Gap_Filling_Supplementary_Material 2

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

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

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

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)

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
343	-95.875	35.625	0.129	251
344	-95.625	35.625	0.148	252
345	-95.375	35.625	0.164	251
346	-95.125	35.625	0.175	250
347	-94.875	35.625	0.132	248
348	-94.625	35.625	0.127	247
349	-94.375	35.625	0.184	248
350	-94.125	35.625	0.361	236
351	-93.875	35.625	0.347	238
352	-103.375	35.375	0.396	252
353	-103.125	35.375	0.424	244
354	-102.875	35.375	0.452	241
355	-102.625	35.375	0.439	244
356	-102.375	35.375	0.433	252
357	-102.125	35.375	0.340	252
358	-101.875	35.375	0.346	252
359	-101.625	35.375	0.318	250
360	-101.375	35.375	0.432	252
361	-101.125	35.375	0.406	252
362	-100.875	35.375	0.404	252
363	-100.625	35.375	0.386	251
364	-100.375	35.375	0.334	234
365	-100.125	35.375	0.381	252
366	-99.875	35.375	0.412	252
367	-99.625	35.375	0.371	251
368	-99.375	35.375	0.361	252
369	-99.125	35.375	0.373	251
370	-98.875	35.375	0.325	248
371	-98.625	35.375	0.374	242
372	-98.375	35.375	0.358	250
373	-98.125	35.375	0.330	249
374	-97.875	35.375	0.168	235
375	-97.625	35.375	0.119	251
376	-97.375	35.375	0.078	250
377	-97.125	35.375	0.116	248
378	-96.875	35.375	0.160	248
379	-96.625	35.375	0.183	248
380	-96.375	35.375	0.192	249
381	-96.125	35.375	0.212	248
382	-95.875	35.375	0.177	248
383	-95.625	35.375	0.157	238
384	-95.375	35.375	0.193	247
385	-95.125	35.375	0.155	248
386	-94.875	35.375	0.167	248
387	-94.625	35.375	0.179	249
388	-94.375	35.375	0.165	240
389	-94.125	35.375	0.217	248
390	-93.875	35.375	0.393	234
391	-103.375	35.125	0.497	246
392	-103.125	35.125	0.471	243
393	-102.875	35.125	0.445	243
394	-102.625	35.125	0.503	245
395	-102.375	35.125	0.480	252
396	-102.125	35.125	0.389	252
397	-101.875	35.125	0.375	250
398	-101.625	35.125	0.387	250
399	-101.375	35.125	0.473	252
400	-101.125	35.125	0.422	252
401	-100.875	35.125	0.414	252
402	-100.625	35.125	0.438	252
403	-100.375	35.125	0.421	252
404	-100.125	35.125	0.427	252
405	-99.875	35.125	0.419	252
406	-99.625	35.125	0.388	252
407	-99.375	35.125	0.377	251
408	-99.125	35.125	0.401	252
409	-98.875	35.125	0.389	249
410	-98.625	35.125	0.330	238
411	-98.375	35.125	0.330	235
412	-98.125	35.125	0.328	251
413	-97.875	35.125	0.295	250
414	-97.625	35.125	0.218	250
415	-97.375	35.125	0.182	250
416	-97.125	35.125	0.215	252
417	-96.875	35.125	0.216	251
418	-96.625	35.125	0.174	252
419	-96.375	35.125	0.188	251
420	-96.125	35.125	0.272	251
421	-95.875	35.125	0.209	252
422	-95.625	35.125	0.175	243
423	-95.375	35.125	0.246	249
424	-95.125	35.125	0.260	245
425	-94.875	35.125	0.266	250
426	-94.625	35.125	0.241	248
427	-94.375	35.125	0.228	247
428	-94.125	35.125	0.370	241
429	-93.875	35.125	0.355	240
430	-103.375	34.875	0.457	252
431	-103.125	34.875	0.475	250
432	-102.875	34.875	0.501	252
433	-102.625	34.875	0.531	244
434	-102.375	34.875	0.522	252
435	-102.125	34.875	0.450	252
436	-101.875	34.875	0.423	252
437	-101.625	34.875	0.451	251
438	-101.375	34.875	0.454	251
439	-101.125	34.875	0.447	251
440	-100.875	34.875	0.465	252
441	-100.625	34.875	0.463	252
442	-100.375	34.875	0.430	252
443	-100.125	34.875	0.430	252
444	-99.875	34.875	0.421	252
445	-99.625	34.875	0.401	249
446	-99.375	34.875	0.414	252
447	-99.125	34.875	0.401	252
448	-98.875	34.875	0.371	252
449	-98.625	34.875	0.371	238
450	-98.375	34.875	0.289	233
451	-98.125	34.875	0.336	250
452	-97.875	34.875	0.309	250
453	-97.625	34.875	0.277	249
454	-97.375	34.875	0.267	251
455	-97.125	34.875	0.259	252
456	-96.875	34.875	0.263	252
457	-96.625	34.875	0.255	252
458	-96.375	34.875	0.263	251
459	-96.125	34.875	0.275	248
460	-95.875	34.875	0.276	247
461	-95.625	34.875	0.280	238
462	-95.375	34.875	0.270	242
463	-95.125	34.875	0.257	241
464	-94.875	34.875	0.225	244
465	-94.625	34.875	0.395	240
466	-94.375	34.875	0.401	239
467	-94.125	34.875	0.372	239
468	-93.875	34.875	0.313	239
469	-103.375	34.625	0.533	251
470	-103.125	34.625	0.538	252
471	-102.875	34.625	0.543	252
472	-102.625	34.625	0.558	252
473	-102.375	34.625	0.557	252
474	-102.125	34.625	0.529	252
475	-101.875	34.625	0.533	252
476	-101.625	34.625	0.524	252
477	-101.375	34.625	0.497	251
478	-101.125	34.625	0.478	251
479	-100.875	34.625	0.509	251
480	-100.625	34.625	0.462	252
481	-100.375	34.625	0.409	252
482	-100.125	34.625	0.423	252
483	-99.875	34.625	0.444	252
484	-99.625	34.625	0.478	252
485	-99.375	34.625	0.426	252
486	-99.125	34.625	0.398	251
487	-98.875	34.625	0.387	252
488	-98.625	34.625	0.343	252
489	-98.375	34.625	0.323	240
490	-98.125	34.625	0.339	250
491	-97.875	34.625	0.317	250
492	-97.625	34.625	0.331	251
493	-97.375	34.625	0.282	251
494	-97.125	34.625	0.303	250
495	-96.875	34.625	0.321	251
496	-96.625	34.625	0.337	252
497	-96.375	34.625	0.313	251
498	-96.125	34.625	0.307	250
499	-95.875	34.625	0.342	241
500	-95.625	34.625	0.260	205
501	-95.375	34.625	0.269	239
502	-95.125	34.625	0.455	242
503	-94.875	34.625	0.440	241
504	-94.625	34.625	0.441	238
505	-94.375	34.625	0.401	239
506	-94.125	34.625	0.339	239
507	-93.875	34.625	0.305	238
508	-103.375	34.375	0.575	252
509	-103.125	34.375	0.579	252
510	-102.875	34.375	0.578	252
511	-102.625	34.375	0.594	252
512	-102.375	34.375	0.554	252
513	-102.125	34.375	0.519	252
514	-101.875	34.375	0.525	252
515	-101.625	34.375	0.540	252
516	-101.375	34.375	0.519	252
517	-101.125	34.375	0.516	252
518	-100.875	34.375	0.526	252
519	-100.625	34.375	0.498	252
520	-100.375	34.375	0.469	252
521	-100.125	34.375	0.423	252
522	-99.875	34.375	0.395	252
523	-99.625	34.375	0.427	252
524	-99.375	34.375	0.484	252
525	-99.125	34.375	0.426	251
526	-98.875	34.375	0.389	252
527	-98.625	34.375	0.365	249
528	-98.375	34.375	0.368	252
529	-98.125	34.375	0.358	252
530	-97.875	34.375	0.342	251
531	-97.625	34.375	0.315	251
532	-97.375	34.375	0.270	251
533	-97.125	34.375	0.242	251
534	-96.875	34.375	0.328	251
535	-96.625	34.375	0.294	251
536	-96.375	34.375	0.313	250
537	-96.125	34.375	0.306	221
538	-95.875	34.375	0.333	240
539	-95.625	34.375	0.326	232
540	-95.375	34.375	0.330	243
541	-95.125	34.375	0.460	240
542	-94.875	34.375	0.406	238
543	-94.625	34.375	0.373	236
544	-94.375	34.375	0.353	236
545	-94.125	34.375	0.305	236
546	-93.875	34.375	0.265	236
547	-103.375	34.125	0.604	248
548	-103.125	34.125	0.609	251
549	-102.875	34.125	0.642	251
550	-102.625	34.125	0.616	252
551	-102.375	34.125	0.586	252
552	-102.125	34.125	0.549	252
553	-101.875	34.125	0.596	252
554	-101.625	34.125	0.590	250
555	-101.375	34.125	0.564	252
556	-101.125	34.125	0.534	252
557	-100.875	34.125	0.526	252
558	-100.625	34.125	0.506	252
559	-100.375	34.125	0.511	252
560	-100.125	34.125	0.469	252
561	-99.875	34.125	0.446	252
562	-99.625	34.125	0.415	252
563	-99.375	34.125	0.443	252
564	-99.125	34.125	0.454	252
565	-98.875	34.125	0.425	248
566	-98.625	34.125	0.393	252
567	-98.375	34.125	0.369	252
568	-98.125	34.125	0.353	251
569	-97.875	34.125	0.339	252
570	-97.625	34.125	0.345	251
571	-97.375	34.125	0.306	251
572	-97.125	34.125	0.243	252
573	-96.875	34.125	0.260	245
574	-96.625	34.125	0.304	252
575	-96.375	34.125	0.300	251
576	-96.125	34.125	0.349	251
577	-95.875	34.125	0.346	251
578	-95.625	34.125	0.344	246
579	-95.375	34.125	0.353	243
580	-95.125	34.125	0.318	251
581	-94.875	34.125	0.406	242
582	-94.625	34.125	0.342	243
583	-94.375	34.125	0.317	242
584	-94.125	34.125	0.254	242
585	-93.875	34.125	0.274	243
586	-103.375	33.875	0.501	252
587	-103.125	33.875	0.560	252
588	-102.875	33.875	0.622	252
589	-102.625	33.875	0.597	252
590	-102.375	33.875	0.588	252
591	-102.125	33.875	0.558	252
592	-101.875	33.875	0.611	252
593	-101.625	33.875	0.616	252
594	-101.375	33.875	0.556	252
595	-101.125	33.875	0.526	252
596	-100.875	33.875	0.512	252
597	-100.625	33.875	0.473	252
598	-100.375	33.875	0.485	252
599	-100.125	33.875	0.474	252
600	-99.875	33.875	0.450	252
601	-99.625	33.875	0.438	252
602	-99.375	33.875	0.432	252
603	-99.125	33.875	0.406	252
604	-98.875	33.875	0.405	252
605	-98.625	33.875	0.379	252
606	-98.375	33.875	0.341	252
607	-98.125	33.875	0.339	251
608	-97.875	33.875	0.343	251
609	-97.625	33.875	0.331	251
610	-97.375	33.875	0.313	251
611	-97.125	33.875	0.290	252
612	-96.875	33.875	0.438	248
613	-96.625	33.875	0.459	243
614	-96.375	33.875	0.341	252
615	-96.125	33.875	0.349	251
616	-95.875	33.875	0.414	251
617	-95.625	33.875	0.418	246
618	-95.375	33.875	0.422	245
619	-95.125	33.875	0.424	244
620	-94.875	33.875	0.378	243
621	-94.625	33.875	0.320	245
622	-94.375	33.875	0.278	251
623	-94.125	33.875	0.312	248
624	-93.875	33.875	0.336	246
625	-103.375	33.625	0.522	252
626	-103.125	33.625	0.572	252
627	-102.875	33.625	0.649	252
628	-102.625	33.625	0.636	252
629	-102.375	33.625	0.551	252
630	-102.125	33.625	0.584	252
631	-101.875	33.625	0.598	252
632	-101.625	33.625	0.611	252
633	-101.375	33.625	0.575	252
634	-101.125	33.625	0.556	252
635	-100.875	33.625	0.526	252
636	-100.625	33.625	0.447	252
637	-100.375	33.625	0.466	252
638	-100.125	33.625	0.403	238
639	-99.875	33.625	0.417	252
640	-99.625	33.625	0.429	252
641	-99.375	33.625	0.378	252
642	-99.125	33.625	0.356	252
643	-98.875	33.625	0.357	252
644	-98.625	33.625	0.354	252
645	-98.375	33.625	0.348	252
646	-98.125	33.625	0.307	251
647	-97.875	33.625	0.308	252
648	-97.625	33.625	0.343	252
649	-97.375	33.625	0.370	252
650	-97.125	33.625	0.345	252
651	-96.875	33.625	0.309	252
652	-96.625	33.625	0.363	252
653	-96.375	33.625	0.354	252
654	-96.125	33.625	0.416	251
655	-95.875	33.625	0.446	251
656	-95.625	33.625	0.435	246
657	-95.375	33.625	0.443	245
658	-95.125	33.625	0.415	251
659	-94.875	33.625	0.390	250
660	-94.625	33.625	0.364	247
661	-94.375	33.625	0.371	252
662	-94.125	33.625	0.357	246
663	-93.875	33.625	0.349	244
664	-103.375	33.375	0.503	252
665	-103.125	33.375	0.558	252
666	-102.875	33.375	0.602	252
667	-102.625	33.375	0.635	252
668	-102.375	33.375	0.569	252
669	-102.125	33.375	0.658	252
670	-101.875	33.375	0.639	252
671	-101.625	33.375	0.607	252
672	-101.375	33.375	0.582	252
673	-101.125	33.375	0.566	252
674	-100.875	33.375	0.499	252
675	-100.625	33.375	0.464	252
676	-100.375	33.375	0.422	252
677	-100.125	33.375	0.405	252
678	-99.875	33.375	0.423	252
679	-99.625	33.375	0.399	252
680	-99.375	33.375	0.380	252
681	-99.125	33.375	0.348	252
682	-98.875	33.375	0.331	252
683	-98.625	33.375	0.332	252
684	-98.375	33.375	0.324	251
685	-98.125	33.375	0.313	251
686	-97.875	33.375	0.345	251
687	-97.625	33.375	0.356	252
688	-97.375	33.375	0.404	251
689	-97.125	33.375	0.360	252
690	-96.875	33.375	0.405	252
691	-96.625	33.375	0.428	244
692	-96.375	33.375	0.456	252
693	-96.125	33.375	0.471	252
694	-95.875	33.375	0.410	239
695	-95.625	33.375	0.423	247
696	-95.375	33.375	0.447	245
697	-95.125	33.375	0.394	244
698	-94.875	33.375	0.385	251
699	-94.625	33.375	0.407	243
700	-94.375	33.375	0.344	246
701	-94.125	33.375	0.366	252
702	-93.875	33.375	0.382	239
703	-103.375	33.125	0.565	252
704	-103.125	33.125	0.594	252
705	-102.875	33.125	0.632	252
706	-102.625	33.125	0.628	252
707	-102.375	33.125	0.641	252
708	-102.125	33.125	0.625	252
709	-101.875	33.125	0.588	252
710	-101.625	33.125	0.564	252
711	-101.375	33.125	0.558	252
712	-101.125	33.125	0.515	252
713	-100.875	33.125	0.492	252
714	-100.625	33.125	0.402	252
715	-100.375	33.125	0.373	252
716	-100.125	33.125	0.395	252
717	-99.875	33.125	0.407	252
718	-99.625	33.125	0.383	252
719	-99.375	33.125	0.406	252
720	-99.125	33.125	0.368	252
721	-98.875	33.125	0.328	252
722	-98.625	33.125	0.299	251
723	-98.375	33.125	0.321	251
724	-98.125	33.125	0.355	251
725	-97.875	33.125	0.366	251
726	-97.625	33.125	0.368	251
727	-97.375	33.125	0.298	252
728	-97.125	33.125	0.361	252
729	-96.875	33.125	0.404	252
730	-96.625	33.125	0.420	252
731	-96.375	33.125	0.482	252
732	-96.125	33.125	0.394	252
733	-95.875	33.125	0.407	252
734	-95.625	33.125	0.428	251
735	-95.375	33.125	0.411	251
736	-95.125	33.125	0.351	252
737	-94.875	33.125	0.371	250
738	-94.625	33.125	0.337	240
739	-94.375	33.125	0.336	250
740	-94.125	33.125	0.332	248
741	-93.875	33.125	0.416	249

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
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463	-95.125	34.875	-0.578	241
464	-94.875	34.875	-0.547	244
465	-94.625	34.875	-0.138	240
466	-94.375	34.875	-0.052	239
467	-94.125	34.875	-0.002	239
468	-93.875	34.875	0.077	239
469	-103.375	34.625	-0.214	251
470	-103.125	34.625	-0.212	252
471	-102.875	34.625	-0.219	252
472	-102.625	34.625	-0.186	252
473	-102.375	34.625	-0.198	252
474	-102.125	34.625	-0.236	252
475	-101.875	34.625	-0.261	252
476	-101.625	34.625	-0.288	252
477	-101.375	34.625	-0.377	251
478	-101.125	34.625	-0.421	251
479	-100.875	34.625	-0.418	251
480	-100.625	34.625	-0.430	252
481	-100.375	34.625	-0.452	252
482	-100.125	34.625	-0.455	252
483	-99.875	34.625	-0.437	252
484	-99.625	34.625	-0.418	252
485	-99.375	34.625	-0.447	252
486	-99.125	34.625	-0.461	251
487	-98.875	34.625	-0.528	252
488	-98.625	34.625	-0.591	252
489	-98.375	34.625	-0.598	240
490	-98.125	34.625	-0.532	250
491	-97.875	34.625	-0.580	250
492	-97.625	34.625	-0.624	251
493	-97.375	34.625	-0.656	251
494	-97.125	34.625	-0.654	250
495	-96.875	34.625	-0.639	251
496	-96.625	34.625	-0.647	252
497	-96.375	34.625	-0.663	251
498	-96.125	34.625	-0.674	250
499	-95.875	34.625	-0.672	241
500	-95.625	34.625	-0.685	205
501	-95.375	34.625	-0.537	239
502	-95.125	34.625	-0.192	242
503	-94.875	34.625	-0.068	241
504	-94.625	34.625	0.028	238
505	-94.375	34.625	0.054	239
506	-94.125	34.625	0.073	239
507	-93.875	34.625	0.107	238
508	-103.375	34.375	-0.146	252
509	-103.125	34.375	-0.137	252
510	-102.875	34.375	-0.168	252
511	-102.625	34.375	-0.161	252
512	-102.375	34.375	-0.222	252
513	-102.125	34.375	-0.262	252
514	-101.875	34.375	-0.280	252
515	-101.625	34.375	-0.280	252
516	-101.375	34.375	-0.348	252
517	-101.125	34.375	-0.366	252
518	-100.875	34.375	-0.400	252
519	-100.625	34.375	-0.430	252
520	-100.375	34.375	-0.440	252
521	-100.125	34.375	-0.463	252
522	-99.875	34.375	-0.472	252
523	-99.625	34.375	-0.477	252
524	-99.375	34.375	-0.475	252
525	-99.125	34.375	-0.509	251
526	-98.875	34.375	-0.517	252
527	-98.625	34.375	-0.575	249
528	-98.375	34.375	-0.586	252
529	-98.125	34.375	-0.621	252
530	-97.875	34.375	-0.601	251
531	-97.625	34.375	-0.631	251
532	-97.375	34.375	-0.706	251
533	-97.125	34.375	-0.697	251
534	-96.875	34.375	-0.663	251
535	-96.625	34.375	-0.726	251
536	-96.375	34.375	-0.688	250
537	-96.125	34.375	-0.737	221
538	-95.875	34.375	-0.666	240
539	-95.625	34.375	-0.592	232
540	-95.375	34.375	-0.527	243
541	-95.125	34.375	-0.048	240
542	-94.875	34.375	0.024	238
543	-94.625	34.375	0.076	236
544	-94.375	34.375	0.096	236
545	-94.125	34.375	0.134	236
546	-93.875	34.375	0.166	236
547	-103.375	34.125	-0.009	248
548	-103.125	34.125	-0.089	251
549	-102.875	34.125	-0.048	251
550	-102.625	34.125	-0.130	252
551	-102.375	34.125	-0.173	252
552	-102.125	34.125	-0.180	252
553	-101.875	34.125	-0.209	252
554	-101.625	34.125	-0.234	250
555	-101.375	34.125	-0.283	252
556	-101.125	34.125	-0.320	252
557	-100.875	34.125	-0.384	252
558	-100.625	34.125	-0.453	252
559	-100.375	34.125	-0.455	252
560	-100.125	34.125	-0.496	252
561	-99.875	34.125	-0.501	252
562	-99.625	34.125	-0.541	252
563	-99.375	34.125	-0.559	252
564	-99.125	34.125	-0.541	252
565	-98.875	34.125	-0.569	248
566	-98.625	34.125	-0.557	252
567	-98.375	34.125	-0.614	252
568	-98.125	34.125	-0.656	251
569	-97.875	34.125	-0.621	252
570	-97.625	34.125	-0.638	251
571	-97.375	34.125	-0.702	251
572	-97.125	34.125	-0.740	252
573	-96.875	34.125	-0.744	245
574	-96.625	34.125	-0.753	252
575	-96.375	34.125	-0.745	251
576	-96.125	34.125	-0.726	251
577	-95.875	34.125	-0.729	251
578	-95.625	34.125	-0.668	246
579	-95.375	34.125	-0.650	243
580	-95.125	34.125	-0.627	251
581	-94.875	34.125	-0.152	242
582	-94.625	34.125	-0.086	243
583	-94.375	34.125	0.023	242
584	-94.125	34.125	0.070	242
585	-93.875	34.125	0.012	243
586	-103.375	33.875	-0.262	252
587	-103.125	33.875	-0.217	252
588	-102.875	33.875	-0.180	252
589	-102.625	33.875	-0.223	252
590	-102.375	33.875	-0.212	252
591	-102.125	33.875	-0.198	252
592	-101.875	33.875	-0.190	252
593	-101.625	33.875	-0.260	252
594	-101.375	33.875	-0.303	252
595	-101.125	33.875	-0.329	252
596	-100.875	33.875	-0.423	252
597	-100.625	33.875	-0.470	252
598	-100.375	33.875	-0.480	252
599	-100.125	33.875	-0.508	252
600	-99.875	33.875	-0.532	252
601	-99.625	33.875	-0.558	252
602	-99.375	33.875	-0.591	252
603	-99.125	33.875	-0.587	252
604	-98.875	33.875	-0.603	252
605	-98.625	33.875	-0.625	252
606	-98.375	33.875	-0.653	252
607	-98.125	33.875	-0.672	251
608	-97.875	33.875	-0.701	251
609	-97.625	33.875	-0.715	251
610	-97.375	33.875	-0.756	251
611	-97.125	33.875	-0.755	252
612	-96.875	33.875	-0.611	248
613	-96.625	33.875	-0.536	243
614	-96.375	33.875	-0.785	252
615	-96.125	33.875	-0.785	251
616	-95.875	33.875	-0.751	251
617	-95.625	33.875	-0.729	246
618	-95.375	33.875	-0.695	245
619	-95.125	33.875	-0.682	244
620	-94.875	33.875	-0.642	243
621	-94.625	33.875	-0.536	245
622	-94.375	33.875	-0.593	251
623	-94.125	33.875	-0.567	248
624	-93.875	33.875	-0.716	246
625	-103.375	33.625	-0.328	252
626	-103.125	33.625	-0.259	252
627	-102.875	33.625	-0.182	252
628	-102.625	33.625	-0.163	252
629	-102.375	33.625	-0.183	252
630	-102.125	33.625	-0.170	252
631	-101.875	33.625	-0.203	252
632	-101.625	33.625	-0.260	252
633	-101.375	33.625	-0.324	252
634	-101.125	33.625	-0.329	252
635	-100.875	33.625	-0.421	252
636	-100.625	33.625	-0.468	252
637	-100.375	33.625	-0.467	252
638	-100.125	33.625	-0.545	238
639	-99.875	33.625	-0.533	252
640	-99.625	33.625	-0.553	252
641	-99.375	33.625	-0.609	252
642	-99.125	33.625	-0.633	252
643	-98.875	33.625	-0.632	252
644	-98.625	33.625	-0.658	252
645	-98.375	33.625	-0.674	252
646	-98.125	33.625	-0.695	251
647	-97.875	33.625	-0.712	252
648	-97.625	33.625	-0.745	252
649	-97.375	33.625	-0.743	252
650	-97.125	33.625	-0.743	252
651	-96.875	33.625	-0.718	252
652	-96.625	33.625	-0.730	252
653	-96.375	33.625	-0.724	252
654	-96.125	33.625	-0.749	251
655	-95.875	33.625	-0.779	251
656	-95.625	33.625	-0.750	246
657	-95.375	33.625	-0.733	245
658	-95.125	33.625	-0.697	251
659	-94.875	33.625	-0.663	250
660	-94.625	33.625	-0.673	247
661	-94.375	33.625	-0.690	252
662	-94.125	33.625	-0.652	246
663	-93.875	33.625	-0.674	244
664	-103.375	33.375	-0.316	252
665	-103.125	33.375	-0.265	252
666	-102.875	33.375	-0.219	252
667	-102.625	33.375	-0.151	252
668	-102.375	33.375	-0.144	252
669	-102.125	33.375	-0.115	252
670	-101.875	33.375	-0.165	252
671	-101.625	33.375	-0.246	252
672	-101.375	33.375	-0.312	252
673	-101.125	33.375	-0.387	252
674	-100.875	33.375	-0.459	252
675	-100.625	33.375	-0.470	252
676	-100.375	33.375	-0.484	252
677	-100.125	33.375	-0.527	252
678	-99.875	33.375	-0.558	252
679	-99.625	33.375	-0.581	252
680	-99.375	33.375	-0.584	252
681	-99.125	33.375	-0.617	252
682	-98.875	33.375	-0.643	252
683	-98.625	33.375	-0.685	252
684	-98.375	33.375	-0.696	251
685	-98.125	33.375	-0.717	251
686	-97.875	33.375	-0.702	251
687	-97.625	33.375	-0.742	252
688	-97.375	33.375	-0.729	251
689	-97.125	33.375	-0.668	252
690	-96.875	33.375	-0.683	252
691	-96.625	33.375	-0.722	244
692	-96.375	33.375	-0.727	252
693	-96.125	33.375	-0.724	252
694	-95.875	33.375	-0.697	239
695	-95.625	33.375	-0.720	247
696	-95.375	33.375	-0.759	245
697	-95.125	33.375	-0.687	244
698	-94.875	33.375	-0.688	251
699	-94.625	33.375	-0.633	243
700	-94.375	33.375	-0.614	246
701	-94.125	33.375	-0.743	252
702	-93.875	33.375	-0.552	239
703	-103.375	33.125	-0.285	252
704	-103.125	33.125	-0.289	252
705	-102.875	33.125	-0.234	252
706	-102.625	33.125	-0.167	252
707	-102.375	33.125	-0.181	252
708	-102.125	33.125	-0.210	252
709	-101.875	33.125	-0.277	252
710	-101.625	33.125	-0.346	252
711	-101.375	33.125	-0.383	252
712	-101.125	33.125	-0.464	252
713	-100.875	33.125	-0.484	252
714	-100.625	33.125	-0.494	252
715	-100.375	33.125	-0.564	252
716	-100.125	33.125	-0.573	252
717	-99.875	33.125	-0.568	252
718	-99.625	33.125	-0.606	252
719	-99.375	33.125	-0.609	252
720	-99.125	33.125	-0.617	252
721	-98.875	33.125	-0.669	252
722	-98.625	33.125	-0.726	251
723	-98.375	33.125	-0.696	251
724	-98.125	33.125	-0.681	251
725	-97.875	33.125	-0.705	251
726	-97.625	33.125	-0.710	251
727	-97.375	33.125	-0.719	252
728	-97.125	33.125	-0.724	252
729	-96.875	33.125	-0.743	252
730	-96.625	33.125	-0.743	252
731	-96.375	33.125	-0.722	252
732	-96.125	33.125	-0.690	252
733	-95.875	33.125	-0.743	252
734	-95.625	33.125	-0.744	251
735	-95.375	33.125	-0.734	251
736	-95.125	33.125	-0.672	252
737	-94.875	33.125	-0.677	250
738	-94.625	33.125	-0.608	240
739	-94.375	33.125	-0.551	250
740	-94.125	33.125	-0.528	248
741	-93.875	33.125	-0.634	249

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
5	-102.375	37.625	-0.104	251
6	-102.125	37.625	0.024	221
7	-101.875	37.625	-0.026	236
8	-101.625	37.625	-0.109	252
9	-101.375	37.625	-0.067	252
10	-101.125	37.625	-0.030	252
11	-100.875	37.625	-0.086	251
12	-100.625	37.625	-0.120	249
13	-100.375	37.625	-0.178	248
14	-100.125	37.625	-0.055	225
15	-99.875	37.625	-0.176	247
16	-99.625	37.625	-0.165	247
17	-99.375	37.625	-0.130	247
18	-99.125	37.625	-0.042	234
19	-98.875	37.625	-0.039	232
20	-98.625	37.625	-0.133	249
21	-98.375	37.625	-0.185	251
22	-98.125	37.625	-0.233	251
23	-97.875	37.625	-0.307	236
24	-97.625	37.625	-0.254	250
25	-97.375	37.625	-0.392	249
26	-97.125	37.625	-0.356	247
27	-96.875	37.625	-0.375	247
28	-96.625	37.625	-0.475	243
29	-96.375	37.625	-0.440	241
30	-96.125	37.625	-0.496	241
31	-95.875	37.625	-0.522	243
32	-95.625	37.625	-0.499	244
33	-95.375	37.625	-0.465	242
34	-95.125	37.625	-0.463	241
35	-94.875	37.625	-0.438	245
36	-94.625	37.625	-0.467	241
37	-94.375	37.625	-0.444	238
38	-94.125	37.625	-0.405	233
39	-93.875	37.625	-0.358	233
40	-103.375	37.375	-0.208	251
41	-103.125	37.375	-0.154	251
42	-102.875	37.375	-0.001	233
43	-102.625	37.375	0.042	235
44	-102.375	37.375	-0.027	251
45	-102.125	37.375	-0.029	250
46	-101.875	37.375	-0.053	251
47	-101.625	37.375	0.058	241
48	-101.375	37.375	-0.159	242
49	-101.125	37.375	-0.211	241
50	-100.875	37.375	-0.282	240
51	-100.625	37.375	-0.338	234
52	-100.375	37.375	-0.345	227
53	-100.125	37.375	-0.172	244
54	-99.875	37.375	-0.240	203
55	-99.625	37.375	-0.171	231
56	-99.375	37.375	-0.168	231
57	-99.125	37.375	-0.204	248
58	-98.875	37.375	-0.275	245
59	-98.625	37.375	-0.201	248
60	-98.375	37.375	-0.202	249
61	-98.125	37.375	-0.362	221
62	-97.875	37.375	-0.322	231
63	-97.625	37.375	-0.298	249
64	-97.375	37.375	-0.312	251
65	-97.125	37.375	-0.416	248
66	-96.875	37.375	-0.434	245
67	-96.625	37.375	-0.463	243
68	-96.375	37.375	-0.450	241
69	-96.125	37.375	-0.499	241
70	-95.875	37.375	-0.537	243
71	-95.625	37.375	-0.515	243
72	-95.375	37.375	-0.325	228
73	-95.125	37.375	-0.335	236
74	-94.875	37.375	-0.413	241
75	-94.625	37.375	-0.413	237
76	-94.375	37.375	-0.444	236
77	-94.125	37.375	-0.467	233
78	-93.875	37.375	-0.413	237
79	-103.375	37.125	-0.031	237
80	-103.125	37.125	0.015	237
81	-102.875	37.125	0.010	251
82	-102.625	37.125	-0.012	251
83	-102.375	37.125	-0.050	251
84	-102.125	37.125	-0.008	251
85	-101.875	37.125	-0.045	251
86	-101.625	37.125	0.093	242
87	-101.375	37.125	-0.025	251
88	-101.125	37.125	-0.023	252
89	-100.875	37.125	-0.055	250
90	-100.625	37.125	-0.188	252
91	-100.375	37.125	-0.229	250
92	-100.125	37.125	-0.174	225
93	-99.875	37.125	-0.230	242
94	-99.625	37.125	-0.159	244
95	-99.375	37.125	-0.229	243
96	-99.125	37.125	-0.295	247
97	-98.875	37.125	-0.298	245
98	-98.625	37.125	-0.277	248
99	-98.375	37.125	-0.263	221
100	-98.125	37.125	-0.279	228
101	-97.875	37.125	-0.282	250
102	-97.625	37.125	-0.302	250
103	-97.375	37.125	-0.339	249
104	-97.125	37.125	-0.381	251
105	-96.875	37.125	-0.495	247
106	-96.625	37.125	-0.467	242
107	-96.375	37.125	-0.419	240
108	-96.125	37.125	-0.423	241
109	-95.875	37.125	-0.471	243
110	-95.625	37.125	-0.463	247
111	-95.375	37.125	-0.487	239
112	-95.125	37.125	-0.528	244
113	-94.875	37.125	-0.302	226
114	-94.625	37.125	-0.435	241
115	-94.375	37.125	-0.451	236
116	-94.125	37.125	-0.507	243
117	-93.875	37.125	-0.516	242
118	-103.375	36.875	-0.191	252
119	-103.125	36.875	-0.187	251
120	-102.875	36.875	-0.152	251
121	-102.625	36.875	-0.183	250
122	-102.375	36.875	-0.165	251
123	-102.125	36.875	-0.158	251
124	-101.875	36.875	-0.149	252
125	-101.625	36.875	-0.212	252
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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
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668	-102.375	33.375	0.496	252
669	-102.125	33.375	0.634	252
670	-101.875	33.375	0.583	252
671	-101.625	33.375	0.550	252
672	-101.375	33.375	0.541	252
673	-101.125	33.375	0.545	252
674	-100.875	33.375	0.473	252
675	-100.625	33.375	0.434	252
676	-100.375	33.375	0.379	252
677	-100.125	33.375	0.352	252
678	-99.875	33.375	0.397	252
679	-99.625	33.375	0.360	252
680	-99.375	33.375	0.351	252
681	-99.125	33.375	0.322	252
682	-98.875	33.375	0.317	252
683	-98.625	33.375	0.303	252
684	-98.375	33.375	0.263	251
685	-98.125	33.375	0.249	251
686	-97.875	33.375	0.285	251
687	-97.625	33.375	0.309	252
688	-97.375	33.375	0.363	251
689	-97.125	33.375	0.344	252
690	-96.875	33.375	0.387	252
691	-96.625	33.375	0.419	244
692	-96.375	33.375	0.451	252
693	-96.125	33.375	0.448	252
694	-95.875	33.375	0.392	239
695	-95.625	33.375	0.400	247
696	-95.375	33.375	0.426	245
697	-95.125	33.375	0.383	244
698	-94.875	33.375	0.380	251
699	-94.625	33.375	0.384	243
700	-94.375	33.375	0.331	246
701	-94.125	33.375	0.341	252
702	-93.875	33.375	0.364	239
703	-103.375	33.125	0.524	252
704	-103.125	33.125	0.561	252
705	-102.875	33.125	0.588	252
706	-102.625	33.125	0.583	252
707	-102.375	33.125	0.616	252
708	-102.125	33.125	0.605	252
709	-101.875	33.125	0.570	252
710	-101.625	33.125	0.507	252
711	-101.375	33.125	0.517	252
712	-101.125	33.125	0.476	252
713	-100.875	33.125	0.470	252
714	-100.625	33.125	0.384	252
715	-100.375	33.125	0.325	252
716	-100.125	33.125	0.352	252
717	-99.875	33.125	0.373	252
718	-99.625	33.125	0.338	252
719	-99.375	33.125	0.379	252
720	-99.125	33.125	0.341	252
721	-98.875	33.125	0.294	252
722	-98.625	33.125	0.253	251
723	-98.375	33.125	0.248	251
724	-98.125	33.125	0.267	251
725	-97.875	33.125	0.306	251
726	-97.625	33.125	0.332	251
727	-97.375	33.125	0.284	252
728	-97.125	33.125	0.362	252
729	-96.875	33.125	0.382	252
730	-96.625	33.125	0.399	252
731	-96.375	33.125	0.453	252
732	-96.125	33.125	0.365	252
733	-95.875	33.125	0.371	252
734	-95.625	33.125	0.413	251
735	-95.375	33.125	0.393	251
736	-95.125	33.125	0.342	252
737	-94.875	33.125	0.354	250
738	-94.625	33.125	0.344	240
739	-94.375	33.125	0.319	250
740	-94.125	33.125	0.324	248
741	-93.875	33.125	0.395	249

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
127	-101.125	36.875	-0.356	252
128	-100.875	36.875	-0.403	252
129	-100.625	36.875	-0.482	250
130	-100.375	36.875	-0.496	249
131	-100.125	36.875	-0.537	250
132	-99.875	36.875	-0.480	246
133	-99.625	36.875	-0.528	249
134	-99.375	36.875	-0.527	246
135	-99.125	36.875	-0.576	248
136	-98.875	36.875	-0.551	250
137	-98.625	36.875	-0.494	241
138	-98.375	36.875	-0.364	247
139	-98.125	36.875	-0.414	250
140	-97.875	36.875	-0.439	249
141	-97.625	36.875	-0.456	250
142	-97.375	36.875	-0.478	251
143	-97.125	36.875	-0.599	250
144	-96.875	36.875	-0.706	250
145	-96.625	36.875	-0.668	246
146	-96.375	36.875	-0.655	246
147	-96.125	36.875	-0.644	246
148	-95.875	36.875	-0.659	246
149	-95.625	36.875	-0.649	247
150	-95.375	36.875	-0.646	245
151	-95.125	36.875	-0.668	247
152	-94.875	36.875	-0.644	244
153	-94.625	36.875	-0.628	244
154	-94.375	36.875	-0.578	244
155	-94.125	36.875	-0.602	238
156	-93.875	36.875	-0.569	241
157	-103.375	36.625	-0.363	252
158	-103.125	36.625	-0.358	252
159	-102.875	36.625	-0.369	251
160	-102.625	36.625	-0.377	250
161	-102.375	36.625	-0.374	251
162	-102.125	36.625	-0.352	251
163	-101.875	36.625	-0.348	252
164	-101.625	36.625	-0.365	252
165	-101.375	36.625	-0.412	251
166	-101.125	36.625	-0.403	249
167	-100.875	36.625	-0.437	251
168	-100.625	36.625	-0.497	251
169	-100.375	36.625	-0.533	251
170	-100.125	36.625	-0.540	249
171	-99.875	36.625	-0.535	249
172	-99.625	36.625	-0.547	250
173	-99.375	36.625	-0.559	251
174	-99.125	36.625	-0.548	247
175	-98.875	36.625	-0.545	250
176	-98.625	36.625	-0.472	250
177	-98.375	36.625	-0.416	250
178	-98.125	36.625	-0.464	251
179	-97.875	36.625	-0.450	250
180	-97.625	36.625	-0.460	250
181	-97.375	36.625	-0.515	250
182	-97.125	36.625	-0.626	249
183	-96.875	36.625	-0.682	250
184	-96.625	36.625	-0.696	247
185	-96.375	36.625	-0.662	247
186	-96.125	36.625	-0.688	239
187	-95.875	36.625	-0.697	246
188	-95.625	36.625	-0.697	245
189	-95.375	36.625	-0.690	246
190	-95.125	36.625	-0.663	245
191	-94.875	36.625	-0.707	245
192	-94.625	36.625	-0.672	245
193	-94.375	36.625	-0.590	245
194	-94.125	36.625	-0.548	234
195	-93.875	36.625	-0.500	242
196	-103.375	36.375	-0.322	251
197	-103.125	36.375	-0.360	251
198	-102.875	36.375	-0.303	251
199	-102.625	36.375	-0.352	252
200	-102.375	36.375	-0.350	252
201	-102.125	36.375	-0.362	252
202	-101.875	36.375	-0.367	252
203	-101.625	36.375	-0.350	251
204	-101.375	36.375	-0.376	252
205	-101.125	36.375	-0.459	250
206	-100.875	36.375	-0.511	247
207	-100.625	36.375	-0.515	250
208	-100.375	36.375	-0.531	251
209	-100.125	36.375	-0.541	252
210	-99.875	36.375	-0.537	249
211	-99.625	36.375	-0.531	250
212	-99.375	36.375	-0.501	250
213	-99.125	36.375	-0.492	248
214	-98.875	36.375	-0.525	249
215	-98.625	36.375	-0.520	249
216	-98.375	36.375	-0.438	249
217	-98.125	36.375	-0.465	242
218	-97.875	36.375	-0.449	250
219	-97.625	36.375	-0.501	249
220	-97.375	36.375	-0.549	249
221	-97.125	36.375	-0.584	247
222	-96.875	36.375	-0.665	248
223	-96.625	36.375	-0.627	247
224	-96.375	36.375	-0.644	247
225	-96.125	36.375	-0.626	246
226	-95.875	36.375	-0.693	247
227	-95.625	36.375	-0.716	246
228	-95.375	36.375	-0.631	247
229	-95.125	36.375	-0.625	247
230	-94.875	36.375	-0.561	236
231	-94.625	36.375	-0.640	244
232	-94.375	36.375	-0.537	244
233	-94.125	36.375	-0.614	244
234	-93.875	36.375	-0.649	240
235	-103.375	36.125	-0.237	247
236	-103.125	36.125	-0.352	252
237	-102.875	36.125	-0.289	252
238	-102.625	36.125	-0.331	252
239	-102.375	36.125	-0.353	252
240	-102.125	36.125	-0.389	252
241	-101.875	36.125	-0.382	252
242	-101.625	36.125	-0.432	251
243	-101.375	36.125	-0.485	252
244	-101.125	36.125	-0.472	251
245	-100.875	36.125	-0.496	251
246	-100.625	36.125	-0.518	251
247	-100.375	36.125	-0.594	234
248	-100.125	36.125	-0.529	247
249	-99.875	36.125	-0.544	247
250	-99.625	36.125	-0.559	248
251	-99.375	36.125	-0.465	250
252	-99.125	36.125	-0.511	249
253	-98.875	36.125	-0.497	249
254	-98.625	36.125	-0.502	249
255	-98.375	36.125	-0.511	239
256	-98.125	36.125	-0.447	242
257	-97.875	36.125	-0.482	240
258	-97.625	36.125	-0.584	238
259	-97.375	36.125	-0.651	232
260	-97.125	36.125	-0.585	248
261	-96.875	36.125	-0.556	248
262	-96.625	36.125	-0.578	248
263	-96.375	36.125	-0.615	248
264	-96.125	36.125	-0.695	248
265	-95.875	36.125	-0.711	247
266	-95.625	36.125	-0.714	248
267	-95.375	36.125	-0.682	243
268	-95.125	36.125	-0.686	248
269	-94.875	36.125	-0.651	246
270	-94.625	36.125	-0.562	244
271	-94.375	36.125	-0.574	243
272	-94.125	36.125	-0.620	236
273	-93.875	36.125	-0.584	239
274	-103.375	35.875	-0.376	252
275	-103.125	35.875	-0.215	240
276	-102.875	35.875	-0.326	252
277	-102.625	35.875	-0.312	252
278	-102.375	35.875	-0.334	252
279	-102.125	35.875	-0.377	252
280	-101.875	35.875	-0.475	251
281	-101.625	35.875	-0.463	252
282	-101.375	35.875	-0.461	252
283	-101.125	35.875	-0.459	251
284	-100.875	35.875	-0.509	250
285	-100.625	35.875	-0.549	233
286	-100.375	35.875	-0.597	229
287	-100.125	35.875	-0.568	251
288	-99.875	35.875	-0.585	248
289	-99.625	35.875	-0.567	251
290	-99.375	35.875	-0.550	252
291	-99.125	35.875	-0.504	249
292	-98.875	35.875	-0.443	249
293	-98.625	35.875	-0.468	236
294	-98.375	35.875	-0.511	238
295	-98.125	35.875	-0.471	250
296	-97.875	35.875	-0.457	249
297	-97.625	35.875	-0.527	249
298	-97.375	35.875	-0.603	249
299	-97.125	35.875	-0.600	248
300	-96.875	35.875	-0.600	248
301	-96.625	35.875	-0.671	248
302	-96.375	35.875	-0.606	248
303	-96.125	35.875	-0.657	247
304	-95.875	35.875	-0.722	247
305	-95.625	35.875	-0.688	247
306	-95.375	35.875	-0.751	248
307	-95.125	35.875	-0.746	247
308	-94.875	35.875	-0.683	247
309	-94.625	35.875	-0.503	246
310	-94.375	35.875	-0.613	240
311	-94.125	35.875	-0.321	232
312	-93.875	35.875	-0.275	231
313	-103.375	35.625	-0.397	252
314	-103.125	35.625	-0.254	246
315	-102.875	35.625	-0.254	246
316	-102.625	35.625	-0.351	248
317	-102.375	35.625	-0.383	252
318	-102.125	35.625	-0.440	252
319	-101.875	35.625	-0.460	252
320	-101.625	35.625	-0.522	252
321	-101.375	35.625	-0.471	252
322	-101.125	35.625	-0.487	252
323	-100.875	35.625	-0.493	252
324	-100.625	35.625	-0.550	252
325	-100.375	35.625	-0.546	252
326	-100.125	35.625	-0.617	230
327	-99.875	35.625	-0.558	252
328	-99.625	35.625	-0.590	249
329	-99.375	35.625	-0.571	251
330	-99.125	35.625	-0.495	251
331	-98.875	35.625	-0.429	243
332	-98.625	35.625	-0.445	238
333	-98.375	35.625	-0.516	237
334	-98.125	35.625	-0.469	250
335	-97.875	35.625	-0.566	239
336	-97.625	35.625	-0.573	251
337	-97.375	35.625	-0.604	251
338	-97.125	35.625	-0.594	248
339	-96.875	35.625	-0.668	248
340	-96.625	35.625	-0.684	249
341	-96.375	35.625	-0.635	249
342	-96.125	35.625	-0.657	248
343	-95.875	35.625	-0.715	251
344	-95.625	35.625	-0.754	252
345	-95.375	35.625	-0.731	251
346	-95.125	35.625	-0.700	250
347	-94.875	35.625	-0.753	248
348	-94.625	35.625	-0.632	247
349	-94.375	35.625	-0.621	248
350	-94.125	35.625	-0.262	236
351	-93.875	35.625	-0.152	238
352	-103.375	35.375	-0.426	252
353	-103.125	35.375	-0.304	244
354	-102.875	35.375	-0.297	241
355	-102.625	35.375	-0.332	244
356	-102.375	35.375	-0.436	252
357	-102.125	35.375	-0.506	252
358	-101.875	35.375	-0.505	252
359	-101.625	35.375	-0.480	250
360	-101.375	35.375	-0.411	252
361	-101.125	35.375	-0.466	252
362	-100.875	35.375	-0.506	252
363	-100.625	35.375	-0.503	251
364	-100.375	35.375	-0.571	234
365	-100.125	35.375	-0.502	252
366	-99.875	35.375	-0.513	252
367	-99.625	35.375	-0.558	251
368	-99.375	35.375	-0.545	252
369	-99.125	35.375	-0.470	251
370	-98.875	35.375	-0.462	248
371	-98.625	35.375	-0.459	242
372	-98.375	35.375	-0.485	250
373	-98.125	35.375	-0.548	249
374	-97.875	35.375	-0.635	235
375	-97.625	35.375	-0.567	251
376	-97.375	35.375	-0.623	250
377	-97.125	35.375	-0.650	248
378	-96.875	35.375	-0.708	248
379	-96.625	35.375	-0.684	248
380	-96.375	35.375	-0.695	249
381	-96.125	35.375	-0.701	248
382	-95.875	35.375	-0.721	248
383	-95.625	35.375	-0.701	238
384	-95.375	35.375	-0.716	247
385	-95.125	35.375	-0.733	248
386	-94.875	35.375	-0.762	248
387	-94.625	35.375	-0.764	249
388	-94.375	35.375	-0.652	240
389	-94.125	35.375	-0.683	248
390	-93.875	35.375	-0.319	234
391	-103.375	35.125	-0.293	246
392	-103.125	35.125	-0.344	243
393	-102.875	35.125	-0.326	243
394	-102.625	35.125	-0.276	245
395	-102.375	35.125	-0.407	252
396	-102.125	35.125	-0.444	252
397	-101.875	35.125	-0.416	250
398	-101.625	35.125	-0.425	250
399	-101.375	35.125	-0.408	252
400	-101.125	35.125	-0.454	252
401	-100.875	35.125	-0.488	252
402	-100.625	35.125	-0.478	252
403	-100.375	35.125	-0.499	252
404	-100.125	35.125	-0.498	252
405	-99.875	35.125	-0.497	252
406	-99.625	35.125	-0.511	252
407	-99.375	35.125	-0.485	251
408	-99.125	35.125	-0.476	252
409	-98.875	35.125	-0.483	249
410	-98.625	35.125	-0.524	238
411	-98.375	35.125	-0.486	235
412	-98.125	35.125	-0.467	251
413	-97.875	35.125	-0.552	250
414	-97.625	35.125	-0.638	250
415	-97.375	35.125	-0.633	250
416	-97.125	35.125	-0.601	252
417	-96.875	35.125	-0.669	251
418	-96.625	35.125	-0.689	252
419	-96.375	35.125	-0.722	251
420	-96.125	35.125	-0.652	251
421	-95.875	35.125	-0.693	252
422	-95.625	35.125	-0.657	243
423	-95.375	35.125	-0.637	249
424	-95.125	35.125	-0.568	245
425	-94.875	35.125	-0.611	250
426	-94.625	35.125	-0.660	248
427	-94.375	35.125	-0.607	247
428	-94.125	35.125	-0.286	241
429	-93.875	35.125	-0.182	240
430	-103.375	34.875	-0.402	252
431	-103.125	34.875	-0.380	250
432	-102.875	34.875	-0.350	252
433	-102.625	34.875	-0.218	244
434	-102.375	34.875	-0.256	252
435	-102.125	34.875	-0.385	252
436	-101.875	34.875	-0.406	252
437	-101.625	34.875	-0.361	251
438	-101.375	34.875	-0.429	251
439	-101.125	34.875	-0.457	251
440	-100.875	34.875	-0.453	252
441	-100.625	34.875	-0.470	252
442	-100.375	34.875	-0.477	252
443	-100.125	34.875	-0.467	252
444	-99.875	34.875	-0.484	252
445	-99.625	34.875	-0.490	249
446	-99.375	34.875	-0.435	252
447	-99.125	34.875	-0.492	252
448	-98.875	34.875	-0.560	252
449	-98.625	34.875	-0.614	238
450	-98.375	34.875	-0.613	233
451	-98.125	34.875	-0.520	250
452	-97.875	34.875	-0.551	250
453	-97.625	34.875	-0.647	249
454	-97.375	34.875	-0.671	251
455	-97.125	34.875	-0.681	252
456	-96.875	34.875	-0.652	252
457	-96.625	34.875	-0.669	252
458	-96.375	34.875	-0.663	251
459	-96.125	34.875	-0.672	248
460	-95.875	34.875	-0.659	247
461	-95.625	34.875	-0.631	238
462	-95.375	34.875	-0.582	242
463	-95.125	34.875	-0.584	241
464	-94.875	34.875	-0.544	244
465	-94.625	34.875	-0.139	240
466	-94.375	34.875	-0.060	239
467	-94.125	34.875	-0.013	239
468	-93.875	34.875	0.065	239
469	-103.375	34.625	-0.278	251
470	-103.125	34.625	-0.257	252
471	-102.875	34.625	-0.266	252
472	-102.625	34.625	-0.221	252
473	-102.375	34.625	-0.228	252
474	-102.125	34.625	-0.273	252
475	-101.875	34.625	-0.283	252
476	-101.625	34.625	-0.315	252
477	-101.375	34.625	-0.407	251
478	-101.125	34.625	-0.447	251
479	-100.875	34.625	-0.463	251
480	-100.625	34.625	-0.457	252
481	-100.375	34.625	-0.480	252
482	-100.125	34.625	-0.475	252
483	-99.875	34.625	-0.449	252
484	-99.625	34.625	-0.432	252
485	-99.375	34.625	-0.434	252
486	-99.125	34.625	-0.465	251
487	-98.875	34.625	-0.534	252
488	-98.625	34.625	-0.593	252
489	-98.375	34.625	-0.584	240
490	-98.125	34.625	-0.536	250
491	-97.875	34.625	-0.557	250
492	-97.625	34.625	-0.598	251
493	-97.375	34.625	-0.661	251
494	-97.125	34.625	-0.659	250
495	-96.875	34.625	-0.651	251
496	-96.625	34.625	-0.655	252
497	-96.375	34.625	-0.668	251
498	-96.125	34.625	-0.677	250
499	-95.875	34.625	-0.684	241
500	-95.625	34.625	-0.699	205
501	-95.375	34.625	-0.534	239
502	-95.125	34.625	-0.208	242
503	-94.875	34.625	-0.099	241
504	-94.625	34.625	0.029	238
505	-94.375	34.625	0.023	239
506	-94.125	34.625	0.051	239
507	-93.875	34.625	0.101	238
508	-103.375	34.375	-0.181	252
509	-103.125	34.375	-0.181	252
510	-102.875	34.375	-0.213	252
511	-102.625	34.375	-0.187	252
512	-102.375	34.375	-0.232	252
513	-102.125	34.375	-0.288	252
514	-101.875	34.375	-0.310	252
515	-101.625	34.375	-0.297	252
516	-101.375	34.375	-0.391	252
517	-101.125	34.375	-0.401	252
518	-100.875	34.375	-0.441	252
519	-100.625	34.375	-0.473	252
520	-100.375	34.375	-0.469	252
521	-100.125	34.375	-0.480	252
522	-99.875	34.375	-0.495	252
523	-99.625	34.375	-0.512	252
524	-99.375	34.375	-0.502	252
525	-99.125	34.375	-0.510	251
526	-98.875	34.375	-0.522	252
527	-98.625	34.375	-0.574	249
528	-98.375	34.375	-0.591	252
529	-98.125	34.375	-0.632	252
530	-97.875	34.375	-0.588	251
531	-97.625	34.375	-0.629	251
532	-97.375	34.375	-0.728	251
533	-97.125	34.375	-0.715	251
534	-96.875	34.375	-0.669	251
535	-96.625	34.375	-0.739	251
536	-96.375	34.375	-0.677	250
537	-96.125	34.375	-0.744	221
538	-95.875	34.375	-0.680	240
539	-95.625	34.375	-0.618	232
540	-95.375	34.375	-0.532	243
541	-95.125	34.375	-0.067	240
542	-94.875	34.375	0.018	238
543	-94.625	34.375	0.070	236
544	-94.375	34.375	0.082	236
545	-94.125	34.375	0.129	236
546	-93.875	34.375	0.134	236
547	-103.375	34.125	-0.053	248
548	-103.125	34.125	-0.101	251
549	-102.875	34.125	-0.061	251
550	-102.625	34.125	-0.141	252
551	-102.375	34.125	-0.177	252
552	-102.125	34.125	-0.199	252
553	-101.875	34.125	-0.225	252
554	-101.625	34.125	-0.257	250
555	-101.375	34.125	-0.304	252
556	-101.125	34.125	-0.321	252
557	-100.875	34.125	-0.409	252
558	-100.625	34.125	-0.473	252
559	-100.375	34.125	-0.486	252
560	-100.125	34.125	-0.505	252
561	-99.875	34.125	-0.507	252
562	-99.625	34.125	-0.550	252
563	-99.375	34.125	-0.564	252
564	-99.125	34.125	-0.552	252
565	-98.875	34.125	-0.569	248
566	-98.625	34.125	-0.549	252
567	-98.375	34.125	-0.610	252
568	-98.125	34.125	-0.660	251
569	-97.875	34.125	-0.604	252
570	-97.625	34.125	-0.635	251
571	-97.375	34.125	-0.717	251
572	-97.125	34.125	-0.750	252
573	-96.875	34.125	-0.741	245
574	-96.625	34.125	-0.739	252
575	-96.375	34.125	-0.741	251
576	-96.125	34.125	-0.735	251
577	-95.875	34.125	-0.756	251
578	-95.625	34.125	-0.670	246
579	-95.375	34.125	-0.655	243
580	-95.125	34.125	-0.632	251
581	-94.875	34.125	-0.155	242
582	-94.625	34.125	-0.082	243
583	-94.375	34.125	0.019	242
584	-94.125	34.125	0.070	242
585	-93.875	34.125	0.006	243
586	-103.375	33.875	-0.307	252
587	-103.125	33.875	-0.272	252
588	-102.875	33.875	-0.220	252
589	-102.625	33.875	-0.257	252
590	-102.375	33.875	-0.244	252
591	-102.125	33.875	-0.212	252
592	-101.875	33.875	-0.196	252
593	-101.625	33.875	-0.284	252
594	-101.375	33.875	-0.328	252
595	-101.125	33.875	-0.332	252
596	-100.875	33.875	-0.425	252
597	-100.625	33.875	-0.473	252
598	-100.375	33.875	-0.504	252
599	-100.125	33.875	-0.534	252
600	-99.875	33.875	-0.550	252
601	-99.625	33.875	-0.559	252
602	-99.375	33.875	-0.598	252
603	-99.125	33.875	-0.592	252
604	-98.875	33.875	-0.602	252
605	-98.625	33.875	-0.626	252
606	-98.375	33.875	-0.653	252
607	-98.125	33.875	-0.686	251
608	-97.875	33.875	-0.705	251
609	-97.625	33.875	-0.728	251
610	-97.375	33.875	-0.751	251
611	-97.125	33.875	-0.761	252
612	-96.875	33.875	-0.616	248
613	-96.625	33.875	-0.556	243
614	-96.375	33.875	-0.777	252
615	-96.125	33.875	-0.787	251
616	-95.875	33.875	-0.760	251
617	-95.625	33.875	-0.728	246
618	-95.375	33.875	-0.697	245
619	-95.125	33.875	-0.684	244
620	-94.875	33.875	-0.648	243
621	-94.625	33.875	-0.522	245
622	-94.375	33.875	-0.598	251
623	-94.125	33.875	-0.572	248
624	-93.875	33.875	-0.703	246
625	-103.375	33.625	-0.348	252
626	-103.125	33.625	-0.299	252
627	-102.875	33.625	-0.215	252
628	-102.625	33.625	-0.200	252
629	-102.375	33.625	-0.215	252
630	-102.125	33.625	-0.163	252
631	-101.875	33.625	-0.215	252
632	-101.625	33.625	-0.286	252
633	-101.375	33.625	-0.354	252
634	-101.125	33.625	-0.332	252
635	-100.875	33.625	-0.417	252
636	-100.625	33.625	-0.463	252
637	-100.375	33.625	-0.478	252
638	-100.125	33.625	-0.584	238
639	-99.875	33.625	-0.535	252
640	-99.625	33.625	-0.555	252
641	-99.375	33.625	-0.606	252
642	-99.125	33.625	-0.625	252
643	-98.875	33.625	-0.631	252
644	-98.625	33.625	-0.666	252
645	-98.375	33.625	-0.688	252
646	-98.125	33.625	-0.698	251
647	-97.875	33.625	-0.723	252
648	-97.625	33.625	-0.760	252
649	-97.375	33.625	-0.751	252
650	-97.125	33.625	-0.737	252
651	-96.875	33.625	-0.700	252
652	-96.625	33.625	-0.724	252
653	-96.375	33.625	-0.725	252
654	-96.125	33.625	-0.731	251
655	-95.875	33.625	-0.772	251
656	-95.625	33.625	-0.719	246
657	-95.375	33.625	-0.716	245
658	-95.125	33.625	-0.704	251
659	-94.875	33.625	-0.673	250
660	-94.625	33.625	-0.667	247
661	-94.375	33.625	-0.675	252
662	-94.125	33.625	-0.647	246
663	-93.875	33.625	-0.666	244
664	-103.375	33.375	-0.314	252
665	-103.125	33.375	-0.275	252
666	-102.875	33.375	-0.241	252
667	-102.625	33.375	-0.161	252
668	-102.375	33.375	-0.175	252
669	-102.125	33.375	-0.130	252
670	-101.875	33.375	-0.202	252
671	-101.625	33.375	-0.265	252
672	-101.375	33.375	-0.340	252
673	-101.125	33.375	-0.383	252
674	-100.875	33.375	-0.459	252
675	-100.625	33.375	-0.473	252
676	-100.375	33.375	-0.481	252
677	-100.125	33.375	-0.522	252
678	-99.875	33.375	-0.551	252
679	-99.625	33.375	-0.581	252
680	-99.375	33.375	-0.592	252
681	-99.125	33.375	-0.604	252
682	-98.875	33.375	-0.633	252
683	-98.625	33.375	-0.691	252
684	-98.375	33.375	-0.704	251
685	-98.125	33.375	-0.725	251
686	-97.875	33.375	-0.709	251
687	-97.625	33.375	-0.750	252
688	-97.375	33.375	-0.722	251
689	-97.125	33.375	-0.644	252
690	-96.875	33.375	-0.673	252
691	-96.625	33.375	-0.717	244
692	-96.375	33.375	-0.723	252
693	-96.125	33.375	-0.714	252
694	-95.875	33.375	-0.690	239
695	-95.625	33.375	-0.702	247
696	-95.375	33.375	-0.746	245
697	-95.125	33.375	-0.667	244
698	-94.875	33.375	-0.674	251
699	-94.625	33.375	-0.618	243
700	-94.375	33.375	-0.616	246
701	-94.125	33.375	-0.753	252
702	-93.875	33.375	-0.560	239
703	-103.375	33.125	-0.282	252
704	-103.125	33.125	-0.267	252
705	-102.875	33.125	-0.230	252
706	-102.625	33.125	-0.182	252
707	-102.375	33.125	-0.157	252
708	-102.125	33.125	-0.219	252
709	-101.875	33.125	-0.293	252
710	-101.625	33.125	-0.376	252
711	-101.375	33.125	-0.395	252
712	-101.125	33.125	-0.484	252
713	-100.875	33.125	-0.483	252
714	-100.625	33.125	-0.485	252
715	-100.375	33.125	-0.569	252
716	-100.125	33.125	-0.584	252
717	-99.875	33.125	-0.560	252
718	-99.625	33.125	-0.597	252
719	-99.375	33.125	-0.602	252
720	-99.125	33.125	-0.607	252
721	-98.875	33.125	-0.664	252
722	-98.625	33.125	-0.727	251
723	-98.375	33.125	-0.712	251
724	-98.125	33.125	-0.704	251
725	-97.875	33.125	-0.719	251
726	-97.625	33.125	-0.699	251
727	-97.375	33.125	-0.716	252
728	-97.125	33.125	-0.710	252
729	-96.875	33.125	-0.732	252
730	-96.625	33.125	-0.730	252
731	-96.375	33.125	-0.733	252
732	-96.125	33.125	-0.685	252
733	-95.875	33.125	-0.731	252
734	-95.625	33.125	-0.719	251
735	-95.375	33.125	-0.742	251
736	-95.125	33.125	-0.644	252
737	-94.875	33.125	-0.660	250
738	-94.625	33.125	-0.583	240
739	-94.375	33.125	-0.570	250
740	-94.125	33.125	-0.552	248
741	-93.875	33.125	-0.638	249

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
327	-99.875	35.625	-0.447	252
328	-99.625	35.625	-0.479	249
329	-99.375	35.625	-0.463	251
330	-99.125	35.625	-0.389	251
331	-98.875	35.625	-0.338	243
332	-98.625	35.625	-0.354	238
333	-98.375	35.625	-0.425	237
334	-98.125	35.625	-0.370	250
335	-97.875	35.625	-0.481	239
336	-97.625	35.625	-0.512	251
337	-97.375	35.625	-0.550	251
338	-97.125	35.625	-0.532	248
339	-96.875	35.625	-0.598	248
340	-96.625	35.625	-0.617	249
341	-96.375	35.625	-0.574	249
342	-96.125	35.625	-0.595	248
343	-95.875	35.625	-0.660	251
344	-95.625	35.625	-0.694	252
345	-95.375	35.625	-0.671	251
346	-95.125	35.625	-0.651	250
347	-94.875	35.625	-0.716	248
348	-94.625	35.625	-0.598	247
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
355	-102.625	35.375	-0.189	244
356	-102.375	35.375	-0.295	252
357	-102.125	35.375	-0.378	252
358	-101.875	35.375	-0.384	252
359	-101.625	35.375	-0.353	250
360	-101.375	35.375	-0.264	252
361	-101.125	35.375	-0.319	252
362	-100.875	35.375	-0.371	252
363	-100.625	35.375	-0.374	251
364	-100.375	35.375	-0.456	234
365	-100.125	35.375	-0.375	252
366	-99.875	35.375	-0.389	252
367	-99.625	35.375	-0.444	251
368	-99.375	35.375	-0.435	252
369	-99.125	35.375	-0.363	251
370	-98.875	35.375	-0.369	248
371	-98.625	35.375	-0.364	242
372	-98.375	35.375	-0.384	250
373	-98.125	35.375	-0.447	249
374	-97.875	35.375	-0.560	235
375	-97.625	35.375	-0.507	251
376	-97.375	35.375	-0.571	250
377	-97.125	35.375	-0.594	248
378	-96.875	35.375	-0.643	248
379	-96.625	35.375	-0.622	248
380	-96.375	35.375	-0.636	249
381	-96.125	35.375	-0.647	248
382	-95.875	35.375	-0.666	248
383	-95.625	35.375	-0.646	238
384	-95.375	35.375	-0.655	247
385	-95.125	35.375	-0.680	248
386	-94.875	35.375	-0.715	248
387	-94.625	35.375	-0.728	249
388	-94.375	35.375	-0.617	240
389	-94.125	35.375	-0.640	248
390	-93.875	35.375	-0.257	234
391	-103.375	35.125	-0.168	246
392	-103.125	35.125	-0.205	243
393	-102.875	35.125	-0.179	243
394	-102.625	35.125	-0.123	245
395	-102.375	35.125	-0.255	252
396	-102.125	35.125	-0.305	252
397	-101.875	35.125	-0.274	250
398	-101.625	35.125	-0.285	250
399	-101.375	35.125	-0.256	252
400	-101.125	35.125	-0.309	252
401	-100.875	35.125	-0.349	252
402	-100.625	35.125	-0.346	252
403	-100.375	35.125	-0.372	252
404	-100.125	35.125	-0.368	252
405	-99.875	35.125	-0.364	252
406	-99.625	35.125	-0.390	252
407	-99.375	35.125	-0.376	251
408	-99.125	35.125	-0.375	252
409	-98.875	35.125	-0.379	249
410	-98.625	35.125	-0.425	238
411	-98.375	35.125	-0.391	235
412	-98.125	35.125	-0.370	251
413	-97.875	35.125	-0.462	250
414	-97.625	35.125	-0.560	250
415	-97.375	35.125	-0.574	250
416	-97.125	35.125	-0.539	252
417	-96.875	35.125	-0.603	251
418	-96.625	35.125	-0.628	252
419	-96.375	35.125	-0.665	251
420	-96.125	35.125	-0.595	251
421	-95.875	35.125	-0.636	252
422	-95.625	35.125	-0.600	243
423	-95.375	35.125	-0.575	249
424	-95.125	35.125	-0.515	245
425	-94.875	35.125	-0.557	250
426	-94.625	35.125	-0.614	248
427	-94.375	35.125	-0.564	247
428	-94.125	35.125	-0.226	241
429	-93.875	35.125	-0.114	240
430	-103.375	34.875	-0.247	252
431	-103.125	34.875	-0.212	250
432	-102.875	34.875	-0.189	252
433	-102.625	34.875	-0.050	244
434	-102.375	34.875	-0.100	252
435	-102.125	34.875	-0.227	252
436	-101.875	34.875	-0.259	252
437	-101.625	34.875	-0.220	251
438	-101.375	34.875	-0.287	251
439	-101.125	34.875	-0.316	251
440	-100.875	34.875	-0.310	252
441	-100.625	34.875	-0.330	252
442	-100.375	34.875	-0.347	252
443	-100.125	34.875	-0.338	252
444	-99.875	34.875	-0.356	252
445	-99.625	34.875	-0.374	249
446	-99.375	34.875	-0.311	252
447	-99.125	34.875	-0.377	252
448	-98.875	34.875	-0.454	252
449	-98.625	34.875	-0.530	238
450	-98.375	34.875	-0.531	233
451	-98.125	34.875	-0.433	250
452	-97.875	34.875	-0.455	250
453	-97.625	34.875	-0.559	249
454	-97.375	34.875	-0.593	251
455	-97.125	34.875	-0.613	252
456	-96.875	34.875	-0.583	252
457	-96.625	34.875	-0.599	252
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459	-96.125	34.875	-0.619	248
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462	-95.375	34.875	-0.516	242
463	-95.125	34.875	-0.537	241
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465	-94.625	34.875	-0.066	240
466	-94.375	34.875	0.014	239
467	-94.125	34.875	0.058	239
468	-93.875	34.875	0.118	239
469	-103.375	34.625	-0.095	251
470	-103.125	34.625	-0.083	252
471	-102.875	34.625	-0.092	252
472	-102.625	34.625	-0.045	252
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478	-101.125	34.625	-0.307	251
479	-100.875	34.625	-0.328	251
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481	-100.375	34.625	-0.351	252
482	-100.125	34.625	-0.348	252
483	-99.875	34.625	-0.318	252
484	-99.625	34.625	-0.300	252
485	-99.375	34.625	-0.316	252
486	-99.125	34.625	-0.356	251
487	-98.875	34.625	-0.427	252
488	-98.625	34.625	-0.489	252
489	-98.375	34.625	-0.490	240
490	-98.125	34.625	-0.441	250
491	-97.875	34.625	-0.465	250
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493	-97.375	34.625	-0.578	251
494	-97.125	34.625	-0.579	250
495	-96.875	34.625	-0.571	251
496	-96.625	34.625	-0.576	252
497	-96.375	34.625	-0.596	251
498	-96.125	34.625	-0.615	250
499	-95.875	34.625	-0.630	241
500	-95.625	34.625	-0.646	205
501	-95.375	34.625	-0.477	239
502	-95.125	34.625	-0.128	242
503	-94.875	34.625	-0.025	241
504	-94.625	34.625	0.099	238
505	-94.375	34.625	0.102	239
506	-94.125	34.625	0.106	239
507	-93.875	34.625	0.157	238
508	-103.375	34.375	-0.003	252
509	-103.125	34.375	-0.013	252
510	-102.875	34.375	-0.039	252
511	-102.625	34.375	-0.008	252
512	-102.375	34.375	-0.062	252
513	-102.125	34.375	-0.135	252
514	-101.875	34.375	-0.156	252
515	-101.625	34.375	-0.143	252
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517	-101.125	34.375	-0.259	252
518	-100.875	34.375	-0.304	252
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520	-100.375	34.375	-0.340	252
521	-100.125	34.375	-0.353	252
522	-99.875	34.375	-0.367	252
523	-99.625	34.375	-0.385	252
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525	-99.125	34.375	-0.394	251
526	-98.875	34.375	-0.408	252
527	-98.625	34.375	-0.465	249
528	-98.375	34.375	-0.483	252
529	-98.125	34.375	-0.527	252
530	-97.875	34.375	-0.490	251
531	-97.625	34.375	-0.544	251
532	-97.375	34.375	-0.652	251
533	-97.125	34.375	-0.647	251
534	-96.875	34.375	-0.594	251
535	-96.625	34.375	-0.666	251
536	-96.375	34.375	-0.605	250
537	-96.125	34.375	-0.683	221
538	-95.875	34.375	-0.613	240
539	-95.625	34.375	-0.555	232
540	-95.375	34.375	-0.478	243
541	-95.125	34.375	0.015	240
542	-94.875	34.375	0.093	238
543	-94.625	34.375	0.141	236
544	-94.375	34.375	0.145	236
545	-94.125	34.375	0.165	236
546	-93.875	34.375	0.189	236
547	-103.375	34.125	0.122	248
548	-103.125	34.125	0.083	251
549	-102.875	34.125	0.127	251
550	-102.625	34.125	0.041	252
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553	-101.875	34.125	-0.081	252
554	-101.625	34.125	-0.104	250
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557	-100.875	34.125	-0.271	252
558	-100.625	34.125	-0.342	252
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560	-100.125	34.125	-0.379	252
561	-99.875	34.125	-0.390	252
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563	-99.375	34.125	-0.443	252
564	-99.125	34.125	-0.434	252
565	-98.875	34.125	-0.461	248
566	-98.625	34.125	-0.438	252
567	-98.375	34.125	-0.496	252
568	-98.125	34.125	-0.554	251
569	-97.875	34.125	-0.502	252
570	-97.625	34.125	-0.543	251
571	-97.375	34.125	-0.648	251
572	-97.125	34.125	-0.681	252
573	-96.875	34.125	-0.676	245
574	-96.625	34.125	-0.682	252
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576	-96.125	34.125	-0.668	251
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580	-95.125	34.125	-0.576	251
581	-94.875	34.125	-0.071	242
582	-94.625	34.125	-0.006	243
583	-94.375	34.125	0.093	242
584	-94.125	34.125	0.130	242
585	-93.875	34.125	0.080	243
586	-103.375	33.875	-0.136	252
587	-103.125	33.875	-0.093	252
588	-102.875	33.875	-0.033	252
589	-102.625	33.875	-0.083	252
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592	-101.875	33.875	-0.048	252
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595	-101.125	33.875	-0.191	252
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597	-100.625	33.875	-0.342	252
598	-100.375	33.875	-0.376	252
599	-100.125	33.875	-0.405	252
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603	-99.125	33.875	-0.481	252
604	-98.875	33.875	-0.489	252
605	-98.625	33.875	-0.514	252
606	-98.375	33.875	-0.544	252
607	-98.125	33.875	-0.582	251
608	-97.875	33.875	-0.616	251
609	-97.625	33.875	-0.641	251
610	-97.375	33.875	-0.674	251
611	-97.125	33.875	-0.695	252
612	-96.875	33.875	-0.541	248
613	-96.625	33.875	-0.479	243
614	-96.375	33.875	-0.719	252
615	-96.125	33.875	-0.721	251
616	-95.875	33.875	-0.688	251
617	-95.625	33.875	-0.660	246
618	-95.375	33.875	-0.623	245
619	-95.125	33.875	-0.616	244
620	-94.875	33.875	-0.585	243
621	-94.625	33.875	-0.445	245
622	-94.375	33.875	-0.537	251
623	-94.125	33.875	-0.521	248
624	-93.875	33.875	-0.642	246
625	-103.375	33.625	-0.175	252
626	-103.125	33.625	-0.120	252
627	-102.875	33.625	-0.034	252
628	-102.625	33.625	-0.027	252
629	-102.375	33.625	-0.057	252
630	-102.125	33.625	-0.011	252
631	-101.875	33.625	-0.074	252
632	-101.625	33.625	-0.143	252
633	-101.375	33.625	-0.208	252
634	-101.125	33.625	-0.189	252
635	-100.875	33.625	-0.271	252
636	-100.625	33.625	-0.325	252
637	-100.375	33.625	-0.347	252
638	-100.125	33.625	-0.458	238
639	-99.875	33.625	-0.412	252
640	-99.625	33.625	-0.433	252
641	-99.375	33.625	-0.492	252
642	-99.125	33.625	-0.515	252
643	-98.875	33.625	-0.527	252
644	-98.625	33.625	-0.563	252
645	-98.375	33.625	-0.589	252
646	-98.125	33.625	-0.606	251
647	-97.875	33.625	-0.640	252
648	-97.625	33.625	-0.684	252
649	-97.375	33.625	-0.671	252
650	-97.125	33.625	-0.662	252
651	-96.875	33.625	-0.638	252
652	-96.625	33.625	-0.665	252
653	-96.375	33.625	-0.667	252
654	-96.125	33.625	-0.655	251
655	-95.875	33.625	-0.695	251
656	-95.625	33.625	-0.648	246
657	-95.375	33.625	-0.643	245
658	-95.125	33.625	-0.627	251
659	-94.875	33.625	-0.590	250
660	-94.625	33.625	-0.592	247
661	-94.375	33.625	-0.596	252
662	-94.125	33.625	-0.579	246
663	-93.875	33.625	-0.600	244
664	-103.375	33.375	-0.134	252
665	-103.125	33.375	-0.091	252
666	-102.875	33.375	-0.059	252
667	-102.625	33.375	0.013	252
668	-102.375	33.375	-0.016	252
669	-102.125	33.375	0.025	252
670	-101.875	33.375	-0.058	252
671	-101.625	33.375	-0.123	252
672	-101.375	33.375	-0.200	252
673	-101.125	33.375	-0.238	252
674	-100.875	33.375	-0.312	252
675	-100.625	33.375	-0.333	252
676	-100.375	33.375	-0.352	252
677	-100.125	33.375	-0.391	252
678	-99.875	33.375	-0.426	252
679	-99.625	33.375	-0.463	252
680	-99.375	33.375	-0.478	252
681	-99.125	33.375	-0.497	252
682	-98.875	33.375	-0.529	252
683	-98.625	33.375	-0.593	252
684	-98.375	33.375	-0.617	251
685	-98.125	33.375	-0.640	251
686	-97.875	33.375	-0.616	251
687	-97.625	33.375	-0.668	252
688	-97.375	33.375	-0.632	251
689	-97.125	33.375	-0.565	252
690	-96.875	33.375	-0.608	252
691	-96.625	33.375	-0.657	244
692	-96.375	33.375	-0.651	252
693	-96.125	33.375	-0.638	252
694	-95.875	33.375	-0.626	239
695	-95.625	33.375	-0.637	247
696	-95.375	33.375	-0.669	245
697	-95.125	33.375	-0.583	244
698	-94.875	33.375	-0.579	251
699	-94.625	33.375	-0.521	243
700	-94.375	33.375	-0.544	246
701	-94.125	33.375	-0.690	252
702	-93.875	33.375	-0.476	239
703	-103.375	33.125	-0.093	252
704	-103.125	33.125	-0.081	252
705	-102.875	33.125	-0.046	252
706	-102.625	33.125	-0.004	252
707	-102.375	33.125	0.008	252
708	-102.125	33.125	-0.066	252
709	-101.875	33.125	-0.141	252
710	-101.625	33.125	-0.229	252
711	-101.375	33.125	-0.250	252
712	-101.125	33.125	-0.342	252
713	-100.875	33.125	-0.343	252
714	-100.625	33.125	-0.352	252
715	-100.375	33.125	-0.440	252
716	-100.125	33.125	-0.457	252
717	-99.875	33.125	-0.434	252
718	-99.625	33.125	-0.477	252
719	-99.375	33.125	-0.489	252
720	-99.125	33.125	-0.499	252
721	-98.875	33.125	-0.567	252
722	-98.625	33.125	-0.637	251
723	-98.375	33.125	-0.622	251
724	-98.125	33.125	-0.617	251
725	-97.875	33.125	-0.632	251
726	-97.625	33.125	-0.607	251
727	-97.375	33.125	-0.632	252
728	-97.125	33.125	-0.641	252
729	-96.875	33.125	-0.674	252
730	-96.625	33.125	-0.662	252
731	-96.375	33.125	-0.655	252
732	-96.125	33.125	-0.612	252
733	-95.875	33.125	-0.666	252
734	-95.625	33.125	-0.645	251
735	-95.375	33.125	-0.665	251
736	-95.125	33.125	-0.564	252
737	-94.875	33.125	-0.573	250
738	-94.625	33.125	-0.509	240
739	-94.375	33.125	-0.515	250
740	-94.125	33.125	-0.500	248
741	-93.875	33.125	-0.562	249

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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