SOIL MOISTURE AND GEOPHYSICAL COVARIATES CORRELATION ANALYSIS

Data Preparation

Preparation of soil moisture information layers for the region of interest

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.

Preparation of geophysical covariates layers for the region of interest

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

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

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

Soil texture aggregation in four general classes a resampling of data to 0.25 degrees over the region of interest

Topographic Wetness Index

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

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

Topographic Index over region of interest. 0.25x0.25 degrees, dimensionless units

Correlation Analysis

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)

Conceptual temporal and correlation analysis between soil moisture and covariates (e.g. precipitation)

Temporal Correlation Analysis

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.

MEAN Monthly Soil Moisture Values

Temporal Correlation between Monthly Mean Soil Moisture and Precipitation
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)
Temporal Correlation Mean Soil Moistre and Precipitation
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
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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
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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
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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
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578 -95.625 34.125 0.344 246
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580 -95.125 34.125 0.318 251
581 -94.875 34.125 0.406 242
582 -94.625 34.125 0.342 243
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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
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602 -99.375 33.875 0.432 252
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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
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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
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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
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686 -97.875 33.375 0.345 251
687 -97.625 33.375 0.356 252
688 -97.375 33.375 0.404 251
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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
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714 -100.625 33.125 0.402 252
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717 -99.875 33.125 0.407 252
718 -99.625 33.125 0.383 252
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720 -99.125 33.125 0.368 252
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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
Temporal Correlation between Monthly Mean Soil Moisture and Maximum Temperature
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)
Temporal Correlation Mean Soil Moistre and Max Temperature
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
7 -101.875 37.625 -0.135 236
8 -101.625 37.625 -0.218 252
9 -101.375 37.625 -0.180 252
10 -101.125 37.625 -0.126 252
11 -100.875 37.625 -0.183 251
12 -100.625 37.625 -0.220 249
13 -100.375 37.625 -0.298 248
14 -100.125 37.625 -0.183 225
15 -99.875 37.625 -0.288 247
16 -99.625 37.625 -0.282 247
17 -99.375 37.625 -0.246 247
18 -99.125 37.625 -0.164 234
19 -98.875 37.625 -0.160 232
20 -98.625 37.625 -0.246 249
21 -98.375 37.625 -0.290 251
22 -98.125 37.625 -0.335 251
23 -97.875 37.625 -0.383 236
24 -97.625 37.625 -0.340 250
25 -97.375 37.625 -0.468 249
26 -97.125 37.625 -0.429 247
27 -96.875 37.625 -0.460 247
28 -96.625 37.625 -0.554 243
29 -96.375 37.625 -0.525 241
30 -96.125 37.625 -0.573 241
31 -95.875 37.625 -0.596 243
32 -95.625 37.625 -0.570 244
33 -95.375 37.625 -0.532 242
34 -95.125 37.625 -0.530 241
35 -94.875 37.625 -0.506 245
36 -94.625 37.625 -0.532 241
37 -94.375 37.625 -0.497 238
38 -94.125 37.625 -0.462 233
39 -93.875 37.625 -0.413 233
40 -103.375 37.375 -0.316 251
41 -103.125 37.375 -0.264 251
42 -102.875 37.375 -0.132 233
43 -102.625 37.375 -0.078 235
44 -102.375 37.375 -0.136 251
45 -102.125 37.375 -0.132 250
46 -101.875 37.375 -0.161 251
47 -101.625 37.375 -0.061 241
48 -101.375 37.375 -0.249 242
49 -101.125 37.375 -0.304 241
50 -100.875 37.375 -0.367 240
51 -100.625 37.375 -0.430 234
52 -100.375 37.375 -0.444 227
53 -100.125 37.375 -0.292 244
54 -99.875 37.375 -0.327 203
55 -99.625 37.375 -0.297 231
56 -99.375 37.375 -0.295 231
57 -99.125 37.375 -0.311 248
58 -98.875 37.375 -0.390 245
59 -98.625 37.375 -0.315 248
60 -98.375 37.375 -0.307 249
61 -98.125 37.375 -0.442 221
62 -97.875 37.375 -0.407 231
63 -97.625 37.375 -0.388 249
64 -97.375 37.375 -0.398 251
65 -97.125 37.375 -0.497 248
66 -96.875 37.375 -0.518 245
67 -96.625 37.375 -0.544 243
68 -96.375 37.375 -0.536 241
69 -96.125 37.375 -0.583 241
70 -95.875 37.375 -0.617 243
71 -95.625 37.375 -0.592 243
72 -95.375 37.375 -0.403 228
73 -95.125 37.375 -0.410 236
74 -94.875 37.375 -0.484 241
75 -94.625 37.375 -0.477 237
76 -94.375 37.375 -0.512 236
77 -94.125 37.375 -0.525 233
78 -93.875 37.375 -0.474 237
79 -103.375 37.125 -0.152 237
80 -103.125 37.125 -0.119 237
81 -102.875 37.125 -0.110 251
82 -102.625 37.125 -0.133 251
83 -102.375 37.125 -0.174 251
84 -102.125 37.125 -0.122 251
85 -101.875 37.125 -0.142 251
86 -101.625 37.125 -0.036 242
87 -101.375 37.125 -0.134 251
88 -101.125 37.125 -0.134 252
89 -100.875 37.125 -0.177 250
90 -100.625 37.125 -0.307 252
91 -100.375 37.125 -0.342 250
92 -100.125 37.125 -0.305 225
93 -99.875 37.125 -0.355 242
94 -99.625 37.125 -0.288 244
95 -99.375 37.125 -0.353 243
96 -99.125 37.125 -0.410 247
97 -98.875 37.125 -0.413 245
98 -98.625 37.125 -0.394 248
99 -98.375 37.125 -0.370 221
100 -98.125 37.125 -0.370 228
101 -97.875 37.125 -0.379 250
102 -97.625 37.125 -0.389 250
103 -97.375 37.125 -0.423 249
104 -97.125 37.125 -0.458 251
105 -96.875 37.125 -0.579 247
106 -96.625 37.125 -0.547 242
107 -96.375 37.125 -0.506 240
108 -96.125 37.125 -0.505 241
109 -95.875 37.125 -0.551 243
110 -95.625 37.125 -0.545 247
111 -95.375 37.125 -0.560 239
112 -95.125 37.125 -0.595 244
113 -94.875 37.125 -0.382 226
114 -94.625 37.125 -0.501 241
115 -94.375 37.125 -0.517 236
116 -94.125 37.125 -0.565 243
117 -93.875 37.125 -0.570 242
118 -103.375 36.875 -0.299 252
119 -103.125 36.875 -0.295 251
120 -102.875 36.875 -0.271 251
121 -102.625 36.875 -0.309 250
122 -102.375 36.875 -0.290 251
123 -102.125 36.875 -0.285 251
124 -101.875 36.875 -0.270 252
125 -101.625 36.875 -0.328 252
126 -101.375 36.875 -0.385 252
127 -101.125 36.875 -0.313 252
128 -100.875 36.875 -0.378 252
129 -100.625 36.875 -0.443 250
130 -100.375 36.875 -0.473 249
131 -100.125 36.875 -0.507 250
132 -99.875 36.875 -0.463 246
133 -99.625 36.875 -0.508 249
134 -99.375 36.875 -0.505 246
135 -99.125 36.875 -0.539 248
136 -98.875 36.875 -0.499 250
137 -98.625 36.875 -0.472 241
138 -98.375 36.875 -0.361 247
139 -98.125 36.875 -0.414 250
140 -97.875 36.875 -0.433 249
141 -97.625 36.875 -0.442 250
142 -97.375 36.875 -0.479 251
143 -97.125 36.875 -0.572 250
144 -96.875 36.875 -0.670 250
145 -96.625 36.875 -0.636 246
146 -96.375 36.875 -0.632 246
147 -96.125 36.875 -0.610 246
148 -95.875 36.875 -0.635 246
149 -95.625 36.875 -0.648 247
150 -95.375 36.875 -0.639 245
151 -95.125 36.875 -0.656 247
152 -94.875 36.875 -0.653 244
153 -94.625 36.875 -0.625 244
154 -94.375 36.875 -0.576 244
155 -94.125 36.875 -0.617 238
156 -93.875 36.875 -0.591 241
157 -103.375 36.625 -0.275 252
158 -103.125 36.625 -0.266 252
159 -102.875 36.625 -0.276 251
160 -102.625 36.625 -0.299 250
161 -102.375 36.625 -0.300 251
162 -102.125 36.625 -0.280 251
163 -101.875 36.625 -0.298 252
164 -101.625 36.625 -0.328 252
165 -101.375 36.625 -0.393 251
166 -101.125 36.625 -0.377 249
167 -100.875 36.625 -0.411 251
168 -100.625 36.625 -0.470 251
169 -100.375 36.625 -0.510 251
170 -100.125 36.625 -0.529 249
171 -99.875 36.625 -0.515 249
172 -99.625 36.625 -0.530 250
173 -99.375 36.625 -0.529 251
174 -99.125 36.625 -0.517 247
175 -98.875 36.625 -0.491 250
176 -98.625 36.625 -0.447 250
177 -98.375 36.625 -0.394 250
178 -98.125 36.625 -0.435 251
179 -97.875 36.625 -0.436 250
180 -97.625 36.625 -0.462 250
181 -97.375 36.625 -0.505 250
182 -97.125 36.625 -0.597 249
183 -96.875 36.625 -0.636 250
184 -96.625 36.625 -0.659 247
185 -96.375 36.625 -0.637 247
186 -96.125 36.625 -0.678 239
187 -95.875 36.625 -0.703 246
188 -95.625 36.625 -0.711 245
189 -95.375 36.625 -0.685 246
190 -95.125 36.625 -0.674 245
191 -94.875 36.625 -0.714 245
192 -94.625 36.625 -0.678 245
193 -94.375 36.625 -0.603 245
194 -94.125 36.625 -0.574 234
195 -93.875 36.625 -0.511 242
196 -103.375 36.375 -0.237 251
197 -103.125 36.375 -0.273 251
198 -102.875 36.375 -0.237 251
199 -102.625 36.375 -0.291 252
200 -102.375 36.375 -0.275 252
201 -102.125 36.375 -0.287 252
202 -101.875 36.375 -0.315 252
203 -101.625 36.375 -0.319 251
204 -101.375 36.375 -0.353 252
205 -101.125 36.375 -0.420 250
206 -100.875 36.375 -0.490 247
207 -100.625 36.375 -0.485 250
208 -100.375 36.375 -0.513 251
209 -100.125 36.375 -0.510 252
210 -99.875 36.375 -0.496 249
211 -99.625 36.375 -0.497 250
212 -99.375 36.375 -0.484 250
213 -99.125 36.375 -0.467 248
214 -98.875 36.375 -0.512 249
215 -98.625 36.375 -0.484 249
216 -98.375 36.375 -0.424 249
217 -98.125 36.375 -0.458 242
218 -97.875 36.375 -0.444 250
219 -97.625 36.375 -0.502 249
220 -97.375 36.375 -0.544 249
221 -97.125 36.375 -0.564 247
222 -96.875 36.375 -0.637 248
223 -96.625 36.375 -0.607 247
224 -96.375 36.375 -0.642 247
225 -96.125 36.375 -0.613 246
226 -95.875 36.375 -0.686 247
227 -95.625 36.375 -0.701 246
228 -95.375 36.375 -0.643 247
229 -95.125 36.375 -0.640 247
230 -94.875 36.375 -0.562 236
231 -94.625 36.375 -0.647 244
232 -94.375 36.375 -0.556 244
233 -94.125 36.375 -0.598 244
234 -93.875 36.375 -0.656 240
235 -103.375 36.125 -0.139 247
236 -103.125 36.125 -0.257 252
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Temporal Correlation between Monthly Mean Soil Moisture and Minimum Temperature
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)
Temporal Correlation Mean Soil Moistre and Min Temperature
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
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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

MEDIAN Monthly Soil Moisture Values

Temporal Correlation between Monthly Mean Soil Moisture and Precipitation
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)
Temporal Correlation Median Soil Moistre and Precipitation
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
103 -97.375 37.125 0.203 249
104 -97.125 37.125 0.121 251
105 -96.875 37.125 0.088 247
106 -96.625 37.125 0.208 242
107 -96.375 37.125 0.227 240
108 -96.125 37.125 0.225 241
109 -95.875 37.125 0.187 243
110 -95.625 37.125 0.209 247
111 -95.375 37.125 0.184 239
112 -95.125 37.125 0.178 244
113 -94.875 37.125 0.281 226
114 -94.625 37.125 0.197 241
115 -94.375 37.125 0.204 236
116 -94.125 37.125 0.214 243
117 -93.875 37.125 0.189 242
118 -103.375 36.875 0.311 252
119 -103.125 36.875 0.320 251
120 -102.875 36.875 0.317 251
121 -102.625 36.875 0.285 250
122 -102.375 36.875 0.283 251
123 -102.125 36.875 0.319 251
124 -101.875 36.875 0.348 252
125 -101.625 36.875 0.299 252
126 -101.375 36.875 0.239 252
127 -101.125 36.875 0.351 252
128 -100.875 36.875 0.348 252
129 -100.625 36.875 0.306 250
130 -100.375 36.875 0.316 249
131 -100.125 36.875 0.276 250
132 -99.875 36.875 0.305 246
133 -99.625 36.875 0.256 249
134 -99.375 36.875 0.262 246
135 -99.125 36.875 0.225 248
136 -98.875 36.875 0.250 250
137 -98.625 36.875 0.253 241
138 -98.375 36.875 0.375 247
139 -98.125 36.875 0.335 250
140 -97.875 36.875 0.302 249
141 -97.625 36.875 0.232 250
142 -97.375 36.875 0.178 251
143 -97.125 36.875 0.077 250
144 -96.875 36.875 0.033 250
145 -96.625 36.875 0.108 246
146 -96.375 36.875 0.115 246
147 -96.125 36.875 0.159 246
148 -95.875 36.875 0.151 246
149 -95.625 36.875 0.158 247
150 -95.375 36.875 0.115 245
151 -95.125 36.875 0.108 247
152 -94.875 36.875 0.175 244
153 -94.625 36.875 0.113 244
154 -94.375 36.875 0.143 244
155 -94.125 36.875 0.195 238
156 -93.875 36.875 0.167 241
157 -103.375 36.625 0.330 252
158 -103.125 36.625 0.322 252
159 -102.875 36.625 0.294 251
160 -102.625 36.625 0.296 250
161 -102.375 36.625 0.306 251
162 -102.125 36.625 0.340 251
163 -101.875 36.625 0.350 252
164 -101.625 36.625 0.345 252
165 -101.375 36.625 0.330 251
166 -101.125 36.625 0.334 249
167 -100.875 36.625 0.321 251
168 -100.625 36.625 0.313 251
169 -100.375 36.625 0.299 251
170 -100.125 36.625 0.314 249
171 -99.875 36.625 0.326 249
172 -99.625 36.625 0.265 250
173 -99.375 36.625 0.247 251
174 -99.125 36.625 0.264 247
175 -98.875 36.625 0.256 250
176 -98.625 36.625 0.312 250
177 -98.375 36.625 0.369 250
178 -98.125 36.625 0.309 251
179 -97.875 36.625 0.307 250
180 -97.625 36.625 0.284 250
181 -97.375 36.625 0.214 250
182 -97.125 36.625 0.088 249
183 -96.875 36.625 0.076 250
184 -96.625 36.625 0.098 247
185 -96.375 36.625 0.142 247
186 -96.125 36.625 0.109 239
187 -95.875 36.625 0.105 246
188 -95.625 36.625 0.101 245
189 -95.375 36.625 0.098 246
190 -95.125 36.625 0.132 245
191 -94.875 36.625 0.129 245
192 -94.625 36.625 0.130 245
193 -94.375 36.625 0.119 245
194 -94.125 36.625 0.177 234
195 -93.875 36.625 0.191 242
196 -103.375 36.375 0.341 251
197 -103.125 36.375 0.297 251
198 -102.875 36.375 0.340 251
199 -102.625 36.375 0.342 252
200 -102.375 36.375 0.349 252
201 -102.125 36.375 0.355 252
202 -101.875 36.375 0.343 252
203 -101.625 36.375 0.370 251
204 -101.375 36.375 0.359 252
205 -101.125 36.375 0.309 250
206 -100.875 36.375 0.262 247
207 -100.625 36.375 0.272 250
208 -100.375 36.375 0.299 251
209 -100.125 36.375 0.297 252
210 -99.875 36.375 0.352 249
211 -99.625 36.375 0.300 250
212 -99.375 36.375 0.320 250
213 -99.125 36.375 0.270 248
214 -98.875 36.375 0.303 249
215 -98.625 36.375 0.330 249
216 -98.375 36.375 0.372 249
217 -98.125 36.375 0.305 242
218 -97.875 36.375 0.299 250
219 -97.625 36.375 0.280 249
220 -97.375 36.375 0.207 249
221 -97.125 36.375 0.187 247
222 -96.875 36.375 0.123 248
223 -96.625 36.375 0.139 247
224 -96.375 36.375 0.134 247
225 -96.125 36.375 0.139 246
226 -95.875 36.375 0.097 247
227 -95.625 36.375 0.094 246
228 -95.375 36.375 0.144 247
229 -95.125 36.375 0.129 247
230 -94.875 36.375 0.196 236
231 -94.625 36.375 0.111 244
232 -94.375 36.375 0.100 244
233 -94.125 36.375 0.095 244
234 -93.875 36.375 0.123 240
235 -103.375 36.125 0.383 247
236 -103.125 36.125 0.332 252
237 -102.875 36.125 0.341 252
238 -102.625 36.125 0.312 252
239 -102.375 36.125 0.322 252
240 -102.125 36.125 0.350 252
241 -101.875 36.125 0.314 252
242 -101.625 36.125 0.323 251
243 -101.375 36.125 0.282 252
244 -101.125 36.125 0.308 251
245 -100.875 36.125 0.310 251
246 -100.625 36.125 0.322 251
247 -100.375 36.125 0.210 234
248 -100.125 36.125 0.335 247
249 -99.875 36.125 0.349 247
250 -99.625 36.125 0.338 248
251 -99.375 36.125 0.357 250
252 -99.125 36.125 0.261 249
253 -98.875 36.125 0.301 249
254 -98.625 36.125 0.371 249
255 -98.375 36.125 0.299 239
256 -98.125 36.125 0.324 242
257 -97.875 36.125 0.278 240
258 -97.625 36.125 0.207 238
259 -97.375 36.125 0.093 232
260 -97.125 36.125 0.121 248
261 -96.875 36.125 0.194 248
262 -96.625 36.125 0.170 248
263 -96.375 36.125 0.132 248
264 -96.125 36.125 0.117 248
265 -95.875 36.125 0.071 247
266 -95.625 36.125 0.093 248
267 -95.375 36.125 0.128 243
268 -95.125 36.125 0.163 248
269 -94.875 36.125 0.141 246
270 -94.625 36.125 0.134 244
271 -94.375 36.125 0.106 243
272 -94.125 36.125 0.103 236
273 -93.875 36.125 0.155 239
274 -103.375 35.875 0.324 252
275 -103.125 35.875 0.387 240
276 -102.875 35.875 0.368 252
277 -102.625 35.875 0.370 252
278 -102.375 35.875 0.400 252
279 -102.125 35.875 0.341 252
280 -101.875 35.875 0.292 251
281 -101.625 35.875 0.299 252
282 -101.375 35.875 0.333 252
283 -101.125 35.875 0.321 251
284 -100.875 35.875 0.298 250
285 -100.625 35.875 0.265 233
286 -100.375 35.875 0.269 229
287 -100.125 35.875 0.323 251
288 -99.875 35.875 0.292 248
289 -99.625 35.875 0.346 251
290 -99.375 35.875 0.362 252
291 -99.125 35.875 0.322 249
292 -98.875 35.875 0.381 249
293 -98.625 35.875 0.344 236
294 -98.375 35.875 0.310 238
295 -98.125 35.875 0.327 250
296 -97.875 35.875 0.298 249
297 -97.625 35.875 0.148 249
298 -97.375 35.875 0.106 249
299 -97.125 35.875 0.077 248
300 -96.875 35.875 0.099 248
301 -96.625 35.875 0.117 248
302 -96.375 35.875 0.239 248
303 -96.125 35.875 0.169 247
304 -95.875 35.875 0.124 247
305 -95.625 35.875 0.142 247
306 -95.375 35.875 0.148 248
307 -95.125 35.875 0.090 247
308 -94.875 35.875 0.147 247
309 -94.625 35.875 0.202 246
310 -94.375 35.875 0.123 240
311 -94.125 35.875 0.298 232
312 -93.875 35.875 0.305 231
313 -103.375 35.625 0.299 252
314 -103.125 35.625 0.398 246
315 -102.875 35.625 0.416 246
316 -102.625 35.625 0.377 248
317 -102.375 35.625 0.420 252
318 -102.125 35.625 0.321 252
319 -101.875 35.625 0.314 252
320 -101.625 35.625 0.261 252
321 -101.375 35.625 0.331 252
322 -101.125 35.625 0.316 252
323 -100.875 35.625 0.339 252
324 -100.625 35.625 0.321 252
325 -100.375 35.625 0.307 252
326 -100.125 35.625 0.214 230
327 -99.875 35.625 0.335 252
328 -99.625 35.625 0.355 249
329 -99.375 35.625 0.348 251
330 -99.125 35.625 0.350 251
331 -98.875 35.625 0.323 243
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456 -96.875 34.875 0.255 252
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472 -102.625 34.625 0.483 252
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482 -100.125 34.625 0.391 252
483 -99.875 34.625 0.402 252
484 -99.625 34.625 0.439 252
485 -99.375 34.625 0.398 252
486 -99.125 34.625 0.371 251
487 -98.875 34.625 0.344 252
488 -98.625 34.625 0.317 252
489 -98.375 34.625 0.316 240
490 -98.125 34.625 0.320 250
491 -97.875 34.625 0.315 250
492 -97.625 34.625 0.341 251
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494 -97.125 34.625 0.287 250
495 -96.875 34.625 0.301 251
496 -96.625 34.625 0.312 252
497 -96.375 34.625 0.301 251
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500 -95.625 34.625 0.260 205
501 -95.375 34.625 0.242 239
502 -95.125 34.625 0.420 242
503 -94.875 34.625 0.417 241
504 -94.625 34.625 0.398 238
505 -94.375 34.625 0.368 239
506 -94.125 34.625 0.296 239
507 -93.875 34.625 0.239 238
508 -103.375 34.375 0.533 252
509 -103.125 34.375 0.519 252
510 -102.875 34.375 0.512 252
511 -102.625 34.375 0.531 252
512 -102.375 34.375 0.509 252
513 -102.125 34.375 0.465 252
514 -101.875 34.375 0.475 252
515 -101.625 34.375 0.481 252
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517 -101.125 34.375 0.446 252
518 -100.875 34.375 0.453 252
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520 -100.375 34.375 0.423 252
521 -100.125 34.375 0.399 252
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524 -99.375 34.375 0.429 252
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526 -98.875 34.375 0.358 252
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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
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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
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580 -95.125 34.125 0.294 251
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584 -94.125 34.125 0.230 242
585 -93.875 34.125 0.236 243
586 -103.375 33.875 0.411 252
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588 -102.875 33.875 0.533 252
589 -102.625 33.875 0.527 252
590 -102.375 33.875 0.535 252
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592 -101.875 33.875 0.555 252
593 -101.625 33.875 0.555 252
594 -101.375 33.875 0.498 252
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596 -100.875 33.875 0.469 252
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598 -100.375 33.875 0.437 252
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600 -99.875 33.875 0.408 252
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608 -97.875 33.875 0.293 251
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610 -97.375 33.875 0.278 251
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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
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671 -101.625 33.375 0.550 252
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673 -101.125 33.375 0.545 252
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676 -100.375 33.375 0.379 252
677 -100.125 33.375 0.352 252
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680 -99.375 33.375 0.351 252
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688 -97.375 33.375 0.363 251
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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
Temporal Correlation between Monthly Mean Soil Moisture and Maximum Temperature
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)
Temporal Correlation Median Soil Moistre and Max Temperature
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
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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
Temporal Correlation between Monthly Mean Soil Moisture and Minimum Temperature
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)
Temporal Correlation Median Soil Moistre and Min Temperature
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
232 -94.375 36.375 -0.498 244
233 -94.125 36.375 -0.584 244
234 -93.875 36.375 -0.622 240
235 -103.375 36.125 -0.107 247
236 -103.125 36.125 -0.228 252
237 -102.875 36.125 -0.170 252
238 -102.625 36.125 -0.208 252
239 -102.375 36.125 -0.231 252
240 -102.125 36.125 -0.265 252
241 -101.875 36.125 -0.271 252
242 -101.625 36.125 -0.313 251
243 -101.375 36.125 -0.368 252
244 -101.125 36.125 -0.349 251
245 -100.875 36.125 -0.371 251
246 -100.625 36.125 -0.392 251
247 -100.375 36.125 -0.482 234
248 -100.125 36.125 -0.401 247
249 -99.875 36.125 -0.418 247
250 -99.625 36.125 -0.442 248
251 -99.375 36.125 -0.350 250
252 -99.125 36.125 -0.412 249
253 -98.875 36.125 -0.402 249
254 -98.625 36.125 -0.400 249
255 -98.375 36.125 -0.422 239
256 -98.125 36.125 -0.356 242
257 -97.875 36.125 -0.389 240
258 -97.625 36.125 -0.494 238
259 -97.375 36.125 -0.579 232
260 -97.125 36.125 -0.503 248
261 -96.875 36.125 -0.473 248
262 -96.625 36.125 -0.503 248
263 -96.375 36.125 -0.556 248
264 -96.125 36.125 -0.649 248
265 -95.875 36.125 -0.670 247
266 -95.625 36.125 -0.661 248
267 -95.375 36.125 -0.628 243
268 -95.125 36.125 -0.637 248
269 -94.875 36.125 -0.610 246
270 -94.625 36.125 -0.517 244
271 -94.375 36.125 -0.537 243
272 -94.125 36.125 -0.592 236
273 -93.875 36.125 -0.557 239
274 -103.375 35.875 -0.238 252
275 -103.125 35.875 -0.069 240
276 -102.875 35.875 -0.196 252
277 -102.625 35.875 -0.183 252
278 -102.375 35.875 -0.202 252
279 -102.125 35.875 -0.255 252
280 -101.875 35.875 -0.356 251
281 -101.625 35.875 -0.342 252
282 -101.375 35.875 -0.342 252
283 -101.125 35.875 -0.338 251
284 -100.875 35.875 -0.383 250
285 -100.625 35.875 -0.438 233
286 -100.375 35.875 -0.487 229
287 -100.125 35.875 -0.448 251
288 -99.875 35.875 -0.471 248
289 -99.625 35.875 -0.453 251
290 -99.375 35.875 -0.439 252
291 -99.125 35.875 -0.404 249
292 -98.875 35.875 -0.339 249
293 -98.625 35.875 -0.383 236
294 -98.375 35.875 -0.423 238
295 -98.125 35.875 -0.368 250
296 -97.875 35.875 -0.355 249
297 -97.625 35.875 -0.440 249
298 -97.375 35.875 -0.526 249
299 -97.125 35.875 -0.527 248
300 -96.875 35.875 -0.530 248
301 -96.625 35.875 -0.603 248
302 -96.375 35.875 -0.541 248
303 -96.125 35.875 -0.600 247
304 -95.875 35.875 -0.667 247
305 -95.625 35.875 -0.632 247
306 -95.375 35.875 -0.688 248
307 -95.125 35.875 -0.704 247
308 -94.875 35.875 -0.640 247
309 -94.625 35.875 -0.460 246
310 -94.375 35.875 -0.575 240
311 -94.125 35.875 -0.269 232
312 -93.875 35.875 -0.228 231
313 -103.375 35.625 -0.266 252
314 -103.125 35.625 -0.119 246
315 -102.875 35.625 -0.117 246
316 -102.625 35.625 -0.216 248
317 -102.375 35.625 -0.242 252
318 -102.125 35.625 -0.307 252
319 -101.875 35.625 -0.333 252
320 -101.625 35.625 -0.403 252
321 -101.375 35.625 -0.344 252
322 -101.125 35.625 -0.358 252
323 -100.875 35.625 -0.360 252
324 -100.625 35.625 -0.427 252
325 -100.375 35.625 -0.427 252
326 -100.125 35.625 -0.512 230
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328 -99.625 35.625 -0.479 249
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334 -98.125 35.625 -0.370 250
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336 -97.625 35.625 -0.512 251
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342 -96.125 35.625 -0.595 248
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345 -95.375 35.625 -0.671 251
346 -95.125 35.625 -0.651 250
347 -94.875 35.625 -0.716 248
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349 -94.375 35.625 -0.596 248
350 -94.125 35.625 -0.213 236
351 -93.875 35.625 -0.105 238
352 -103.375 35.375 -0.308 252
353 -103.125 35.375 -0.182 244
354 -102.875 35.375 -0.162 241
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356 -102.375 35.375 -0.295 252
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Temporal Correlation Plots

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 Analysis

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.

MEAN Monthly Soil Moisture Values

Spatial Correlation between Monthly Mean Soil Moisture and Precipitation
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)
Spatial Correlation Mean Soil Moistre and Precipitation
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
Spatial Correlation between Monthly Mean Soil Moisture and Maximum Temperature
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)
Spatial Correlation Mean Soil Moistre and Max Temperature
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
Spatial Correlation between Monthly Mean Soil Moisture and Minimum Temperature
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)
Spatial Correlation Mean Soil Moistre and Min Temperature
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
Spatial Correlation between Monthly Mean Soil Moisture and Soil Texture
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)
Spatial Correlation Mean Soil Moistre and Soil Texture
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
Spatial Correlation between Monthly Mean Soil Moisture and TWI
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)
Spatial Correlation Mean Soil Moistre and TWI
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

MEDIAN Monthly Soil Moisture Values

Spatial Correlation between Monthly Median Soil Moisture and Precipitation
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)
Spatial Correlation Median Soil Moistre and Precipitation
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
Spatial Correlation between Monthly Median Soil Moisture and Maximum Temperature
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)
Spatial Correlation Median Soil Moistre and Max Temperature
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
Spatial Correlation between Monthly Median Soil Moisture and Minimum Temperature
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)
Spatial Correlation Median Soil Moistre and Min Temperature
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
Spatial Correlation between Monthly Median Soil Moisture and Soil Texture
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)
Spatial Correlation Median Soil Moistre and Soil Texture
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
Spatial Correlation between Monthly Median Soil Moisture and Topographic Wetness Index
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)
Spatial Correlation Median Soil Moistre and TWI
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

Spatial Correlation Plots

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'

Results

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)
Mean correlation derived from temporal and spatial analysis using mean and median soil moisture layers, and geophysical covariates
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

Conclusions

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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