728 |
1995_08 | -0.190 | 731 |
1995_09 | -0.135 | 730 |
1995_10 | 0.088 | 732 |
1995_11 | -0.074 | 720 |
1995_12 | -0.153 | 496 |
1996_01 | -0.227 | 544 |
1996_02 | -0.247 | 720 |
1996_03 | -0.289 | 728 |
1996_04 | -0.310 | 733 |
1996_05 | -0.283 | 717 |
1996_06 | -0.222 | 740 |
1996_07 | -0.153 | 712 |
1996_08 | 0.068 | 737 |
1996_09 | -0.098 | 738 |
1996_10 | -0.179 | 732 |
1996_11 | -0.274 | 737 |
1996_12 | -0.271 | 587 |
1997_01 | -0.172 | 532 |
1997_02 | -0.231 | 617 |
1997_03 | -0.352 | 731 |
1997_04 | -0.154 | 716 |
1997_05 | -0.312 | 714 |
1997_06 | -0.294 | 725 |
1997_07 | -0.212 | 715 |
1997_08 | -0.066 | 729 |
1997_09 | -0.052 | 718 |
1997_10 | -0.253 | 730 |
1997_11 | -0.313 | 716 |
1997_12 | -0.353 | 702 |
1998_01 | -0.350 | 702 |
1998_02 | -0.319 | 721 |
1998_03 | -0.358 | 729 |
1998_04 | -0.281 | 733 |
1998_05 | -0.295 | 732 |
1998_06 | -0.294 | 731 |
1998_07 | -0.142 | 737 |
1998_08 | 0.012 | 732 |
1998_09 | -0.234 | 734 |
1998_10 | -0.212 | 739 |
1998_11 | -0.207 | 740 |
1998_12 | -0.271 | 721 |
1999_01 | -0.246 | 690 |
1999_02 | -0.319 | 727 |
1999_03 | -0.287 | 740 |
1999_04 | -0.283 | 737 |
1999_05 | -0.286 | 735 |
1999_06 | -0.259 | 738 |
1999_07 | -0.202 | 735 |
1999_08 | -0.020 | 730 |
1999_09 | -0.155 | 736 |
1999_10 | -0.148 | 720 |
1999_11 | -0.324 | 730 |
1999_12 | -0.260 | 708 |
2000_01 | -0.267 | 695 |
2000_02 | -0.311 | 727 |
2000_03 | -0.243 | 741 |
2000_04 | -0.305 | 736 |
2000_05 | -0.297 | 734 |
2000_06 | -0.262 | 736 |
2000_07 | -0.121 | 738 |
2000_08 | -0.171 | 734 |
2000_09 | -0.254 | 733 |
2000_10 | -0.206 | 739 |
2000_11 | -0.259 | 741 |
2000_12 | -0.308 | 705 |
2001_01 | -0.334 | 696 |
2001_02 | -0.260 | 677 |
2001_03 | -0.277 | 689 |
2001_04 | -0.340 | 732 |
2001_05 | -0.154 | 711 |
2001_06 | -0.237 | 735 |
2001_07 | -0.210 | 730 |
2001_08 | -0.209 | 734 |
2001_09 | -0.294 | 739 |
2001_10 | -0.300 | 738 |
2001_11 | -0.314 | 735 |
2001_12 | -0.346 | 719 |
2002_01 | -0.273 | 683 |
2002_02 | -0.312 | 706 |
2002_03 | -0.367 | 714 |
2002_04 | -0.374 | 735 |
2002_05 | -0.344 | 726 |
2002_06 | -0.346 | 740 |
2002_07 | -0.327 | 741 |
2002_08 | -0.238 | 741 |
2002_09 | -0.235 | 741 |
2002_10 | -0.253 | 741 |
2002_11 | -0.295 | 740 |
2002_12 | -0.319 | 740 |
2003_01 | -0.321 | 726 |
2003_02 | -0.332 | 729 |
2003_03 | -0.305 | 700 |
2003_04 | -0.274 | 701 |
2003_05 | -0.239 | 700 |
2003_06 | 0.000 | 700 |
2003_07 | -0.203 | 701 |
2003_08 | -0.262 | 740 |
2003_09 | -0.260 | 741 |
2003_10 | -0.203 | 741 |
2003_11 | -0.346 | 741 |
2003_12 | -0.284 | 738 |
2004_01 | -0.325 | 731 |
2004_02 | -0.350 | 738 |
2004_03 | -0.325 | 741 |
2004_04 | -0.245 | 741 |
2004_05 | -0.336 | 741 |
2004_06 | -0.295 | 741 |
2004_07 | -0.184 | 741 |
2004_08 | -0.194 | 741 |
2004_09 | -0.186 | 741 |
2004_10 | -0.228 | 741 |
2004_11 | -0.245 | 741 |
2004_12 | -0.360 | 736 |
2005_01 | -0.318 | 725 |
2005_02 | -0.308 | 739 |
2005_03 | -0.346 | 741 |
2005_04 | -0.333 | 741 |
2005_05 | -0.278 | 741 |
2005_06 | -0.214 | 741 |
2005_07 | -0.249 | 741 |
2005_08 | -0.036 | 741 |
2005_09 | -0.184 | 741 |
2005_10 | -0.197 | 741 |
2005_11 | -0.305 | 741 |
2005_12 | -0.241 | 707 |
2006_01 | -0.242 | 728 |
2006_02 | -0.291 | 737 |
2006_03 | -0.355 | 741 |
2006_04 | -0.339 | 741 |
2006_05 | -0.325 | 741 |
2006_06 | -0.279 | 741 |
2006_07 | -0.154 | 741 |
2006_08 | 0.185 | 741 |
2006_09 | -0.120 | 741 |
2006_10 | -0.227 | 741 |
2006_11 | -0.367 | 741 |
2006_12 | -0.301 | 737 |
2007_01 | -0.342 | 714 |
2007_02 | -0.279 | 741 |
2007_03 | -0.299 | 741 |
2007_04 | -0.265 | 741 |
2007_05 | -0.324 | 741 |
2007_06 | -0.308 | 741 |
2007_07 | -0.343 | 741 |
2007_08 | -0.304 | 741 |
2007_09 | -0.260 | 741 |
2007_10 | -0.309 | 741 |
2007_11 | -0.319 | 741 |
2007_12 | -0.303 | 740 |
2008_01 | -0.257 | 740 |
2008_02 | -0.283 | 741 |
2008_03 | -0.336 | 741 |
2008_04 | -0.337 | 741 |
2008_05 | -0.297 | 741 |
2008_06 | -0.264 | 741 |
2008_07 | -0.146 | 741 |
2008_08 | -0.191 | 741 |
2008_09 | -0.151 | 741 |
2008_10 | 0.109 | 741 |
2008_11 | -0.179 | 741 |
2008_12 | -0.221 | 741 |
2009_01 | -0.285 | 740 |
2009_02 | -0.311 | 741 |
2009_03 | -0.309 | 741 |
2009_04 | -0.247 | 741 |
2009_05 | -0.350 | 741 |
2009_06 | -0.255 | 741 |
2009_07 | -0.164 | 741 |
2009_08 | -0.176 | 741 |
2009_09 | -0.299 | 741 |
2009_10 | -0.236 | 741 |
2009_11 | -0.273 | 741 |
2009_12 | -0.297 | 741 |
2010_01 | -0.324 | 741 |
2010_02 | -0.359 | 738 |
2010_03 | -0.329 | 738 |
2010_04 | -0.345 | 738 |
2010_05 | -0.334 | 741 |
2010_06 | -0.276 | 741 |
2010_07 | -0.254 | 741 |
2010_08 | -0.120 | 741 |
2010_09 | -0.301 | 741 |
2010_10 | -0.216 | 739 |
2010_11 | -0.180 | 741 |
2010_12 | -0.204 | 741 |
2011_01 | -0.265 | 738 |
2011_02 | -0.313 | 739 |
2011_03 | -0.269 | 741 |
2011_04 | -0.306 | 741 |
2011_05 | -0.344 | 741 |
2011_06 | -0.330 | 741 |
2011_07 | -0.245 | 741 |
2011_08 | -0.168 | 741 |
2011_09 | -0.161 | 741 |
2011_10 | -0.260 | 741 |
2011_11 | -0.271 | 741 |
2011_12 | -0.280 | 741 |
2012_01 | -0.316 | 739 |
2012_02 | -0.288 | 707 |
2012_03 | -0.346 | 717 |
2012_04 | -0.267 | 741 |
2012_05 | -0.275 | 741 |
2012_06 | -0.260 | 741 |
2012_07 | -0.204 | 741 |
2012_08 | -0.180 | 741 |
2012_09 | -0.200 | 741 |
2012_10 | -0.294 | 733 |
2012_11 | -0.203 | 740 |
2012_12 | -0.215 | 741 |
2013_01 | -0.244 | 738 |
2013_02 | -0.284 | 711 |
2013_03 | -0.262 | 720 |
2013_04 | -0.319 | 724 |
2013_05 | -0.333 | 730 |
2013_06 | -0.287 | 735 |
2013_07 | -0.206 | 739 |
2013_08 | -0.069 | 739 |
2013_09 | -0.056 | 739 |
2013_10 | -0.222 | 724 |
2013_11 | -0.210 | 739 |
2013_12 | -0.294 | 735 |
2014_01 | -0.260 | 738 |
2014_02 | -0.301 | 711 |
2014_03 | -0.301 | 741 |
2014_04 | -0.333 | 741 |
2014_05 | -0.318 | 741 |
2014_06 | -0.222 | 741 |
2014_07 | -0.198 | 741 |
2014_08 | -0.158 | 741 |
2014_09 | -0.024 | 741 |
2014_10 | -0.164 | 737 |
2014_11 | -0.204 | 740 |
2014_12 | -0.263 | 741 |
2015_01 | -0.269 | 734 |
2015_02 | -0.273 | 721 |
2015_03 | -0.363 | 724 |
2015_04 | -0.336 | 732 |
2015_05 | -0.360 | 741 |
2015_06 | -0.331 | 741 |
2015_07 | -0.253 | 741 |
2015_08 | -0.061 | 741 |
2015_09 | -0.178 | 741 |
2015_10 | 0.226 | 734 |
2015_11 | -0.313 | 741 |
2015_12 | -0.295 | 741 |
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation")
TWI <- read.csv("TWI_region_interest_montlhy_pixel_values.csv", header = TRUE, sep = ',', dec = '.')
TWI[1:4] <- NULL
TWI <- replace(TWI, TWI == -9999, NA)
base_matrix <- read.csv("dates.csv", header = TRUE, sep = ',', dec = '.')
final_spatial_correlation <- base_matrix
names(final_spatial_correlation)[2] <- paste('Corr_TWI')
for (i in 1:252) {
correlation <- cor(SoilMoisture_MEDIAN[i], TWI[i],
use = 'pairwise.complete.obs', method = 'pearson')
number_values <- sum(is.na(SoilMoisture_MEDIAN[i]))
number_values <- 741 - number_values
final_spatial_correlation[i,2] <- correlation
final_spatial_correlation[i,3] <- number_values
}
mean_spatial_corr_medianSM_TWIt <- round(mean(final_spatial_correlation$Corr_TWI), digits = 3)
kable(final_spatial_correlation, caption = 'Spatial Correlation Median Soil Moistre and TWI', digits = 3)
Month | Corr_TWI | Number_of_pairs |
---|---|---|
1995_01 | -0.018 | 466 |
1995_02 | -0.066 | 597 |
1995_03 | -0.136 | 724 |
1995_04 | -0.088 | 709 |
1995_05 | -0.078 | 676 |
1995_06 | -0.122 | 731 |
1995_07 | -0.084 | 728 |
1995_08 | -0.104 | 731 |
1995_09 | 0.020 | 730 |
1995_10 | 0.086 | 732 |
1995_11 | 0.093 | 720 |
1995_12 | -0.021 | 496 |
1996_01 | -0.074 | 544 |
1996_02 | -0.098 | 720 |
1996_03 | -0.058 | 728 |
1996_04 | -0.061 | 733 |
1996_05 | -0.085 | 717 |
1996_06 | -0.092 | 740 |
1996_07 | -0.001 | 712 |
1996_08 | -0.004 | 737 |
1996_09 | 0.083 | 738 |
1996_10 | 0.013 | 732 |
1996_11 | -0.080 | 737 |
1996_12 | -0.110 | 587 |
1997_01 | -0.013 | 532 |
1997_02 | -0.136 | 617 |
1997_03 | -0.094 | 731 |
1997_04 | -0.043 | 716 |
1997_05 | -0.086 | 714 |
1997_06 | -0.092 | 725 |
1997_07 | -0.100 | 715 |
1997_08 | -0.085 | 729 |
1997_09 | -0.023 | 718 |
1997_10 | -0.122 | 730 |
1997_11 | -0.149 | 716 |
1997_12 | -0.109 | 702 |
1998_01 | -0.124 | 702 |
1998_02 | -0.120 | 721 |
1998_03 | -0.125 | 729 |
1998_04 | -0.123 | 733 |
1998_05 | -0.132 | 732 |
1998_06 | -0.097 | 731 |
1998_07 | -0.094 | 737 |
1998_08 | 0.010 | 732 |
1998_09 | -0.066 | 734 |
1998_10 | -0.082 | 739 |
1998_11 | -0.097 | 740 |
1998_12 | -0.105 | 721 |
1999_01 | -0.126 | 690 |
1999_02 | -0.128 | 727 |
1999_03 | -0.141 | 740 |
1999_04 | -0.114 | 737 |
1999_05 | -0.093 | 735 |
1999_06 | -0.104 | 738 |
1999_07 | -0.037 | 735 |
1999_08 | 0.020 | 730 |
1999_09 | -0.068 | 736 |
1999_10 | -0.027 | 720 |
1999_11 | -0.111 | 730 |
1999_12 | -0.110 | 708 |
2000_01 | -0.115 | 695 |
2000_02 | -0.134 | 727 |
2000_03 | -0.126 | 741 |
2000_04 | -0.095 | 736 |
2000_05 | -0.104 | 734 |
2000_06 | -0.080 | 736 |
2000_07 | -0.029 | 738 |
2000_08 | -0.093 | 734 |
2000_09 | -0.079 | 733 |
2000_10 | -0.074 | 739 |
2000_11 | -0.055 | 741 |
2000_12 | -0.093 | 705 |
2001_01 | -0.080 | 696 |
2001_02 | -0.108 | 677 |
2001_03 | -0.119 | 689 |
2001_04 | -0.073 | 732 |
2001_05 | -0.040 | 711 |
2001_06 | -0.097 | 735 |
2001_07 | -0.058 | 730 |
2001_08 | -0.050 | 734 |
2001_09 | -0.064 | 739 |
2001_10 | -0.081 | 738 |
2001_11 | -0.096 | 735 |
2001_12 | -0.088 | 719 |
2002_01 | -0.107 | 683 |
2002_02 | -0.124 | 706 |
2002_03 | -0.071 | 714 |
2002_04 | -0.092 | 735 |
2002_05 | -0.103 | 726 |
2002_06 | -0.117 | 740 |
2002_07 | -0.081 | 741 |
2002_08 | -0.078 | 741 |
2002_09 | -0.090 | 741 |
2002_10 | -0.051 | 741 |
2002_11 | -0.091 | 740 |
2002_12 | -0.101 | 740 |
2003_01 | -0.112 | 726 |
2003_02 | -0.099 | 729 |
2003_03 | -0.104 | 700 |
2003_04 | -0.094 | 701 |
2003_05 | -0.082 | 700 |
2003_06 | -0.022 | 700 |
2003_07 | -0.039 | 701 |
2003_08 | -0.073 | 740 |
2003_09 | -0.080 | 741 |
2003_10 | -0.098 | 741 |
2003_11 | -0.090 | 741 |
2003_12 | -0.109 | 738 |
2004_01 | -0.099 | 731 |
2004_02 | -0.125 | 738 |
2004_03 | -0.112 | 741 |
2004_04 | -0.109 | 741 |
2004_05 | -0.099 | 741 |
2004_06 | -0.077 | 741 |
2004_07 | -0.077 | 741 |
2004_08 | -0.065 | 741 |
2004_09 | -0.021 | 741 |
2004_10 | -0.032 | 741 |
2004_11 | -0.070 | 741 |
2004_12 | -0.121 | 736 |
2005_01 | -0.117 | 725 |
2005_02 | -0.116 | 739 |
2005_03 | -0.130 | 741 |
2005_04 | -0.129 | 741 |
2005_05 | -0.085 | 741 |
2005_06 | -0.121 | 741 |
2005_07 | -0.080 | 741 |
2005_08 | -0.039 | 741 |
2005_09 | -0.082 | 741 |
2005_10 | -0.047 | 741 |
2005_11 | -0.104 | 741 |
2005_12 | -0.110 | 707 |
2006_01 | -0.118 | 728 |
2006_02 | -0.095 | 737 |
2006_03 | -0.118 | 741 |
2006_04 | -0.121 | 741 |
2006_05 | -0.133 | 741 |
2006_06 | -0.103 | 741 |
2006_07 | -0.104 | 741 |
2006_08 | 0.013 | 741 |
2006_09 | 0.000 | 741 |
2006_10 | -0.045 | 741 |
2006_11 | -0.109 | 741 |
2006_12 | -0.095 | 737 |
2007_01 | -0.075 | 714 |
2007_02 | -0.122 | 741 |
2007_03 | -0.121 | 741 |
2007_04 | -0.163 | 741 |
2007_05 | -0.133 | 741 |
2007_06 | -0.127 | 741 |
2007_07 | -0.100 | 741 |
2007_08 | -0.060 | 741 |
2007_09 | -0.076 | 741 |
2007_10 | -0.110 | 741 |
2007_11 | -0.094 | 741 |
2007_12 | -0.090 | 740 |
2008_01 | -0.100 | 740 |
2008_02 | -0.119 | 741 |
2008_03 | -0.120 | 741 |
2008_04 | -0.129 | 741 |
2008_05 | -0.131 | 741 |
2008_06 | -0.134 | 741 |
2008_07 | -0.069 | 741 |
2008_08 | -0.053 | 741 |
2008_09 | -0.051 | 741 |
2008_10 | 0.018 | 741 |
2008_11 | -0.091 | 741 |
2008_12 | -0.092 | 741 |
2009_01 | -0.097 | 740 |
2009_02 | -0.119 | 741 |
2009_03 | -0.102 | 741 |
2009_04 | -0.153 | 741 |
2009_05 | -0.129 | 741 |
2009_06 | -0.072 | 741 |
2009_07 | -0.013 | 741 |
2009_08 | -0.034 | 741 |
2009_09 | -0.085 | 741 |
2009_10 | -0.069 | 741 |
2009_11 | -0.102 | 741 |
2009_12 | -0.063 | 741 |
2010_01 | -0.103 | 741 |
2010_02 | -0.103 | 738 |
2010_03 | -0.141 | 738 |
2010_04 | -0.146 | 738 |
2010_05 | -0.139 | 741 |
2010_06 | -0.129 | 741 |
2010_07 | -0.087 | 741 |
2010_08 | -0.034 | 741 |
2010_09 | -0.097 | 741 |
2010_10 | -0.071 | 739 |
2010_11 | -0.092 | 741 |
2010_12 | -0.092 | 741 |
2011_01 | -0.101 | 738 |
2011_02 | -0.113 | 739 |
2011_03 | -0.126 | 741 |
2011_04 | -0.125 | 741 |
2011_05 | -0.123 | 741 |
2011_06 | -0.122 | 741 |
2011_07 | -0.066 | 741 |
2011_08 | -0.087 | 741 |
2011_09 | -0.054 | 741 |
2011_10 | -0.062 | 741 |
2011_11 | -0.121 | 741 |
2011_12 | -0.113 | 741 |
2012_01 | -0.107 | 739 |
2012_02 | -0.128 | 707 |
2012_03 | -0.134 | 717 |
2012_04 | -0.140 | 741 |
2012_05 | -0.127 | 741 |
2012_06 | -0.084 | 741 |
2012_07 | -0.028 | 741 |
2012_08 | -0.014 | 741 |
2012_09 | -0.034 | 741 |
2012_10 | -0.030 | 733 |
2012_11 | -0.081 | 740 |
2012_12 | -0.098 | 741 |
2013_01 | -0.075 | 738 |
2013_02 | -0.114 | 711 |
2013_03 | -0.123 | 720 |
2013_04 | -0.142 | 724 |
2013_05 | -0.137 | 730 |
2013_06 | -0.121 | 735 |
2013_07 | -0.076 | 739 |
2013_08 | -0.124 | 739 |
2013_09 | -0.102 | 739 |
2013_10 | -0.094 | 724 |
2013_11 | -0.086 | 739 |
2013_12 | -0.075 | 735 |
2014_01 | -0.116 | 738 |
2014_02 | -0.128 | 711 |
2014_03 | -0.130 | 741 |
2014_04 | -0.118 | 741 |
2014_05 | -0.107 | 741 |
2014_06 | -0.117 | 741 |
2014_07 | -0.093 | 741 |
2014_08 | -0.072 | 741 |
2014_09 | 0.053 | 741 |
2014_10 | -0.054 | 737 |
2014_11 | -0.073 | 740 |
2014_12 | -0.101 | 741 |
2015_01 | -0.061 | 734 |
2015_02 | -0.097 | 721 |
2015_03 | -0.112 | 724 |
2015_04 | -0.122 | 732 |
2015_05 | -0.103 | 741 |
2015_06 | -0.099 | 741 |
2015_07 | -0.076 | 741 |
2015_08 | -0.078 | 741 |
2015_09 | -0.117 | 741 |
2015_10 | 0.069 | 734 |
2015_11 | -0.100 | 741 |
2015_12 | -0.112 | 741 |
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/Correlation/spatial_correlation")
#MEAN Soil Moisture vs Precipitation
data <- read.csv('Spatial_Correlation_region_interest_MEAN_montlhy_SoilMoist_daymet_Precipitation.csv')
data$X <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Month, y = Corr_Precipitation, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Month, y = Corr_Precipitation)) +
labs (title = paste0('Spatial correlation, Mean Soil Moisture and Daymet Precipitation.
', x), x = 'Monthly layer', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEAN Soil Moisture vs Max Temperature
data <- read.csv('Spatial_Correlation_region_interest_MEAN_montlhy_SoilMoist_daymet_Temperature_Max.csv')
data$X <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Month, y = Corr_Temperature_Max, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Month, y = Corr_Temperature_Max)) +
labs (title = paste0('Spatial correlation, Mean Soil Moisture and Daymet Max Temperature.
', x), x = 'Monthly layer', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEAN Soil Moisture vs Min Temperature
data <- read.csv('Spatial_Correlation_region_interest_MEAN_montlhy_SoilMoist_daymet_Temperature_Min.csv')
data$X <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Month, y = Corr_Temperature_Min, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Month, y = Corr_Temperature_Min)) +
labs (title = paste0('Spatial correlation, Mean Soil Moisture and Daymet Min Temperature.
', x), x = 'Monthly layer', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEAN Soil Moisture vs Soil Texture
data <- read.csv('Spatial_Correlation_region_interest_MEAN_montlhy_SoilMoist_SoilTexture.csv')
data$X <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Month, y = Corr_Soil_Texture, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Month, y = Corr_Soil_Texture)) +
labs (title = paste0('Spatial correlation, Mean Soil Moisture and Soil Texture.
', x), x = 'Monthly layer', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEAN Soil Moisture vs Topographic Wetness Index#
data <- read.csv('Spatial_Correlation_region_interest_MEAN_montlhy_SoilMoist_TWI.csv')
data$X <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Month, y = Corr_TWI, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Month, y = Corr_TWI)) +
labs (title = paste0('Spatial correlation, Mean Soil Moisture and TWI.
', x), x = 'Monthly layer', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEDIAN Soil Moisture vs Precipitation
data <- read.csv('Spatial_Correlation_region_interest_MEDIAN_montlhy_SoilMoist_daymet_Precipitation.csv')
data$X <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Month, y = Corr_Precipitation, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Month, y = Corr_Precipitation)) +
labs (title = paste0('Spatial correlation, Median Soil Moisture and Daymet Precipitation.
', x), x = 'Monthly layer', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEDIAN Soil Moisture vs Max Temperature
data <- read.csv('Spatial_Correlation_region_interest_MEDIAN_montlhy_SoilMoist_daymet_Temperature_Max.csv')
data$X <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Month, y = Corr_Temperature_Max, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Month, y = Corr_Temperature_Max)) +
labs (title = paste0('Spatial correlation, Median Soil Moisture and Daymet Max Temperature.
', x), x = 'Monthly layer', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEDIAN Soil Moisture vs Min Temperature
data <- read.csv('Spatial_Correlation_region_interest_MEDIAN_montlhy_SoilMoist_daymet_Temperature_Min.csv')
data$X <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Month, y = Corr_Temperature_Min, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Month, y = Corr_Temperature_Min)) +
labs (title = paste0('Spatial correlation, Median Soil Moisture and Daymet Min Temperature.
', x), x = 'Monthly layer', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEDIAN Soil Moisture vs Soil Texture
data <- read.csv('Spatial_Correlation_region_interest_MEDIAN_montlhy_SoilMoist_SoilTexture.csv')
data$X <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Month, y = Corr_Soil_Texture, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Month, y = Corr_Soil_Texture)) +
labs (title = paste0('Spatial correlation, Median Soil Moisture and Soil Texture.
', x), x = 'Monthly layer', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#MEDIAN Soil Moisture vs Topographic Wetness Index
data <- read.csv('Spatial_Correlation_region_interest_MEDIAN_montlhy_SoilMoist_TWI.csv')
data$X <- NULL
x <- mean(data[,2])
x <- round(x, digits = 3)
x <- as.character(x)
x <- paste0('Mean Correlation ', x)
ggplot(data = data) +
geom_point(mapping = aes(x = Month, y = Corr_TWI, color = Number_of_pairs)) +
geom_smooth(mapping = aes(x = Month, y = Corr_TWI)) +
labs (title = paste0('Spatial correlation, Median Soil Moisture and TWI.
', x), x = 'Monthly layer', y = 'Correlation', color = "Number of Pairs")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
The results show the mean correlation value for each series of data derived both for temporal and spatial analysis between mean and median monthly soil moisture layers, and monthly values for ancillary information. Temporal analysis depicts the mean of 741 correlation values, precipitation show the highest positive correlation, while maximum temperature represents the highest values describing and inverse correlation. Spatial analysis shows the means of 252 correlation values, precipitation and minimum temperature are the variables describing the highest general correlation values, whereas max temperature shows a negative and lower correlation than the previous variables. As maximum temperature might be more affected by evaporation and transpiration process above soil level, it seems not be related to soil water content. In the other hand, minimum temperature might be more related to the limitation of evaporation, then preserving water in soil for longer periods, giving a chance to stabilize moisture in soil and then being better estimated by the remote sensors.
results <- matrix(data = NA, ncol = 5, nrow = 5)
results <- as.data.frame(results)
names_results <- c("Covariate", "Mean Temporal Correlation(Mean Monthly Values)", "Mean Temporal Correlation(Median Monthly Values)", "Mean Spatial Correlation(Mean Monthly Values)", "Mean Spatial Correlation(Median Monthly Values)")
names(results) <- names_results
results$Covariate <- c("Precipitation", "Max Temperature", "Min Temperature", "Soil Texture", "TWI")
results[1,2] <- mean_temp_corr_meanSM_Prcp
results[2,2] <- mean_temp_corr_meanSM_MaxTemp
results[3,2] <- mean_temp_corr_meanSM_MinTemp
results[4,2] <- NA
results[5,2] <- NA
results[1,3] <- mean_temp_corr_medianSM_Prcp
results[2,3] <- mean_temp_corr_medianSM_MaxTemp
results[3,3] <- mean_temp_corr_medianSM_MinTemp
results[4,3] <- NA
results[5,3] <- NA
results[1,4] <- mean_spatial_corr_meanSM_Prcp
results[2,4] <- mean_spatial_corr_meanSM_MaxTemp
results[3,4] <- mean_spatial_corr_meanSM_MinTemp
results[4,4] <- mean_spatial_corr_meanSM_SoilText
results[5,4] <- mean_spatial_corr_meanSM_TWI
results[1,5] <- mean_spatial_corr_medianSM_Prcp
results[2,5] <- mean_spatial_corr_medianSM_MaxTemp
results[3,5] <- mean_spatial_corr_medianSM_MinTemp
results[4,5] <- mean_spatial_corr_medianSM_SoilText
results[5,5] <- mean_spatial_corr_medianSM_TWIt
kable(results, caption = 'Mean correlation derived from temporal and spatial analysis using mean and median soil moisture layers, and geophysical covariates', digits = 3)
Covariate | Mean Temporal Correlation(Mean Monthly Values) | Mean Temporal Correlation(Median Monthly Values) | Mean Spatial Correlation(Mean Monthly Values) | Mean Spatial Correlation(Median Monthly Values) |
---|---|---|---|---|
Precipitation | 0.348 | 0.310 | 0.566 | 0.555 |
Max Temperature | -0.467 | -0.488 | -0.219 | -0.215 |
Min Temperature | -0.363 | -0.389 | 0.382 | 0.389 |
Soil Texture | NA | NA | -0.246 | -0.249 |
TWI | NA | NA | -0.087 | -0.087 |
Derived from theses temporal and spatial correlation analysis, we establish that the better modeling strategy for soil moisture is regarding the spatial distribution of both Precipitation and Minimum temperature values. Correlation values between these two variables and soil moisture layers tend to be higher and more consistent than regarding temporal approach. These two variables are then used in further steps of our analyses to predict soil moisture over areas where satellites cannot retrieve soil moisture information and estimate its values. Regarding the correlation between ancillary variables and mean and median values, monthly mean soil moisture values were selected as the base for further analysis, as its values does not show a significant shift from median values. Mean values better correspond the statistical parameter used to generate temperature monthly layers, one of the covariates selected now on for soil moisture prediction It is clear that considering temporal analysis for soil moisture predictions, our approach should have regarded a time series gap filling technique, but using spatial analysis as our base line, we propose the use of generalized linear models to predict soil moisture over areas were original data is not available. This way we also diminish for seasonality effects as every monthly layer is treated independently, using all valid pixels in both soil moisture layer and defined covariates, to build up an individual linear regression equation for each month and then to predict new soil moisture values.
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