SOIL MOISTURE AND GEOPHYSICAL COVARIATES CORRELATION ANALYSIS

Data Preparation

Preparation of soil moisture information layers for the region of interest

For this study, we took advantage of the ESA CCI soil moisture combined data set, as it offers a better option as referred in other soil moisture measuring systems (Entekhabi et al. 2014). Although data is available from November 1978, high uncertainty is reported until 1991 (Dorigo et al. 2015), thus we initially decided to download data from January 1995 to December 2015. In addition, validation data from the North America Soil Moisture Database (NASMD) over the region of interest is available from January 1996 to December 2012 (252 Montlhy information layers).

As original ESA CCI data was acquired for a global coverage, soil moisture daily values were cropped to the conterminous territory of the United States (CONUS). Monthly stacks were generated in order to calculate basic metrics (including mean and median values) for each month of the study period, obtaining single values to describe soil moisture monthly behavior over each pixel. Finally, data were cropped to the region of interest over Oklahoma and surrounding areas.

Preparation of geophysical covariates layers for the region of interest

In order to explore relationships between soil moisture and some physical variables, ancillary layers were generated for Precipitation, Maximum Air Temperature, Minimum Air Temperature, Soil Texture, and Topographic Wetness index. These selected variables are known to work as drivers for water input in soil, thus its importance in soil moisture inference (Koster and Suarez 2001; Seneviratne et al. 2010; Entekhabi et al. 2014).

Meteorological data

Meteorological data was acquired at 1-km spatial resolution monthly layers produced by the Daily Surface Weather and Climatological Summaries (DAYMET) (Thornton et al. 2018). Total monthly precipitation, as well as monthly average air temperature raster layers from January 1996 to December 2012 were cropped to the region of interest, projected to the WGS84 Lat-Long coordinate system and resampled to 0.25 degrees by means of nearest neighbor method (ngb) (J. A. Parker, Kenyon, and Troxel 1983).

Soil Texture

Soil texture data was obtained from the US soil survey geographic database (USDA 2016) at state-level for Arkansas, Colorado, Kansas, Missouri, New Mexico, Oklahoma and Texas. Original texture classes from each state classification were grouped in four major categories (Coarse, Medium, Medium-fine and Fine) regarding the texture triangle from US Department of Agriculture (USDA) and its modification proposed by (Bertermann et al. 2013).

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

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

Topographic Wetness Index

Besides the more explicit topographic features (primary attributes), the influence of topography over the soil properties and water distribution in the landscape can be expressed by means of wetness indexes derived from compound topographic attributes (Wilson and Gallant 2000). The most widely used index to describe flow and concentration of water in soil is the topographic wetness index (TWI) (Beven and Kirkby 1979).

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

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

To calculate TWI, we used a digital elevation model at 250m pixel size, generated by (Hengl, Heuvelink, and Stein 2004) as input and we applied a basic terrain analysis function from SAGA GIS tools, which generates a set of topographic parameters, including TWI (Conrad et al. 2015). The output was then resampled using nearest neighbor method (J. A. Parker, Kenyon, and Troxel 1983), to 0.25 degrees pixel size to match with soil moisture monthly layers as well as the other ancillary layers generated previously.

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

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

Correlation Analysis

In order to define the covariates used to model soil moisture, correlation analysis, both temporal and spatial were performed regarding ancillary layer of information generated in the previous step. As shown in Figure below, each valid pixel from in every monthly layer of soil moisture information in the region of interest was compared against the same pixel in every covariate layer. Monthly mean and median values from soil moisture layers were used for correlation analysis.

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

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

Temporal Correlation Analysis

For temporal correlation analysis, the set of data was extracted from each valid pixel along the 252 monthly layers of soil moisture (both Mean and Median monthly values), as well as the correspondent pixels from precipitation, minimum temperature, maximum temperature, this way, generating time series of all data for each pixel. This means, each pixel might have a different number of valid values in the time series, no greater than 252. Correlation values were calculated for each time series of 252 monthly values, in each of the 741 pixels with the region of interest. Soil texture and topographic wetness index analysis were not performed as their values are static along time period.

TWI and Soil Texture are not used in temporal correlation analysis, as these values are static in every month across the study period.

MEAN Monthly Soil Moisture Values

Temporal Correlation between Monthly Mean Soil Moisture and Precipitation
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

SoilMoisture_MEAN <- read.csv("SoilMoisture_region_interest_MEAN_monthly_pixel_values_v45.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_monthly_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 0.212 212
2 -103.125 37.625 0.393 223
3 -102.875 37.625 0.436 205
4 -102.625 37.625 0.382 212
5 -102.375 37.625 0.383 230
6 -102.125 37.625 0.377 232
7 -101.875 37.625 0.399 234
8 -101.625 37.625 0.382 251
9 -101.375 37.625 0.376 252
10 -101.125 37.625 0.415 252
11 -100.875 37.625 0.385 251
12 -100.625 37.625 0.355 249
13 -100.375 37.625 0.171 231
14 -100.125 37.625 0.417 225
15 -99.875 37.625 0.369 235
16 -99.625 37.625 0.387 232
17 -99.375 37.625 0.409 232
18 -99.125 37.625 0.397 234
19 -98.875 37.625 0.369 232
20 -98.625 37.625 0.193 222
21 -98.375 37.625 0.145 224
22 -98.125 37.625 0.151 230
23 -97.875 37.625 0.201 237
24 -97.625 37.625 0.264 241
25 -97.375 37.625 0.173 242
26 -97.125 37.625 0.143 242
27 -96.875 37.625 0.142 242
28 -96.625 37.625 0.084 243
29 -96.375 37.625 0.154 241
30 -96.125 37.625 0.195 241
31 -95.875 37.625 0.177 243
32 -95.625 37.625 0.201 234
33 -95.375 37.625 0.257 230
34 -95.125 37.625 0.244 236
35 -94.875 37.625 0.267 230
36 -94.625 37.625 0.288 229
37 -94.375 37.625 0.225 235
38 -94.125 37.625 0.278 233
39 -93.875 37.625 0.254 233
40 -103.375 37.375 0.379 223
41 -103.125 37.375 0.388 229
42 -102.875 37.375 0.450 222
43 -102.625 37.375 0.470 224
44 -102.375 37.375 0.469 221
45 -102.125 37.375 0.459 234
46 -101.875 37.375 0.454 233
47 -101.625 37.375 0.413 240
48 -101.375 37.375 0.410 252
49 -101.125 37.375 0.331 239
50 -100.875 37.375 0.385 239
51 -100.625 37.375 0.356 249
52 -100.375 37.375 0.223 226
53 -100.125 37.375 0.427 231
54 -99.875 37.375 0.407 235
55 -99.625 37.375 0.401 231
56 -99.375 37.375 0.404 231
57 -99.125 37.375 0.377 238
58 -98.875 37.375 0.318 244
59 -98.625 37.375 0.311 172
60 -98.375 37.375 0.317 248
61 -98.125 37.375 0.204 221
62 -97.875 37.375 0.294 250
63 -97.625 37.375 0.255 249
64 -97.375 37.375 0.117 250
65 -97.125 37.375 0.131 241
66 -96.875 37.375 0.153 242
67 -96.625 37.375 0.146 243
68 -96.375 37.375 0.176 241
69 -96.125 37.375 0.179 241
70 -95.875 37.375 0.143 243
71 -95.625 37.375 0.161 244
72 -95.375 37.375 0.259 228
73 -95.125 37.375 0.251 229
74 -94.875 37.375 0.277 231
75 -94.625 37.375 0.237 232
76 -94.375 37.375 0.284 226
77 -94.125 37.375 0.222 233
78 -93.875 37.375 0.213 237
79 -103.375 37.125 0.388 235
80 -103.125 37.125 0.452 235
81 -102.875 37.125 0.483 236
82 -102.625 37.125 0.485 237
83 -102.375 37.125 0.551 225
84 -102.125 37.125 0.536 219
85 -101.875 37.125 0.400 243
86 -101.625 37.125 0.388 241
87 -101.375 37.125 0.470 239
88 -101.125 37.125 0.274 196
89 -100.875 37.125 0.471 239
90 -100.625 37.125 0.251 232
91 -100.375 37.125 0.428 239
92 -100.125 37.125 0.409 236
93 -99.875 37.125 0.417 236
94 -99.625 37.125 0.340 245
95 -99.375 37.125 0.372 236
96 -99.125 37.125 0.383 239
97 -98.875 37.125 0.354 244
98 -98.625 37.125 0.369 234
99 -98.375 37.125 0.288 222
100 -98.125 37.125 0.354 239
101 -97.875 37.125 0.290 241
102 -97.625 37.125 0.225 250
103 -97.375 37.125 0.215 240
104 -97.125 37.125 0.002 235
105 -96.875 37.125 0.230 234
106 -96.625 37.125 0.207 239
107 -96.375 37.125 0.191 240
108 -96.125 37.125 0.192 241
109 -95.875 37.125 0.265 235
110 -95.625 37.125 0.281 237
111 -95.375 37.125 0.185 245
112 -95.125 37.125 0.201 238
113 -94.875 37.125 0.304 226
114 -94.625 37.125 0.265 245
115 -94.375 37.125 0.285 236
116 -94.125 37.125 0.299 229
117 -93.875 37.125 0.257 231
118 -103.375 36.875 0.449 234
119 -103.125 36.875 0.503 219
120 -102.875 36.875 0.523 224
121 -102.625 36.875 0.481 237
122 -102.375 36.875 0.474 237
123 -102.125 36.875 0.539 221
124 -101.875 36.875 0.562 219
125 -101.625 36.875 0.388 245
126 -101.375 36.875 0.361 246
127 -101.125 36.875 0.390 251
128 -100.875 36.875 0.446 246
129 -100.625 36.875 0.354 249
130 -100.375 36.875 0.374 249
131 -100.125 36.875 0.374 245
132 -99.875 36.875 0.390 234
133 -99.625 36.875 0.309 246
134 -99.375 36.875 0.289 247
135 -99.125 36.875 0.326 246
136 -98.875 36.875 0.318 246
137 -98.625 36.875 0.302 246
138 -98.375 36.875 0.405 249
139 -98.125 36.875 0.320 249
140 -97.875 36.875 0.292 249
141 -97.625 36.875 0.243 247
142 -97.375 36.875 0.167 251
143 -97.125 36.875 0.101 250
144 -96.875 36.875 0.077 250
145 -96.625 36.875 0.063 246
146 -96.375 36.875 0.157 237
147 -96.125 36.875 0.141 242
148 -95.875 36.875 0.242 237
149 -95.625 36.875 0.239 234
150 -95.375 36.875 0.173 239
151 -95.125 36.875 0.179 238
152 -94.875 36.875 0.284 227
153 -94.625 36.875 0.300 225
154 -94.375 36.875 0.246 241
155 -94.125 36.875 0.199 238
156 -93.875 36.875 0.254 230
157 -103.375 36.625 0.494 236
158 -103.125 36.625 0.494 237
159 -102.875 36.625 0.448 244
160 -102.625 36.625 0.459 236
161 -102.375 36.625 0.504 235
162 -102.125 36.625 0.493 221
163 -101.875 36.625 0.433 241
164 -101.625 36.625 0.315 250
165 -101.375 36.625 0.335 245
166 -101.125 36.625 0.385 249
167 -100.875 36.625 0.342 250
168 -100.625 36.625 0.357 250
169 -100.375 36.625 0.319 251
170 -100.125 36.625 0.363 246
171 -99.875 36.625 0.382 247
172 -99.625 36.625 0.231 229
173 -99.375 36.625 0.340 247
174 -99.125 36.625 0.363 247
175 -98.875 36.625 0.215 236
176 -98.625 36.625 0.310 250
177 -98.375 36.625 0.410 246
178 -98.125 36.625 0.341 250
179 -97.875 36.625 0.279 250
180 -97.625 36.625 0.259 247
181 -97.375 36.625 0.213 250
182 -97.125 36.625 0.159 247
183 -96.875 36.625 0.121 249
184 -96.625 36.625 0.198 238
185 -96.375 36.625 0.181 238
186 -96.125 36.625 0.244 230
187 -95.875 36.625 0.149 242
188 -95.625 36.625 0.188 233
189 -95.375 36.625 0.146 241
190 -95.125 36.625 0.246 231
191 -94.875 36.625 0.142 239
192 -94.625 36.625 0.284 235
193 -94.375 36.625 0.299 225
194 -94.125 36.625 0.288 223
195 -93.875 36.625 0.314 228
196 -103.375 36.375 0.510 236
197 -103.125 36.375 0.514 238
198 -102.875 36.375 0.519 237
199 -102.625 36.375 0.485 236
200 -102.375 36.375 0.484 238
201 -102.125 36.375 0.511 220
202 -101.875 36.375 0.465 234
203 -101.625 36.375 0.489 222
204 -101.375 36.375 0.329 251
205 -101.125 36.375 0.367 249
206 -100.875 36.375 0.266 246
207 -100.625 36.375 0.316 249
208 -100.375 36.375 0.314 251
209 -100.125 36.375 0.321 252
210 -99.875 36.375 0.366 247
211 -99.625 36.375 0.353 237
212 -99.375 36.375 0.368 242
213 -99.125 36.375 0.311 248
214 -98.875 36.375 0.313 248
215 -98.625 36.375 0.368 248
216 -98.375 36.375 0.309 243
217 -98.125 36.375 0.329 249
218 -97.875 36.375 0.268 250
219 -97.625 36.375 0.258 247
220 -97.375 36.375 0.235 247
221 -97.125 36.375 0.194 249
222 -96.875 36.375 0.182 247
223 -96.625 36.375 0.194 241
224 -96.375 36.375 0.116 247
225 -96.125 36.375 0.111 236
226 -95.875 36.375 0.105 247
227 -95.625 36.375 0.195 240
228 -95.375 36.375 0.130 243
229 -95.125 36.375 0.248 234
230 -94.875 36.375 0.297 231
231 -94.625 36.375 0.276 224
232 -94.375 36.375 0.286 227
233 -94.125 36.375 0.189 235
234 -93.875 36.375 0.315 221
235 -103.375 36.125 0.526 229
236 -103.125 36.125 0.543 240
237 -102.875 36.125 0.560 238
238 -102.625 36.125 0.564 238
239 -102.375 36.125 0.554 238
240 -102.125 36.125 0.497 243
241 -101.875 36.125 0.526 224
242 -101.625 36.125 0.364 247
243 -101.375 36.125 0.502 227
244 -101.125 36.125 0.361 250
245 -100.875 36.125 0.346 251
246 -100.625 36.125 0.281 246
247 -100.375 36.125 0.346 247
248 -100.125 36.125 0.371 247
249 -99.875 36.125 0.375 247
250 -99.625 36.125 0.364 251
251 -99.375 36.125 0.407 241
252 -99.125 36.125 0.395 240
253 -98.875 36.125 0.343 248
254 -98.625 36.125 0.315 237
255 -98.375 36.125 0.344 249
256 -98.125 36.125 0.351 252
257 -97.875 36.125 0.295 247
258 -97.625 36.125 0.250 250
259 -97.375 36.125 0.231 249
260 -97.125 36.125 0.265 239
261 -96.875 36.125 0.236 239
262 -96.625 36.125 0.127 248
263 -96.375 36.125 0.083 248
264 -96.125 36.125 0.073 248
265 -95.875 36.125 0.095 247
266 -95.625 36.125 0.160 239
267 -95.375 36.125 0.250 241
268 -95.125 36.125 0.271 232
269 -94.875 36.125 0.273 229
270 -94.625 36.125 0.278 228
271 -94.375 36.125 0.115 237
272 -94.125 36.125 0.105 242
273 -93.875 36.125 0.297 225
274 -103.375 35.875 0.426 246
275 -103.125 35.875 0.480 240
276 -102.875 35.875 0.557 229
277 -102.625 35.875 0.555 238
278 -102.375 35.875 0.575 225
279 -102.125 35.875 0.543 225
280 -101.875 35.875 0.529 226
281 -101.625 35.875 0.331 252
282 -101.375 35.875 0.384 247
283 -101.125 35.875 0.364 251
284 -100.875 35.875 0.361 246
285 -100.625 35.875 0.328 251
286 -100.375 35.875 0.346 247
287 -100.125 35.875 0.378 247
288 -99.875 35.875 0.377 248
289 -99.625 35.875 0.400 248
290 -99.375 35.875 0.438 248
291 -99.125 35.875 0.469 239
292 -98.875 35.875 0.445 248
293 -98.625 35.875 0.386 248
294 -98.375 35.875 0.386 247
295 -98.125 35.875 0.335 250
296 -97.875 35.875 0.227 249
297 -97.625 35.875 0.103 237
298 -97.375 35.875 0.137 248
299 -97.125 35.875 0.191 240
300 -96.875 35.875 0.146 240
301 -96.625 35.875 0.060 240
302 -96.375 35.875 0.027 237
303 -96.125 35.875 0.138 247
304 -95.875 35.875 0.226 239
305 -95.625 35.875 0.274 232
306 -95.375 35.875 0.209 243
307 -95.125 35.875 0.156 244
308 -94.875 35.875 0.215 241
309 -94.625 35.875 0.312 231
310 -94.375 35.875 0.250 229
311 -94.125 35.875 0.270 232
312 -93.875 35.875 0.333 231
313 -103.375 35.625 0.492 241
314 -103.125 35.625 0.490 246
315 -102.875 35.625 0.481 236
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318 -102.125 35.625 0.402 236
319 -101.875 35.625 0.373 251
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321 -101.375 35.625 0.366 251
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330 -99.125 35.625 0.407 250
331 -98.875 35.625 0.398 249
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333 -98.375 35.625 0.362 249
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335 -97.875 35.625 0.190 243
336 -97.625 35.625 0.103 247
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339 -96.875 35.625 0.135 248
340 -96.625 35.625 0.062 241
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344 -95.625 35.625 0.228 243
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352 -103.375 35.375 0.397 246
353 -103.125 35.375 0.425 244
354 -102.875 35.375 0.435 241
355 -102.625 35.375 0.442 236
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357 -102.125 35.375 0.456 233
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361 -101.125 35.375 0.382 251
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364 -100.375 35.375 0.384 248
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368 -99.375 35.375 0.407 248
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375 -97.625 35.375 0.128 250
376 -97.375 35.375 0.085 249
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379 -96.625 35.375 0.221 241
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382 -95.875 35.375 0.224 240
383 -95.625 35.375 0.256 245
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387 -94.625 35.375 0.213 243
388 -94.375 35.375 0.285 234
389 -94.125 35.375 0.235 242
390 -93.875 35.375 0.325 234
391 -103.375 35.125 0.451 246
392 -103.125 35.125 0.480 243
393 -102.875 35.125 0.450 243
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395 -102.375 35.125 0.508 236
396 -102.125 35.125 0.480 237
397 -101.875 35.125 0.453 240
398 -101.625 35.125 0.448 239
399 -101.375 35.125 0.445 250
400 -101.125 35.125 0.389 251
401 -100.875 35.125 0.423 251
402 -100.625 35.125 0.439 251
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404 -100.125 35.125 0.450 250
405 -99.875 35.125 0.426 250
406 -99.625 35.125 0.420 251
407 -99.375 35.125 0.372 252
408 -99.125 35.125 0.369 252
409 -98.875 35.125 0.382 252
410 -98.625 35.125 0.292 241
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422 -95.625 35.125 0.255 247
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425 -94.875 35.125 0.359 242
426 -94.625 35.125 0.246 246
427 -94.375 35.125 0.255 246
428 -94.125 35.125 0.343 241
429 -93.875 35.125 0.334 240
430 -103.375 34.875 0.456 251
431 -103.125 34.875 0.516 244
432 -102.875 34.875 0.524 243
433 -102.625 34.875 0.529 235
434 -102.375 34.875 0.558 245
435 -102.125 34.875 0.424 251
436 -101.875 34.875 0.435 252
437 -101.625 34.875 0.416 251
438 -101.375 34.875 0.436 250
439 -101.125 34.875 0.434 250
440 -100.875 34.875 0.442 252
441 -100.625 34.875 0.479 251
442 -100.375 34.875 0.472 251
443 -100.125 34.875 0.440 250
444 -99.875 34.875 0.408 250
445 -99.625 34.875 0.426 252
446 -99.375 34.875 0.409 252
447 -99.125 34.875 0.383 252
448 -98.875 34.875 0.308 248
449 -98.625 34.875 0.333 248
450 -98.375 34.875 0.304 248
451 -98.125 34.875 0.325 250
452 -97.875 34.875 0.276 250
453 -97.625 34.875 0.274 249
454 -97.375 34.875 0.233 251
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456 -96.875 34.875 0.203 252
457 -96.625 34.875 0.311 247
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479 -100.875 34.625 0.499 251
480 -100.625 34.625 0.469 251
481 -100.375 34.625 0.417 251
482 -100.125 34.625 0.431 251
483 -99.875 34.625 0.398 252
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485 -99.375 34.625 0.422 252
486 -99.125 34.625 0.381 250
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488 -98.625 34.625 0.329 251
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491 -97.875 34.625 0.416 245
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501 -95.375 34.625 0.331 242
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504 -94.625 34.625 0.424 238
505 -94.375 34.625 0.399 239
506 -94.125 34.625 0.350 239
507 -93.875 34.625 0.327 238
508 -103.375 34.375 0.575 251
509 -103.125 34.375 0.600 250
510 -102.875 34.375 0.598 252
511 -102.625 34.375 0.606 252
512 -102.375 34.375 0.573 252
513 -102.125 34.375 0.538 252
514 -101.875 34.375 0.575 248
515 -101.625 34.375 0.529 252
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517 -101.125 34.375 0.522 250
518 -100.875 34.375 0.513 251
519 -100.625 34.375 0.498 251
520 -100.375 34.375 0.455 251
521 -100.125 34.375 0.389 252
522 -99.875 34.375 0.346 252
523 -99.625 34.375 0.368 251
524 -99.375 34.375 0.436 252
525 -99.125 34.375 0.395 251
526 -98.875 34.375 0.370 252
527 -98.625 34.375 0.356 252
528 -98.375 34.375 0.354 250
529 -98.125 34.375 0.337 252
530 -97.875 34.375 0.436 245
531 -97.625 34.375 0.296 251
532 -97.375 34.375 0.241 251
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534 -96.875 34.375 0.247 252
535 -96.625 34.375 0.250 251
536 -96.375 34.375 0.322 245
537 -96.125 34.375 0.326 244
538 -95.875 34.375 0.298 219
539 -95.625 34.375 0.352 240
540 -95.375 34.375 0.330 240
541 -95.125 34.375 0.346 240
542 -94.875 34.375 0.404 238
543 -94.625 34.375 0.414 236
544 -94.375 34.375 0.394 236
545 -94.125 34.375 0.361 236
546 -93.875 34.375 0.330 236
547 -103.375 34.125 0.497 251
548 -103.125 34.125 0.610 251
549 -102.875 34.125 0.661 250
550 -102.625 34.125 0.628 252
551 -102.375 34.125 0.638 244
552 -102.125 34.125 0.537 251
553 -101.875 34.125 0.551 251
554 -101.625 34.125 0.608 252
555 -101.375 34.125 0.547 252
556 -101.125 34.125 0.527 252
557 -100.875 34.125 0.522 252
558 -100.625 34.125 0.501 252
559 -100.375 34.125 0.496 252
560 -100.125 34.125 0.428 252
561 -99.875 34.125 0.392 251
562 -99.625 34.125 0.361 252
563 -99.375 34.125 0.416 252
564 -99.125 34.125 0.414 252
565 -98.875 34.125 0.408 252
566 -98.625 34.125 0.334 252
567 -98.375 34.125 0.369 250
568 -98.125 34.125 0.337 251
569 -97.875 34.125 0.372 247
570 -97.625 34.125 0.273 251
571 -97.375 34.125 0.259 251
572 -97.125 34.125 0.259 252
573 -96.875 34.125 0.278 252
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575 -96.375 34.125 0.278 251
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577 -95.875 34.125 0.346 251
578 -95.625 34.125 0.330 251
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580 -95.125 34.125 0.349 245
581 -94.875 34.125 0.376 242
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583 -94.375 34.125 0.365 242
584 -94.125 34.125 0.343 242
585 -93.875 34.125 0.372 243
586 -103.375 33.875 0.528 252
587 -103.125 33.875 0.577 251
588 -102.875 33.875 0.614 252
589 -102.625 33.875 0.591 252
590 -102.375 33.875 0.625 252
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592 -101.875 33.875 0.610 252
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594 -101.375 33.875 0.549 252
595 -101.125 33.875 0.507 252
596 -100.875 33.875 0.510 252
597 -100.625 33.875 0.492 252
598 -100.375 33.875 0.483 252
599 -100.125 33.875 0.434 252
600 -99.875 33.875 0.414 252
601 -99.625 33.875 0.393 252
602 -99.375 33.875 0.399 252
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604 -98.875 33.875 0.371 252
605 -98.625 33.875 0.323 250
606 -98.375 33.875 0.287 252
607 -98.125 33.875 0.311 250
608 -97.875 33.875 0.320 250
609 -97.625 33.875 0.281 251
610 -97.375 33.875 0.285 251
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612 -96.875 33.875 0.448 244
613 -96.625 33.875 0.427 245
614 -96.375 33.875 0.414 251
615 -96.125 33.875 0.364 251
616 -95.875 33.875 0.403 251
617 -95.625 33.875 0.404 251
618 -95.375 33.875 0.406 251
619 -95.125 33.875 0.402 250
620 -94.875 33.875 0.284 227
621 -94.625 33.875 0.386 242
622 -94.375 33.875 0.376 243
623 -94.125 33.875 0.360 244
624 -93.875 33.875 0.350 246
625 -103.375 33.625 0.520 252
626 -103.125 33.625 0.552 252
627 -102.875 33.625 0.617 252
628 -102.625 33.625 0.623 252
629 -102.375 33.625 0.548 252
630 -102.125 33.625 0.551 252
631 -101.875 33.625 0.576 252
632 -101.625 33.625 0.594 252
633 -101.375 33.625 0.578 252
634 -101.125 33.625 0.540 252
635 -100.875 33.625 0.506 252
636 -100.625 33.625 0.462 252
637 -100.375 33.625 0.447 252
638 -100.125 33.625 0.416 252
639 -99.875 33.625 0.370 252
640 -99.625 33.625 0.379 252
641 -99.375 33.625 0.350 252
642 -99.125 33.625 0.326 252
643 -98.875 33.625 0.320 252
644 -98.625 33.625 0.309 251
645 -98.375 33.625 0.275 252
646 -98.125 33.625 0.298 251
647 -97.875 33.625 0.297 252
648 -97.625 33.625 0.320 252
649 -97.375 33.625 0.349 252
650 -97.125 33.625 0.342 252
651 -96.875 33.625 0.332 252
652 -96.625 33.625 0.374 252
653 -96.375 33.625 0.393 252
654 -96.125 33.625 0.394 251
655 -95.875 33.625 0.451 251
656 -95.625 33.625 0.457 251
657 -95.375 33.625 0.415 251
658 -95.125 33.625 0.381 251
659 -94.875 33.625 0.357 251
660 -94.625 33.625 0.406 248
661 -94.375 33.625 0.413 248
662 -94.125 33.625 0.417 250
663 -93.875 33.625 0.352 250
664 -103.375 33.375 0.505 249
665 -103.125 33.375 0.560 252
666 -102.875 33.375 0.591 252
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668 -102.375 33.375 0.565 252
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671 -101.625 33.375 0.552 252
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674 -100.875 33.375 0.482 252
675 -100.625 33.375 0.457 252
676 -100.375 33.375 0.414 252
677 -100.125 33.375 0.403 252
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680 -99.375 33.375 0.381 251
681 -99.125 33.375 0.321 252
682 -98.875 33.375 0.300 252
683 -98.625 33.375 0.282 252
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690 -96.875 33.375 0.393 252
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696 -95.375 33.375 0.416 251
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699 -94.625 33.375 0.422 242
700 -94.375 33.375 0.405 242
701 -94.125 33.375 0.406 252
702 -93.875 33.375 0.384 250
703 -103.375 33.125 0.559 250
704 -103.125 33.125 0.592 252
705 -102.875 33.125 0.648 252
706 -102.625 33.125 0.632 252
707 -102.375 33.125 0.649 252
708 -102.125 33.125 0.626 252
709 -101.875 33.125 0.595 252
710 -101.625 33.125 0.546 252
711 -101.375 33.125 0.532 252
712 -101.125 33.125 0.508 252
713 -100.875 33.125 0.489 252
714 -100.625 33.125 0.421 252
715 -100.375 33.125 0.398 252
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718 -99.625 33.125 0.350 252
719 -99.375 33.125 0.376 251
720 -99.125 33.125 0.345 252
721 -98.875 33.125 0.295 251
722 -98.625 33.125 0.282 251
723 -98.375 33.125 0.296 251
724 -98.125 33.125 0.305 251
725 -97.875 33.125 0.335 251
726 -97.625 33.125 0.335 251
727 -97.375 33.125 0.308 252
728 -97.125 33.125 0.341 252
729 -96.875 33.125 0.377 252
730 -96.625 33.125 0.404 252
731 -96.375 33.125 0.491 245
732 -96.125 33.125 0.405 251
733 -95.875 33.125 0.437 251
734 -95.625 33.125 0.373 251
735 -95.375 33.125 0.387 251
736 -95.125 33.125 0.400 246
737 -94.875 33.125 0.421 243
738 -94.625 33.125 0.422 241
739 -94.375 33.125 0.351 241
740 -94.125 33.125 0.422 242
741 -93.875 33.125 0.379 249
Temporal Correlation between Monthly Mean Soil Moisture and Maximum Temperature
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

Temperature_Max <- read.csv("Daymet_tmax_region_interest_monthly_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_TMax <- 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 -0.417 212
2 -103.125 37.625 -0.271 223
3 -102.875 37.625 -0.171 205
4 -102.625 37.625 -0.167 212
5 -102.375 37.625 -0.207 230
6 -102.125 37.625 -0.141 232
7 -101.875 37.625 -0.173 234
8 -101.625 37.625 -0.186 251
9 -101.375 37.625 -0.170 252
10 -101.125 37.625 -0.026 252
11 -100.875 37.625 -0.089 251
12 -100.625 37.625 -0.118 249
13 -100.375 37.625 -0.297 231
14 -100.125 37.625 -0.132 225
15 -99.875 37.625 -0.224 235
16 -99.625 37.625 -0.192 232
17 -99.375 37.625 -0.152 232
18 -99.125 37.625 -0.148 234
19 -98.875 37.625 -0.111 232
20 -98.625 37.625 -0.183 222
21 -98.375 37.625 -0.285 224
22 -98.125 37.625 -0.303 230
23 -97.875 37.625 -0.215 237
24 -97.625 37.625 -0.094 241
25 -97.375 37.625 -0.312 242
26 -97.125 37.625 -0.421 242
27 -96.875 37.625 -0.538 242
28 -96.625 37.625 -0.651 243
29 -96.375 37.625 -0.604 241
30 -96.125 37.625 -0.528 241
31 -95.875 37.625 -0.515 243
32 -95.625 37.625 -0.431 234
33 -95.375 37.625 -0.307 230
34 -95.125 37.625 -0.288 236
35 -94.875 37.625 -0.373 230
36 -94.625 37.625 -0.377 229
37 -94.375 37.625 -0.478 235
38 -94.125 37.625 -0.455 233
39 -93.875 37.625 -0.499 233
40 -103.375 37.375 -0.319 223
41 -103.125 37.375 -0.227 229
42 -102.875 37.375 -0.129 222
43 -102.625 37.375 -0.056 224
44 -102.375 37.375 -0.064 221
45 -102.125 37.375 -0.044 234
46 -101.875 37.375 -0.103 233
47 -101.625 37.375 -0.136 240
48 -101.375 37.375 -0.100 252
49 -101.125 37.375 -0.112 239
50 -100.875 37.375 -0.142 239
51 -100.625 37.375 -0.246 249
52 -100.375 37.375 -0.325 226
53 -100.125 37.375 -0.235 231
54 -99.875 37.375 -0.253 235
55 -99.625 37.375 -0.285 231
56 -99.375 37.375 -0.293 231
57 -99.125 37.375 -0.338 238
58 -98.875 37.375 -0.283 244
59 -98.625 37.375 -0.230 172
60 -98.375 37.375 -0.224 248
61 -98.125 37.375 -0.409 221
62 -97.875 37.375 -0.221 250
63 -97.625 37.375 -0.240 249
64 -97.375 37.375 -0.437 250
65 -97.125 37.375 -0.456 241
66 -96.875 37.375 -0.485 242
67 -96.625 37.375 -0.623 243
68 -96.375 37.375 -0.601 241
69 -96.125 37.375 -0.590 241
70 -95.875 37.375 -0.587 243
71 -95.625 37.375 -0.556 244
72 -95.375 37.375 -0.342 228
73 -95.125 37.375 -0.325 229
74 -94.875 37.375 -0.331 231
75 -94.625 37.375 -0.351 232
76 -94.375 37.375 -0.345 226
77 -94.125 37.375 -0.525 233
78 -93.875 37.375 -0.565 237
79 -103.375 37.125 -0.202 235
80 -103.125 37.125 -0.154 235
81 -102.875 37.125 -0.120 236
82 -102.625 37.125 -0.103 237
83 -102.375 37.125 -0.013 225
84 -102.125 37.125 0.086 219
85 -101.875 37.125 -0.027 243
86 -101.625 37.125 -0.131 241
87 -101.375 37.125 -0.045 239
88 -101.125 37.125 -0.151 196
89 -100.875 37.125 -0.056 239
90 -100.625 37.125 -0.345 232
91 -100.375 37.125 -0.300 239
92 -100.125 37.125 -0.337 236
93 -99.875 37.125 -0.256 236
94 -99.625 37.125 -0.285 245
95 -99.375 37.125 -0.351 236
96 -99.125 37.125 -0.371 239
97 -98.875 37.125 -0.384 244
98 -98.625 37.125 -0.277 234
99 -98.375 37.125 -0.293 222
100 -98.125 37.125 -0.234 239
101 -97.875 37.125 -0.304 241
102 -97.625 37.125 -0.343 250
103 -97.375 37.125 -0.321 240
104 -97.125 37.125 -0.514 235
105 -96.875 37.125 -0.423 234
106 -96.625 37.125 -0.593 239
107 -96.375 37.125 -0.582 240
108 -96.125 37.125 -0.582 241
109 -95.875 37.125 -0.483 235
110 -95.625 37.125 -0.411 237
111 -95.375 37.125 -0.531 245
112 -95.125 37.125 -0.527 238
113 -94.875 37.125 -0.368 226
114 -94.625 37.125 -0.377 245
115 -94.375 37.125 -0.435 236
116 -94.125 37.125 -0.431 229
117 -93.875 37.125 -0.499 231
118 -103.375 36.875 -0.172 234
119 -103.125 36.875 -0.068 219
120 -102.875 36.875 -0.025 224
121 -102.625 36.875 -0.129 237
122 -102.375 36.875 -0.111 237
123 -102.125 36.875 -0.003 221
124 -101.875 36.875 0.022 219
125 -101.625 36.875 -0.271 245
126 -101.375 36.875 -0.306 246
127 -101.125 36.875 -0.255 251
128 -100.875 36.875 -0.284 246
129 -100.625 36.875 -0.415 249
130 -100.375 36.875 -0.415 249
131 -100.125 36.875 -0.435 245
132 -99.875 36.875 -0.352 234
133 -99.625 36.875 -0.471 246
134 -99.375 36.875 -0.490 247
135 -99.125 36.875 -0.489 246
136 -98.875 36.875 -0.450 246
137 -98.625 36.875 -0.379 246
138 -98.375 36.875 -0.296 249
139 -98.125 36.875 -0.441 249
140 -97.875 36.875 -0.447 249
141 -97.625 36.875 -0.423 247
142 -97.375 36.875 -0.482 251
143 -97.125 36.875 -0.588 250
144 -96.875 36.875 -0.670 250
145 -96.625 36.875 -0.706 246
146 -96.375 36.875 -0.655 237
147 -96.125 36.875 -0.666 242
148 -95.875 36.875 -0.543 237
149 -95.625 36.875 -0.560 234
150 -95.375 36.875 -0.618 239
151 -95.125 36.875 -0.595 238
152 -94.875 36.875 -0.490 227
153 -94.625 36.875 -0.499 225
154 -94.375 36.875 -0.479 241
155 -94.125 36.875 -0.608 238
156 -93.875 36.875 -0.537 230
157 -103.375 36.625 -0.131 236
158 -103.125 36.625 -0.115 237
159 -102.875 36.625 -0.144 244
160 -102.625 36.625 -0.179 236
161 -102.375 36.625 -0.141 235
162 -102.125 36.625 -0.076 221
163 -101.875 36.625 -0.226 241
164 -101.625 36.625 -0.391 250
165 -101.375 36.625 -0.401 245
166 -101.125 36.625 -0.364 249
167 -100.875 36.625 -0.402 250
168 -100.625 36.625 -0.458 250
169 -100.375 36.625 -0.481 251
170 -100.125 36.625 -0.489 246
171 -99.875 36.625 -0.451 247
172 -99.625 36.625 -0.496 229
173 -99.375 36.625 -0.436 247
174 -99.125 36.625 -0.446 247
175 -98.875 36.625 -0.511 236
176 -98.625 36.625 -0.431 250
177 -98.375 36.625 -0.373 246
178 -98.125 36.625 -0.393 250
179 -97.875 36.625 -0.447 250
180 -97.625 36.625 -0.452 247
181 -97.375 36.625 -0.498 250
182 -97.125 36.625 -0.564 247
183 -96.875 36.625 -0.653 249
184 -96.625 36.625 -0.617 238
185 -96.375 36.625 -0.656 238
186 -96.125 36.625 -0.550 230
187 -95.875 36.625 -0.671 242
188 -95.625 36.625 -0.605 233
189 -95.375 36.625 -0.609 241
190 -95.125 36.625 -0.554 231
191 -94.875 36.625 -0.681 239
192 -94.625 36.625 -0.533 235
193 -94.375 36.625 -0.459 225
194 -94.125 36.625 -0.462 223
195 -93.875 36.625 -0.374 228
196 -103.375 36.375 -0.073 236
197 -103.125 36.375 -0.077 238
198 -102.875 36.375 -0.085 237
199 -102.625 36.375 -0.176 236
200 -102.375 36.375 -0.147 238
201 -102.125 36.375 -0.062 220
202 -101.875 36.375 -0.212 234
203 -101.625 36.375 -0.144 222
204 -101.375 36.375 -0.354 251
205 -101.125 36.375 -0.405 249
206 -100.875 36.375 -0.511 246
207 -100.625 36.375 -0.485 249
208 -100.375 36.375 -0.492 251
209 -100.125 36.375 -0.476 252
210 -99.875 36.375 -0.475 247
211 -99.625 36.375 -0.409 237
212 -99.375 36.375 -0.344 242
213 -99.125 36.375 -0.413 248
214 -98.875 36.375 -0.439 248
215 -98.625 36.375 -0.424 248
216 -98.375 36.375 -0.449 243
217 -98.125 36.375 -0.420 249
218 -97.875 36.375 -0.467 250
219 -97.625 36.375 -0.495 247
220 -97.375 36.375 -0.487 247
221 -97.125 36.375 -0.469 249
222 -96.875 36.375 -0.616 247
223 -96.625 36.375 -0.588 241
224 -96.375 36.375 -0.677 247
225 -96.125 36.375 -0.608 236
226 -95.875 36.375 -0.688 247
227 -95.625 36.375 -0.615 240
228 -95.375 36.375 -0.599 243
229 -95.125 36.375 -0.554 234
230 -94.875 36.375 -0.507 231
231 -94.625 36.375 -0.524 224
232 -94.375 36.375 -0.423 227
233 -94.125 36.375 -0.512 235
234 -93.875 36.375 -0.408 221
235 -103.375 36.125 -0.019 229
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Temporal Correlation between Monthly Mean Soil Moisture and Minimum Temperature
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

Temperature_Min <- read.csv("Daymet_tmin_region_interest_monthly_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_Tmin <- 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 -0.335 212
2 -103.125 37.625 -0.163 223
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553 -101.875 34.125 -0.060 251
554 -101.625 34.125 0.021 252
555 -101.375 34.125 -0.090 252
556 -101.125 34.125 -0.159 252
557 -100.875 34.125 -0.198 252
558 -100.625 34.125 -0.287 252
559 -100.375 34.125 -0.308 252
560 -100.125 34.125 -0.363 252
561 -99.875 34.125 -0.397 251
562 -99.625 34.125 -0.426 252
563 -99.375 34.125 -0.402 252
564 -99.125 34.125 -0.428 252
565 -98.875 34.125 -0.414 252
566 -98.625 34.125 -0.460 252
567 -98.375 34.125 -0.475 250
568 -98.125 34.125 -0.532 251
569 -97.875 34.125 -0.468 247
570 -97.625 34.125 -0.645 251
571 -97.375 34.125 -0.691 251
572 -97.125 34.125 -0.675 252
573 -96.875 34.125 -0.686 252
574 -96.625 34.125 -0.700 252
575 -96.375 34.125 -0.698 251
576 -96.125 34.125 -0.695 251
577 -95.875 34.125 -0.648 251
578 -95.625 34.125 -0.622 251
579 -95.375 34.125 -0.631 251
580 -95.125 34.125 -0.502 245
581 -94.875 34.125 -0.381 242
582 -94.625 34.125 -0.329 243
583 -94.375 34.125 -0.123 242
584 -94.125 34.125 -0.056 242
585 -93.875 34.125 -0.128 243
586 -103.375 33.875 0.008 252
587 -103.125 33.875 0.035 251
588 -102.875 33.875 0.044 252
589 -102.625 33.875 0.085 252
590 -102.375 33.875 0.065 252
591 -102.125 33.875 0.071 252
592 -101.875 33.875 0.068 252
593 -101.625 33.875 -0.026 252
594 -101.375 33.875 -0.079 252
595 -101.125 33.875 -0.159 252
596 -100.875 33.875 -0.253 252
597 -100.625 33.875 -0.312 252
598 -100.375 33.875 -0.328 252
599 -100.125 33.875 -0.386 252
600 -99.875 33.875 -0.401 252
601 -99.625 33.875 -0.455 252
602 -99.375 33.875 -0.473 252
603 -99.125 33.875 -0.492 252
604 -98.875 33.875 -0.505 252
605 -98.625 33.875 -0.475 250
606 -98.375 33.875 -0.530 252
607 -98.125 33.875 -0.566 250
608 -97.875 33.875 -0.610 250
609 -97.625 33.875 -0.664 251
610 -97.375 33.875 -0.701 251
611 -97.125 33.875 -0.719 252
612 -96.875 33.875 -0.506 244
613 -96.625 33.875 -0.571 245
614 -96.375 33.875 -0.598 251
615 -96.125 33.875 -0.704 251
616 -95.875 33.875 -0.689 251
617 -95.625 33.875 -0.664 251
618 -95.375 33.875 -0.663 251
619 -95.125 33.875 -0.631 250
620 -94.875 33.875 -0.616 227
621 -94.625 33.875 -0.441 242
622 -94.375 33.875 -0.390 243
623 -94.125 33.875 -0.345 244
624 -93.875 33.875 -0.603 246
625 -103.375 33.625 -0.139 252
626 -103.125 33.625 -0.071 252
627 -102.875 33.625 0.003 252
628 -102.625 33.625 0.040 252
629 -102.375 33.625 0.069 252
630 -102.125 33.625 0.034 252
631 -101.875 33.625 -0.011 252
632 -101.625 33.625 -0.029 252
633 -101.375 33.625 -0.080 252
634 -101.125 33.625 -0.169 252
635 -100.875 33.625 -0.271 252
636 -100.625 33.625 -0.331 252
637 -100.375 33.625 -0.344 252
638 -100.125 33.625 -0.374 252
639 -99.875 33.625 -0.423 252
640 -99.625 33.625 -0.461 252
641 -99.375 33.625 -0.523 252
642 -99.125 33.625 -0.551 252
643 -98.875 33.625 -0.548 252
644 -98.625 33.625 -0.572 251
645 -98.375 33.625 -0.571 252
646 -98.125 33.625 -0.618 251
647 -97.875 33.625 -0.643 252
648 -97.625 33.625 -0.684 252
649 -97.375 33.625 -0.688 252
650 -97.125 33.625 -0.672 252
651 -96.875 33.625 -0.586 252
652 -96.625 33.625 -0.687 252
653 -96.375 33.625 -0.617 252
654 -96.125 33.625 -0.687 251
655 -95.875 33.625 -0.664 251
656 -95.625 33.625 -0.642 251
657 -95.375 33.625 -0.683 251
658 -95.125 33.625 -0.637 251
659 -94.875 33.625 -0.613 251
660 -94.625 33.625 -0.608 248
661 -94.375 33.625 -0.598 248
662 -94.125 33.625 -0.581 250
663 -93.875 33.625 -0.588 250
664 -103.375 33.375 -0.100 249
665 -103.125 33.375 -0.073 252
666 -102.875 33.375 -0.038 252
667 -102.625 33.375 0.032 252
668 -102.375 33.375 0.036 252
669 -102.125 33.375 0.056 252
670 -101.875 33.375 -0.015 252
671 -101.625 33.375 -0.108 252
672 -101.375 33.375 -0.143 252
673 -101.125 33.375 -0.228 252
674 -100.875 33.375 -0.324 252
675 -100.625 33.375 -0.346 252
676 -100.375 33.375 -0.358 252
677 -100.125 33.375 -0.369 252
678 -99.875 33.375 -0.414 252
679 -99.625 33.375 -0.458 252
680 -99.375 33.375 -0.446 251
681 -99.125 33.375 -0.544 252
682 -98.875 33.375 -0.582 252
683 -98.625 33.375 -0.632 252
684 -98.375 33.375 -0.650 251
685 -98.125 33.375 -0.659 251
686 -97.875 33.375 -0.650 251
687 -97.625 33.375 -0.644 252
688 -97.375 33.375 -0.617 251
689 -97.125 33.375 -0.556 252
690 -96.875 33.375 -0.658 252
691 -96.625 33.375 -0.534 245
692 -96.375 33.375 -0.639 251
693 -96.125 33.375 -0.693 252
694 -95.875 33.375 -0.669 251
695 -95.625 33.375 -0.627 251
696 -95.375 33.375 -0.689 251
697 -95.125 33.375 -0.577 250
698 -94.875 33.375 -0.591 248
699 -94.625 33.375 -0.487 242
700 -94.375 33.375 -0.457 242
701 -94.125 33.375 -0.684 252
702 -93.875 33.375 -0.625 250
703 -103.375 33.125 -0.072 250
704 -103.125 33.125 -0.006 252
705 -102.875 33.125 0.033 252
706 -102.625 33.125 0.035 252
707 -102.375 33.125 0.057 252
708 -102.125 33.125 0.002 252
709 -101.875 33.125 -0.068 252
710 -101.625 33.125 -0.055 252
711 -101.375 33.125 -0.243 252
712 -101.125 33.125 -0.311 252
713 -100.875 33.125 -0.348 252
714 -100.625 33.125 -0.327 252
715 -100.375 33.125 -0.401 252
716 -100.125 33.125 -0.400 252
717 -99.875 33.125 -0.419 252
718 -99.625 33.125 -0.478 252
719 -99.375 33.125 -0.481 251
720 -99.125 33.125 -0.524 252
721 -98.875 33.125 -0.605 251
722 -98.625 33.125 -0.651 251
723 -98.375 33.125 -0.671 251
724 -98.125 33.125 -0.663 251
725 -97.875 33.125 -0.675 251
726 -97.625 33.125 -0.677 251
727 -97.375 33.125 -0.707 252
728 -97.125 33.125 -0.704 252
729 -96.875 33.125 -0.684 252
730 -96.625 33.125 -0.676 252
731 -96.375 33.125 -0.601 245
732 -96.125 33.125 -0.675 251
733 -95.875 33.125 -0.704 251
734 -95.625 33.125 -0.686 251
735 -95.375 33.125 -0.697 251
736 -95.125 33.125 -0.500 246
737 -94.875 33.125 -0.496 243
738 -94.625 33.125 -0.465 241
739 -94.375 33.125 -0.505 241
740 -94.125 33.125 -0.401 242
741 -93.875 33.125 -0.531 249

MEDIAN Monthly Soil Moisture Values

Temporal Correlation between Monthly Median Soil Moisture and Precipitation
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

SoilMoisture_MEDIAN <- read.csv("SoilMoisture_region_interest_MEDIAN_monthly_pixel_values_v45.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_monthly_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 0.193 212
2 -103.125 37.625 0.317 223
3 -102.875 37.625 0.373 205
4 -102.625 37.625 0.307 212
5 -102.375 37.625 0.336 230
6 -102.125 37.625 0.340 232
7 -101.875 37.625 0.336 234
8 -101.625 37.625 0.295 251
9 -101.375 37.625 0.297 252
10 -101.125 37.625 0.340 252
11 -100.875 37.625 0.286 251
12 -100.625 37.625 0.317 249
13 -100.375 37.625 0.160 231
14 -100.125 37.625 0.388 225
15 -99.875 37.625 0.337 235
16 -99.625 37.625 0.310 232
17 -99.375 37.625 0.330 232
18 -99.125 37.625 0.320 234
19 -98.875 37.625 0.329 232
20 -98.625 37.625 0.205 222
21 -98.375 37.625 0.162 224
22 -98.125 37.625 0.175 230
23 -97.875 37.625 0.233 237
24 -97.625 37.625 0.246 241
25 -97.375 37.625 0.141 242
26 -97.125 37.625 0.144 242
27 -96.875 37.625 0.125 242
28 -96.625 37.625 0.096 243
29 -96.375 37.625 0.168 241
30 -96.125 37.625 0.187 241
31 -95.875 37.625 0.168 243
32 -95.625 37.625 0.200 234
33 -95.375 37.625 0.247 230
34 -95.125 37.625 0.221 236
35 -94.875 37.625 0.263 230
36 -94.625 37.625 0.294 229
37 -94.375 37.625 0.209 235
38 -94.125 37.625 0.254 233
39 -93.875 37.625 0.228 233
40 -103.375 37.375 0.316 223
41 -103.125 37.375 0.341 229
42 -102.875 37.375 0.406 222
43 -102.625 37.375 0.412 224
44 -102.375 37.375 0.426 221
45 -102.125 37.375 0.423 234
46 -101.875 37.375 0.389 233
47 -101.625 37.375 0.320 240
48 -101.375 37.375 0.355 252
49 -101.125 37.375 0.283 239
50 -100.875 37.375 0.293 239
51 -100.625 37.375 0.276 249
52 -100.375 37.375 0.235 226
53 -100.125 37.375 0.389 231
54 -99.875 37.375 0.358 235
55 -99.625 37.375 0.350 231
56 -99.375 37.375 0.320 231
57 -99.125 37.375 0.331 238
58 -98.875 37.375 0.307 244
59 -98.625 37.375 0.284 172
60 -98.375 37.375 0.316 248
61 -98.125 37.375 0.216 221
62 -97.875 37.375 0.285 250
63 -97.625 37.375 0.255 249
64 -97.375 37.375 0.116 250
65 -97.125 37.375 0.131 241
66 -96.875 37.375 0.122 242
67 -96.625 37.375 0.150 243
68 -96.375 37.375 0.188 241
69 -96.125 37.375 0.177 241
70 -95.875 37.375 0.130 243
71 -95.625 37.375 0.156 244
72 -95.375 37.375 0.246 228
73 -95.125 37.375 0.248 229
74 -94.875 37.375 0.260 231
75 -94.625 37.375 0.238 232
76 -94.375 37.375 0.268 226
77 -94.125 37.375 0.200 233
78 -93.875 37.375 0.185 237
79 -103.375 37.125 0.351 235
80 -103.125 37.125 0.408 235
81 -102.875 37.125 0.427 236
82 -102.625 37.125 0.440 237
83 -102.375 37.125 0.507 225
84 -102.125 37.125 0.509 219
85 -101.875 37.125 0.306 243
86 -101.625 37.125 0.344 241
87 -101.375 37.125 0.382 239
88 -101.125 37.125 0.229 196
89 -100.875 37.125 0.418 239
90 -100.625 37.125 0.244 232
91 -100.375 37.125 0.366 239
92 -100.125 37.125 0.375 236
93 -99.875 37.125 0.367 236
94 -99.625 37.125 0.281 245
95 -99.375 37.125 0.293 236
96 -99.125 37.125 0.335 239
97 -98.875 37.125 0.303 244
98 -98.625 37.125 0.342 234
99 -98.375 37.125 0.295 222
100 -98.125 37.125 0.323 239
101 -97.875 37.125 0.298 241
102 -97.625 37.125 0.261 250
103 -97.375 37.125 0.221 240
104 -97.125 37.125 0.003 235
105 -96.875 37.125 0.207 234
106 -96.625 37.125 0.187 239
107 -96.375 37.125 0.178 240
108 -96.125 37.125 0.180 241
109 -95.875 37.125 0.263 235
110 -95.625 37.125 0.263 237
111 -95.375 37.125 0.179 245
112 -95.125 37.125 0.186 238
113 -94.875 37.125 0.291 226
114 -94.625 37.125 0.261 245
115 -94.375 37.125 0.261 236
116 -94.125 37.125 0.284 229
117 -93.875 37.125 0.219 231
118 -103.375 36.875 0.394 234
119 -103.125 36.875 0.460 219
120 -102.875 36.875 0.494 224
121 -102.625 36.875 0.436 237
122 -102.375 36.875 0.436 237
123 -102.125 36.875 0.504 221
124 -101.875 36.875 0.543 219
125 -101.625 36.875 0.330 245
126 -101.375 36.875 0.309 246
127 -101.125 36.875 0.326 251
128 -100.875 36.875 0.397 246
129 -100.625 36.875 0.327 249
130 -100.375 36.875 0.348 249
131 -100.125 36.875 0.346 245
132 -99.875 36.875 0.358 234
133 -99.625 36.875 0.264 246
134 -99.375 36.875 0.232 247
135 -99.125 36.875 0.290 246
136 -98.875 36.875 0.297 246
137 -98.625 36.875 0.290 246
138 -98.375 36.875 0.403 249
139 -98.125 36.875 0.325 249
140 -97.875 36.875 0.305 249
141 -97.625 36.875 0.251 247
142 -97.375 36.875 0.186 251
143 -97.125 36.875 0.101 250
144 -96.875 36.875 0.076 250
145 -96.625 36.875 0.059 246
146 -96.375 36.875 0.140 237
147 -96.125 36.875 0.127 242
148 -95.875 36.875 0.214 237
149 -95.625 36.875 0.222 234
150 -95.375 36.875 0.153 239
151 -95.125 36.875 0.174 238
152 -94.875 36.875 0.280 227
153 -94.625 36.875 0.299 225
154 -94.375 36.875 0.235 241
155 -94.125 36.875 0.169 238
156 -93.875 36.875 0.223 230
157 -103.375 36.625 0.439 236
158 -103.125 36.625 0.448 237
159 -102.875 36.625 0.428 244
160 -102.625 36.625 0.420 236
161 -102.375 36.625 0.460 235
162 -102.125 36.625 0.449 221
163 -101.875 36.625 0.358 241
164 -101.625 36.625 0.284 250
165 -101.375 36.625 0.290 245
166 -101.125 36.625 0.340 249
167 -100.875 36.625 0.311 250
168 -100.625 36.625 0.323 250
169 -100.375 36.625 0.298 251
170 -100.125 36.625 0.339 246
171 -99.875 36.625 0.366 247
172 -99.625 36.625 0.213 229
173 -99.375 36.625 0.294 247
174 -99.125 36.625 0.331 247
175 -98.875 36.625 0.201 236
176 -98.625 36.625 0.293 250
177 -98.375 36.625 0.399 246
178 -98.125 36.625 0.321 250
179 -97.875 36.625 0.284 250
180 -97.625 36.625 0.264 247
181 -97.375 36.625 0.225 250
182 -97.125 36.625 0.144 247
183 -96.875 36.625 0.082 249
184 -96.625 36.625 0.155 238
185 -96.375 36.625 0.146 238
186 -96.125 36.625 0.238 230
187 -95.875 36.625 0.138 242
188 -95.625 36.625 0.191 233
189 -95.375 36.625 0.130 241
190 -95.125 36.625 0.247 231
191 -94.875 36.625 0.150 239
192 -94.625 36.625 0.266 235
193 -94.375 36.625 0.282 225
194 -94.125 36.625 0.254 223
195 -93.875 36.625 0.268 228
196 -103.375 36.375 0.455 236
197 -103.125 36.375 0.476 238
198 -102.875 36.375 0.486 237
199 -102.625 36.375 0.448 236
200 -102.375 36.375 0.428 238
201 -102.125 36.375 0.467 220
202 -101.875 36.375 0.394 234
203 -101.625 36.375 0.440 222
204 -101.375 36.375 0.276 251
205 -101.125 36.375 0.315 249
206 -100.875 36.375 0.244 246
207 -100.625 36.375 0.270 249
208 -100.375 36.375 0.285 251
209 -100.125 36.375 0.293 252
210 -99.875 36.375 0.333 247
211 -99.625 36.375 0.334 237
212 -99.375 36.375 0.326 242
213 -99.125 36.375 0.253 248
214 -98.875 36.375 0.273 248
215 -98.625 36.375 0.342 248
216 -98.375 36.375 0.290 243
217 -98.125 36.375 0.324 249
218 -97.875 36.375 0.272 250
219 -97.625 36.375 0.269 247
220 -97.375 36.375 0.252 247
221 -97.125 36.375 0.178 249
222 -96.875 36.375 0.169 247
223 -96.625 36.375 0.176 241
224 -96.375 36.375 0.091 247
225 -96.125 36.375 0.120 236
226 -95.875 36.375 0.102 247
227 -95.625 36.375 0.177 240
228 -95.375 36.375 0.117 243
229 -95.125 36.375 0.235 234
230 -94.875 36.375 0.277 231
231 -94.625 36.375 0.249 224
232 -94.375 36.375 0.252 227
233 -94.125 36.375 0.149 235
234 -93.875 36.375 0.264 221
235 -103.375 36.125 0.457 229
236 -103.125 36.125 0.489 240
237 -102.875 36.125 0.487 238
238 -102.625 36.125 0.489 238
239 -102.375 36.125 0.489 238
240 -102.125 36.125 0.432 243
241 -101.875 36.125 0.461 224
242 -101.625 36.125 0.329 247
243 -101.375 36.125 0.442 227
244 -101.125 36.125 0.317 250
245 -100.875 36.125 0.306 251
246 -100.625 36.125 0.265 246
247 -100.375 36.125 0.328 247
248 -100.125 36.125 0.351 247
249 -99.875 36.125 0.351 247
250 -99.625 36.125 0.333 251
251 -99.375 36.125 0.353 241
252 -99.125 36.125 0.350 240
253 -98.875 36.125 0.294 248
254 -98.625 36.125 0.300 237
255 -98.375 36.125 0.340 249
256 -98.125 36.125 0.337 252
257 -97.875 36.125 0.285 247
258 -97.625 36.125 0.260 250
259 -97.375 36.125 0.225 249
260 -97.125 36.125 0.249 239
261 -96.875 36.125 0.210 239
262 -96.625 36.125 0.094 248
263 -96.375 36.125 0.054 248
264 -96.125 36.125 0.073 248
265 -95.875 36.125 0.093 247
266 -95.625 36.125 0.158 239
267 -95.375 36.125 0.225 241
268 -95.125 36.125 0.265 232
269 -94.875 36.125 0.243 229
270 -94.625 36.125 0.258 228
271 -94.375 36.125 0.098 237
272 -94.125 36.125 0.085 242
273 -93.875 36.125 0.274 225
274 -103.375 35.875 0.371 246
275 -103.125 35.875 0.414 240
276 -102.875 35.875 0.503 229
277 -102.625 35.875 0.483 238
278 -102.375 35.875 0.528 225
279 -102.125 35.875 0.474 225
280 -101.875 35.875 0.460 226
281 -101.625 35.875 0.292 252
282 -101.375 35.875 0.326 247
283 -101.125 35.875 0.287 251
284 -100.875 35.875 0.310 246
285 -100.625 35.875 0.286 251
286 -100.375 35.875 0.303 247
287 -100.125 35.875 0.356 247
288 -99.875 35.875 0.346 248
289 -99.625 35.875 0.383 248
290 -99.375 35.875 0.403 248
291 -99.125 35.875 0.440 239
292 -98.875 35.875 0.408 248
293 -98.625 35.875 0.370 248
294 -98.375 35.875 0.364 247
295 -98.125 35.875 0.336 250
296 -97.875 35.875 0.216 249
297 -97.625 35.875 0.093 237
298 -97.375 35.875 0.124 248
299 -97.125 35.875 0.183 240
300 -96.875 35.875 0.138 240
301 -96.625 35.875 0.038 240
302 -96.375 35.875 0.011 237
303 -96.125 35.875 0.123 247
304 -95.875 35.875 0.205 239
305 -95.625 35.875 0.269 232
306 -95.375 35.875 0.211 243
307 -95.125 35.875 0.141 244
308 -94.875 35.875 0.195 241
309 -94.625 35.875 0.270 231
310 -94.375 35.875 0.217 229
311 -94.125 35.875 0.238 232
312 -93.875 35.875 0.292 231
313 -103.375 35.625 0.436 241
314 -103.125 35.625 0.437 246
315 -102.875 35.625 0.440 236
316 -102.625 35.625 0.361 248
317 -102.375 35.625 0.398 248
318 -102.125 35.625 0.334 236
319 -101.875 35.625 0.314 251
320 -101.625 35.625 0.360 250
321 -101.375 35.625 0.298 251
322 -101.125 35.625 0.295 252
323 -100.875 35.625 0.313 252
324 -100.625 35.625 0.317 248
325 -100.375 35.625 0.328 248
326 -100.125 35.625 0.315 248
327 -99.875 35.625 0.368 248
328 -99.625 35.625 0.386 248
329 -99.375 35.625 0.388 248
330 -99.125 35.625 0.379 250
331 -98.875 35.625 0.384 249
332 -98.625 35.625 0.395 242
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540 -95.375 34.375 0.302 240
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620 -94.875 33.875 0.266 227
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651 -96.875 33.625 0.303 252
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700 -94.375 33.375 0.387 242
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729 -96.875 33.125 0.377 252
730 -96.625 33.125 0.390 252
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734 -95.625 33.125 0.343 251
735 -95.375 33.125 0.359 251
736 -95.125 33.125 0.392 246
737 -94.875 33.125 0.396 243
738 -94.625 33.125 0.376 241
739 -94.375 33.125 0.335 241
740 -94.125 33.125 0.372 242
741 -93.875 33.125 0.317 249
Temporal Correlation between Monthly Median Soil Moisture and Maximum Temperature
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

Temperature_Max <- read.csv("Daymet_tmax_region_interest_monthly_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_TMax <- 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 -0.438 212
2 -103.125 37.625 -0.314 223
3 -102.875 37.625 -0.216 205
4 -102.625 37.625 -0.230 212
5 -102.375 37.625 -0.257 230
6 -102.125 37.625 -0.209 232
7 -101.875 37.625 -0.251 234
8 -101.625 37.625 -0.249 251
9 -101.375 37.625 -0.202 252
10 -101.125 37.625 -0.071 252
11 -100.875 37.625 -0.133 251
12 -100.625 37.625 -0.150 249
13 -100.375 37.625 -0.315 231
14 -100.125 37.625 -0.197 225
15 -99.875 37.625 -0.286 235
16 -99.625 37.625 -0.250 232
17 -99.375 37.625 -0.215 232
18 -99.125 37.625 -0.212 234
19 -98.875 37.625 -0.158 232
20 -98.625 37.625 -0.211 222
21 -98.375 37.625 -0.300 224
22 -98.125 37.625 -0.301 230
23 -97.875 37.625 -0.200 237
24 -97.625 37.625 -0.116 241
25 -97.375 37.625 -0.340 242
26 -97.125 37.625 -0.429 242
27 -96.875 37.625 -0.549 242
28 -96.625 37.625 -0.650 243
29 -96.375 37.625 -0.591 241
30 -96.125 37.625 -0.527 241
31 -95.875 37.625 -0.516 243
32 -95.625 37.625 -0.432 234
33 -95.375 37.625 -0.321 230
34 -95.125 37.625 -0.315 236
35 -94.875 37.625 -0.372 230
36 -94.625 37.625 -0.384 229
37 -94.375 37.625 -0.501 235
38 -94.125 37.625 -0.481 233
39 -93.875 37.625 -0.526 233
40 -103.375 37.375 -0.381 223
41 -103.125 37.375 -0.290 229
42 -102.875 37.375 -0.179 222
43 -102.625 37.375 -0.090 224
44 -102.375 37.375 -0.118 221
45 -102.125 37.375 -0.126 234
46 -101.875 37.375 -0.156 233
47 -101.625 37.375 -0.187 240
48 -101.375 37.375 -0.123 252
49 -101.125 37.375 -0.149 239
50 -100.875 37.375 -0.190 239
51 -100.625 37.375 -0.280 249
52 -100.375 37.375 -0.309 226
53 -100.125 37.375 -0.274 231
54 -99.875 37.375 -0.301 235
55 -99.625 37.375 -0.328 231
56 -99.375 37.375 -0.370 231
57 -99.125 37.375 -0.376 238
58 -98.875 37.375 -0.318 244
59 -98.625 37.375 -0.279 172
60 -98.375 37.375 -0.253 248
61 -98.125 37.375 -0.414 221
62 -97.875 37.375 -0.237 250
63 -97.625 37.375 -0.268 249
64 -97.375 37.375 -0.459 250
65 -97.125 37.375 -0.478 241
66 -96.875 37.375 -0.517 242
67 -96.625 37.375 -0.625 243
68 -96.375 37.375 -0.611 241
69 -96.125 37.375 -0.597 241
70 -95.875 37.375 -0.592 243
71 -95.625 37.375 -0.562 244
72 -95.375 37.375 -0.365 228
73 -95.125 37.375 -0.355 229
74 -94.875 37.375 -0.348 231
75 -94.625 37.375 -0.362 232
76 -94.375 37.375 -0.369 226
77 -94.125 37.375 -0.534 233
78 -93.875 37.375 -0.587 237
79 -103.375 37.125 -0.252 235
80 -103.125 37.125 -0.220 235
81 -102.875 37.125 -0.193 236
82 -102.625 37.125 -0.148 237
83 -102.375 37.125 -0.057 225
84 -102.125 37.125 0.039 219
85 -101.875 37.125 -0.081 243
86 -101.625 37.125 -0.192 241
87 -101.375 37.125 -0.106 239
88 -101.125 37.125 -0.186 196
89 -100.875 37.125 -0.091 239
90 -100.625 37.125 -0.353 232
91 -100.375 37.125 -0.340 239
92 -100.125 37.125 -0.372 236
93 -99.875 37.125 -0.305 236
94 -99.625 37.125 -0.331 245
95 -99.375 37.125 -0.419 236
96 -99.125 37.125 -0.405 239
97 -98.875 37.125 -0.431 244
98 -98.625 37.125 -0.334 234
99 -98.375 37.125 -0.310 222
100 -98.125 37.125 -0.260 239
101 -97.875 37.125 -0.307 241
102 -97.625 37.125 -0.333 250
103 -97.375 37.125 -0.341 240
104 -97.125 37.125 -0.540 235
105 -96.875 37.125 -0.457 234
106 -96.625 37.125 -0.597 239
107 -96.375 37.125 -0.603 240
108 -96.125 37.125 -0.599 241
109 -95.875 37.125 -0.504 235
110 -95.625 37.125 -0.445 237
111 -95.375 37.125 -0.553 245
112 -95.125 37.125 -0.543 238
113 -94.875 37.125 -0.377 226
114 -94.625 37.125 -0.378 245
115 -94.375 37.125 -0.469 236
116 -94.125 37.125 -0.453 229
117 -93.875 37.125 -0.533 231
118 -103.375 36.875 -0.239 234
119 -103.125 36.875 -0.105 219
120 -102.875 36.875 -0.072 224
121 -102.625 36.875 -0.189 237
122 -102.375 36.875 -0.161 237
123 -102.125 36.875 -0.049 221
124 -101.875 36.875 -0.017 219
125 -101.625 36.875 -0.344 245
126 -101.375 36.875 -0.360 246
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670 -101.875 33.375 -0.159 252
671 -101.625 33.375 -0.253 252
672 -101.375 33.375 -0.300 252
673 -101.125 33.375 -0.382 252
674 -100.875 33.375 -0.475 252
675 -100.625 33.375 -0.491 252
676 -100.375 33.375 -0.506 252
677 -100.125 33.375 -0.507 252
678 -99.875 33.375 -0.543 252
679 -99.625 33.375 -0.587 252
680 -99.375 33.375 -0.557 251
681 -99.125 33.375 -0.647 252
682 -98.875 33.375 -0.683 252
683 -98.625 33.375 -0.732 252
684 -98.375 33.375 -0.748 251
685 -98.125 33.375 -0.746 251
686 -97.875 33.375 -0.747 251
687 -97.625 33.375 -0.732 252
688 -97.375 33.375 -0.716 251
689 -97.125 33.375 -0.638 252
690 -96.875 33.375 -0.709 252
691 -96.625 33.375 -0.603 245
692 -96.375 33.375 -0.708 251
693 -96.125 33.375 -0.765 252
694 -95.875 33.375 -0.749 251
695 -95.625 33.375 -0.706 251
696 -95.375 33.375 -0.754 251
697 -95.125 33.375 -0.663 250
698 -94.875 33.375 -0.684 248
699 -94.625 33.375 -0.582 242
700 -94.375 33.375 -0.574 242
701 -94.125 33.375 -0.765 252
702 -93.875 33.375 -0.712 250
703 -103.375 33.125 -0.227 250
704 -103.125 33.125 -0.160 252
705 -102.875 33.125 -0.139 252
706 -102.625 33.125 -0.151 252
707 -102.375 33.125 -0.091 252
708 -102.125 33.125 -0.163 252
709 -101.875 33.125 -0.224 252
710 -101.625 33.125 -0.206 252
711 -101.375 33.125 -0.400 252
712 -101.125 33.125 -0.469 252
713 -100.875 33.125 -0.486 252
714 -100.625 33.125 -0.459 252
715 -100.375 33.125 -0.550 252
716 -100.125 33.125 -0.542 252
717 -99.875 33.125 -0.541 252
718 -99.625 33.125 -0.594 252
719 -99.375 33.125 -0.598 251
720 -99.125 33.125 -0.629 252
721 -98.875 33.125 -0.700 251
722 -98.625 33.125 -0.746 251
723 -98.375 33.125 -0.771 251
724 -98.125 33.125 -0.759 251
725 -97.875 33.125 -0.768 251
726 -97.625 33.125 -0.780 251
727 -97.375 33.125 -0.787 252
728 -97.125 33.125 -0.753 252
729 -96.875 33.125 -0.724 252
730 -96.625 33.125 -0.721 252
731 -96.375 33.125 -0.699 245
732 -96.125 33.125 -0.750 251
733 -95.875 33.125 -0.781 251
734 -95.625 33.125 -0.760 251
735 -95.375 33.125 -0.783 251
736 -95.125 33.125 -0.588 246
737 -94.875 33.125 -0.605 243
738 -94.625 33.125 -0.561 241
739 -94.375 33.125 -0.572 241
740 -94.125 33.125 -0.534 242
741 -93.875 33.125 -0.632 249
Temporal Correlation between Monthly Median Soil Moisture and Minimum Temperature
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

Temperature_Min <- read.csv("Daymet_tmin_region_interest_monthly_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_TMin <- 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 -0.362 212
2 -103.125 37.625 -0.217 223
3 -102.875 37.625 -0.087 205
4 -102.625 37.625 -0.110 212
5 -102.375 37.625 -0.139 230
6 -102.125 37.625 -0.115 232
7 -101.875 37.625 -0.151 234
8 -101.625 37.625 -0.147 251
9 -101.375 37.625 -0.103 252
10 -101.125 37.625 0.013 252
11 -100.875 37.625 -0.041 251
12 -100.625 37.625 -0.053 249
13 -100.375 37.625 -0.248 231
14 -100.125 37.625 -0.072 225
15 -99.875 37.625 -0.176 235
16 -99.625 37.625 -0.137 232
17 -99.375 37.625 -0.094 232
18 -99.125 37.625 -0.094 234
19 -98.875 37.625 -0.044 232
20 -98.625 37.625 -0.121 222
21 -98.375 37.625 -0.202 224
22 -98.125 37.625 -0.218 230
23 -97.875 37.625 -0.126 237
24 -97.625 37.625 -0.026 241
25 -97.375 37.625 -0.265 242
26 -97.125 37.625 -0.358 242
27 -96.875 37.625 -0.484 242
28 -96.625 37.625 -0.584 243
29 -96.375 37.625 -0.517 241
30 -96.125 37.625 -0.450 241
31 -95.875 37.625 -0.437 243
32 -95.625 37.625 -0.353 234
33 -95.375 37.625 -0.246 230
34 -95.125 37.625 -0.252 236
35 -94.875 37.625 -0.303 230
36 -94.625 37.625 -0.316 229
37 -94.375 37.625 -0.444 235
38 -94.125 37.625 -0.421 233
39 -93.875 37.625 -0.470 233
40 -103.375 37.375 -0.271 223
41 -103.125 37.375 -0.167 229
42 -102.875 37.375 -0.047 222
43 -102.625 37.375 0.034 224
44 -102.375 37.375 -0.004 221
45 -102.125 37.375 -0.021 234
46 -101.875 37.375 -0.051 233
47 -101.625 37.375 -0.077 240
48 -101.375 37.375 -0.024 252
49 -101.125 37.375 -0.052 239
50 -100.875 37.375 -0.091 239
51 -100.625 37.375 -0.172 249
52 -100.375 37.375 -0.219 226
53 -100.125 37.375 -0.151 231
54 -99.875 37.375 -0.187 235
55 -99.625 37.375 -0.203 231
56 -99.375 37.375 -0.246 231
57 -99.125 37.375 -0.261 238
58 -98.875 37.375 -0.214 244
59 -98.625 37.375 -0.188 172
60 -98.375 37.375 -0.149 248
61 -98.125 37.375 -0.339 221
62 -97.875 37.375 -0.144 250
63 -97.625 37.375 -0.181 249
64 -97.375 37.375 -0.389 250
65 -97.125 37.375 -0.400 241
66 -96.875 37.375 -0.446 242
67 -96.625 37.375 -0.560 243
68 -96.375 37.375 -0.534 241
69 -96.125 37.375 -0.516 241
70 -95.875 37.375 -0.517 243
71 -95.625 37.375 -0.489 244
72 -95.375 37.375 -0.287 228
73 -95.125 37.375 -0.280 229
74 -94.875 37.375 -0.273 231
75 -94.625 37.375 -0.298 232
76 -94.375 37.375 -0.298 226
77 -94.125 37.375 -0.472 233
78 -93.875 37.375 -0.530 237
79 -103.375 37.125 -0.135 235
80 -103.125 37.125 -0.090 235
81 -102.875 37.125 -0.058 236
82 -102.625 37.125 -0.014 237
83 -102.375 37.125 0.080 225
84 -102.125 37.125 0.166 219
85 -101.875 37.125 0.003 243
86 -101.625 37.125 -0.074 241
87 -101.375 37.125 0.007 239
88 -101.125 37.125 -0.092 196
89 -100.875 37.125 0.033 239
90 -100.625 37.125 -0.243 232
91 -100.375 37.125 -0.208 239
92 -100.125 37.125 -0.239 236
93 -99.875 37.125 -0.178 236
94 -99.625 37.125 -0.211 245
95 -99.375 37.125 -0.296 236
96 -99.125 37.125 -0.281 239
97 -98.875 37.125 -0.315 244
98 -98.625 37.125 -0.217 234
99 -98.375 37.125 -0.205 222
100 -98.125 37.125 -0.158 239
101 -97.875 37.125 -0.209 241
102 -97.625 37.125 -0.249 250
103 -97.375 37.125 -0.252 240
104 -97.125 37.125 -0.477 235
105 -96.875 37.125 -0.362 234
106 -96.625 37.125 -0.529 239
107 -96.375 37.125 -0.525 240
108 -96.125 37.125 -0.522 241
109 -95.875 37.125 -0.415 235
110 -95.625 37.125 -0.361 237
111 -95.375 37.125 -0.479 245
112 -95.125 37.125 -0.475 238
113 -94.875 37.125 -0.297 226
114 -94.625 37.125 -0.308 245
115 -94.375 37.125 -0.396 236
116 -94.125 37.125 -0.379 229
117 -93.875 37.125 -0.466 231
118 -103.375 36.875 -0.106 234
119 -103.125 36.875 0.027 219
120 -102.875 36.875 0.072 224
121 -102.625 36.875 -0.048 237
122 -102.375 36.875 -0.028 237
123 -102.125 36.875 0.079 221
124 -101.875 36.875 0.114 219
125 -101.625 36.875 -0.220 245
126 -101.375 36.875 -0.230 246
127 -101.125 36.875 -0.176 251
128 -100.875 36.875 -0.181 246
129 -100.625 36.875 -0.326 249
130 -100.375 36.875 -0.304 249
131 -100.125 36.875 -0.322 245
132 -99.875 36.875 -0.251 234
133 -99.625 36.875 -0.365 246
134 -99.375 36.875 -0.406 247
135 -99.125 36.875 -0.398 246
136 -98.875 36.875 -0.360 246
137 -98.625 36.875 -0.290 246
138 -98.375 36.875 -0.192 249
139 -98.125 36.875 -0.329 249
140 -97.875 36.875 -0.341 249
141 -97.625 36.875 -0.321 247
142 -97.375 36.875 -0.386 251
143 -97.125 36.875 -0.504 250
144 -96.875 36.875 -0.608 250
145 -96.625 36.875 -0.649 246
146 -96.375 36.875 -0.586 237
147 -96.125 36.875 -0.606 242
148 -95.875 36.875 -0.490 237
149 -95.625 36.875 -0.502 234
150 -95.375 36.875 -0.558 239
151 -95.125 36.875 -0.532 238
152 -94.875 36.875 -0.436 227
153 -94.625 36.875 -0.439 225
154 -94.375 36.875 -0.440 241
155 -94.125 36.875 -0.586 238
156 -93.875 36.875 -0.496 230
157 -103.375 36.625 -0.052 236
158 -103.125 36.625 -0.027 237
159 -102.875 36.625 -0.053 244
160 -102.625 36.625 -0.092 236
161 -102.375 36.625 -0.059 235
162 -102.125 36.625 0.021 221
163 -101.875 36.625 -0.165 241
164 -101.625 36.625 -0.302 250
165 -101.375 36.625 -0.297 245
166 -101.125 36.625 -0.251 249
167 -100.875 36.625 -0.301 250
168 -100.625 36.625 -0.358 250
169 -100.375 36.625 -0.382 251
170 -100.125 36.625 -0.380 246
171 -99.875 36.625 -0.336 247
172 -99.625 36.625 -0.414 229
173 -99.375 36.625 -0.366 247
174 -99.125 36.625 -0.354 247
175 -98.875 36.625 -0.447 236
176 -98.625 36.625 -0.365 250
177 -98.375 36.625 -0.292 246
178 -98.125 36.625 -0.311 250
179 -97.875 36.625 -0.347 250
180 -97.625 36.625 -0.366 247
181 -97.375 36.625 -0.407 250
182 -97.125 36.625 -0.486 247
183 -96.875 36.625 -0.612 249
184 -96.625 36.625 -0.563 238
185 -96.375 36.625 -0.595 238
186 -96.125 36.625 -0.494 230
187 -95.875 36.625 -0.607 242
188 -95.625 36.625 -0.538 233
189 -95.375 36.625 -0.533 241
190 -95.125 36.625 -0.481 231
191 -94.875 36.625 -0.646 239
192 -94.625 36.625 -0.477 235
193 -94.375 36.625 -0.420 225
194 -94.125 36.625 -0.421 223
195 -93.875 36.625 -0.342 228
196 -103.375 36.375 -0.015 236
197 -103.125 36.375 0.014 238
198 -102.875 36.375 0.022 237
199 -102.625 36.375 -0.080 236
200 -102.375 36.375 -0.050 238
201 -102.125 36.375 0.042 220
202 -101.875 36.375 -0.116 234
203 -101.625 36.375 -0.061 222
204 -101.375 36.375 -0.267 251
205 -101.125 36.375 -0.308 249
206 -100.875 36.375 -0.404 246
207 -100.625 36.375 -0.392 249
208 -100.375 36.375 -0.404 251
209 -100.125 36.375 -0.388 252
210 -99.875 36.375 -0.404 247
211 -99.625 36.375 -0.332 237
212 -99.375 36.375 -0.270 242
213 -99.125 36.375 -0.343 248
214 -98.875 36.375 -0.361 248
215 -98.625 36.375 -0.361 248
216 -98.375 36.375 -0.393 243
217 -98.125 36.375 -0.331 249
218 -97.875 36.375 -0.376 250
219 -97.625 36.375 -0.403 247
220 -97.375 36.375 -0.390 247
221 -97.125 36.375 -0.399 249
222 -96.875 36.375 -0.550 247
223 -96.625 36.375 -0.514 241
224 -96.375 36.375 -0.605 247
225 -96.125 36.375 -0.534 236
226 -95.875 36.375 -0.630 247
227 -95.625 36.375 -0.564 240
228 -95.375 36.375 -0.541 243
229 -95.125 36.375 -0.510 234
230 -94.875 36.375 -0.470 231
231 -94.625 36.375 -0.486 224
232 -94.375 36.375 -0.404 227
233 -94.125 36.375 -0.500 235
234 -93.875 36.375 -0.396 221
235 -103.375 36.125 0.064 229
236 -103.125 36.125 0.004 240
237 -102.875 36.125 0.076 238
238 -102.625 36.125 0.038 238
239 -102.375 36.125 0.021 238
240 -102.125 36.125 -0.036 243
241 -101.875 36.125 0.045 224
242 -101.625 36.125 -0.227 247
243 -101.375 36.125 -0.083 227
244 -101.125 36.125 -0.313 250
245 -100.875 36.125 -0.355 251
246 -100.625 36.125 -0.404 246
247 -100.375 36.125 -0.389 247
248 -100.125 36.125 -0.386 247
249 -99.875 36.125 -0.386 247
250 -99.625 36.125 -0.406 251
251 -99.375 36.125 -0.313 241
252 -99.125 36.125 -0.288 240
253 -98.875 36.125 -0.350 248
254 -98.625 36.125 -0.467 237
255 -98.375 36.125 -0.341 249
256 -98.125 36.125 -0.273 252
257 -97.875 36.125 -0.307 247
258 -97.625 36.125 -0.419 250
259 -97.375 36.125 -0.346 249
260 -97.125 36.125 -0.380 239
261 -96.875 36.125 -0.434 239
262 -96.625 36.125 -0.526 248
263 -96.375 36.125 -0.681 248
264 -96.125 36.125 -0.687 248
265 -95.875 36.125 -0.682 247
266 -95.625 36.125 -0.480 239
267 -95.375 36.125 -0.513 241
268 -95.125 36.125 -0.538 232
269 -94.875 36.125 -0.517 229
270 -94.625 36.125 -0.417 228
271 -94.375 36.125 -0.603 237
272 -94.125 36.125 -0.614 242
273 -93.875 36.125 -0.474 225
274 -103.375 35.875 -0.194 246
275 -103.125 35.875 -0.023 240
276 -102.875 35.875 0.119 229
277 -102.625 35.875 -0.004 238
278 -102.375 35.875 0.165 225
279 -102.125 35.875 0.068 225
280 -101.875 35.875 -0.035 226
281 -101.625 35.875 -0.297 252
282 -101.375 35.875 -0.317 247
283 -101.125 35.875 -0.372 251
284 -100.875 35.875 -0.393 246
285 -100.625 35.875 -0.398 251
286 -100.375 35.875 -0.380 247
287 -100.125 35.875 -0.388 247
288 -99.875 35.875 -0.366 248
289 -99.625 35.875 -0.422 248
290 -99.375 35.875 -0.327 248
291 -99.125 35.875 -0.241 239
292 -98.875 35.875 -0.270 248
293 -98.625 35.875 -0.342 248
294 -98.375 35.875 -0.359 247
295 -98.125 35.875 -0.331 250
296 -97.875 35.875 -0.346 249
297 -97.625 35.875 -0.516 237
298 -97.375 35.875 -0.523 248
299 -97.125 35.875 -0.443 240
300 -96.875 35.875 -0.513 240
301 -96.625 35.875 -0.533 240
302 -96.375 35.875 -0.660 237
303 -96.125 35.875 -0.652 247
304 -95.875 35.875 -0.580 239
305 -95.625 35.875 -0.523 232
306 -95.375 35.875 -0.621 243
307 -95.125 35.875 -0.630 244
308 -94.875 35.875 -0.566 241
309 -94.625 35.875 -0.437 231
310 -94.375 35.875 -0.559 229
311 -94.125 35.875 -0.511 232
312 -93.875 35.875 -0.356 231
313 -103.375 35.625 -0.101 241
314 -103.125 35.625 -0.062 246
315 -102.875 35.625 -0.023 236
316 -102.625 35.625 -0.226 248
317 -102.375 35.625 -0.229 248
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320 -101.625 35.625 -0.232 250
321 -101.375 35.625 -0.330 251
322 -101.125 35.625 -0.360 252
323 -100.875 35.625 -0.376 252
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325 -100.375 35.625 -0.415 248
326 -100.125 35.625 -0.393 248
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331 -98.875 35.625 -0.279 249
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336 -97.625 35.625 -0.547 247
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339 -96.875 35.625 -0.581 248
340 -96.625 35.625 -0.560 241
341 -96.375 35.625 -0.472 243
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343 -95.875 35.625 -0.534 243
344 -95.625 35.625 -0.530 243
345 -95.375 35.625 -0.615 217
346 -95.125 35.625 -0.607 245
347 -94.875 35.625 -0.585 237
348 -94.625 35.625 -0.601 236
349 -94.375 35.625 -0.516 237
350 -94.125 35.625 -0.551 236
351 -93.875 35.625 -0.326 240
352 -103.375 35.375 -0.255 246
353 -103.125 35.375 -0.136 244
354 -102.875 35.375 -0.174 241
355 -102.625 35.375 -0.094 236
356 -102.375 35.375 -0.101 236
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358 -101.875 35.375 -0.384 250
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Temporal Correlation Plots

X axis show the number of pixels, starting in the upper left corner and finishing in n the lower right corner.

setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

#MEAN Soil Moisture vs Precipitation

data <- read.csv('Temporal_Correlation_region_interest_MEAN_monthly_SoilMoist_v45_daymet_Prcp.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_monthly_SoilMoist_v45_daymet_TMax.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_monthly_SoilMoist_v45_daymet_TMin.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_monthly_SoilMoist_v45_daymet_Prcp.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_monthly_SoilMoist_v45_daymet_TMax.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_monthly_SoilMoist_v45_daymet_TMin.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_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'

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/Gap_Filling_paper_definitive/Correlation_4.5")

SoilMoisture_MEAN <- read.csv("SoilMoisture_region_interest_MEAN_monthly_pixel_values_v45.csv", header = TRUE, sep = ',', dec = '.')
SoilMoisture_MEAN[1:4] <- NULL
SoilMoisture_MEAN <- replace(SoilMoisture_MEAN, SoilMoisture_MEAN == -9999, NA)

Precipitation <- read.csv("Daymet_prcp_region_interest_monthly_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 0.636 325
1995_02 0.554 562
1995_03 0.692 723
1995_04 0.741 724
1995_05 0.406 680
1995_06 0.503 733
1995_07 0.272 735
1995_08 0.179 733
1995_09 0.363 732
1995_10 0.223 734
1995_11 0.332 730
1995_12 0.315 406
1996_01 0.651 428
1996_02 0.506 691
1996_03 0.686 736
1996_04 0.793 740
1996_05 0.529 734
1996_06 0.355 739
1996_07 0.049 698
1996_08 0.100 740
1996_09 0.178 740
1996_10 0.566 732
1996_11 0.677 737
1996_12 0.356 483
1997_01 0.469 404
1997_02 0.551 598
1997_03 0.766 736
1997_04 0.167 727
1997_05 0.460 726
1997_06 0.453 734
1997_07 0.205 730
1997_08 0.254 737
1997_09 0.387 726
1997_10 0.525 733
1997_11 0.542 727
1997_12 0.492 662
1998_01 0.609 606
1998_02 0.363 710
1998_03 0.675 716
1998_04 0.588 730
1998_05 0.588 730
1998_06 0.637 726
1998_07 0.483 733
1998_08 0.277 724
1998_09 0.789 737
1998_10 0.401 741
1998_11 0.623 741
1998_12 0.569 695
1999_01 0.446 589
1999_02 0.668 719
1999_03 0.578 739
1999_04 0.456 739
1999_05 0.693 733
1999_06 0.549 737
1999_07 0.103 729
1999_08 0.100 725
1999_09 0.507 732
1999_10 0.343 712
1999_11 0.605 727
1999_12 0.635 692
2000_01 0.536 602
2000_02 0.794 725
2000_03 0.261 741
2000_04 0.369 735
2000_05 0.733 733
2000_06 0.642 732
2000_07 0.457 732
2000_08 -0.111 726
2000_09 0.628 729
2000_10 0.006 738
2000_11 0.462 741
2000_12 0.567 640
2001_01 0.517 598
2001_02 0.601 589
2001_03 0.378 614
2001_04 0.665 723
2001_05 0.181 692
2001_06 0.706 730
2001_07 0.330 725
2001_08 0.361 730
2001_09 0.511 738
2001_10 0.795 734
2001_11 0.424 733
2001_12 0.594 710
2002_01 0.578 568
2002_02 0.504 646
2002_03 0.646 693
2002_04 0.706 730
2002_05 0.731 719
2002_06 0.481 741
2002_07 0.281 741
2002_08 0.202 741
2002_09 0.314 741
2002_10 0.226 741
2002_11 0.384 740
2002_12 0.482 739
2003_01 0.257 665
2003_02 0.637 684
2003_03 0.637 568
2003_04 0.527 569
2003_05 0.600 569
2003_06 0.078 569
2003_07 0.391 569
2003_08 0.383 736
2003_09 0.471 741
2003_10 0.613 741
2003_11 0.611 741
2003_12 0.704 730
2004_01 0.620 654
2004_02 0.349 737
2004_03 0.585 741
2004_04 0.475 741
2004_05 0.741 741
2004_06 0.417 741
2004_07 0.420 741
2004_08 -0.255 741
2004_09 -0.158 741
2004_10 0.368 741
2004_11 0.386 741
2004_12 0.506 707
2005_01 0.592 649
2005_02 0.592 737
2005_03 0.498 741
2005_04 0.543 741
2005_05 0.373 741
2005_06 0.388 741
2005_07 0.199 741
2005_08 0.262 741
2005_09 0.415 741
2005_10 0.323 741
2005_11 0.336 741
2005_12 0.539 637
2006_01 0.540 654
2006_02 0.478 723
2006_03 0.590 741
2006_04 0.752 741
2006_05 0.567 741
2006_06 0.500 741
2006_07 0.295 741
2006_08 0.298 741
2006_09 0.266 741
2006_10 0.337 741
2006_11 0.674 741
2006_12 0.062 702
2007_01 0.555 701
2007_02 0.648 722
2007_03 0.074 741
2007_04 0.577 741
2007_05 0.534 741
2007_06 0.680 741
2007_07 0.599 741
2007_08 0.212 741
2007_09 0.456 741
2007_10 0.761 741
2007_11 0.504 741
2007_12 0.579 740
2008_01 0.617 737
2008_02 0.812 739
2008_03 0.771 741
2008_04 0.834 741
2008_05 0.664 741
2008_06 0.776 741
2008_07 0.309 741
2008_08 0.164 741
2008_09 0.587 741
2008_10 0.197 741
2008_11 0.660 741
2008_12 0.686 736
2009_01 0.596 741
2009_02 0.773 740
2009_03 0.782 741
2009_04 0.577 741
2009_05 0.683 741
2009_06 0.303 741
2009_07 0.351 741
2009_08 0.505 741
2009_09 0.704 741
2009_10 0.599 741
2009_11 0.702 741
2009_12 0.642 738
2010_01 0.571 735
2010_02 0.467 737
2010_03 0.634 741
2010_04 0.139 741
2010_05 0.551 741
2010_06 0.509 741
2010_07 0.306 741
2010_08 0.200 741
2010_09 0.660 741
2010_10 0.260 741
2010_11 0.641 741
2010_12 0.484 741
2011_01 0.473 741
2011_02 0.736 741
2011_03 0.661 741
2011_04 0.785 741
2011_05 0.826 741
2011_06 0.416 741
2011_07 0.231 741
2011_08 0.602 741
2011_09 0.538 741
2011_10 0.361 741
2011_11 0.665 741
2011_12 0.471 739
2012_01 0.580 736
2012_02 0.660 741
2012_03 0.810 741
2012_04 0.552 741
2012_05 0.481 741
2012_06 0.274 741
2012_07 0.476 741
2012_08 0.369 741
2012_09 0.454 741
2012_10 0.588 741
2012_11 0.654 741
2012_12 0.530 738
2013_01 0.695 723
2013_02 0.549 741
2013_03 0.758 741
2013_04 0.806 741
2013_05 0.811 741
2013_06 0.200 741
2013_07 0.464 741
2013_08 0.521 741
2013_09 0.178 741
2013_10 0.739 741
2013_11 0.617 741
2013_12 0.685 736
2014_01 0.615 729
2014_02 0.517 741
2014_03 0.798 741
2014_04 0.782 741
2014_05 0.622 741
2014_06 0.344 741
2014_07 0.361 741
2014_08 0.286 741
2014_09 0.482 741
2014_10 0.699 741
2014_11 0.406 741
2014_12 0.708 739
2015_01 0.451 721
2015_02 0.633 741
2015_03 0.753 741
2015_04 0.464 741
2015_05 0.588 741
2015_06 0.501 741
2015_07 0.273 741
2015_08 0.497 741
2015_09 0.408 741
2015_10 0.018 741
2015_11 0.700 741
2015_12 0.647 740
Spatial Correlation between Monthly Mean Soil Moisture and Maximum Temperature
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

Temperature_Max <- read.csv("Daymet_tmax_region_interest_monthly_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_Tmax <- 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 0.090 325
1995_02 -0.070 562
1995_03 0.108 723
1995_04 0.104 724
1995_05 -0.084 680
1995_06 -0.220 733
1995_07 -0.452 735
1995_08 0.110 733
1995_09 0.134 732
1995_10 0.219 734
1995_11 -0.216 730
1995_12 0.167 406
1996_01 -0.285 428
1996_02 -0.241 691
1996_03 -0.175 736
1996_04 -0.254 740
1996_05 -0.705 734
1996_06 -0.452 739
1996_07 -0.377 698
1996_08 -0.126 740
1996_09 0.155 740
1996_10 0.043 732
1996_11 -0.348 737
1996_12 -0.187 483
1997_01 0.130 404
1997_02 0.348 598
1997_03 -0.209 736
1997_04 0.305 727
1997_05 0.166 726
1997_06 -0.103 734
1997_07 -0.287 730
1997_08 -0.310 737
1997_09 -0.042 726
1997_10 0.116 733
1997_11 -0.074 727
1997_12 0.169 662
1998_01 -0.226 606
1998_02 0.297 710
1998_03 0.121 716
1998_04 -0.063 730
1998_05 -0.356 730
1998_06 -0.481 726
1998_07 -0.511 733
1998_08 -0.289 724
1998_09 -0.365 737
1998_10 -0.098 741
1998_11 -0.221 741
1998_12 0.042 695
1999_01 -0.289 589
1999_02 -0.262 719
1999_03 -0.116 739
1999_04 0.156 739
1999_05 -0.003 733
1999_06 -0.344 737
1999_07 -0.288 729
1999_08 -0.184 725
1999_09 -0.224 732
1999_10 0.232 712
1999_11 0.065 727
1999_12 0.141 692
2000_01 -0.233 602
2000_02 -0.380 725
2000_03 -0.119 741
2000_04 -0.363 735
2000_05 -0.597 733
2000_06 -0.429 732
2000_07 -0.549 732
2000_08 -0.107 726
2000_09 -0.283 729
2000_10 0.378 738
2000_11 0.302 741
2000_12 -0.199 640
2001_01 0.181 598
2001_02 0.089 589
2001_03 0.256 614
2001_04 0.047 723
2001_05 -0.162 692
2001_06 -0.644 730
2001_07 -0.569 725
2001_08 -0.096 730
2001_09 -0.533 738
2001_10 -0.479 734
2001_11 0.445 733
2001_12 0.223 710
2002_01 0.002 568
2002_02 -0.013 646
2002_03 -0.129 693
2002_04 -0.131 730
2002_05 -0.657 719
2002_06 -0.748 741
2002_07 -0.236 741
2002_08 -0.188 741
2002_09 0.229 741
2002_10 0.217 741
2002_11 0.192 740
2002_12 0.424 739
2003_01 -0.492 665
2003_02 -0.217 684
2003_03 -0.274 568
2003_04 -0.192 569
2003_05 -0.286 569
2003_06 0.006 569
2003_07 -0.225 569
2003_08 -0.055 736
2003_09 -0.369 741
2003_10 -0.299 741
2003_11 0.253 741
2003_12 -0.314 730
2004_01 -0.177 654
2004_02 -0.180 737
2004_03 0.005 741
2004_04 0.019 741
2004_05 -0.638 741
2004_06 -0.451 741
2004_07 -0.380 741
2004_08 -0.023 741
2004_09 0.127 741
2004_10 0.295 741
2004_11 0.398 741
2004_12 -0.003 707
2005_01 -0.069 649
2005_02 0.219 737
2005_03 0.187 741
2005_04 -0.020 741
2005_05 0.138 741
2005_06 -0.266 741
2005_07 -0.130 741
2005_08 0.220 741
2005_09 0.068 741
2005_10 0.184 741
2005_11 -0.126 741
2005_12 -0.189 637
2006_01 -0.119 654
2006_02 -0.303 723
2006_03 0.202 741
2006_04 -0.155 741
2006_05 -0.469 741
2006_06 -0.656 741
2006_07 -0.348 741
2006_08 -0.244 741
2006_09 0.041 741
2006_10 0.061 741
2006_11 -0.010 741
2006_12 0.370 702
2007_01 0.388 701
2007_02 -0.252 722
2007_03 0.331 741
2007_04 0.093 741
2007_05 0.386 741
2007_06 -0.133 741
2007_07 -0.475 741
2007_08 0.116 741
2007_09 -0.152 741
2007_10 -0.339 741
2007_11 0.050 741
2007_12 0.188 740
2008_01 -0.390 737
2008_02 -0.324 739
2008_03 -0.122 741
2008_04 -0.298 741
2008_05 -0.404 741
2008_06 -0.696 741
2008_07 -0.232 741
2008_08 -0.264 741
2008_09 -0.194 741
2008_10 -0.268 741
2008_11 -0.452 741
2008_12 -0.239 736
2009_01 -0.512 741
2009_02 -0.383 740
2009_03 -0.331 741
2009_04 -0.390 741
2009_05 -0.332 741
2009_06 -0.195 741
2009_07 -0.475 741
2009_08 -0.537 741
2009_09 -0.309 741
2009_10 -0.208 741
2009_11 -0.157 741
2009_12 0.126 738
2010_01 -0.358 735
2010_02 0.143 737
2010_03 -0.091 741
2010_04 0.450 741
2010_05 0.069 741
2010_06 -0.435 741
2010_07 -0.013 741
2010_08 0.017 741
2010_09 -0.543 741
2010_10 0.086 741
2010_11 0.032 741
2010_12 -0.392 741
2011_01 -0.368 741
2011_02 -0.190 741
2011_03 -0.443 741
2011_04 -0.366 741
2011_05 -0.617 741
2011_06 -0.554 741
2011_07 -0.212 741
2011_08 -0.399 741
2011_09 -0.300 741
2011_10 0.147 741
2011_11 -0.119 741
2011_12 0.620 739
2012_01 -0.028 736
2012_02 0.162 741
2012_03 0.066 741
2012_04 -0.403 741
2012_05 -0.226 741
2012_06 -0.500 741
2012_07 -0.053 741
2012_08 -0.041 741
2012_09 -0.103 741
2012_10 -0.164 741
2012_11 -0.374 741
2012_12 0.024 738
2013_01 0.157 723
2013_02 0.014 741
2013_03 -0.579 741
2013_04 -0.184 741
2013_05 -0.659 741
2013_06 -0.748 741
2013_07 -0.265 741
2013_08 -0.581 741
2013_09 -0.191 741
2013_10 -0.206 741
2013_11 -0.228 741
2013_12 -0.039 736
2014_01 -0.478 729
2014_02 -0.267 741
2014_03 -0.424 741
2014_04 -0.311 741
2014_05 -0.539 741
2014_06 -0.594 741
2014_07 -0.519 741
2014_08 -0.375 741
2014_09 -0.232 741
2014_10 -0.301 741
2014_11 -0.150 741
2014_12 -0.252 739
2015_01 0.222 721
2015_02 -0.436 741
2015_03 -0.632 741
2015_04 -0.136 741
2015_05 0.386 741
2015_06 -0.183 741
2015_07 -0.228 741
2015_08 -0.430 741
2015_09 -0.508 741
2015_10 -0.080 741
2015_11 0.311 741
2015_12 0.391 740
Spatial Correlation between Monthly Mean Soil Moisture and Minimum Temperature
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

Temperature_Min <- read.csv("Daymet_tmin_region_interest_monthly_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_Tmin <- 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 0.518 325
1995_02 0.523 562
1995_03 0.645 723
1995_04 0.530 724
1995_05 0.287 680
1995_06 0.469 733
1995_07 0.256 735
1995_08 0.402 733
1995_09 0.430 732
1995_10 0.419 734
1995_11 0.185 730
1995_12 0.384 406
1996_01 0.390 428
1996_02 0.149 691
1996_03 0.451 736
1996_04 0.505 740
1996_05 0.260 734
1996_06 0.203 739
1996_07 -0.022 698
1996_08 0.074 740
1996_09 0.254 740
1996_10 0.455 732
1996_11 0.470 737
1996_12 0.334 483
1997_01 0.357 404
1997_02 0.551 598
1997_03 0.631 736
1997_04 0.321 727
1997_05 0.513 726
1997_06 0.518 734
1997_07 0.310 730
1997_08 0.123 737
1997_09 0.224 726
1997_10 0.599 733
1997_11 0.531 727
1997_12 0.606 662
1998_01 0.534 606
1998_02 0.616 710
1998_03 0.696 716
1998_04 0.574 730
1998_05 0.505 730
1998_06 0.436 726
1998_07 -0.064 733
1998_08 -0.135 724
1998_09 0.349 737
1998_10 0.253 741
1998_11 0.358 741
1998_12 0.615 695
1999_01 0.290 589
1999_02 0.655 719
1999_03 0.396 739
1999_04 0.522 739
1999_05 0.397 733
1999_06 0.427 737
1999_07 0.332 729
1999_08 -0.115 725
1999_09 -0.009 732
1999_10 0.425 712
1999_11 0.655 727
1999_12 0.566 692
2000_01 0.458 602
2000_02 0.479 725
2000_03 0.441 741
2000_04 0.353 735
2000_05 0.471 733
2000_06 0.383 732
2000_07 0.043 732
2000_08 0.159 726
2000_09 0.231 729
2000_10 0.425 738
2000_11 0.535 741
2000_12 0.429 640
2001_01 0.417 598
2001_02 0.424 589
2001_03 0.417 614
2001_04 0.677 723
2001_05 0.166 692
2001_06 0.213 730
2001_07 0.139 725
2001_08 0.216 730
2001_09 0.435 738
2001_10 0.513 734
2001_11 0.551 733
2001_12 0.708 710
2002_01 0.445 568
2002_02 0.641 646
2002_03 0.749 693
2002_04 0.678 730
2002_05 0.527 719
2002_06 0.391 741
2002_07 0.621 741
2002_08 0.365 741
2002_09 0.381 741
2002_10 0.420 741
2002_11 0.411 740
2002_12 0.597 739
2003_01 0.104 665
2003_02 0.395 684
2003_03 0.457 568
2003_04 0.477 569
2003_05 0.337 569
2003_06 0.258 569
2003_07 0.384 569
2003_08 0.319 736
2003_09 0.265 741
2003_10 0.221 741
2003_11 0.645 741
2003_12 0.539 730
2004_01 0.360 654
2004_02 0.505 737
2004_03 0.508 741
2004_04 0.272 741
2004_05 0.611 741
2004_06 0.511 741
2004_07 0.241 741
2004_08 0.271 741
2004_09 0.157 741
2004_10 0.473 741
2004_11 0.491 741
2004_12 0.497 707
2005_01 0.323 649
2005_02 0.408 737
2005_03 0.521 741
2005_04 0.563 741
2005_05 0.350 741
2005_06 0.307 741
2005_07 0.405 741
2005_08 0.385 741
2005_09 0.408 741
2005_10 0.399 741
2005_11 0.509 741
2005_12 0.246 637
2006_01 0.440 654
2006_02 0.485 723
2006_03 0.514 741
2006_04 0.583 741
2006_05 0.506 741
2006_06 0.098 741
2006_07 0.001 741
2006_08 -0.177 741
2006_09 0.161 741
2006_10 0.318 741
2006_11 0.640 741
2006_12 0.613 702
2007_01 0.575 701
2007_02 0.306 722
2007_03 0.616 741
2007_04 0.453 741
2007_05 0.681 741
2007_06 0.714 741
2007_07 0.729 741
2007_08 0.626 741
2007_09 0.568 741
2007_10 0.641 741
2007_11 0.606 741
2007_12 0.538 740
2008_01 0.435 737
2008_02 0.396 739
2008_03 0.627 741
2008_04 0.599 741
2008_05 0.567 741
2008_06 0.430 741
2008_07 0.241 741
2008_08 0.316 741
2008_09 0.461 741
2008_10 0.094 741
2008_11 0.377 741
2008_12 0.289 736
2009_01 0.314 741
2009_02 0.488 740
2009_03 0.505 741
2009_04 0.366 741
2009_05 0.611 741
2009_06 0.455 741
2009_07 0.074 741
2009_08 0.099 741
2009_09 0.548 741
2009_10 0.494 741
2009_11 0.614 741
2009_12 0.649 738
2010_01 0.506 735
2010_02 0.544 737
2010_03 0.597 741
2010_04 0.634 741
2010_05 0.608 741
2010_06 0.417 741
2010_07 0.509 741
2010_08 0.201 741
2010_09 0.458 741
2010_10 0.395 741
2010_11 0.594 741
2010_12 0.197 741
2011_01 0.406 741
2011_02 0.507 741
2011_03 0.453 741
2011_04 0.518 741
2011_05 0.575 741
2011_06 0.289 741
2011_07 0.109 741
2011_08 -0.111 741
2011_09 -0.078 741
2011_10 0.407 741
2011_11 0.483 741
2011_12 0.563 739
2012_01 0.515 736
2012_02 0.589 741
2012_03 0.705 741
2012_04 0.502 741
2012_05 0.456 741
2012_06 0.311 741
2012_07 0.248 741
2012_08 0.277 741
2012_09 0.375 741
2012_10 0.482 741
2012_11 0.229 741
2012_12 0.533 738
2013_01 0.478 723
2013_02 0.503 741
2013_03 0.294 741
2013_04 0.687 741
2013_05 0.508 741
2013_06 0.337 741
2013_07 0.225 741
2013_08 0.064 741
2013_09 0.048 741
2013_10 0.526 741
2013_11 0.443 741
2013_12 0.527 736
2014_01 0.142 729
2014_02 0.264 741
2014_03 0.379 741
2014_04 0.477 741
2014_05 0.523 741
2014_06 0.395 741
2014_07 -0.029 741
2014_08 0.239 741
2014_09 0.120 741
2014_10 0.292 741
2014_11 0.476 741
2014_12 0.544 739
2015_01 0.333 721
2015_02 0.165 741
2015_03 0.635 741
2015_04 0.702 741
2015_05 0.685 741
2015_06 0.627 741
2015_07 0.478 741
2015_08 0.028 741
2015_09 0.178 741
2015_10 0.063 741
2015_11 0.652 741
2015_12 0.626 740
Spatial Correlation between Monthly Mean Soil Moisture and Soil Texture
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

Soil_Texture <- read.csv("Soil_Texture_region_interest_monthly_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 -0.316 325
1995_02 -0.359 562
1995_03 -0.344 723
1995_04 -0.347 724
1995_05 -0.286 680
1995_06 -0.334 733
1995_07 -0.274 735
1995_08 -0.251 733
1995_09 -0.244 732
1995_10 -0.219 734
1995_11 -0.312 730
1995_12 -0.220 406
1996_01 -0.335 428
1996_02 -0.319 691
1996_03 -0.351 736
1996_04 -0.354 740
1996_05 -0.316 734
1996_06 -0.279 739
1996_07 -0.226 698
1996_08 -0.170 740
1996_09 -0.277 740
1996_10 -0.308 732
1996_11 -0.358 737
1996_12 -0.287 483
1997_01 -0.242 404
1997_02 -0.282 598
1997_03 -0.373 736
1997_04 -0.266 727
1997_05 -0.336 726
1997_06 -0.353 734
1997_07 -0.261 730
1997_08 -0.180 737
1997_09 -0.218 726
1997_10 -0.320 733
1997_11 -0.354 727
1997_12 -0.363 662
1998_01 -0.399 606
1998_02 -0.360 710
1998_03 -0.383 716
1998_04 -0.348 730
1998_05 -0.329 730
1998_06 -0.366 726
1998_07 -0.203 733
1998_08 -0.106 724
1998_09 -0.306 737
1998_10 -0.263 741
1998_11 -0.275 741
1998_12 -0.325 695
1999_01 -0.317 589
1999_02 -0.360 719
1999_03 -0.340 739
1999_04 -0.333 739
1999_05 -0.340 733
1999_06 -0.307 737
1999_07 -0.295 729
1999_08 -0.189 725
1999_09 -0.240 732
1999_10 -0.291 712
1999_11 -0.367 727
1999_12 -0.311 692
2000_01 -0.359 602
2000_02 -0.349 725
2000_03 -0.306 741
2000_04 -0.351 735
2000_05 -0.351 733
2000_06 -0.322 732
2000_07 -0.197 732
2000_08 -0.248 726
2000_09 -0.314 729
2000_10 -0.293 738
2000_11 -0.345 741
2000_12 -0.396 640
2001_01 -0.389 598
2001_02 -0.347 589
2001_03 -0.293 614
2001_04 -0.370 723
2001_05 -0.233 692
2001_06 -0.296 730
2001_07 -0.292 725
2001_08 -0.288 730
2001_09 -0.347 738
2001_10 -0.353 734
2001_11 -0.350 733
2001_12 -0.382 710
2002_01 -0.333 568
2002_02 -0.390 646
2002_03 -0.381 693
2002_04 -0.376 730
2002_05 -0.379 719
2002_06 -0.382 741
2002_07 -0.382 741
2002_08 -0.297 741
2002_09 -0.292 741
2002_10 -0.313 741
2002_11 -0.342 740
2002_12 -0.360 739
2003_01 -0.334 665
2003_02 -0.363 684
2003_03 -0.313 568
2003_04 -0.296 569
2003_05 -0.281 569
2003_06 -0.224 569
2003_07 -0.264 569
2003_08 -0.266 736
2003_09 -0.316 741
2003_10 -0.262 741
2003_11 -0.392 741
2003_12 -0.324 730
2004_01 -0.364 654
2004_02 -0.380 737
2004_03 -0.363 741
2004_04 -0.311 741
2004_05 -0.372 741
2004_06 -0.367 741
2004_07 -0.288 741
2004_08 -0.296 741
2004_09 -0.223 741
2004_10 -0.280 741
2004_11 -0.325 741
2004_12 -0.351 707
2005_01 -0.365 649
2005_02 -0.344 737
2005_03 -0.381 741
2005_04 -0.359 741
2005_05 -0.342 741
2005_06 -0.298 741
2005_07 -0.283 741
2005_08 -0.238 741
2005_09 -0.270 741
2005_10 -0.286 741
2005_11 -0.362 741
2005_12 -0.294 637
2006_01 -0.328 654
2006_02 -0.357 723
2006_03 -0.376 741
2006_04 -0.370 741
2006_05 -0.365 741
2006_06 -0.318 741
2006_07 -0.218 741
2006_08 -0.097 741
2006_09 -0.241 741
2006_10 -0.307 741
2006_11 -0.397 741
2006_12 -0.366 702
2007_01 -0.407 701
2007_02 -0.357 722
2007_03 -0.353 741
2007_04 -0.353 741
2007_05 -0.376 741
2007_06 -0.359 741
2007_07 -0.372 741
2007_08 -0.345 741
2007_09 -0.337 741
2007_10 -0.347 741
2007_11 -0.377 741
2007_12 -0.391 740
2008_01 -0.348 737
2008_02 -0.356 739
2008_03 -0.387 741
2008_04 -0.370 741
2008_05 -0.345 741
2008_06 -0.309 741
2008_07 -0.239 741
2008_08 -0.286 741
2008_09 -0.277 741
2008_10 -0.171 741
2008_11 -0.303 741
2008_12 -0.332 736
2009_01 -0.355 741
2009_02 -0.361 740
2009_03 -0.360 741
2009_04 -0.325 741
2009_05 -0.384 741
2009_06 -0.325 741
2009_07 -0.278 741
2009_08 -0.266 741
2009_09 -0.346 741
2009_10 -0.341 741
2009_11 -0.341 741
2009_12 -0.387 738
2010_01 -0.387 735
2010_02 -0.412 737
2010_03 -0.384 741
2010_04 -0.380 741
2010_05 -0.387 741
2010_06 -0.322 741
2010_07 -0.338 741
2010_08 -0.251 741
2010_09 -0.363 741
2010_10 -0.356 741
2010_11 -0.337 741
2010_12 -0.357 741
2011_01 -0.375 741
2011_02 -0.377 741
2011_03 -0.324 741
2011_04 -0.346 741
2011_05 -0.374 741
2011_06 -0.354 741
2011_07 -0.294 741
2011_08 -0.250 741
2011_09 -0.278 741
2011_10 -0.371 741
2011_11 -0.358 741
2011_12 -0.379 739
2012_01 -0.387 736
2012_02 -0.354 741
2012_03 -0.366 741
2012_04 -0.334 741
2012_05 -0.355 741
2012_06 -0.352 741
2012_07 -0.324 741
2012_08 -0.325 741
2012_09 -0.319 741
2012_10 -0.377 741
2012_11 -0.342 741
2012_12 -0.344 738
2013_01 -0.363 723
2013_02 -0.377 741
2013_03 -0.341 741
2013_04 -0.374 741
2013_05 -0.363 741
2013_06 -0.341 741
2013_07 -0.329 741
2013_08 -0.226 741
2013_09 -0.255 741
2013_10 -0.334 741
2013_11 -0.337 741
2013_12 -0.389 736
2014_01 -0.352 729
2014_02 -0.363 741
2014_03 -0.359 741
2014_04 -0.374 741
2014_05 -0.362 741
2014_06 -0.321 741
2014_07 -0.311 741
2014_08 -0.284 741
2014_09 -0.243 741
2014_10 -0.297 741
2014_11 -0.350 741
2014_12 -0.363 739
2015_01 -0.365 721
2015_02 -0.373 741
2015_03 -0.391 741
2015_04 -0.384 741
2015_05 -0.394 741
2015_06 -0.378 741
2015_07 -0.335 741
2015_08 -0.247 741
2015_09 -0.291 741
2015_10 -0.203 741
2015_11 -0.384 741
2015_12 -0.380 740
Spatial Correlation between Monthly Mean Soil Moisture and TWI
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

TWI <- read.csv("TWI_region_interest_monthly_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 -0.041 325
1995_02 -0.060 562
1995_03 -0.131 723
1995_04 -0.086 724
1995_05 -0.091 680
1995_06 -0.131 733
1995_07 -0.066 735
1995_08 -0.094 733
1995_09 -0.020 732
1995_10 0.002 734
1995_11 -0.010 730
1995_12 -0.073 406
1996_01 -0.047 428
1996_02 -0.100 691
1996_03 -0.077 736
1996_04 -0.085 740
1996_05 -0.096 734
1996_06 -0.082 739
1996_07 -0.021 698
1996_08 -0.027 740
1996_09 0.010 740
1996_10 -0.033 732
1996_11 -0.096 737
1996_12 -0.114 483
1997_01 0.000 404
1997_02 -0.156 598
1997_03 -0.094 736
1997_04 -0.040 727
1997_05 -0.088 726
1997_06 -0.104 734
1997_07 -0.090 730
1997_08 -0.090 737
1997_09 -0.059 726
1997_10 -0.092 733
1997_11 -0.109 727
1997_12 -0.105 662
1998_01 -0.116 606
1998_02 -0.114 710
1998_03 -0.118 716
1998_04 -0.124 730
1998_05 -0.131 730
1998_06 -0.094 726
1998_07 -0.095 733
1998_08 -0.009 724
1998_09 -0.080 737
1998_10 -0.096 741
1998_11 -0.117 741
1998_12 -0.107 695
1999_01 -0.102 589
1999_02 -0.127 719
1999_03 -0.131 739
1999_04 -0.123 739
1999_05 -0.098 733
1999_06 -0.105 737
1999_07 -0.066 729
1999_08 -0.036 725
1999_09 -0.073 732
1999_10 -0.043 712
1999_11 -0.117 727
1999_12 -0.117 692
2000_01 -0.121 602
2000_02 -0.141 725
2000_03 -0.130 741
2000_04 -0.097 735
2000_05 -0.121 733
2000_06 -0.081 732
2000_07 -0.050 732
2000_08 -0.074 726
2000_09 -0.079 729
2000_10 -0.088 738
2000_11 -0.074 741
2000_12 -0.101 640
2001_01 -0.095 598
2001_02 -0.104 589
2001_03 -0.058 614
2001_04 -0.097 723
2001_05 -0.048 692
2001_06 -0.100 730
2001_07 -0.068 725
2001_08 -0.055 730
2001_09 -0.081 738
2001_10 -0.094 734
2001_11 -0.089 733
2001_12 -0.082 710
2002_01 -0.105 568
2002_02 -0.121 646
2002_03 -0.088 693
2002_04 -0.101 730
2002_05 -0.114 719
2002_06 -0.123 741
2002_07 -0.092 741
2002_08 -0.063 741
2002_09 -0.074 741
2002_10 -0.065 741
2002_11 -0.093 740
2002_12 -0.085 739
2003_01 -0.116 665
2003_02 -0.101 684
2003_03 -0.093 568
2003_04 -0.084 569
2003_05 -0.066 569
2003_06 -0.035 569
2003_07 -0.028 569
2003_08 -0.037 736
2003_09 -0.084 741
2003_10 -0.090 741
2003_11 -0.090 741
2003_12 -0.105 730
2004_01 -0.098 654
2004_02 -0.101 737
2004_03 -0.099 741
2004_04 -0.089 741
2004_05 -0.093 741
2004_06 -0.079 741
2004_07 -0.072 741
2004_08 -0.057 741
2004_09 -0.012 741
2004_10 -0.042 741
2004_11 -0.065 741
2004_12 -0.116 707
2005_01 -0.099 649
2005_02 -0.106 737
2005_03 -0.111 741
2005_04 -0.112 741
2005_05 -0.071 741
2005_06 -0.105 741
2005_07 -0.063 741
2005_08 -0.053 741
2005_09 -0.068 741
2005_10 -0.043 741
2005_11 -0.092 741
2005_12 -0.086 637
2006_01 -0.112 654
2006_02 -0.104 723
2006_03 -0.107 741
2006_04 -0.101 741
2006_05 -0.123 741
2006_06 -0.095 741
2006_07 -0.079 741
2006_08 -0.010 741
2006_09 -0.013 741
2006_10 -0.040 741
2006_11 -0.104 741
2006_12 -0.097 702
2007_01 -0.077 701
2007_02 -0.117 722
2007_03 -0.118 741
2007_04 -0.134 741
2007_05 -0.116 741
2007_06 -0.111 741
2007_07 -0.103 741
2007_08 -0.072 741
2007_09 -0.075 741
2007_10 -0.103 741
2007_11 -0.101 741
2007_12 -0.112 740
2008_01 -0.119 737
2008_02 -0.113 739
2008_03 -0.114 741
2008_04 -0.122 741
2008_05 -0.113 741
2008_06 -0.114 741
2008_07 -0.075 741
2008_08 -0.068 741
2008_09 -0.071 741
2008_10 -0.035 741
2008_11 -0.102 741
2008_12 -0.105 736
2009_01 -0.110 741
2009_02 -0.116 740
2009_03 -0.104 741
2009_04 -0.123 741
2009_05 -0.109 741
2009_06 -0.082 741
2009_07 -0.035 741
2009_08 -0.058 741
2009_09 -0.075 741
2009_10 -0.074 741
2009_11 -0.101 741
2009_12 -0.070 738
2010_01 -0.104 735
2010_02 -0.111 737
2010_03 -0.127 741
2010_04 -0.121 741
2010_05 -0.123 741
2010_06 -0.114 741
2010_07 -0.073 741
2010_08 -0.061 741
2010_09 -0.092 741
2010_10 -0.088 741
2010_11 -0.098 741
2010_12 -0.102 741
2011_01 -0.107 741
2011_02 -0.124 741
2011_03 -0.114 741
2011_04 -0.115 741
2011_05 -0.113 741
2011_06 -0.097 741
2011_07 -0.064 741
2011_08 -0.092 741
2011_09 -0.070 741
2011_10 -0.083 741
2011_11 -0.121 741
2011_12 -0.113 739
2012_01 -0.104 736
2012_02 -0.119 741
2012_03 -0.129 741
2012_04 -0.132 741
2012_05 -0.118 741
2012_06 -0.099 741
2012_07 -0.033 741
2012_08 -0.023 741
2012_09 -0.048 741
2012_10 -0.066 741
2012_11 -0.096 741
2012_12 -0.096 738
2013_01 -0.125 723
2013_02 -0.125 741
2013_03 -0.118 741
2013_04 -0.132 741
2013_05 -0.129 741
2013_06 -0.116 741
2013_07 -0.103 741
2013_08 -0.139 741
2013_09 -0.112 741
2013_10 -0.110 741
2013_11 -0.103 741
2013_12 -0.091 736
2014_01 -0.130 729
2014_02 -0.126 741
2014_03 -0.125 741
2014_04 -0.110 741
2014_05 -0.097 741
2014_06 -0.117 741
2014_07 -0.100 741
2014_08 -0.084 741
2014_09 -0.030 741
2014_10 -0.095 741
2014_11 -0.098 741
2014_12 -0.114 739
2015_01 -0.087 721
2015_02 -0.109 741
2015_03 -0.112 741
2015_04 -0.119 741
2015_05 -0.112 741
2015_06 -0.101 741
2015_07 -0.094 741
2015_08 -0.087 741
2015_09 -0.111 741
2015_10 -0.048 741
2015_11 -0.111 741
2015_12 -0.122 740

MEDIAN Monthly Soil Moisture Values

Spatial Correlation between Monthly Median Soil Moisture and Precipitation
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

SoilMoisture_MEDIAN <- read.csv("SoilMoisture_region_interest_MEDIAN_monthly_pixel_values_v45.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_monthly_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 0.636 325
1995_02 0.560 562
1995_03 0.661 723
1995_04 0.713 724
1995_05 0.425 680
1995_06 0.522 733
1995_07 0.271 735
1995_08 0.115 733
1995_09 0.404 732
1995_10 0.212 734
1995_11 0.326 730
1995_12 0.306 406
1996_01 0.641 428
1996_02 0.504 691
1996_03 0.694 736
1996_04 0.786 740
1996_05 0.498 734
1996_06 0.356 739
1996_07 0.066 698
1996_08 0.110 740
1996_09 0.113 740
1996_10 0.573 732
1996_11 0.656 737
1996_12 0.350 483
1997_01 0.467 404
1997_02 0.540 598
1997_03 0.759 736
1997_04 0.165 727
1997_05 0.433 726
1997_06 0.462 734
1997_07 0.187 730
1997_08 0.278 737
1997_09 0.361 726
1997_10 0.537 733
1997_11 0.523 727
1997_12 0.495 662
1998_01 0.600 606
1998_02 0.369 710
1998_03 0.685 716
1998_04 0.595 730
1998_05 0.571 730
1998_06 0.650 726
1998_07 0.437 733
1998_08 0.242 724
1998_09 0.791 737
1998_10 0.424 741
1998_11 0.645 741
1998_12 0.572 695
1999_01 0.431 589
1999_02 0.667 719
1999_03 0.576 739
1999_04 0.448 739
1999_05 0.676 733
1999_06 0.540 737
1999_07 0.122 729
1999_08 0.079 725
1999_09 0.536 732
1999_10 0.337 712
1999_11 0.605 727
1999_12 0.652 692
2000_01 0.536 602
2000_02 0.798 725
2000_03 0.266 741
2000_04 0.402 735
2000_05 0.732 733
2000_06 0.652 732
2000_07 0.458 732
2000_08 -0.134 726
2000_09 0.611 729
2000_10 -0.004 738
2000_11 0.447 741
2000_12 0.548 640
2001_01 0.534 598
2001_02 0.583 589
2001_03 0.375 614
2001_04 0.669 723
2001_05 0.213 692
2001_06 0.703 730
2001_07 0.336 725
2001_08 0.333 730
2001_09 0.523 738
2001_10 0.779 734
2001_11 0.367 733
2001_12 0.608 710
2002_01 0.569 568
2002_02 0.513 646
2002_03 0.651 693
2002_04 0.713 730
2002_05 0.734 719
2002_06 0.482 741
2002_07 0.252 741
2002_08 0.185 741
2002_09 0.301 741
2002_10 0.317 741
2002_11 0.405 740
2002_12 0.463 739
2003_01 0.260 665
2003_02 0.636 684
2003_03 0.636 568
2003_04 0.521 569
2003_05 0.623 569
2003_06 0.115 569
2003_07 0.399 569
2003_08 0.395 736
2003_09 0.497 741
2003_10 0.615 741
2003_11 0.626 741
2003_12 0.699 730
2004_01 0.612 654
2004_02 0.345 737
2004_03 0.600 741
2004_04 0.499 741
2004_05 0.735 741
2004_06 0.403 741
2004_07 0.418 741
2004_08 -0.277 741
2004_09 -0.302 741
2004_10 0.397 741
2004_11 0.360 741
2004_12 0.520 707
2005_01 0.590 649
2005_02 0.587 737
2005_03 0.493 741
2005_04 0.536 741
2005_05 0.349 741
2005_06 0.387 741
2005_07 0.221 741
2005_08 0.290 741
2005_09 0.414 741
2005_10 0.316 741
2005_11 0.321 741
2005_12 0.547 637
2006_01 0.532 654
2006_02 0.482 723
2006_03 0.601 741
2006_04 0.777 741
2006_05 0.557 741
2006_06 0.500 741
2006_07 0.258 741
2006_08 0.358 741
2006_09 0.266 741
2006_10 0.396 741
2006_11 0.688 741
2006_12 0.051 702
2007_01 0.557 701
2007_02 0.655 722
2007_03 0.095 741
2007_04 0.588 741
2007_05 0.551 741
2007_06 0.679 741
2007_07 0.601 741
2007_08 0.198 741
2007_09 0.458 741
2007_10 0.769 741
2007_11 0.509 741
2007_12 0.580 740
2008_01 0.605 737
2008_02 0.810 739
2008_03 0.777 741
2008_04 0.835 741
2008_05 0.679 741
2008_06 0.782 741
2008_07 0.265 741
2008_08 0.192 741
2008_09 0.572 741
2008_10 0.218 741
2008_11 0.662 741
2008_12 0.673 736
2009_01 0.607 741
2009_02 0.768 740
2009_03 0.782 741
2009_04 0.592 741
2009_05 0.671 741
2009_06 0.293 741
2009_07 0.347 741
2009_08 0.496 741
2009_09 0.720 741
2009_10 0.593 741
2009_11 0.696 741
2009_12 0.624 738
2010_01 0.562 735
2010_02 0.461 737
2010_03 0.633 741
2010_04 0.105 741
2010_05 0.581 741
2010_06 0.506 741
2010_07 0.318 741
2010_08 0.155 741
2010_09 0.662 741
2010_10 0.246 741
2010_11 0.647 741
2010_12 0.465 741
2011_01 0.474 741
2011_02 0.746 741
2011_03 0.651 741
2011_04 0.780 741
2011_05 0.823 741
2011_06 0.387 741
2011_07 0.206 741
2011_08 0.604 741
2011_09 0.547 741
2011_10 0.401 741
2011_11 0.663 741
2011_12 0.458 739
2012_01 0.565 736
2012_02 0.669 741
2012_03 0.823 741
2012_04 0.544 741
2012_05 0.467 741
2012_06 0.258 741
2012_07 0.455 741
2012_08 0.355 741
2012_09 0.441 741
2012_10 0.597 741
2012_11 0.648 741
2012_12 0.546 738
2013_01 0.700 723
2013_02 0.548 741
2013_03 0.763 741
2013_04 0.814 741
2013_05 0.819 741
2013_06 0.209 741
2013_07 0.411 741
2013_08 0.510 741
2013_09 0.160 741
2013_10 0.741 741
2013_11 0.625 741
2013_12 0.689 736
2014_01 0.617 729
2014_02 0.501 741
2014_03 0.804 741
2014_04 0.782 741
2014_05 0.583 741
2014_06 0.383 741
2014_07 0.349 741
2014_08 0.262 741
2014_09 0.519 741
2014_10 0.710 741
2014_11 0.428 741
2014_12 0.712 739
2015_01 0.446 721
2015_02 0.630 741
2015_03 0.747 741
2015_04 0.495 741
2015_05 0.567 741
2015_06 0.507 741
2015_07 0.280 741
2015_08 0.500 741
2015_09 0.385 741
2015_10 -0.048 741
2015_11 0.701 741
2015_12 0.626 740
Spatial Correlation between Monthly Median Soil Moisture and Maximum Temperature
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

Temperature_Max <- read.csv("Daymet_tmax_region_interest_monthly_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_Tmax <- 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 0.082 325
1995_02 -0.106 562
1995_03 0.085 723
1995_04 0.144 724
1995_05 -0.111 680
1995_06 -0.174 733
1995_07 -0.441 735
1995_08 0.212 733
1995_09 0.134 732
1995_10 0.228 734
1995_11 -0.221 730
1995_12 0.168 406
1996_01 -0.310 428
1996_02 -0.222 691
1996_03 -0.154 736
1996_04 -0.194 740
1996_05 -0.679 734
1996_06 -0.445 739
1996_07 -0.337 698
1996_08 -0.200 740
1996_09 0.161 740
1996_10 0.037 732
1996_11 -0.371 737
1996_12 -0.182 483
1997_01 0.133 404
1997_02 0.345 598
1997_03 -0.217 736
1997_04 0.274 727
1997_05 0.165 726
1997_06 -0.111 734
1997_07 -0.264 730
1997_08 -0.313 737
1997_09 -0.006 726
1997_10 0.096 733
1997_11 -0.075 727
1997_12 0.162 662
1998_01 -0.250 606
1998_02 0.303 710
1998_03 0.098 716
1998_04 -0.059 730
1998_05 -0.365 730
1998_06 -0.530 726
1998_07 -0.479 733
1998_08 -0.224 724
1998_09 -0.393 737
1998_10 -0.072 741
1998_11 -0.223 741
1998_12 0.024 695
1999_01 -0.287 589
1999_02 -0.263 719
1999_03 -0.169 739
1999_04 0.174 739
1999_05 0.006 733
1999_06 -0.333 737
1999_07 -0.286 729
1999_08 -0.167 725
1999_09 -0.210 732
1999_10 0.207 712
1999_11 0.061 727
1999_12 0.123 692
2000_01 -0.249 602
2000_02 -0.387 725
2000_03 -0.133 741
2000_04 -0.378 735
2000_05 -0.599 733
2000_06 -0.466 732
2000_07 -0.537 732
2000_08 -0.085 726
2000_09 -0.271 729
2000_10 0.334 738
2000_11 0.328 741
2000_12 -0.215 640
2001_01 0.168 598
2001_02 0.081 589
2001_03 0.246 614
2001_04 0.043 723
2001_05 -0.184 692
2001_06 -0.642 730
2001_07 -0.586 725
2001_08 -0.127 730
2001_09 -0.561 738
2001_10 -0.476 734
2001_11 0.457 733
2001_12 0.216 710
2002_01 0.011 568
2002_02 -0.063 646
2002_03 -0.173 693
2002_04 -0.144 730
2002_05 -0.655 719
2002_06 -0.746 741
2002_07 -0.227 741
2002_08 -0.182 741
2002_09 0.269 741
2002_10 0.285 741
2002_11 0.178 740
2002_12 0.411 739
2003_01 -0.494 665
2003_02 -0.242 684
2003_03 -0.280 568
2003_04 -0.191 569
2003_05 -0.342 569
2003_06 -0.001 569
2003_07 -0.227 569
2003_08 -0.031 736
2003_09 -0.386 741
2003_10 -0.288 741
2003_11 0.245 741
2003_12 -0.305 730
2004_01 -0.147 654
2004_02 -0.196 737
2004_03 -0.006 741
2004_04 0.055 741
2004_05 -0.639 741
2004_06 -0.441 741
2004_07 -0.414 741
2004_08 -0.039 741
2004_09 0.270 741
2004_10 0.305 741
2004_11 0.374 741
2004_12 -0.008 707
2005_01 -0.070 649
2005_02 0.218 737
2005_03 0.171 741
2005_04 -0.006 741
2005_05 0.137 741
2005_06 -0.279 741
2005_07 -0.141 741
2005_08 0.188 741
2005_09 0.052 741
2005_10 0.188 741
2005_11 -0.088 741
2005_12 -0.202 637
2006_01 -0.137 654
2006_02 -0.310 723
2006_03 0.202 741
2006_04 -0.165 741
2006_05 -0.514 741
2006_06 -0.661 741
2006_07 -0.321 741
2006_08 -0.325 741
2006_09 0.036 741
2006_10 0.109 741
2006_11 -0.016 741
2006_12 0.348 702
2007_01 0.379 701
2007_02 -0.251 722
2007_03 0.365 741
2007_04 0.104 741
2007_05 0.396 741
2007_06 -0.120 741
2007_07 -0.487 741
2007_08 0.106 741
2007_09 -0.145 741
2007_10 -0.342 741
2007_11 0.043 741
2007_12 0.205 740
2008_01 -0.386 737
2008_02 -0.345 739
2008_03 -0.115 741
2008_04 -0.285 741
2008_05 -0.436 741
2008_06 -0.717 741
2008_07 -0.201 741
2008_08 -0.270 741
2008_09 -0.227 741
2008_10 -0.289 741
2008_11 -0.445 741
2008_12 -0.215 736
2009_01 -0.510 741
2009_02 -0.377 740
2009_03 -0.326 741
2009_04 -0.401 741
2009_05 -0.337 741
2009_06 -0.208 741
2009_07 -0.491 741
2009_08 -0.540 741
2009_09 -0.318 741
2009_10 -0.234 741
2009_11 -0.153 741
2009_12 0.116 738
2010_01 -0.356 735
2010_02 0.117 737
2010_03 -0.101 741
2010_04 0.433 741
2010_05 0.035 741
2010_06 -0.460 741
2010_07 -0.066 741
2010_08 0.046 741
2010_09 -0.542 741
2010_10 0.066 741
2010_11 0.009 741
2010_12 -0.406 741
2011_01 -0.378 741
2011_02 -0.192 741
2011_03 -0.425 741
2011_04 -0.347 741
2011_05 -0.629 741
2011_06 -0.546 741
2011_07 -0.188 741
2011_08 -0.386 741
2011_09 -0.300 741
2011_10 0.175 741
2011_11 -0.121 741
2011_12 0.612 739
2012_01 -0.031 736
2012_02 0.144 741
2012_03 0.092 741
2012_04 -0.380 741
2012_05 -0.269 741
2012_06 -0.521 741
2012_07 -0.032 741
2012_08 -0.055 741
2012_09 -0.109 741
2012_10 -0.158 741
2012_11 -0.361 741
2012_12 0.031 738
2013_01 0.151 723
2013_02 -0.021 741
2013_03 -0.588 741
2013_04 -0.173 741
2013_05 -0.653 741
2013_06 -0.758 741
2013_07 -0.278 741
2013_08 -0.577 741
2013_09 -0.218 741
2013_10 -0.234 741
2013_11 -0.247 741
2013_12 -0.063 736
2014_01 -0.489 729
2014_02 -0.293 741
2014_03 -0.418 741
2014_04 -0.311 741
2014_05 -0.555 741
2014_06 -0.606 741
2014_07 -0.546 741
2014_08 -0.402 741
2014_09 -0.255 741
2014_10 -0.291 741
2014_11 -0.150 741
2014_12 -0.265 739
2015_01 0.188 721
2015_02 -0.488 741
2015_03 -0.632 741
2015_04 -0.162 741
2015_05 0.382 741
2015_06 -0.174 741
2015_07 -0.229 741
2015_08 -0.434 741
2015_09 -0.497 741
2015_10 -0.227 741
2015_11 0.282 741
2015_12 0.394 740
Spatial Correlation between Monthly Median Soil Moisture and Minimum Temperature
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

Temperature_Min <- read.csv("Daymet_tmin_region_interest_monthly_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_Tmin <- 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 0.517 325
1995_02 0.501 562
1995_03 0.607 723
1995_04 0.544 724
1995_05 0.302 680
1995_06 0.521 733
1995_07 0.272 735
1995_08 0.438 733
1995_09 0.425 732
1995_10 0.397 734
1995_11 0.165 730
1995_12 0.380 406
1996_01 0.363 428
1996_02 0.178 691
1996_03 0.476 736
1996_04 0.542 740
1996_05 0.292 734
1996_06 0.231 739
1996_07 0.032 698
1996_08 0.007 740
1996_09 0.246 740
1996_10 0.469 732
1996_11 0.439 737
1996_12 0.337 483
1997_01 0.365 404
1997_02 0.543 598
1997_03 0.626 736
1997_04 0.299 727
1997_05 0.508 726
1997_06 0.505 734
1997_07 0.341 730
1997_08 0.160 737
1997_09 0.243 726
1997_10 0.594 733
1997_11 0.518 727
1997_12 0.602 662
1998_01 0.518 606
1998_02 0.613 710
1998_03 0.692 716
1998_04 0.578 730
1998_05 0.493 730
1998_06 0.403 726
1998_07 -0.034 733
1998_08 -0.055 724
1998_09 0.314 737
1998_10 0.303 741
1998_11 0.366 741
1998_12 0.602 695
1999_01 0.298 589
1999_02 0.650 719
1999_03 0.357 739
1999_04 0.542 739
1999_05 0.410 733
1999_06 0.445 737
1999_07 0.312 729
1999_08 -0.106 725
1999_09 0.013 732
1999_10 0.420 712
1999_11 0.645 727
1999_12 0.564 692
2000_01 0.448 602
2000_02 0.476 725
2000_03 0.449 741
2000_04 0.392 735
2000_05 0.480 733
2000_06 0.364 732
2000_07 0.087 732
2000_08 0.178 726
2000_09 0.228 729
2000_10 0.413 738
2000_11 0.548 741
2000_12 0.405 640
2001_01 0.390 598
2001_02 0.413 589
2001_03 0.407 614
2001_04 0.669 723
2001_05 0.173 692
2001_06 0.218 730
2001_07 0.119 725
2001_08 0.190 730
2001_09 0.422 738
2001_10 0.507 734
2001_11 0.553 733
2001_12 0.703 710
2002_01 0.454 568
2002_02 0.619 646
2002_03 0.747 693
2002_04 0.667 730
2002_05 0.534 719
2002_06 0.392 741
2002_07 0.616 741
2002_08 0.385 741
2002_09 0.430 741
2002_10 0.528 741
2002_11 0.403 740
2002_12 0.578 739
2003_01 0.114 665
2003_02 0.376 684
2003_03 0.461 568
2003_04 0.484 569
2003_05 0.328 569
2003_06 0.251 569
2003_07 0.391 569
2003_08 0.363 736
2003_09 0.267 741
2003_10 0.233 741
2003_11 0.641 741
2003_12 0.539 730
2004_01 0.380 654
2004_02 0.494 737
2004_03 0.500 741
2004_04 0.313 741
2004_05 0.615 741
2004_06 0.478 741
2004_07 0.213 741
2004_08 0.267 741
2004_09 0.292 741
2004_10 0.495 741
2004_11 0.465 741
2004_12 0.493 707
2005_01 0.323 649
2005_02 0.407 737
2005_03 0.518 741
2005_04 0.583 741
2005_05 0.353 741
2005_06 0.312 741
2005_07 0.432 741
2005_08 0.361 741
2005_09 0.398 741
2005_10 0.409 741
2005_11 0.535 741
2005_12 0.236 637
2006_01 0.425 654
2006_02 0.476 723
2006_03 0.516 741
2006_04 0.588 741
2006_05 0.490 741
2006_06 0.115 741
2006_07 0.029 741
2006_08 -0.263 741
2006_09 0.159 741
2006_10 0.378 741
2006_11 0.638 741
2006_12 0.619 702
2007_01 0.571 701
2007_02 0.311 722
2007_03 0.644 741
2007_04 0.473 741
2007_05 0.698 741
2007_06 0.722 741
2007_07 0.713 741
2007_08 0.624 741
2007_09 0.581 741
2007_10 0.651 741
2007_11 0.609 741
2007_12 0.548 740
2008_01 0.435 737
2008_02 0.378 739
2008_03 0.632 741
2008_04 0.608 741
2008_05 0.567 741
2008_06 0.444 741
2008_07 0.270 741
2008_08 0.335 741
2008_09 0.433 741
2008_10 0.090 741
2008_11 0.383 741
2008_12 0.305 736
2009_01 0.317 741
2009_02 0.499 740
2009_03 0.517 741
2009_04 0.374 741
2009_05 0.614 741
2009_06 0.460 741
2009_07 0.092 741
2009_08 0.106 741
2009_09 0.549 741
2009_10 0.476 741
2009_11 0.625 741
2009_12 0.631 738
2010_01 0.501 735
2010_02 0.521 737
2010_03 0.595 741
2010_04 0.639 741
2010_05 0.601 741
2010_06 0.425 741
2010_07 0.473 741
2010_08 0.233 741
2010_09 0.452 741
2010_10 0.382 741
2010_11 0.592 741
2010_12 0.183 741
2011_01 0.394 741
2011_02 0.517 741
2011_03 0.475 741
2011_04 0.528 741
2011_05 0.558 741
2011_06 0.311 741
2011_07 0.138 741
2011_08 -0.086 741
2011_09 -0.081 741
2011_10 0.445 741
2011_11 0.483 741
2011_12 0.556 739
2012_01 0.500 736
2012_02 0.582 741
2012_03 0.731 741
2012_04 0.534 741
2012_05 0.440 741
2012_06 0.304 741
2012_07 0.270 741
2012_08 0.276 741
2012_09 0.385 741
2012_10 0.494 741
2012_11 0.242 741
2012_12 0.542 738
2013_01 0.474 723
2013_02 0.469 741
2013_03 0.296 741
2013_04 0.697 741
2013_05 0.536 741
2013_06 0.358 741
2013_07 0.206 741
2013_08 0.084 741
2013_09 0.030 741
2013_10 0.510 741
2013_11 0.445 741
2013_12 0.517 736
2014_01 0.133 729
2014_02 0.242 741
2014_03 0.382 741
2014_04 0.483 741
2014_05 0.539 741
2014_06 0.411 741
2014_07 -0.049 741
2014_08 0.240 741
2014_09 0.124 741
2014_10 0.313 741
2014_11 0.489 741
2014_12 0.530 739
2015_01 0.302 721
2015_02 0.128 741
2015_03 0.638 741
2015_04 0.719 741
2015_05 0.677 741
2015_06 0.634 741
2015_07 0.466 741
2015_08 0.035 741
2015_09 0.204 741
2015_10 -0.062 741
2015_11 0.630 741
2015_12 0.626 740
Spatial Correlation between Monthly Median Soil Moisture and Soil Texture
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

Soil_Texture <- read.csv("Soil_Texture_region_interest_monthly_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 -0.320 325
1995_02 -0.357 562
1995_03 -0.329 723
1995_04 -0.354 724
1995_05 -0.276 680
1995_06 -0.335 733
1995_07 -0.277 735
1995_08 -0.290 733
1995_09 -0.252 732
1995_10 -0.204 734
1995_11 -0.306 730
1995_12 -0.228 406
1996_01 -0.319 428
1996_02 -0.329 691
1996_03 -0.359 736
1996_04 -0.364 740
1996_05 -0.320 734
1996_06 -0.287 739
1996_07 -0.235 698
1996_08 -0.131 740
1996_09 -0.254 740
1996_10 -0.309 732
1996_11 -0.340 737
1996_12 -0.292 483
1997_01 -0.239 404
1997_02 -0.278 598
1997_03 -0.372 736
1997_04 -0.275 727
1997_05 -0.341 726
1997_06 -0.356 734
1997_07 -0.281 730
1997_08 -0.206 737
1997_09 -0.226 726
1997_10 -0.321 733
1997_11 -0.345 727
1997_12 -0.361 662
1998_01 -0.401 606
1998_02 -0.354 710
1998_03 -0.390 716
1998_04 -0.340 730
1998_05 -0.329 730
1998_06 -0.349 726
1998_07 -0.226 733
1998_08 -0.136 724
1998_09 -0.292 737
1998_10 -0.275 741
1998_11 -0.287 741
1998_12 -0.325 695
1999_01 -0.320 589
1999_02 -0.364 719
1999_03 -0.333 739
1999_04 -0.332 739
1999_05 -0.336 733
1999_06 -0.314 737
1999_07 -0.283 729
1999_08 -0.190 725
1999_09 -0.256 732
1999_10 -0.294 712
1999_11 -0.358 727
1999_12 -0.319 692
2000_01 -0.354 602
2000_02 -0.349 725
2000_03 -0.306 741
2000_04 -0.352 735
2000_05 -0.343 733
2000_06 -0.325 732
2000_07 -0.217 732
2000_08 -0.253 726
2000_09 -0.324 729
2000_10 -0.269 738
2000_11 -0.334 741
2000_12 -0.388 640
2001_01 -0.392 598
2001_02 -0.338 589
2001_03 -0.293 614
2001_04 -0.374 723
2001_05 -0.226 692
2001_06 -0.297 730
2001_07 -0.291 725
2001_08 -0.284 730
2001_09 -0.354 738
2001_10 -0.353 734
2001_11 -0.355 733
2001_12 -0.380 710
2002_01 -0.342 568
2002_02 -0.391 646
2002_03 -0.391 693
2002_04 -0.383 730
2002_05 -0.374 719
2002_06 -0.371 741
2002_07 -0.369 741
2002_08 -0.291 741
2002_09 -0.309 741
2002_10 -0.338 741
2002_11 -0.334 740
2002_12 -0.361 739
2003_01 -0.332 665
2003_02 -0.356 684
2003_03 -0.313 568
2003_04 -0.287 569
2003_05 -0.270 569
2003_06 -0.216 569
2003_07 -0.255 569
2003_08 -0.276 736
2003_09 -0.312 741
2003_10 -0.275 741
2003_11 -0.377 741
2003_12 -0.315 730
2004_01 -0.371 654
2004_02 -0.377 737
2004_03 -0.365 741
2004_04 -0.330 741
2004_05 -0.362 741
2004_06 -0.345 741
2004_07 -0.271 741
2004_08 -0.297 741
2004_09 -0.276 741
2004_10 -0.290 741
2004_11 -0.320 741
2004_12 -0.364 707
2005_01 -0.361 649
2005_02 -0.345 737
2005_03 -0.384 741
2005_04 -0.369 741
2005_05 -0.337 741
2005_06 -0.288 741
2005_07 -0.297 741
2005_08 -0.225 741
2005_09 -0.275 741
2005_10 -0.298 741
2005_11 -0.368 741
2005_12 -0.291 637
2006_01 -0.318 654
2006_02 -0.350 723
2006_03 -0.366 741
2006_04 -0.366 741
2006_05 -0.354 741
2006_06 -0.321 741
2006_07 -0.237 741
2006_08 -0.058 741
2006_09 -0.259 741
2006_10 -0.335 741
2006_11 -0.389 741
2006_12 -0.372 702
2007_01 -0.395 701
2007_02 -0.363 722
2007_03 -0.359 741
2007_04 -0.354 741
2007_05 -0.372 741
2007_06 -0.369 741
2007_07 -0.371 741
2007_08 -0.347 741
2007_09 -0.330 741
2007_10 -0.356 741
2007_11 -0.380 741
2007_12 -0.388 740
2008_01 -0.352 737
2008_02 -0.356 739
2008_03 -0.385 741
2008_04 -0.371 741
2008_05 -0.348 741
2008_06 -0.308 741
2008_07 -0.248 741
2008_08 -0.286 741
2008_09 -0.266 741
2008_10 -0.168 741
2008_11 -0.305 741
2008_12 -0.337 736
2009_01 -0.352 741
2009_02 -0.365 740
2009_03 -0.360 741
2009_04 -0.317 741
2009_05 -0.380 741
2009_06 -0.325 741
2009_07 -0.283 741
2009_08 -0.272 741
2009_09 -0.345 741
2009_10 -0.343 741
2009_11 -0.344 741
2009_12 -0.381 738
2010_01 -0.386 735
2010_02 -0.412 737
2010_03 -0.384 741
2010_04 -0.383 741
2010_05 -0.376 741
2010_06 -0.324 741
2010_07 -0.326 741
2010_08 -0.263 741
2010_09 -0.359 741
2010_10 -0.358 741
2010_11 -0.333 741
2010_12 -0.343 741
2011_01 -0.369 741
2011_02 -0.378 741
2011_03 -0.331 741
2011_04 -0.344 741
2011_05 -0.364 741
2011_06 -0.359 741
2011_07 -0.307 741
2011_08 -0.257 741
2011_09 -0.280 741
2011_10 -0.387 741
2011_11 -0.345 741
2011_12 -0.382 739
2012_01 -0.385 736
2012_02 -0.352 741
2012_03 -0.373 741
2012_04 -0.338 741
2012_05 -0.346 741
2012_06 -0.354 741
2012_07 -0.329 741
2012_08 -0.311 741
2012_09 -0.326 741
2012_10 -0.383 741
2012_11 -0.350 741
2012_12 -0.339 738
2013_01 -0.362 723
2013_02 -0.369 741
2013_03 -0.339 741
2013_04 -0.371 741
2013_05 -0.372 741
2013_06 -0.340 741
2013_07 -0.327 741
2013_08 -0.232 741
2013_09 -0.243 741
2013_10 -0.324 741
2013_11 -0.335 741
2013_12 -0.385 736
2014_01 -0.356 729
2014_02 -0.361 741
2014_03 -0.359 741
2014_04 -0.372 741
2014_05 -0.361 741
2014_06 -0.313 741
2014_07 -0.303 741
2014_08 -0.280 741
2014_09 -0.234 741
2014_10 -0.298 741
2014_11 -0.366 741
2014_12 -0.353 739
2015_01 -0.356 721
2015_02 -0.377 741
2015_03 -0.396 741
2015_04 -0.376 741
2015_05 -0.390 741
2015_06 -0.380 741
2015_07 -0.337 741
2015_08 -0.243 741
2015_09 -0.300 741
2015_10 -0.134 741
2015_11 -0.382 741
2015_12 -0.375 740
Spatial Correlation between Monthly Median Soil Moisture and Topographic Wetness Index
setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

TWI <- read.csv("TWI_region_interest_monthly_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_TWI <- 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 -0.049 325
1995_02 -0.064 562
1995_03 -0.130 723
1995_04 -0.108 724
1995_05 -0.102 680
1995_06 -0.122 733
1995_07 -0.081 735
1995_08 -0.080 733
1995_09 -0.020 732
1995_10 0.007 734
1995_11 0.004 730
1995_12 -0.101 406
1996_01 -0.039 428
1996_02 -0.109 691
1996_03 -0.072 736
1996_04 -0.079 740
1996_05 -0.089 734
1996_06 -0.107 739
1996_07 -0.028 698
1996_08 -0.043 740
1996_09 0.028 740
1996_10 -0.042 732
1996_11 -0.091 737
1996_12 -0.113 483
1997_01 -0.007 404
1997_02 -0.152 598
1997_03 -0.103 736
1997_04 -0.054 727
1997_05 -0.085 726
1997_06 -0.104 734
1997_07 -0.093 730
1997_08 -0.101 737
1997_09 -0.042 726
1997_10 -0.106 733
1997_11 -0.121 727
1997_12 -0.108 662
1998_01 -0.120 606
1998_02 -0.115 710
1998_03 -0.114 716
1998_04 -0.114 730
1998_05 -0.123 730
1998_06 -0.093 726
1998_07 -0.092 733
1998_08 -0.017 724
1998_09 -0.084 737
1998_10 -0.107 741
1998_11 -0.104 741
1998_12 -0.106 695
1999_01 -0.101 589
1999_02 -0.121 719
1999_03 -0.129 739
1999_04 -0.124 739
1999_05 -0.094 733
1999_06 -0.099 737
1999_07 -0.058 729
1999_08 -0.022 725
1999_09 -0.076 732
1999_10 -0.050 712
1999_11 -0.104 727
1999_12 -0.115 692
2000_01 -0.120 602
2000_02 -0.139 725
2000_03 -0.130 741
2000_04 -0.105 735
2000_05 -0.118 733
2000_06 -0.082 732
2000_07 -0.055 732
2000_08 -0.080 726
2000_09 -0.077 729
2000_10 -0.086 738
2000_11 -0.071 741
2000_12 -0.103 640
2001_01 -0.086 598
2001_02 -0.099 589
2001_03 -0.063 614
2001_04 -0.092 723
2001_05 -0.053 692
2001_06 -0.098 730
2001_07 -0.066 725
2001_08 -0.054 730
2001_09 -0.089 738
2001_10 -0.103 734
2001_11 -0.108 733
2001_12 -0.083 710
2002_01 -0.106 568
2002_02 -0.132 646
2002_03 -0.091 693
2002_04 -0.106 730
2002_05 -0.118 719
2002_06 -0.113 741
2002_07 -0.089 741
2002_08 -0.068 741
2002_09 -0.077 741
2002_10 -0.076 741
2002_11 -0.091 740
2002_12 -0.084 739
2003_01 -0.120 665
2003_02 -0.097 684
2003_03 -0.091 568
2003_04 -0.077 569
2003_05 -0.078 569
2003_06 -0.026 569
2003_07 -0.011 569
2003_08 -0.045 736
2003_09 -0.079 741
2003_10 -0.089 741
2003_11 -0.098 741
2003_12 -0.100 730
2004_01 -0.096 654
2004_02 -0.110 737
2004_03 -0.111 741
2004_04 -0.104 741
2004_05 -0.094 741
2004_06 -0.075 741
2004_07 -0.062 741
2004_08 -0.057 741
2004_09 -0.032 741
2004_10 -0.050 741
2004_11 -0.060 741
2004_12 -0.125 707
2005_01 -0.097 649
2005_02 -0.106 737
2005_03 -0.113 741
2005_04 -0.114 741
2005_05 -0.070 741
2005_06 -0.107 741
2005_07 -0.063 741
2005_08 -0.044 741
2005_09 -0.064 741
2005_10 -0.044 741
2005_11 -0.093 741
2005_12 -0.090 637
2006_01 -0.116 654
2006_02 -0.106 723
2006_03 -0.101 741
2006_04 -0.108 741
2006_05 -0.117 741
2006_06 -0.091 741
2006_07 -0.078 741
2006_08 -0.008 741
2006_09 -0.016 741
2006_10 -0.060 741
2006_11 -0.107 741
2006_12 -0.101 702
2007_01 -0.091 701
2007_02 -0.121 722
2007_03 -0.108 741
2007_04 -0.139 741
2007_05 -0.119 741
2007_06 -0.112 741
2007_07 -0.097 741
2007_08 -0.069 741
2007_09 -0.076 741
2007_10 -0.104 741
2007_11 -0.106 741
2007_12 -0.112 740
2008_01 -0.118 737
2008_02 -0.117 739
2008_03 -0.113 741
2008_04 -0.119 741
2008_05 -0.119 741
2008_06 -0.119 741
2008_07 -0.072 741
2008_08 -0.068 741
2008_09 -0.063 741
2008_10 -0.035 741
2008_11 -0.104 741
2008_12 -0.102 736
2009_01 -0.106 741
2009_02 -0.113 740
2009_03 -0.100 741
2009_04 -0.128 741
2009_05 -0.107 741
2009_06 -0.081 741
2009_07 -0.040 741
2009_08 -0.058 741
2009_09 -0.077 741
2009_10 -0.081 741
2009_11 -0.105 741
2009_12 -0.063 738
2010_01 -0.103 735
2010_02 -0.113 737
2010_03 -0.128 741
2010_04 -0.128 741
2010_05 -0.124 741
2010_06 -0.114 741
2010_07 -0.072 741
2010_08 -0.059 741
2010_09 -0.091 741
2010_10 -0.089 741
2010_11 -0.108 741
2010_12 -0.094 741
2011_01 -0.104 741
2011_02 -0.122 741
2011_03 -0.116 741
2011_04 -0.116 741
2011_05 -0.111 741
2011_06 -0.100 741
2011_07 -0.064 741
2011_08 -0.088 741
2011_09 -0.068 741
2011_10 -0.099 741
2011_11 -0.116 741
2011_12 -0.116 739
2012_01 -0.111 736
2012_02 -0.122 741
2012_03 -0.127 741
2012_04 -0.133 741
2012_05 -0.120 741
2012_06 -0.095 741
2012_07 -0.037 741
2012_08 -0.015 741
2012_09 -0.049 741
2012_10 -0.069 741
2012_11 -0.095 741
2012_12 -0.095 738
2013_01 -0.118 723
2013_02 -0.129 741
2013_03 -0.121 741
2013_04 -0.126 741
2013_05 -0.125 741
2013_06 -0.118 741
2013_07 -0.087 741
2013_08 -0.138 741
2013_09 -0.110 741
2013_10 -0.112 741
2013_11 -0.100 741
2013_12 -0.098 736
2014_01 -0.128 729
2014_02 -0.131 741
2014_03 -0.123 741
2014_04 -0.111 741
2014_05 -0.102 741
2014_06 -0.114 741
2014_07 -0.100 741
2014_08 -0.075 741
2014_09 -0.026 741
2014_10 -0.096 741
2014_11 -0.106 741
2014_12 -0.113 739
2015_01 -0.083 721
2015_02 -0.118 741
2015_03 -0.112 741
2015_04 -0.115 741
2015_05 -0.113 741
2015_06 -0.103 741
2015_07 -0.091 741
2015_08 -0.093 741
2015_09 -0.119 741
2015_10 -0.037 741
2015_11 -0.114 741
2015_12 -0.119 740

Spatial Correlation Plots

setwd("E:/Dropbox/UDEL/Gap_Filling_paper_definitive/Correlation_4.5")

#MEAN Soil Moisture vs Precipitation

data <- read.csv('Spatial_Correlation_region_interest_MEAN_monthly_SoilMoist_v45_daymet_Prcp.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_monthly_SoilMoist_v45_daymet_TMax.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_monthly_SoilMoist_v45_daymet_TMin.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_monthly_SoilMoist_v45_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_monthly_SoilMoist_v45_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_monthly_SoilMoist_v45_daymet_Prcp.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_monthly_SoilMoist_v45_daymet_TMax.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_monthly_SoilMoist_v45_daymet_TMin.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_monthly_SoilMoist_v45_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_monthly_SoilMoist_v45_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_TMax
results[3,2] <- mean_temp_corr_meanSM_Tmin
results[4,2] <- NA
results[5,2] <- NA
results[1,3] <- mean_temp_corr_medianSM_Prcp
results[2,3] <- mean_temp_corr_medianSM_TMax
results[3,3] <- mean_temp_corr_medianSM_TMin
results[4,3] <- NA
results[5,3] <- NA
results[1,4] <- mean_spatial_corr_meanSM_Prcp
results[2,4] <- mean_spatial_corr_meanSM_Tmax
results[3,4] <- mean_spatial_corr_meanSM_Tmin
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_Tmax
results[3,5] <- mean_spatial_corr_medianSM_Tmin
results[4,5] <- mean_spatial_corr_medianSM_SoilText
results[5,5] <- mean_spatial_corr_medianSM_TWI

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 0.359 0.328 0.494 0.493
Max Temperature -0.432 -0.453 -0.151 -0.155
Min Temperature -0.325 -0.351 0.408 0.410
Soil Texture NA NA -0.325 -0.325
TWI NA NA -0.091 -0.091

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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