GENERATION OF MODELED SOIL MOISTURE LAYERS

Overview

This document describes the overall process to model Soil Moisture in Oklahoma and surrounding areas performing geospatial interpolation (Ordinary Kriging) and General Linear Model regression regarding the relationship between monthly mean soil moisture and, monthly mean minimum air temperature and total precipitation. Soil Moisture product was acquired from the European Space Agency Climate Change Initiative (CCI version 3.2).

Objectives

This workflow performs two approaches to model soil moisture over those pixels where soil moisture was not remotely retrieved and processed by the ESA CCI product (version 3.2). The region of interest is an area of 465,777 km2, covering the state of Oklahoma and some portions of the surrounding states.

According to the availability of ground-truth data aimed to validate the modeled soil moisture values, as well as define the base correlation between satellite soil moisture estimates and ground-truth data, a period from January 2000 to September 2012 was established.

This workflow shows two approaches to model soil moisture for one single month, which can be any of the months of the established study period. Regarding the availability of spatial gaps to perform the analysis, different percentages of valid data (25% and 50%) were artificially removed to test the modeling methods under different scenarios of gap presence; with 100% of available data, 75 % and 50% respectively.

First approach aims to soil moisture modeling by means of Ordinary Kriging interpolation (Hengl, Heuvelink, and Stein 2004; Stein 1999; Cressie 1990), regarding the pixel values nearby No Data (invalid) pixels, as well as the spatial distribution of both valid and invalid pixels.

The second approach is based on Generalized Linear Model regression, regarding the relationship between soil moisture as response variable and two or more predictors (Gareth et al. 2013). As result of previous analysis, independent linear correlation between soil moisture and meteorological parameters such as precipitation and minimum air temperature was found, this way, the conceptual assumption for using linear models is fulfilled.

Both proposed techniques for soil moisture modeling are evaluated and validated using cross validation approach, as well as Pearson correlation with soil moisture field data derived from the North America Soil Moisture Database Network (Quiring et al. 2016) over the region of interest.

Workflow for soil moisture modeling and the gap-filling over the region of interest, regarding 100%, 75% and 50% of available valid pixels in each monthly layer. Cross-validation as well as ground-truth validation is also described.

Workflow for soil moisture modeling and the gap-filling over the region of interest, regarding 100%, 75% and 50% of available valid pixels in each monthly layer. Cross-validation as well as ground-truth validation is also described.

Data sources

The CCI product is a soil moisture database developed by the European Space Agency in Collaboration with the Vienna University of Technology (TU Wien) (Dorigo et al. 2015). This gathers historical records from a set of sensors, providing global soil moisture measurements daily since November 1978, at 0.25 degrees of spatial resolution.

Meteorological data used in this workflow was acquired from Daymet (Daily Surface Weather and Climatological Data), an initiative supported by NASA through the Earth Science Data and Information System and the Terrestrial Ecosystem program (Thornton et al. 2018). Daymet provides gridded estimates of seven surface parameters at 1-km spatial resolution over North America. Original data was cropped to the region of interest, then projected to WGS 84 Lat-Long coordinate system and resampled to 0.25 degrees by means of nearest neighbor method (ngb) (J. a Parker, Kenyon, and Troxel 1983).

Validation data was acquired from the North American Soil Moisture Database (Quiring et al. 2016). The NASMD is a collection of field soil moisture measurements from 33 observation networks, comprising over 1,800 sites in the United States and Canada. For the sake of comparison between satellite soil moisture and modeled soil moisture outputs, mean monthly soil moisture values from the NASMD were calculated for each station.

Data preparation

Import of one soil moisture monthly layer (February 2011), as well as predefined monthly covariates (precipitation and min air temperature) for the same month as soil moisture.

Transformation of soil moisture layer into Spatial Points Data Frame, the format needed to run kriging interpolation method using 100% of available valid pixels in the selected month.

#Set working directory where the described files below must be located
setwd("E:/Dropbox/UDEL/Oklahoma_Gap_Filling/General_Modeling")

rm(list = ls())

#Data needed to reproduce the process along the study period (153 months).
#This script runs individually for each month.

### 153 Soil Moisture layers (January 2000 - September 2012)
### 153 Total montlhy precipitation layers (January 2000 - September 2012)
### 153 Average montlhy minimum temperature layers (January 2000 - September 2012)
### 153 Montlhy fiedl soil moisture records over the region of interest, from nasmd (January 2000 - September 2012)
### Reference raster layer of the reion of interest (wgs84)

#This seed needs to be set to generate the same sampling outputs in some parts of the process. This parameter must not be modified

set.seed(3456)

#This is the number of layer that defines the month to be procesees (e.g. 140 = Febraruay 2011)
i = 134

#This is the projections that will assign metric coordinates to the data to be interpolated based on Ordinary Kriging.
albers_proj <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0+y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs"

#This is the original projection in which the original soil moisture data is provided.
wgs84 <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"

raster_reference <- raster('Oklahoma_raster_frame__.tif')

raster_reference_pixels <- as(raster_reference, 'SpatialPolygonsDataFrame')
raster_reference_pixels <- spTransform(raster_reference_pixels, CRSobj=CRS(albers_proj))

#These are the list of files including all input data needed to reproduce the whole workflow.
sm_files <- list.files(pattern = "mean_med_ssd_min_max.tif")
prcp_files <- list.files(pattern = "daymet_prcp")
tmin_files <- list.files(pattern = "daymet_tmin")
nasmdb_files <- list.files(pattern = "validation_nasmdb")


sm_monthly_ref <- raster(sm_files[i], band =1)
sm_monthly_ref <- as.data.frame(sm_monthly_ref, xy=TRUE)
names(sm_monthly_ref)[3] <- 'original_soil_moist'

sm_raster <- raster(sm_files[i], band =1)
sm_raster_krig <- as(sm_raster, 'SpatialPointsDataFrame')
sm_raster_krig <- spTransform(sm_raster_krig, albers_proj)

sm_raster <- na.omit(as.data.frame(sm_raster[[1]], xy=TRUE))

names(sm_raster)[3] <- 'soil_moist'
names(sm_raster_krig)[1] <- 'soil_moist'

prcp_raster <- raster(prcp_files[i], band = 1)
prcp_montlhy_ref <- as.data.frame(prcp_raster, xy=TRUE)
names(prcp_montlhy_ref)[3] <- 'prcp'

tmin_raster <- raster(tmin_files[i], band = 1)
tmin_montlhy_ref <- as.data.frame(prcp_raster, xy=TRUE)
names(tmin_montlhy_ref)[3] <- 'tmin'

covariates <- stack(prcp_raster, tmin_raster)
names(covariates)[1] <- 'prcp'
names(covariates)[2] <- 'tmin'
#Generation of table to store correlation and rmse outputs from kriging interpoaltion method
df <- data.frame(YYMM=character(), correlation_krig=numeric(), rmse_krig=numeric(), 
                 correlation_glm=numeric(), rmse_glm=numeric(), nfold_krig=numeric(), 
                 nfold_glm=numeric())
df$YYMM <- as.character()
layer_name_sm <- substr(sm_files[i], 20,26)
print(layer_name_sm)
layer_name_prcp <- substr(prcp_files[i], 13,19)
print(layer_name_prcp)
layer_name_tmin <- substr(tmin_files[i], 13,19)
print(layer_name_tmin)
layer_name_nasmd <- substr(sm_files[i], 20,26)
print(layer_name_nasmd)
df[1,1] <- layer_name_sm

accuracy_report_100 <- df
accuracy_report_75 <- df
accuracy_report_50 <- df

Kriging spatial interpolation of available valid pixels

This section runs Kriging Spatial Interpolation method using 100%, 75% and 50% of available valid pixels in the soil moisture monthly layer. The number of valid pixels varies according to every monthly layer.

Kriging 100% of available valid pixels

#generation of a model to fit variogram
model_100 <- autofitVariogram(soil_moist~1, input_data = sm_raster_krig, verbose= FALSE, 
                              GLS.model=TRUE)

#application of kriging spatial interpolation using the previous fittef model
sm_kriging_100 <- autoKrige(soil_moist~1, input_data = sm_raster_krig, GLS.model = TRUE, new_data = raster_reference_pixels)

#save the R file, which contains the variogram model
saveRDS(model_100, file = paste0('variogram_model_100_', layer_name_sm))
write.csv(model_100$exp_var, file = paste0('variogram_model_100_exp_var_', layer_name_sm, '.csv'))
write.csv(model_100$var_model, file = paste0('variogram_model_100_var_model_', layer_name_sm, '.csv'))

#definition of predicted values and associated standard error
sm_kriging_100_output <- sm_kriging_100$krige_output
sm_kriging_100_output <- spTransform(sm_kriging_100_output, wgs84)

pred_100 <- rasterize(sm_kriging_100_output, raster_reference, 'var1.pred')
std_err_100 <- rasterize(sm_kriging_100_output, raster_reference, 'var1.stdev', na.rm = FALSE)

#export of outputs to raster files
writeRaster(stack(pred_100, std_err_100), file=paste0('ordKriging_', layer_name_sm, 
            '_pred_error_sm_100.tif'), overwrite = TRUE)

Kriging 75% of available valid pixels

#slecction of training data, 75% of valid available pixels
trainIndex75 <- createDataPartition(sm_raster[,3], p = .75, list = FALSE, times = 1)
Train75 <- sm_raster[ trainIndex75,] 
Test75 <- sm_raster[-trainIndex75,]

coordinates(Train75) =~ x+y
projection(Train75) <- wgs84

Train75 <- spTransform(Train75, CRSobj = albers_proj)

#generation of  a model to fit variogram
model_75 <- autofitVariogram(soil_moist~1, input_data = Train75, verbose= FALSE, 
                             GLS.model=TRUE)

#application of kriging spatial interpolation using the previous fittef model
sm_kriging_75 <- autoKrige(soil_moist~1, input_data = Train75, GLS.model = TRUE, new_data = raster_reference_pixels)

saveRDS(model_75, file = paste0('variogram_model_75_', layer_name_sm))
write.csv(model_75$exp_var, file = paste0('variogram_model_75_exp_var_', layer_name_sm, '.csv'))
write.csv(model_75$var_model, file = paste0('variogram_model_75_var_model_', layer_name_sm, '.csv'))

#definition of predicted values and associated standard error
sm_kriging_75_output <- sm_kriging_75$krige_output
sm_kriging_75_output <- spTransform(sm_kriging_75_output, wgs84)

pred_75 <- rasterize(sm_kriging_75_output, raster_reference, 'var1.pred')
std_err_75 <- rasterize(sm_kriging_75_output, raster_reference, 'var1.stdev', na.rm = FALSE)

#export of outputs to raster files
writeRaster(stack(pred_75, std_err_75), file=paste0('ordKriging_', layer_name_sm, 
            '_pred_error_sm_75.tif'), overwrite = TRUE)

Kriging 50% of available valid pixels

#slecction of training data, 50% of valid available pixels
trainIndex50 <- createDataPartition(sm_raster[,3], p = .5, list = FALSE, times = 1)
Train50 <- sm_raster[ trainIndex50,]
Test50 <- sm_raster[-trainIndex50,]

coordinates(Train50) =~ x+y
projection(Train50) <- wgs84

Train50 <- spTransform(Train50, CRSobj = albers_proj)

#generation of  a model to fit variogram
model_50 <- autofitVariogram(soil_moist~1, input_data = Train50, verbose= FALSE, 
                             GLS.model=TRUE)

#application of kriging spatial interpolation using the previous fittef model
sm_kriging_50 <- autoKrige(soil_moist~1, input_data = Train50, GLS.model = TRUE, new_data = raster_reference_pixels)

saveRDS(model_50, file = paste0('variogram_model_50_', layer_name_sm))
write.csv(model_50$exp_var, file = paste0('variogram_model_50_exp_var_', layer_name_sm, '.csv'))
write.csv(model_50$var_model, file = paste0('variogram_model_50_var_model_', layer_name_sm, '.csv'))

#definition of predicted values and associated standard error
sm_kriging_50_output <- sm_kriging_50$krige_output
sm_kriging_50_output <- spTransform(sm_kriging_50_output, wgs84)

pred_50 <- rasterize(sm_kriging_50_output, raster_reference, 'var1.pred')
std_err_50 <- rasterize(sm_kriging_50_output, raster_reference, 'var1.stdev', na.rm = FALSE)

#export of outputs to raster files
writeRaster(stack(pred_50, std_err_50), file=paste0('ordKriging_', layer_name_sm, 
            '_pred_error_sm_50.tif'), overwrite = TRUE)

Cross validation for kriging spatial interpolation model

This function takes the same model used in the autoKrige function to predict over different subsets based on an iterative process.

Cross validation is applied to the outputs of kriging interpolation using 100%, 75% and 50% of available valid pixels. autoKrige.cv removes a predefined percent of the available valid data and then predicts new values over them, this process is performed 10 times in this section, ensuring every valid pixel is removed once and then predicted.

Only the model is validated in this process, as no predicted values over areas with original invalid pixels can be validated.

100% of valid pixels

sm_kriging_cv_100_10fold <- autoKrige.cv(soil_moist~1, 
                                     input_data = sm_raster_krig, nfold = 10)

acc_report_100_krig <- accuracy_report_100[, -c(4,5,7)]

accuracy_report_100[1,6] <- 10
accuracy_report_100[1,2] <- round(cor(sm_kriging_cv_100_10fold$krige.cv_output$observed, 
                                      sm_kriging_cv_100_10fold$krige.cv_output$var1.pred)
                                  ^2, 3)
accuracy_report_100[1,3] <- round(rmse(sm_kriging_cv_100_10fold$krige.cv_output$observed, 
                                       sm_kriging_cv_100_10fold$krige.cv_output$var1.pred)
                                  ,3)

sm_kriging_cv_100_10fold_output <- as.data.frame(sm_kriging_cv_100_10fold$krige.cv_output)
sm_kriging_cv_100_10fold_output <- transform(sm_kriging_cv_100_10fold_output, 
                                     YY = substr(layer_name_sm, 1, 4), 
                                     MM = substr(layer_name_sm, 6, 7))

write.csv(sm_kriging_cv_100_10fold_output, file = paste0('cross_validation_kriging_100_', 
                        layer_name_nasmd, '.csv'))

acc_report_100_krig[1,4] <- 10
acc_report_100_krig[1,2] <- round(cor(sm_kriging_cv_100_10fold$krige.cv_output$observed, 
                                      sm_kriging_cv_100_10fold$krige.cv_output$var1.pred)
                                  ^2, 3)
acc_report_100_krig[1,3] <- round(rmse(sm_kriging_cv_100_10fold$krige.cv_output$observed, 
                                       sm_kriging_cv_100_10fold$krige.cv_output$var1.pred)
                                  ,3)

75% of valid pixels

sm_kriging_cv_75_10fold <- autoKrige.cv(soil_moist~1, input_data = Train75, nfold = 10)

acc_report_75_krig <- accuracy_report_75[, -c(4,5,7)]

accuracy_report_75[1,6] <- 10
accuracy_report_75[1,2] <- round(cor(sm_kriging_cv_75_10fold$krige.cv_output$observed, 
                                     sm_kriging_cv_75_10fold$krige.cv_output$var1.pred)
                                 ^2, 3)
accuracy_report_75[1,3] <- round(rmse(sm_kriging_cv_75_10fold$krige.cv_output$observed, 
                                      sm_kriging_cv_75_10fold$krige.cv_output$var1.pred)
                                 ,3)

sm_kriging_cv_75_10fold_output <- as.data.frame(sm_kriging_cv_75_10fold$krige.cv_output)
sm_kriging_cv_75_10fold_output <- transform(sm_kriging_cv_75_10fold_output, 
                                     YY = substr(layer_name_sm, 1, 4), 
                                     MM = substr(layer_name_sm, 6, 7))

write.csv(sm_kriging_cv_75_10fold_output, file = paste0('cross_validation_kriging_75_', 
                        layer_name_nasmd, '.csv'))

acc_report_75_krig[1,4] <- 10
acc_report_75_krig[1,2] <- round(cor(sm_kriging_cv_75_10fold$krige.cv_output$observed, 
                                      sm_kriging_cv_75_10fold$krige.cv_output$var1.pred)
                                 ^2, 3)
acc_report_75_krig[1,3] <- round(rmse(sm_kriging_cv_75_10fold$krige.cv_output$observed, 
                                       sm_kriging_cv_75_10fold$krige.cv_output$var1.pred)
                                 ,3)

50% of valid pixels

sm_kriging_cv_50_10fold <- autoKrige.cv(soil_moist~1, input_data = Train50, nfold = 10)

acc_report_50_krig <- accuracy_report_50[, -c(4,5,7)]

accuracy_report_50[1,6] <- 10
accuracy_report_50[1,2] <- round(cor(sm_kriging_cv_50_10fold$krige.cv_output$observed, 
                                     sm_kriging_cv_50_10fold$krige.cv_output$var1.pred)
                                 ^2, 3)
accuracy_report_50[1,3] <- round(rmse(sm_kriging_cv_50_10fold$krige.cv_output$observed, 
                                      sm_kriging_cv_50_10fold$krige.cv_output$var1.pred)
                                 ,3)

sm_kriging_cv_50_10fold_output <- as.data.frame(sm_kriging_cv_50_10fold$krige.cv_output)
sm_kriging_cv_50_10fold_output <- transform(sm_kriging_cv_50_10fold_output, 
                                     YY = substr(layer_name_sm, 1, 4), 
                                     MM = substr(layer_name_sm, 6, 7))

write.csv(sm_kriging_cv_50_10fold_output, file = paste0('cross_validation_kriging_50_', 
                        layer_name_nasmd, '.csv'))

acc_report_50_krig[1,4] <- 10
acc_report_50_krig[1,2] <- round(cor(sm_kriging_cv_50_10fold$krige.cv_output$observed, 
                                      sm_kriging_cv_50_10fold$krige.cv_output$var1.pred)
                                 ^2, 3)
acc_report_50_krig[1,3] <- round(rmse(sm_kriging_cv_50_10fold$krige.cv_output$observed, 
                                       sm_kriging_cv_50_10fold$krige.cv_output$var1.pred)
                                 ,3)

Report of kriging model outputs

This section brings together the Kriging models generated by autokrige function, as well as the semivariogram explaining the spatial distribution of the actual data and the parameters to the best fitted model. Finally, cross validation is reported for each cases using different percentages fin put data for kriging model generation.

100% of available data

#Kriging Model 100% of available data
plot(sm_kriging_100, col=colorRampPalette(c("tan4", "tan2", "yellow1", "yellowgreen", 
    "green", "springgreen2", "steelblue1", "steelblue4"))(20))

75% of available data

#Kriging Model 75% of available data
plot(sm_kriging_75, col=colorRampPalette(c("tan4", "tan2", "yellow1", "yellowgreen", 
    "green", "springgreen2", "steelblue1", "steelblue4"))(20))

50% of available data

#Kriging Model 50% of available data
plot(sm_kriging_50, col=colorRampPalette(c("tan4", "tan2", "yellow1", "yellowgreen", 
    "green", "springgreen2", "steelblue1", "steelblue4"))(20))

Report of semivariograms from kriging models

100% of available data

#Semivariogram model using 100% of available data
kable(model_100$exp_var, caption = 'Explained Variance', digits = 5)
Explained Variance
np dist gamma dir.hor dir.ver id
1417 25209.54 0.00009 0 0 var1
1360 35912.78 0.00013 0 0 var1
1875 49964.56 0.00014 0 0 var1
8668 74270.54 0.00022 0 0 var1
11497 109931.10 0.00031 0 0 var1
21198 154759.37 0.00041 0 0 var1
26327 207769.58 0.00056 0 0 var1
27943 261715.56 0.00068 0 0 var1
37749 323494.40 0.00089 0 0 var1
kable(model_100$var_model, caption = 'Variance Model', digits = 5)
Variance Model
model psill range kappa ang1 ang2 ang3 anis1 anis2
Nug 0.00006 0 0.0 0 0 0 1 1
Ste 0.00654 2685935 0.6 0 0 0 1 1

75% of available data

#Semivariogram model using 75% of available data
kable(model_75$exp_var, caption = 'Explained Variance', digits = 5)
Explained Variance
np dist gamma dir.hor dir.ver id
793 25221.68 0.00009 0 0 var1
771 35921.69 0.00014 0 0 var1
1064 50027.58 0.00014 0 0 var1
4842 74156.92 0.00021 0 0 var1
6439 109660.70 0.00029 0 0 var1
11871 154614.76 0.00040 0 0 var1
14742 207638.85 0.00054 0 0 var1
15626 261629.06 0.00065 0 0 var1
20789 323420.90 0.00083 0 0 var1
kable(model_75$var_model, caption = 'Variance Model', digits = 5)
Variance Model
model psill range kappa ang1 ang2 ang3 anis1 anis2
Nug 0.00003 0 0 0 0 0 1 1
Sph 0.02056 12941523 0 0 0 0 1 1

50% of available data

#Semivariogram model using 50% of available data
kable(model_50$exp_var, caption = 'Explained Variance', digits = 5)
Explained Variance
np dist gamma dir.hor dir.ver id
343 25195.76 0.00010 0 0 var1
327 35906.43 0.00011 0 0 var1
453 49676.15 0.00011 0 0 var1
2192 74238.06 0.00019 0 0 var1
2827 109790.26 0.00029 0 0 var1
5204 154575.84 0.00038 0 0 var1
6535 207294.77 0.00052 0 0 var1
6940 261218.83 0.00063 0 0 var1
9481 322828.60 0.00081 0 0 var1
kable(model_50$var_model, caption = 'Variance Model', digits = 5)
Variance Model
model psill range kappa ang1 ang2 ang3 anis1 anis2
Nug 0.00005 0 0.0 0 0 0 1 1
Ste 0.00503 2258067 0.6 0 0 0 1 1

Cross validation for kriging outputs

Cross validation is reported for each cases using different percentages of input data (100%, 75% and 50% of available valid pixels) for kriging model generation.

#10 folds cross validation for model built with 100% of available valid pixels in the region of interest.
kable(acc_report_100_krig, caption = 'Cross validation output using 100% of valid data')
Cross validation output using 100% of valid data
YYMM correlation_krig rmse_krig nfold_krig
2011_02 0.935 0.01 10
write.csv(acc_report_100_krig, file = paste0('cv_kriging_100_', layer_name_sm, '.csv'))

#10 folds cross validation for model built with 75% of available valid pixels in the region of interest.
kable(acc_report_75_krig, caption = 'Cross validation output using 75% of valid data')
Cross validation output using 75% of valid data
YYMM correlation_krig rmse_krig nfold_krig
2011_02 0.929 0.01 10
write.csv(acc_report_75_krig, file = paste0('cv_kriging_75_', layer_name_sm, '.csv'))

#10 folds cross validation for model built with 50% of available valid pixels in the region of interest.
kable(acc_report_50_krig, caption = 'Cross validation output using 50% of valid data')
Cross validation output using 50% of valid data
YYMM correlation_krig rmse_krig nfold_krig
2011_02 0.919 0.011 10
write.csv(acc_report_50_krig, file = paste0('cv_kriging_50_', layer_name_sm, '.csv'))

Multiple regression model

This section generates soil moisture predictions over the region of interest, based on the correlation between soil moisture and meteorological parameters such as total monthly precipitation and monthly average of daily minimum temperature records.

Generation of training subsets

Training subsets with different percentage of valid data. These are the inputs for the multiple regression model.

100% of available data

#This section project the data used for kriging interpolation to a lat-lon reference system that matches with the covariates (precipitation, minimum temperature) references system. 100% of available valid pixels in the region of interest.
sm_raster_krig <- spTransform(sm_raster_krig, CRSobj = wgs84)
covariates_values_100 <- extract(covariates,sm_raster_krig) 

lm_train_100 <- cbind(as.data.frame(sm_raster_krig, xy=TRUE), 
                      data.frame(covariates_values_100))  
lm_train_100 <- data.frame(sm =lm_train_100[1], prcp= lm_train_100[5], 
                           tmin=lm_train_100[6])

75% of available data

#This section project the data used for kriging interpolation to a lat-lon reference system that matches with the covariates (precipitation, minimum temperature) references system. 75% of available valid pixels in the region of interest.
Train75 <- spTransform(Train75, CRSobj = wgs84)
covariates_values_75 <- extract(covariates,Train75) 

lm_train_75 <- cbind(as.data.frame(Train75, xy=TRUE), 
                     data.frame(covariates_values_75))  
lm_train_75 <- data.frame(sm =lm_train_75[1], prcp= lm_train_75[5], 
                          tmin=lm_train_75[6])

50% of available data

#This section project the data used for kriging interpolation to a lat-lon reference system that matches with the covariates (precipitation, minimum temperature) references system. 50% of available valid pixels in the region of interest.
Train50 <- spTransform(Train50, CRSobj = wgs84)
covariates_values_50 <- extract(covariates,Train50) 

lm_train_50 <- cbind(as.data.frame(Train50, xy=TRUE), 
                     data.frame(covariates_values_50))  
lm_train_50 <- data.frame(sm =lm_train_50[1], prcp= lm_train_50[5], 
                          tmin=lm_train_50[6])
###Input and output matrices, Creation of input matrices for model generation, as well as matrices for outputs writing.
sm_and_predictors <- data.frame(sm_monthly_ref[3], prcp_montlhy_ref[3], 
                                tmin_montlhy_ref[3])

sm_monthly_outputs <- sm_monthly_ref
sm_monthly_outputs[4] <- NA
names(sm_monthly_outputs)[4] <- 'predicted_soil_moist'

GLM generation usign different percentages of available data (100%, 75% and 50%)

ctrl1 <- trainControl(method = "repeatedcv", repeats = 1, number=10, savePredictions = TRUE)

#Generation of GLM for predicting soil moisture using precipitation and minimum temperature as covariates. 100% of available valid pixels in the region of interest.
glm_100_10fold <- train(soil_moist~., data=lm_train_100, method = "glm", 
                        trControl = ctrl1)

#Generation of table for GLM 100% of available valid pixels accuracy report
accuracy_report_100[1,7] <- 10
accuracy_report_100[1,4] <- round((glm_100_10fold$results['Rsquared']), 3)
accuracy_report_100[1,5] <- round((glm_100_10fold$results['RMSE']), 3)

accuracy_glm_100_10fold <- glm_100_10fold$pred
accuracy_glm_100_10fold <- transform(accuracy_glm_100_10fold, 
                                     YY = substr(layer_name_sm, 1, 4), 
                                     MM = substr(layer_name_sm, 6, 7))
write.csv(accuracy_glm_100_10fold, file = paste0('cross_validation_glm_100_', 
                        layer_name_nasmd, '.csv'))


#Generation of GLM for predicting soil moisture using precipitation and minimum temperature as covariates. 75% of available valid pixels in the region of interest.
glm_75_10fold <- train(soil_moist~., data=lm_train_75, method = "glm", 
                       trControl = ctrl1)

#Generation of table for GLM 75% of available valid pixels accuracy report
accuracy_report_75[1,7] <- 10
accuracy_report_75[1,4] <- round((glm_75_10fold$results['Rsquared']), 3)
accuracy_report_75[1,5] <- round((glm_75_10fold$results['RMSE']), 3)

accuracy_glm_75_10fold <- glm_75_10fold$pred
accuracy_glm_75_10fold <- transform(accuracy_glm_75_10fold, 
                                     YY = substr(layer_name_sm, 1, 4), 
                                     MM = substr(layer_name_sm, 6, 7))
write.csv(accuracy_glm_75_10fold, file = paste0('cross_validation_glm_75_', 
                        layer_name_nasmd, '.csv'))

#Generation of GLM for predicting soil moisture using precipitation and minimum temperature as covariates. 50% of available valid pixels in the region of interest.
glm_50_10fold <- train(soil_moist~., data=lm_train_50, method = "glm", trControl = ctrl1)

#Generation of table for GLM 50% of available valid pixels accuracy report
accuracy_report_50[1,7] <- 10
accuracy_report_50[1,4] <- round((glm_50_10fold$results['Rsquared']), 3)
accuracy_report_50[1,5] <- round((glm_50_10fold$results['RMSE']), 3)

accuracy_glm_50_10fold <- glm_50_10fold$pred
accuracy_glm_50_10fold <- transform(accuracy_glm_50_10fold, 
                                     YY = substr(layer_name_sm, 1, 4), 
                                     MM = substr(layer_name_sm, 6, 7))
write.csv(accuracy_glm_50_10fold, file = paste0('cross_validation_glm_50_', 
                        layer_name_nasmd, '.csv'))

Generation of soil moisture prediction layers based on multiple regression

sm_prediction_100_10fold <- predict(covariates, glm_100_10fold)

sm_prediction_75_10fold <- predict(covariates, glm_75_10fold)

sm_prediction_50_10fold <- predict(covariates, glm_50_10fold)

names(sm_prediction_100_10fold) <- c('Prediction_100perct_valid_data_10_fold')

#par(mfrow=c(1,2)) 
plot(sm_prediction_100_10fold$Prediction_100perct_valid_data_10_fold, 
     main = '100% valid data 10 fold', col=colorRampPalette(c("tan4", "tan2", "yellow1", 
                "yellowgreen", "green", "springgreen2", "steelblue1", "steelblue4"))(20))

names(sm_prediction_75_10fold) <- c('Prediction_75perct_valid_data_10_fold')

#par(mfrow=c(1,2))
plot(sm_prediction_75_10fold$Prediction_75perct_valid_data_10_fold, 
     main = '75% valid data 10 fold', col=colorRampPalette(c("tan4", "tan2", "yellow1", 
                "yellowgreen", "green", "springgreen2", "steelblue1", "steelblue4"))(20))

names(sm_prediction_50_10fold) <- c('Prediction_50perct_valid_data_10_fold')

#par(mfrow=c(1,2))
plot(sm_prediction_50_10fold$Prediction_50perct_valid_data_10_fold, 
     main = '50% valid data 10 fold', col=colorRampPalette(c("tan4", "tan2", "yellow1", 
                "yellowgreen", "green", "springgreen2", "steelblue1", "steelblue4"))(20))

Overall accuracy report Kriging vs Linear regression Model

kable(accuracy_report_100, caption = 'Accuracy report 100% ofa valid data')
Accuracy report 100% ofa valid data
YYMM correlation_krig rmse_krig correlation_glm rmse_glm nfold_krig nfold_glm
2011_02 0.935 0.01 0.599 0.025 10 10
kable(accuracy_report_75, caption = 'Accuracy report 75% ofa valid data')
Accuracy report 75% ofa valid data
YYMM correlation_krig rmse_krig correlation_glm rmse_glm nfold_krig nfold_glm
2011_02 0.929 0.01 0.587 0.025 10 10
kable(accuracy_report_50, caption = 'Accuracy report 50% ofa valid data')
Accuracy report 50% ofa valid data
YYMM correlation_krig rmse_krig correlation_glm rmse_glm nfold_krig nfold_glm
2011_02 0.919 0.011 0.641 0.023 10 10

Field data validation

Correlation between original satellite data (100%, 75%, 50%) and field data from the NASMDB

Correlation between values from pixels in the original satellite data and field data, is intended to establish the reference correlation value expected between modeled data, both kriging and linear models, and field data.

In the first step, all valid data for the specified month are extracted from the North America Soil Moisture Data Base, this data will be correlated with satellite values over pixels with original satellite data.

#Preparation of references layers from satellite data, subsetting different percentages of valid data (100%, 75%, 50%)

Train100_raster <- sm_raster_krig
gridded(Train100_raster) <- TRUE
Train100_raster <- raster(Train100_raster)

Train75_raster <- Train75
gridded(Train75_raster) <- TRUE
Train75_raster <- raster(Train75_raster)

Train50_raster <- Train50
gridded(Train50_raster) <- TRUE
Train50_raster <- raster(Train50_raster)
#Generation of tables to record correlation outputs between satellite data with different percentage of valid pixels (100%, 75%, 50%) and the data from the NASMDB over the same valid pixels

satellite_field_correlation <- matrix(data = NA, nrow =1 , ncol =6) 
satellite_field_correlation <- as.data.frame(satellite_field_correlation)
headers <- c('ID', 'Layer', 'Correlation', 'RMSE', 'Number_Stations', 'Number_Points')
names(satellite_field_correlation) <- headers
satellite_field_correlation$ID <- i
satellite_field_correlation$Layer <- layer_name_sm

satellite_field_correlation_100 <- satellite_field_correlation
satellite_field_correlation_75 <- satellite_field_correlation
satellite_field_correlation_50 <- satellite_field_correlation

100% of valid data

validation_csv_values_100 <- na.omit(read.csv(nasmdb_files[[i]]))

   if(length(validation_csv_values_100$Station_ID) != 0){
     
coordinates(validation_csv_values_100) <- ~Longitude+Latitude
proj4string(validation_csv_values_100) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0") 
validation_csv_values_100 <- spTransform(validation_csv_values_100, 
                                         CRS(projection(raster_reference)))

}

validation_csv_values_100$extract_values <- extract(Train100_raster, 
                                                    validation_csv_values_100)
validation_table_satellite_100 <- as.data.frame(validation_csv_values_100)

no_stations <- length(validation_table_satellite_100$Station_ID)
no_points <- na.omit(validation_table_satellite_100$extract_values)
no_points <- length(no_points)
correlation <- cor(validation_table_satellite_100$mean_sm_depth_5cm,
                   validation_table_satellite_100$extract_values, 
                   use = 'pairwise.complete.obs')
RMSE <- rmse(na.omit(validation_table_satellite_100$mean_sm_depth_5cm),
                   na.omit(validation_table_satellite_100$extract_values))
  
satellite_field_correlation_100[1, 2] <- layer_name_nasmd
satellite_field_correlation_100[1, 3] <- correlation
satellite_field_correlation_100[1, 4] <- RMSE
satellite_field_correlation_100[1, 5] <- no_stations
satellite_field_correlation_100[1, 6] <- no_points

#Correlation output
kable(satellite_field_correlation_100, 
      caption = paste0('Monthly Satellite 100% valid data vs Field Data correlation '
                       , layer_name_nasmd))
Monthly Satellite 100% valid data vs Field Data correlation 2011_02
ID Layer Correlation RMSE Number_Stations Number_Points
134 2011_02 0.4441071 0.1197464 107 106
write.csv(satellite_field_correlation_100, file = paste0('satellite_nasmdb_100_validation_', layer_name_nasmd, '.csv'))

75% of valid data

validation_csv_values_75 <- na.omit(read.csv(nasmdb_files[[i]]))

   if(length(validation_csv_values_75$Station_ID) != 0){
     
coordinates(validation_csv_values_75) <- ~Longitude+Latitude
proj4string(validation_csv_values_75) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0") 
validation_csv_values_75 <- spTransform(validation_csv_values_75, 
                                        CRS(projection(raster_reference)))

}

validation_csv_values_75$extract_values <- extract(Train75_raster, 
                                                   validation_csv_values_75)
validation_table_satellite_75 <- as.data.frame(validation_csv_values_75)

no_stations <- length(validation_table_satellite_75$Station_ID)
no_points <- na.omit(validation_table_satellite_75$extract_values)
no_points <- length(no_points)
correlation <- cor(validation_table_satellite_75$mean_sm_depth_5cm, 
                   validation_table_satellite_75$extract_values, 
                   use = 'pairwise.complete.obs')
RMSE <- rmse(na.omit(validation_table_satellite_75$mean_sm_depth_5cm),
                   na.omit(validation_table_satellite_75$extract_values))

satellite_field_correlation_75[1, 2] <- layer_name_nasmd
satellite_field_correlation_75[1, 3] <- correlation
satellite_field_correlation_75[1, 4] <- RMSE
satellite_field_correlation_75[1, 5] <- no_stations
satellite_field_correlation_75[1, 6] <- no_points

#Correlation output
kable(satellite_field_correlation_75, 
      caption = paste0('Monthly Satellite 75% valid data vs Field Data correlation ', 
                       layer_name_nasmd))
Monthly Satellite 75% valid data vs Field Data correlation 2011_02
ID Layer Correlation RMSE Number_Stations Number_Points
134 2011_02 0.3931272 0.11936 107 74
write.csv(satellite_field_correlation_75, file = paste0('satellite_nasmdb_75_validation_', layer_name_nasmd, '.csv'))

50% of valid data

validation_csv_values_50 <- na.omit(read.csv(nasmdb_files[[i]]))

   if(length(validation_csv_values_50$Station_ID) != 0){
     
coordinates(validation_csv_values_50) <- ~Longitude+Latitude
proj4string(validation_csv_values_50) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0") 
validation_csv_values_50 <- spTransform(validation_csv_values_50, 
                                        CRS(projection(raster_reference)))

}

validation_csv_values_50$extract_values <- extract(Train50_raster, 
                                                   validation_csv_values_50)
validation_table_satellite_50 <- as.data.frame(validation_csv_values_50)

no_stations <- length(validation_table_satellite_50$Station_ID)
no_points <- na.omit(validation_table_satellite_50$extract_values)
no_points <- length(no_points)
correlation <- cor(validation_table_satellite_50$mean_sm_depth_5cm, 
                   validation_table_satellite_50$extract_values, 
                   use = 'pairwise.complete.obs')
RMSE <- rmse(na.omit(validation_table_satellite_50$mean_sm_depth_5cm),
                   na.omit(validation_table_satellite_50$extract_values))

satellite_field_correlation_50[1, 2] <- layer_name_nasmd
satellite_field_correlation_50[1, 3] <- correlation
satellite_field_correlation_50[1, 4] <- RMSE
satellite_field_correlation_50[1, 5] <- no_stations
satellite_field_correlation_50[1, 6] <- no_points

kable(satellite_field_correlation_50, 
      caption = paste0('Monthly Satellite 50% valid data vs Field Data correlation ', 
                       layer_name_nasmd))
Monthly Satellite 50% valid data vs Field Data correlation 2011_02
ID Layer Correlation RMSE Number_Stations Number_Points
134 2011_02 0.4752477 0.1234172 107 53
write.csv(satellite_field_correlation_50, file = paste0('satellite_nasmdb_50_validation_', layer_name_nasmd, '.csv'))
##Export of points used for validation between satellite data and field data, to shapefile. Also validation tables are exported to CSV files, as well as correlation results

#100%
writeOGR(obj = validation_csv_values_100, dsn = paste0('validation_shapes_100_', layer_name_nasmd), 
         layer = paste0('validation_reference_100_', layer_name_nasmd), 
         driver = 'ESRI Shapefile', overwrite_layer = TRUE)

write.csv(validation_table_satellite_100, 
          file = paste0('validation_reference_satellite_100_', 
                        layer_name_nasmd, '.csv'))

#75%
writeOGR(obj = validation_csv_values_75, dsn = paste0('validation_shapes_75_', layer_name_nasmd), 
         layer = paste0('validation_reference_75_', layer_name_nasmd), 
         driver = 'ESRI Shapefile', overwrite_layer = TRUE)

write.csv(validation_table_satellite_75, 
          file = paste0('validation_reference_satellite_75_', 
                        layer_name_nasmd, '.csv'))

#50%
writeOGR(obj = validation_csv_values_50, dsn = paste0('validation_shapes_50_', layer_name_nasmd), 
         layer = paste0('validation_reference_50_', layer_name_nasmd), 
         driver = 'ESRI Shapefile', overwrite_layer = TRUE)

write.csv(validation_table_satellite_50, 
          file = paste0('validation_reference_satellite_50_', 
                        layer_name_nasmd, '.csv'))

Validation of kriging model outputs

Correlation between kriging model outputs (100%, 75%, 50%) and field data from the North American Soil Moisture Database.

Kiging 100% of valid data

validation_csv_values_100 <- na.omit(read.csv(nasmdb_files[[i]]))

   if(length(validation_csv_values_100$Station_ID) != 0){
     
coordinates(validation_csv_values_100) <- ~Longitude+Latitude
proj4string(validation_csv_values_100) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0") 
validation_csv_values_100 <- spTransform(validation_csv_values_100, 
                                         CRS(projection(raster_reference)))

}

validation_csv_values_100$extract_values <- extract(pred_100, validation_csv_values_100)
validation_table_kriging_100 <- as.data.frame(validation_csv_values_100)

no_stations <- length(validation_table_kriging_100$Station_ID)
no_points <- na.omit(validation_table_kriging_100$extract_values)
no_points <- length(no_points)
correlation <- cor(validation_table_kriging_100$mean_sm_depth_5cm, 
                   validation_table_kriging_100$extract_values, 
                   use = 'pairwise.complete.obs')
RMSE <- rmse(na.omit(validation_table_kriging_100$mean_sm_depth_5cm),
                   na.omit(validation_table_kriging_100$extract_values))

kriging_field_correlation_100 <- satellite_field_correlation
kriging_field_correlation_100[1, 2] <- layer_name_nasmd
kriging_field_correlation_100[1, 3] <- correlation
kriging_field_correlation_100[1, 4] <- RMSE
kriging_field_correlation_100[1, 5] <- no_stations
kriging_field_correlation_100[1, 6] <- no_points

kable(kriging_field_correlation_100, 
      caption = paste0('Monthly Kriging 100% valid data vs Field Data correlation ', 
                       layer_name_nasmd))
Monthly Kriging 100% valid data vs Field Data correlation 2011_02
ID Layer Correlation RMSE Number_Stations Number_Points
134 2011_02 0.462877 0.1107679 107 107
write.csv(kriging_field_correlation_100, file = paste0('kriging_nasmdb_100_validation_', layer_name_nasmd, '.csv'))

Kiging 75% of valid data

validation_csv_values_75 <- na.omit(read.csv(nasmdb_files[[i]]))
 
  if(length(validation_csv_values_75$Station_ID) != 0){
     
coordinates(validation_csv_values_75) <- ~Longitude+Latitude
proj4string(validation_csv_values_75) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0") 
validation_csv_values_75 <- spTransform(validation_csv_values_75, 
                                        CRS(projection(raster_reference)))

}

validation_csv_values_75$extract_values <- extract(pred_75, validation_csv_values_75)
validation_table_kriging_75 <- as.data.frame(validation_csv_values_75)

no_stations <- length(validation_table_kriging_75$Station_ID)
no_points <- na.omit(validation_table_kriging_75$extract_values)
no_points <- length(no_points)
correlation <- cor(validation_table_kriging_75$mean_sm_depth_5cm, 
                   validation_table_kriging_75 $extract_values, 
                   use = 'pairwise.complete.obs')
RMSE <- rmse(na.omit(validation_table_kriging_75$mean_sm_depth_5cm),
                   na.omit(validation_table_kriging_75$extract_values))

kriging_field_correlation_75 <- satellite_field_correlation
kriging_field_correlation_75[1, 2] <- layer_name_nasmd
kriging_field_correlation_75[1, 3] <- correlation
kriging_field_correlation_75[1, 4] <- RMSE
kriging_field_correlation_75[1, 5] <- no_stations
kriging_field_correlation_75[1, 6] <- no_points

kable(kriging_field_correlation_75, 
      caption = paste0('Monthly Kriging 75% valid data vs Field Data correlation ', 
                       layer_name_nasmd))
Monthly Kriging 75% valid data vs Field Data correlation 2011_02
ID Layer Correlation RMSE Number_Stations Number_Points
134 2011_02 0.4636105 0.1108116 107 107
write.csv(kriging_field_correlation_75, file = paste0('kriging_nasmdb_75_validation_', layer_name_nasmd, '.csv'))

Kiging 50% of valid data

validation_csv_values_50 <- na.omit(read.csv(nasmdb_files[[i]]))

   if(length(validation_csv_values_50$Station_ID) != 0){
     
coordinates(validation_csv_values_50) <- ~Longitude+Latitude
proj4string(validation_csv_values_50) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0") 
validation_csv_values_50 <- spTransform(validation_csv_values_50, 
                                        CRS(projection(raster_reference)))

}

validation_csv_values_50$extract_values <- extract(pred_50, validation_csv_values_50)
validation_table_kriging_50 <- as.data.frame(validation_csv_values_50)

no_stations <- length(validation_table_kriging_50$Station_ID)
no_points <- na.omit(validation_table_kriging_50$extract_values)
no_points <- length(no_points)
correlation <- cor(validation_table_kriging_50$mean_sm_depth_5cm, 
                   validation_table_kriging_50$extract_values, 
                   use = 'pairwise.complete.obs')
RMSE <- rmse(na.omit(validation_table_kriging_50$mean_sm_depth_5cm),
                   na.omit(validation_table_kriging_50$extract_values))

kriging_field_correlation_50 <- satellite_field_correlation
kriging_field_correlation_50[1, 2] <- layer_name_nasmd
kriging_field_correlation_50[1, 3] <- correlation
kriging_field_correlation_50[1, 4] <- RMSE
kriging_field_correlation_50[1, 5] <- no_stations
kriging_field_correlation_50[1, 6] <- no_points

kable(kriging_field_correlation_50, 
      caption = paste0('Monthly Kriging 50% valid data vs Field Data correlation ', 
                       layer_name_nasmd))
Monthly Kriging 50% valid data vs Field Data correlation 2011_02
ID Layer Correlation RMSE Number_Stations Number_Points
134 2011_02 0.4727591 0.1108978 107 107
write.csv(kriging_field_correlation_50, file = paste0('kriging_nasmdb_50_validation_', layer_name_nasmd, '.csv'))
#Export of validation tables from kriging models to CSV files, as well as correlation results

#100
write.csv(validation_table_kriging_100, file = paste0('validation_reference_kriging_100', 
                                                      layer_name_nasmd, '.csv'))

#75%
write.csv(validation_table_kriging_75, file = paste0('validation_reference_kriging_75', 
                                                     layer_name_nasmd, '.csv'))

#50%
write.csv(validation_table_kriging_50, file = paste0('validation_reference_kriging_50', 
                                                     layer_name_nasmd, '.csv'))

Validation of linear model outputs

Correlation between linear model outputs (100%, 75%, 50%) and field data from the North American Soil Moisture Database

Linear model 100% of valid data, 10 fold

validation_csv_values_100 <- na.omit(read.csv(nasmdb_files[[i]]))

   if(length(validation_csv_values_100$Station_ID) != 0){
     
coordinates(validation_csv_values_100) <- ~Longitude+Latitude
proj4string(validation_csv_values_100) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0") 
validation_csv_values_100 <- spTransform(validation_csv_values_100, 
                                         CRS(projection(raster_reference)))

}

validation_csv_values_100$extract_values <- extract(sm_prediction_100_10fold, 
                                                    validation_csv_values_100)
validation_table_linearmodel_100_10fold <- as.data.frame(validation_csv_values_100)

write.csv(validation_table_linearmodel_100_10fold, file = paste0('validation_reference_glm_100_', 
                                                                 layer_name_nasmd, '.csv'))

no_stations <- length(validation_table_linearmodel_100_10fold$Station_ID)
no_points <- na.omit(validation_table_linearmodel_100_10fold$extract_values)
no_points <- length(no_points)
correlation <- cor(validation_table_linearmodel_100_10fold$mean_sm_depth_5cm, 
                   validation_table_linearmodel_100_10fold$extract_values, 
                   use = 'pairwise.complete.obs')
RMSE <- rmse(na.omit(validation_table_linearmodel_100_10fold$mean_sm_depth_5cm),
                   na.omit(validation_table_linearmodel_100_10fold$extract_values))

lm_field_correlation_100_10fold <- satellite_field_correlation
lm_field_correlation_100_10fold[1, 2] <- layer_name_nasmd
lm_field_correlation_100_10fold[1, 3] <- correlation
lm_field_correlation_100_10fold[1, 4] <- RMSE
lm_field_correlation_100_10fold[1, 5] <- no_stations
lm_field_correlation_100_10fold[1, 6] <- no_points

kable(lm_field_correlation_100_10fold, 
      caption = paste0('Monthly Linear Model 100% valid data 10 fold vs 
                       Field Data correlation ', 
                       layer_name_nasmd))
Monthly Linear Model 100% valid data 10 fold vs Field Data correlation 2011_02
ID Layer Correlation RMSE Number_Stations Number_Points
134 2011_02 0.4034743 0.1108907 107 107
write.csv(lm_field_correlation_100_10fold, file = paste0('glm10fold_nasmdb_100_validation_', layer_name_nasmd, '.csv'))

Linear Model 75% of valid data, 10 fold

validation_csv_values_75 <- na.omit(read.csv(nasmdb_files[[i]]))

   if(length(validation_csv_values_75$Station_ID) != 0){
     
coordinates(validation_csv_values_75) <- ~Longitude+Latitude
proj4string(validation_csv_values_75) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0") 
validation_csv_values_75 <- spTransform(validation_csv_values_75, 
                                        CRS(projection(raster_reference)))

}

validation_csv_values_75$extract_values <- extract(sm_prediction_75_10fold, 
                                                   validation_csv_values_75)
validation_table_linearmodel_75_10fold <- as.data.frame(validation_csv_values_75)

write.csv(validation_table_linearmodel_75_10fold, file = paste0('validation_reference_glm_75_', 
                                                                 layer_name_nasmd, '.csv'))

no_stations <- length(validation_table_linearmodel_75_10fold$Station_ID)
no_points <- na.omit(validation_table_linearmodel_75_10fold$extract_values)
no_points <- length(no_points)
correlation <- cor(validation_table_linearmodel_75_10fold$mean_sm_depth_5cm, 
                   validation_table_linearmodel_75_10fold$extract_values, 
                   use = 'pairwise.complete.obs')
RMSE <- rmse(na.omit(validation_table_linearmodel_75_10fold$mean_sm_depth_5cm),
                   na.omit(validation_table_linearmodel_75_10fold$extract_values))

lm_field_correlation_75_10fold <- satellite_field_correlation
lm_field_correlation_75_10fold[1, 2] <- layer_name_nasmd
lm_field_correlation_75_10fold[1, 3] <- correlation
lm_field_correlation_75_10fold[1, 4] <- RMSE
lm_field_correlation_75_10fold[1, 5] <- no_stations
lm_field_correlation_75_10fold[1, 6] <- no_points

kable(lm_field_correlation_75_10fold, 
      caption = paste0('Monthly Linear Model 75% valid data 10 fold vs 
                       Field Data correlation ',
                       layer_name_nasmd))
Monthly Linear Model 75% valid data 10 fold vs Field Data correlation 2011_02
ID Layer Correlation RMSE Number_Stations Number_Points
134 2011_02 0.4056848 0.111548 107 107
write.csv(lm_field_correlation_75_10fold, file = paste0('glm10fold_nasmdb_75_validation_', layer_name_nasmd, '.csv'))

Linear Model 50% of valid data, 10 fold

validation_csv_values_50 <- na.omit(read.csv(nasmdb_files[[i]]))

   if(length(validation_csv_values_50$Station_ID) != 0){
     
coordinates(validation_csv_values_50) <- ~Longitude+Latitude
proj4string(validation_csv_values_50) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0") 
validation_csv_values_50 <- spTransform(validation_csv_values_50, 
                                        CRS(projection(raster_reference)))

}

validation_csv_values_50$extract_values <- extract(sm_prediction_50_10fold, 
                                                   validation_csv_values_50)
validation_table_linearmodel_50_10fold <- as.data.frame(validation_csv_values_50)

write.csv(validation_table_linearmodel_50_10fold, file = paste0('validation_reference_glm_50_', 
                                                                 layer_name_nasmd, '.csv'))

no_stations <- length(validation_table_linearmodel_50_10fold$Station_ID)
no_points <- na.omit(validation_table_linearmodel_50_10fold$extract_values)
no_points <- length(no_points)
correlation <- cor(validation_table_linearmodel_50_10fold$mean_sm_depth_5cm, 
                   validation_table_linearmodel_50_10fold$extract_values, 
                   use = 'pairwise.complete.obs')
RMSE <- rmse(na.omit(validation_table_linearmodel_50_10fold$mean_sm_depth_5cm),
                   na.omit(validation_table_linearmodel_50_10fold$extract_values))

lm_field_correlation_50_10fold <- satellite_field_correlation
lm_field_correlation_50_10fold[1, 2] <- layer_name_nasmd
lm_field_correlation_50_10fold[1, 3] <- correlation
lm_field_correlation_50_10fold[1, 4] <- RMSE
lm_field_correlation_50_10fold[1, 5] <- no_stations
lm_field_correlation_50_10fold[1, 6] <- no_points

kable(lm_field_correlation_50_10fold, 
      caption = paste0('Monthly Linear Model 50% valid data 10 fold vs 
                       Field Data correlation ', 
                       layer_name_nasmd))
Monthly Linear Model 50% valid data 10 fold vs Field Data correlation 2011_02
ID Layer Correlation RMSE Number_Stations Number_Points
134 2011_02 0.4085178 0.110114 107 107
write.csv(lm_field_correlation_50_10fold, file = paste0('glm10fold_nasmdb_50_validation_', layer_name_nasmd, '.csv'))

Final correlation report for all models and fiel data

#Final table comparing all cross-validation outputs as well as validation 
#using ground-truth data
Final_report_cross_validation <- read.csv('Final_report_cross_validation.csv')
Final_report_cross_validation <- Final_report_cross_validation[-1]

Final_report_cross_validation[1,4] <- accuracy_report_100[1,2]
Final_report_cross_validation[2,4] <- accuracy_report_75[1,2]
Final_report_cross_validation[3,4] <- accuracy_report_50[1,2]
Final_report_cross_validation[4,4] <- accuracy_report_100[1,4]
Final_report_cross_validation[5,4] <- accuracy_report_75[1,4]
Final_report_cross_validation[6,4] <- accuracy_report_50[1,4]

Final_report_cross_validation[1,5] <- accuracy_report_100[1,3]
Final_report_cross_validation[2,5] <- accuracy_report_75[1,3]
Final_report_cross_validation[3,5] <- accuracy_report_50[1,3]
Final_report_cross_validation[4,5] <- accuracy_report_100[1,5]
Final_report_cross_validation[5,5] <- accuracy_report_75[1,5]
Final_report_cross_validation[6,5] <- accuracy_report_50[1,5]

Final_report_satellite_field_data <- read.csv('Final_report_satellite_field_data.csv')

Final_report_satellite_field_data[1,3] <- satellite_field_correlation_100[1,3]
Final_report_satellite_field_data[1,4] <- satellite_field_correlation_100[1,4]
Final_report_satellite_field_data[1,5] <- satellite_field_correlation_100[1,6]
Final_report_satellite_field_data[2,3] <- satellite_field_correlation_75[1,3]
Final_report_satellite_field_data[2,4] <- satellite_field_correlation_75[1,4]
Final_report_satellite_field_data[2,5] <- satellite_field_correlation_75[1,6]
Final_report_satellite_field_data[3,3] <- satellite_field_correlation_50[1,3]
Final_report_satellite_field_data[3,4] <- satellite_field_correlation_50[1,4]
Final_report_satellite_field_data[3,5] <- satellite_field_correlation_50[1,6]

Final_report_model_outputs_field_data <- read.csv('Final_report_model_outputs_field_data.csv')
  
Final_report_model_outputs_field_data[1,4] <-kriging_field_correlation_100[1,3]
Final_report_model_outputs_field_data[1,5] <-kriging_field_correlation_100[1,4]  
Final_report_model_outputs_field_data[1,6] <-kriging_field_correlation_100[1,6]  
Final_report_model_outputs_field_data[2,4] <-kriging_field_correlation_75[1,3]
Final_report_model_outputs_field_data[2,5] <-kriging_field_correlation_75[1,4]  
Final_report_model_outputs_field_data[2,6] <-kriging_field_correlation_75[1,6]
Final_report_model_outputs_field_data[3,4] <-kriging_field_correlation_50[1,3]
Final_report_model_outputs_field_data[3,5] <-kriging_field_correlation_50[1,4]  
Final_report_model_outputs_field_data[3,6] <-kriging_field_correlation_50[1,6]

Final_report_model_outputs_field_data[4,4] <-lm_field_correlation_100_10fold[1,3]
Final_report_model_outputs_field_data[4,5] <-lm_field_correlation_100_10fold[1,4]  
Final_report_model_outputs_field_data[4,6] <-lm_field_correlation_100_10fold[1,6]  
Final_report_model_outputs_field_data[5,4] <-lm_field_correlation_75_10fold[1,3]
Final_report_model_outputs_field_data[5,5] <-lm_field_correlation_75_10fold[1,4]  
Final_report_model_outputs_field_data[5,6] <-lm_field_correlation_75_10fold[1,6]
Final_report_model_outputs_field_data[6,4] <-lm_field_correlation_50_10fold[1,3]
Final_report_model_outputs_field_data[6,5] <-lm_field_correlation_50_10fold[1,4]  
Final_report_model_outputs_field_data[6,6] <-lm_field_correlation_50_10fold[1,6]

kable(Final_report_cross_validation)
Method Data_Perc Folds Correlation RMSE
Kriging 100 10 0.935 0.010
Kriging 75 10 0.929 0.010
Kriging 50 10 0.919 0.011
Linear 100 10 0.599 0.025
Linear 75 10 0.587 0.025
Linear 50 10 0.641 0.023
write.csv(Final_report_cross_validation, file = paste0('Final_report_cross_validation_', layer_name_nasmd, '.csv'))

kable(Final_report_satellite_field_data)
Reference Data_Perc Correlation RMSE Points_pairs
Satellite_(CCI) 100 0.4441071 0.1197464 106
Satellite_(CCI) 75 0.3931272 0.1193600 74
Satellite_(CCI) 50 0.4752477 0.1234172 53
write.csv(Final_report_satellite_field_data, file = paste0('Final_report_satellite_field_data_', layer_name_nasmd, '.csv'))

kable(Final_report_model_outputs_field_data)
Method Data_Perc Folds Correlation RMSE Points_pairs
Kriging 100 NA 0.4628770 0.1107679 107
Kriging 75 NA 0.4636105 0.1108116 107
Kriging 50 NA 0.4727591 0.1108978 107
Linear 100 10 0.4034743 0.1108907 107
Linear 75 10 0.4056848 0.1115480 107
Linear 50 10 0.4085178 0.1101140 107
write.csv(Final_report_model_outputs_field_data, file = paste0('Final_report_model_outputs_field_data_', layer_name_nasmd, '.csv'))

References

Cressie, Noel. 1990. “The Origins of Kriging.” Mathematical Geology 22 (3): 239-52. https://doi.org/10.1007/BF00889887.

Dorigo, W. A., A. Gruber, R. A M De Jeu, W. Wagner, T. Stacke, A. Loew, C. Albergel, et al. 2015. “Evaluation of the ESA CCI Soil Moisture Product Using Ground-Based Observations.” Remote Sensing of Environment 162 (June): 380-95. https://doi.org/10.1016/j.rse.2014.07.023.

Gareth, James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. Springer Texts in Statistics. Vol. 103. https://doi.org/10.1007/978-1-4614-7138-7.

Hengl, Tomislav, Gerard B M Heuvelink, and Alfred Stein. 2004. “A Generic Framework for Spatial Prediction of Soil Variables Based on Regression-Kriging.” Geoderma 120 (1-2): 75-93. https://doi.org/10.1016/j.geoderma.2003.08.018.

Parker, J a, R V Kenyon, and D E Troxel. 1983. “Comparison of Interpolation Methods for Image Resampling.” IEEE Transactions on Medical Imaging 2 (1): 31-39. https://doi.org/10.1109/42.7784.

Quiring, Steven M., Trent W. Ford, Jessica K. Wang, Angela Khong, Elizabeth Harris, Terra Lindgren, Daniel W. Goldberg, and Zhongxia Li. 2016. “The North American Soil Moisture Database: Development and Applications.” Bulletin of the American Meteorological Society 97 (8): 1441-59. https://doi.org/10.1175/BAMS-D-13-00263.1.

Stein, Michael L. 1999. Interpolation of Spatial Data: Some Theory for Kriging. Springer.

Thornton, M.M., P.E. Thornton, Y. Wei, B.W. Mayer, R.B. Cook, and R.S. Vose. 2018. “Daymet: Monthly Climate Summaries on a 1-Km Grid for North America, Version 3.” Tennessee, USA: ORNL DAAC, Oak Ridge. https://doi.org/10.3334/ORNLDAAC/1345.

Appendix

Maps showing the distribution of field stations from the North American Soil Moisture Database used for each subset of valid data (100%, 75%, 50) for the processed month. (e.g. February 2011)

region_of_interest_grid <- shapefile('Oklahoma_reference_grid__.shp')

#100%
no_points_plot_100 <- as.character(satellite_field_correlation_100$Number_Points[1])
plot(region_of_interest_grid, col = 'azure2', lwd = 0.25, 
     main = 'Points available over the region of interest used for field validation (100%)'
     , cex.main=0.75, xlab = paste0('Number of valid stations for correlation ', no_points_plot_100))
plot(validation_csv_values_100, add = TRUE, col = 'firebrick1', pch = 19)

#75%
no_points_plot_75 <- as.character(satellite_field_correlation_75$Number_Points[1])
plot(region_of_interest_grid, col = 'azure2', lwd = 0.25, 
     main = 'Points available over the region of interest used for field validation (75%)'
     , cex.main=0.75, xlab = paste0('Number of valid stations for correlation ', no_points_plot_75))
plot(validation_csv_values_75, add = TRUE, col = 'firebrick1', pch = 19)

#50%
no_points_plot_50 <- as.character(satellite_field_correlation_50$Number_Points[1])
plot(region_of_interest_grid, col = 'azure2', lwd = 0.25, 
     main = 'Points available over the region of interest used for field validation (50%)'
     , cex.main=0.75, xlab = paste0('Number of valid stations for correlation ', no_points_plot_50))
plot(validation_csv_values_50, add = TRUE, col = 'firebrick1', pch = 19)

Reference data from satellite estimates

#100%
kable(validation_table_satellite_100, 
      caption = paste0('Validation Reference (satellite vs ground-truth data) 100% 
                       valid data', 
                       layer_name_nasmd))
Validation Reference (satellite vs ground-truth data) 100% valid data2011_02
X.1 X Station_ID Year Month mean_sm_depth_5cm extract_values Longitude Latitude
1 30688 30688 10019412 2011 2 0.2411175 0.1871015 -98.0200 34.8000
2 30689 30689 10029412 2011 2 0.2435521 0.2240257 -96.6600 34.7900
3 30690 30690 10039412 2011 2 0.3234629 0.1668676 -99.3400 34.5900
4 30691 30691 10059498 2011 2 0.3532900 0.1764904 -98.6700 36.7800
5 30692 30692 10061112 2011 2 0.1967957 NA -95.6700 34.2500
6 30693 30693 10089412 2011 2 0.1934604 0.1788686 -98.2900 34.9100
8 30695 30695 10119412 2011 2 0.2540407 0.1819853 -99.9000 36.0700
9 30696 30696 10139412 2011 2 0.2551487 0.1715468 -100.5300 36.8000
10 30697 30697 10159412 2011 2 0.2818268 0.1475211 -99.0600 35.4000
11 30698 30698 10169412 2011 2 0.2331411 0.2158529 -95.8700 35.9600
12 30699 30699 10179412 2011 2 0.2706664 0.2098707 -97.2500 36.7500
13 30700 30700 10189412 2011 2 0.3254380 0.1677102 -102.5000 36.6900
14 30701 30701 10199412 2011 2 0.2640646 0.2179539 -96.6300 35.1700
15 30702 30702 10209412 2011 2 0.3112093 0.1925817 -97.6900 36.4100
18 30705 30705 10239412 2011 2 0.2442550 0.1665185 -99.6400 36.8300
19 30706 30706 10259412 2011 2 0.1982011 0.2259330 -97.2700 33.8900
20 30707 30707 10269412 2011 2 0.3206943 0.1727567 -99.2700 35.5900
21 30708 30708 10279412 2011 2 0.1994179 0.2148972 -97.0000 34.8500
23 30710 30710 10299412 2011 2 0.2465456 0.1741186 -99.3500 36.0300
25 30712 30712 10329412 2011 2 0.2709046 0.2311113 -96.3300 34.6100
26 30713 30713 10339412 2011 2 0.2990518 0.1965131 -96.8000 35.6500
27 30714 30714 10349412 2011 2 0.2695300 0.1748596 -98.3600 36.7500
28 30715 30715 10359412 2011 2 0.2521746 0.1918025 -99.7300 35.5500
29 30716 30716 10399412 2011 2 0.3026357 0.2397153 -95.2500 34.2200
30 30717 30717 10419412 2011 2 0.3570429 0.2497672 -94.8500 35.6800
31 30718 30718 10429412 2011 2 0.3171425 0.2204907 -95.8900 36.9100
32 30719 30719 10439412 2011 2 0.2795500 0.2316825 -96.3200 33.9200
33 30720 30720 10449412 2011 2 0.3755546 0.1719271 -98.0400 35.5500
34 30721 30721 10459412 2011 2 0.1883393 0.1854347 -99.8000 35.2000
35 30722 30722 10469412 2011 2 0.3109550 0.2199232 -95.6600 35.3000
36 30723 30723 10479412 2011 2 0.2978971 0.1575720 -98.5000 36.2600
37 30724 30724 10499412 2011 2 0.2566993 0.2207670 -96.4300 36.8400
38 30725 30725 10509412 2011 2 0.2875494 0.1837732 -99.1400 36.7300
39 30726 30726 10519412 2011 2 0.2228443 0.1660368 -98.4700 35.1500
40 30727 30727 10529412 2011 2 0.2593462 0.1489368 -101.6000 36.6000
41 30728 30728 10549499 2011 2 0.3733143 0.1651231 -98.7400 34.2400
42 30729 30729 10559412 2011 2 0.2367632 0.1946599 -97.4800 35.8500
43 30730 30730 10569412 2011 2 0.3742504 0.1939471 -95.6400 35.7500
44 30731 30731 10579612 2011 2 0.2624818 0.2205725 -96.0000 35.8400
45 30732 30732 10589412 2011 2 0.2193125 0.1565697 -98.4800 35.4800
46 30733 30733 10599412 2011 2 0.0126611 0.1675442 -99.0500 34.9900
47 30734 30734 10619412 2011 2 0.3645396 0.1622549 -99.8300 34.6900
48 30735 30735 10629412 2011 2 0.2722127 0.1541432 -101.2300 36.8600
49 30736 30736 10639412 2011 2 0.2874546 0.2406719 -95.5400 34.0300
50 30737 30737 10649412 2011 2 0.4371421 0.2353496 -94.8800 33.8300
51 30738 30738 10669412 2011 2 0.3929139 0.2360119 -94.7800 36.4800
52 30739 30739 10679412 2011 2 0.2431185 0.1600052 -102.8800 36.8300
53 30740 30740 10689412 2011 2 0.3134275 0.1950563 -97.7600 34.5300
55 30742 30742 10719412 2011 2 0.3005504 0.1646411 -98.1100 36.3800
56 30743 30743 10729412 2011 2 0.2428468 0.2284873 -96.0000 34.3100
57 30744 30744 10749412 2011 2 0.1638405 0.1579393 -99.4200 34.8400
58 30745 30745 10759412 2011 2 0.2864296 0.2000756 -97.2100 36.0600
60 30747 30747 10779412 2011 2 0.2653727 0.1949198 -99.0100 36.9900
61 30748 30748 10789412 2011 2 0.2140846 0.2220111 -95.7800 34.8800
62 30749 30749 10809412 2011 2 0.2653454 0.1776427 -98.5700 34.7300
63 30750 30750 10819412 2011 2 0.4142421 0.2385731 -94.8400 36.8900
64 30751 30751 10829412 2011 2 0.3007821 0.1880122 -97.9600 35.2700
65 30752 30752 10859412 2011 2 0.4387112 0.1990605 -96.9100 36.9000
67 30754 30754 10899412 2011 2 0.3655561 0.2109332 -95.6100 36.7400
68 30755 30755 10919412 2011 2 0.2754514 0.2056588 -96.5000 36.0300
69 30756 30756 10959412 2011 2 0.3010093 0.2126226 -96.2600 35.4300
70 30757 30757 10969412 2011 2 0.3250596 0.2133427 -95.9100 35.5800
71 30758 30758 10979412 2011 2 0.3644982 0.2141198 -97.2300 34.7200
72 30759 30759 10989412 2011 2 0.3715582 0.2071612 -96.7700 36.3600
73 30760 30760 10999412 2011 2 0.2713482 0.2000756 -97.0500 36.0000
74 30761 30761 11009912 2011 2 0.2387571 0.1939471 -95.5600 35.8300
76 30763 30763 11029412 2011 2 0.4046039 0.2140599 -95.2700 36.3700
77 30764 30764 11039412 2011 2 0.2600746 0.1673697 -98.9600 35.9000
78 30765 30765 11049412 2011 2 0.3178882 0.1953398 -97.1500 36.3600
79 30766 30766 11069412 2011 2 0.2283450 0.1953839 -97.5900 34.1900
80 30767 30767 11089412 2011 2 0.3090513 0.1589956 -99.0400 36.1900
81 30768 30768 11099412 2011 2 0.3499464 0.2043702 -96.9500 35.3600
82 30769 30769 11109412 2011 2 0.2322625 0.2174215 -96.0400 36.4200
83 30770 30770 11119412 2011 2 0.1808669 0.1905241 -100.2600 36.6000
84 30771 30771 11129412 2011 2 0.2188482 0.1898242 -97.3400 35.5400
85 30772 30772 11139412 2011 2 0.3785246 0.2389061 -95.1800 35.2700
86 30773 30773 11149412 2011 2 0.3887450 0.2000756 -97.1000 36.1200
87 30774 30774 11159412 2011 2 0.1954525 0.2198166 -96.0700 34.8800
88 30775 30775 11179412 2011 2 0.3498904 0.2512968 -94.9900 35.9700
89 30776 30776 11189412 2011 2 0.4238400 0.2276816 -95.0100 34.7100
90 30777 30777 11199412 2011 2 0.2618586 0.1480726 -99.1400 34.4400
91 30778 30778 11209412 2011 2 0.3364304 0.2339444 -96.6800 34.3300
92 30779 30779 11229812 2011 2 0.2672518 0.2148972 -96.8400 34.7900
93 30780 30780 11239412 2011 2 0.3571061 0.2285453 -95.2200 36.7800
94 30781 30781 11241212 2011 2 0.3256629 0.1626039 -98.3200 34.3700
95 30782 30782 11269412 2011 2 0.2795525 0.2034222 -97.5200 34.9800
96 30783 30783 11279412 2011 2 0.2592818 0.1553605 -98.5300 35.8400
97 30784 30784 11289412 2011 2 0.2330579 0.2021369 -97.9900 34.1700
98 30785 30785 11299412 2011 2 0.2284118 0.1649418 -98.7800 35.5100
99 30786 30786 11329412 2011 2 0.3343371 0.2490755 -94.6400 36.0100
100 30787 30787 11339412 2011 2 0.3526150 0.2270108 -95.3500 34.9000
101 30788 30788 11349412 2011 2 0.3601271 0.2241911 -94.6900 34.9800
102 30789 30789 11359412 2011 2 0.2634812 0.1773577 -99.4200 36.4200
103 30790 30790 11369412 2011 2 0.3276068 0.2210402 -96.3400 36.5200
104 30791 30791 1900212 2011 2 0.3063157 0.1918712 -97.4600 35.2400
141 30828 30828 30070512 2011 2 0.0085600 0.1269383 -103.3800 33.3200
142 30829 30829 60079611 2011 2 0.3398168 0.2249230 -96.1800 37.3830
146 30833 30833 60119612 2011 2 0.2873734 0.1748596 -98.2850 36.8810
147 30834 30834 60129612 2011 2 0.3139968 0.2207670 -96.4270 36.8410
148 30835 30835 60139612 2011 2 0.2743646 0.1963522 -97.4850 36.6050
149 30836 30836 60169611 2011 2 0.3002286 0.1589956 -99.1340 36.0610
151 30838 30838 60189711 2011 2 0.3023986 0.1719271 -98.0170 35.5570
154 30841 30841 70480712 2011 2 0.4146116 0.2554975 -94.5829 37.4277
155 30842 30842 70780412 2011 2 0.1586265 0.1489368 -101.5950 36.5993
157 30844 30844 70800212 2011 2 0.3527068 0.2000756 -97.0914 36.1181
158 30845 30845 70810212 2011 2 0.2449762 0.2000756 -97.1082 36.1346
159 30846 30846 71000412 2011 2 0.1231812 0.1250759 -102.7740 33.9557
#75%
kable(validation_table_satellite_75, 
      caption = paste0('Validation Reference (satellite vs ground-truth data) 75% 
                       valid data', 
                       layer_name_nasmd))
Validation Reference (satellite vs ground-truth data) 75% valid data2011_02
X.1 X Station_ID Year Month mean_sm_depth_5cm extract_values Longitude Latitude
1 30688 30688 10019412 2011 2 0.2411175 NA -98.0200 34.8000
2 30689 30689 10029412 2011 2 0.2435521 0.2240257 -96.6600 34.7900
3 30690 30690 10039412 2011 2 0.3234629 0.1668676 -99.3400 34.5900
4 30691 30691 10059498 2011 2 0.3532900 0.1764904 -98.6700 36.7800
5 30692 30692 10061112 2011 2 0.1967957 NA -95.6700 34.2500
6 30693 30693 10089412 2011 2 0.1934604 NA -98.2900 34.9100
8 30695 30695 10119412 2011 2 0.2540407 0.1819853 -99.9000 36.0700
9 30696 30696 10139412 2011 2 0.2551487 0.1715468 -100.5300 36.8000
10 30697 30697 10159412 2011 2 0.2818268 NA -99.0600 35.4000
11 30698 30698 10169412 2011 2 0.2331411 0.2158529 -95.8700 35.9600
12 30699 30699 10179412 2011 2 0.2706664 0.2098707 -97.2500 36.7500
13 30700 30700 10189412 2011 2 0.3254380 0.1677102 -102.5000 36.6900
14 30701 30701 10199412 2011 2 0.2640646 0.2179539 -96.6300 35.1700
15 30702 30702 10209412 2011 2 0.3112093 0.1925817 -97.6900 36.4100
18 30705 30705 10239412 2011 2 0.2442550 0.1665185 -99.6400 36.8300
19 30706 30706 10259412 2011 2 0.1982011 0.2259330 -97.2700 33.8900
20 30707 30707 10269412 2011 2 0.3206943 NA -99.2700 35.5900
21 30708 30708 10279412 2011 2 0.1994179 0.2148972 -97.0000 34.8500
23 30710 30710 10299412 2011 2 0.2465456 0.1741186 -99.3500 36.0300
25 30712 30712 10329412 2011 2 0.2709046 0.2311113 -96.3300 34.6100
26 30713 30713 10339412 2011 2 0.2990518 NA -96.8000 35.6500
27 30714 30714 10349412 2011 2 0.2695300 0.1748596 -98.3600 36.7500
28 30715 30715 10359412 2011 2 0.2521746 0.1918025 -99.7300 35.5500
29 30716 30716 10399412 2011 2 0.3026357 0.2397153 -95.2500 34.2200
30 30717 30717 10419412 2011 2 0.3570429 0.2497672 -94.8500 35.6800
31 30718 30718 10429412 2011 2 0.3171425 NA -95.8900 36.9100
32 30719 30719 10439412 2011 2 0.2795500 NA -96.3200 33.9200
33 30720 30720 10449412 2011 2 0.3755546 0.1719271 -98.0400 35.5500
34 30721 30721 10459412 2011 2 0.1883393 NA -99.8000 35.2000
35 30722 30722 10469412 2011 2 0.3109550 NA -95.6600 35.3000
36 30723 30723 10479412 2011 2 0.2978971 0.1575720 -98.5000 36.2600
37 30724 30724 10499412 2011 2 0.2566993 0.2207670 -96.4300 36.8400
38 30725 30725 10509412 2011 2 0.2875494 0.1837732 -99.1400 36.7300
39 30726 30726 10519412 2011 2 0.2228443 0.1660368 -98.4700 35.1500
40 30727 30727 10529412 2011 2 0.2593462 NA -101.6000 36.6000
41 30728 30728 10549499 2011 2 0.3733143 NA -98.7400 34.2400
42 30729 30729 10559412 2011 2 0.2367632 NA -97.4800 35.8500
43 30730 30730 10569412 2011 2 0.3742504 0.1939471 -95.6400 35.7500
44 30731 30731 10579612 2011 2 0.2624818 0.2205725 -96.0000 35.8400
45 30732 30732 10589412 2011 2 0.2193125 NA -98.4800 35.4800
46 30733 30733 10599412 2011 2 0.0126611 0.1675442 -99.0500 34.9900
47 30734 30734 10619412 2011 2 0.3645396 0.1622549 -99.8300 34.6900
48 30735 30735 10629412 2011 2 0.2722127 0.1541432 -101.2300 36.8600
49 30736 30736 10639412 2011 2 0.2874546 0.2406719 -95.5400 34.0300
50 30737 30737 10649412 2011 2 0.4371421 0.2353496 -94.8800 33.8300
51 30738 30738 10669412 2011 2 0.3929139 0.2360119 -94.7800 36.4800
52 30739 30739 10679412 2011 2 0.2431185 NA -102.8800 36.8300
53 30740 30740 10689412 2011 2 0.3134275 NA -97.7600 34.5300
55 30742 30742 10719412 2011 2 0.3005504 NA -98.1100 36.3800
56 30743 30743 10729412 2011 2 0.2428468 0.2284873 -96.0000 34.3100
57 30744 30744 10749412 2011 2 0.1638405 NA -99.4200 34.8400
58 30745 30745 10759412 2011 2 0.2864296 0.2000756 -97.2100 36.0600
60 30747 30747 10779412 2011 2 0.2653727 0.1949198 -99.0100 36.9900
61 30748 30748 10789412 2011 2 0.2140846 0.2220111 -95.7800 34.8800
62 30749 30749 10809412 2011 2 0.2653454 0.1776427 -98.5700 34.7300
63 30750 30750 10819412 2011 2 0.4142421 0.2385731 -94.8400 36.8900
64 30751 30751 10829412 2011 2 0.3007821 0.1880122 -97.9600 35.2700
65 30752 30752 10859412 2011 2 0.4387112 NA -96.9100 36.9000
67 30754 30754 10899412 2011 2 0.3655561 0.2109332 -95.6100 36.7400
68 30755 30755 10919412 2011 2 0.2754514 NA -96.5000 36.0300
69 30756 30756 10959412 2011 2 0.3010093 0.2126226 -96.2600 35.4300
70 30757 30757 10969412 2011 2 0.3250596 0.2133427 -95.9100 35.5800
71 30758 30758 10979412 2011 2 0.3644982 0.2141198 -97.2300 34.7200
72 30759 30759 10989412 2011 2 0.3715582 NA -96.7700 36.3600
73 30760 30760 10999412 2011 2 0.2713482 0.2000756 -97.0500 36.0000
74 30761 30761 11009912 2011 2 0.2387571 0.1939471 -95.5600 35.8300
76 30763 30763 11029412 2011 2 0.4046039 0.2140599 -95.2700 36.3700
77 30764 30764 11039412 2011 2 0.2600746 0.1673697 -98.9600 35.9000
78 30765 30765 11049412 2011 2 0.3178882 0.1953398 -97.1500 36.3600
79 30766 30766 11069412 2011 2 0.2283450 0.1953839 -97.5900 34.1900
80 30767 30767 11089412 2011 2 0.3090513 NA -99.0400 36.1900
81 30768 30768 11099412 2011 2 0.3499464 0.2043702 -96.9500 35.3600
82 30769 30769 11109412 2011 2 0.2322625 NA -96.0400 36.4200
83 30770 30770 11119412 2011 2 0.1808669 0.1905241 -100.2600 36.6000
84 30771 30771 11129412 2011 2 0.2188482 0.1898242 -97.3400 35.5400
85 30772 30772 11139412 2011 2 0.3785246 NA -95.1800 35.2700
86 30773 30773 11149412 2011 2 0.3887450 0.2000756 -97.1000 36.1200
87 30774 30774 11159412 2011 2 0.1954525 NA -96.0700 34.8800
88 30775 30775 11179412 2011 2 0.3498904 NA -94.9900 35.9700
89 30776 30776 11189412 2011 2 0.4238400 0.2276816 -95.0100 34.7100
90 30777 30777 11199412 2011 2 0.2618586 0.1480726 -99.1400 34.4400
91 30778 30778 11209412 2011 2 0.3364304 0.2339444 -96.6800 34.3300
92 30779 30779 11229812 2011 2 0.2672518 0.2148972 -96.8400 34.7900
93 30780 30780 11239412 2011 2 0.3571061 0.2285453 -95.2200 36.7800
94 30781 30781 11241212 2011 2 0.3256629 0.1626039 -98.3200 34.3700
95 30782 30782 11269412 2011 2 0.2795525 0.2034222 -97.5200 34.9800
96 30783 30783 11279412 2011 2 0.2592818 NA -98.5300 35.8400
97 30784 30784 11289412 2011 2 0.2330579 0.2021369 -97.9900 34.1700
98 30785 30785 11299412 2011 2 0.2284118 NA -98.7800 35.5100
99 30786 30786 11329412 2011 2 0.3343371 0.2490755 -94.6400 36.0100
100 30787 30787 11339412 2011 2 0.3526150 0.2270108 -95.3500 34.9000
101 30788 30788 11349412 2011 2 0.3601271 0.2241911 -94.6900 34.9800
102 30789 30789 11359412 2011 2 0.2634812 NA -99.4200 36.4200
103 30790 30790 11369412 2011 2 0.3276068 0.2210402 -96.3400 36.5200
104 30791 30791 1900212 2011 2 0.3063157 0.1918712 -97.4600 35.2400
141 30828 30828 30070512 2011 2 0.0085600 0.1269383 -103.3800 33.3200
142 30829 30829 60079611 2011 2 0.3398168 0.2249230 -96.1800 37.3830
146 30833 30833 60119612 2011 2 0.2873734 0.1748596 -98.2850 36.8810
147 30834 30834 60129612 2011 2 0.3139968 0.2207670 -96.4270 36.8410
148 30835 30835 60139612 2011 2 0.2743646 0.1963522 -97.4850 36.6050
149 30836 30836 60169611 2011 2 0.3002286 NA -99.1340 36.0610
151 30838 30838 60189711 2011 2 0.3023986 0.1719271 -98.0170 35.5570
154 30841 30841 70480712 2011 2 0.4146116 NA -94.5829 37.4277
155 30842 30842 70780412 2011 2 0.1586265 NA -101.5950 36.5993
157 30844 30844 70800212 2011 2 0.3527068 0.2000756 -97.0914 36.1181
158 30845 30845 70810212 2011 2 0.2449762 0.2000756 -97.1082 36.1346
159 30846 30846 71000412 2011 2 0.1231812 NA -102.7740 33.9557
#50%
kable(validation_table_satellite_50, 
      caption = paste0('Validation Reference (satellite vs ground-truth data) 50% 
                       valid data', 
                       layer_name_nasmd))
Validation Reference (satellite vs ground-truth data) 50% valid data2011_02
X.1 X Station_ID Year Month mean_sm_depth_5cm extract_values Longitude Latitude
1 30688 30688 10019412 2011 2 0.2411175 0.1871015 -98.0200 34.8000
2 30689 30689 10029412 2011 2 0.2435521 NA -96.6600 34.7900
3 30690 30690 10039412 2011 2 0.3234629 0.1668676 -99.3400 34.5900
4 30691 30691 10059498 2011 2 0.3532900 NA -98.6700 36.7800
5 30692 30692 10061112 2011 2 0.1967957 NA -95.6700 34.2500
6 30693 30693 10089412 2011 2 0.1934604 0.1788686 -98.2900 34.9100
8 30695 30695 10119412 2011 2 0.2540407 0.1819853 -99.9000 36.0700
9 30696 30696 10139412 2011 2 0.2551487 0.1715468 -100.5300 36.8000
10 30697 30697 10159412 2011 2 0.2818268 0.1475211 -99.0600 35.4000
11 30698 30698 10169412 2011 2 0.2331411 0.2158529 -95.8700 35.9600
12 30699 30699 10179412 2011 2 0.2706664 NA -97.2500 36.7500
13 30700 30700 10189412 2011 2 0.3254380 0.1677102 -102.5000 36.6900
14 30701 30701 10199412 2011 2 0.2640646 NA -96.6300 35.1700
15 30702 30702 10209412 2011 2 0.3112093 NA -97.6900 36.4100
18 30705 30705 10239412 2011 2 0.2442550 0.1665185 -99.6400 36.8300
19 30706 30706 10259412 2011 2 0.1982011 NA -97.2700 33.8900
20 30707 30707 10269412 2011 2 0.3206943 0.1727567 -99.2700 35.5900
21 30708 30708 10279412 2011 2 0.1994179 NA -97.0000 34.8500
23 30710 30710 10299412 2011 2 0.2465456 NA -99.3500 36.0300
25 30712 30712 10329412 2011 2 0.2709046 0.2311113 -96.3300 34.6100
26 30713 30713 10339412 2011 2 0.2990518 0.1965131 -96.8000 35.6500
27 30714 30714 10349412 2011 2 0.2695300 NA -98.3600 36.7500
28 30715 30715 10359412 2011 2 0.2521746 NA -99.7300 35.5500
29 30716 30716 10399412 2011 2 0.3026357 0.2397153 -95.2500 34.2200
30 30717 30717 10419412 2011 2 0.3570429 NA -94.8500 35.6800
31 30718 30718 10429412 2011 2 0.3171425 0.2204907 -95.8900 36.9100
32 30719 30719 10439412 2011 2 0.2795500 0.2316825 -96.3200 33.9200
33 30720 30720 10449412 2011 2 0.3755546 NA -98.0400 35.5500
34 30721 30721 10459412 2011 2 0.1883393 0.1854347 -99.8000 35.2000
35 30722 30722 10469412 2011 2 0.3109550 NA -95.6600 35.3000
36 30723 30723 10479412 2011 2 0.2978971 0.1575720 -98.5000 36.2600
37 30724 30724 10499412 2011 2 0.2566993 0.2207670 -96.4300 36.8400
38 30725 30725 10509412 2011 2 0.2875494 NA -99.1400 36.7300
39 30726 30726 10519412 2011 2 0.2228443 0.1660368 -98.4700 35.1500
40 30727 30727 10529412 2011 2 0.2593462 NA -101.6000 36.6000
41 30728 30728 10549499 2011 2 0.3733143 NA -98.7400 34.2400
42 30729 30729 10559412 2011 2 0.2367632 0.1946599 -97.4800 35.8500
43 30730 30730 10569412 2011 2 0.3742504 0.1939471 -95.6400 35.7500
44 30731 30731 10579612 2011 2 0.2624818 NA -96.0000 35.8400
45 30732 30732 10589412 2011 2 0.2193125 0.1565697 -98.4800 35.4800
46 30733 30733 10599412 2011 2 0.0126611 0.1675442 -99.0500 34.9900
47 30734 30734 10619412 2011 2 0.3645396 NA -99.8300 34.6900
48 30735 30735 10629412 2011 2 0.2722127 0.1541432 -101.2300 36.8600
49 30736 30736 10639412 2011 2 0.2874546 0.2406719 -95.5400 34.0300
50 30737 30737 10649412 2011 2 0.4371421 0.2353496 -94.8800 33.8300
51 30738 30738 10669412 2011 2 0.3929139 NA -94.7800 36.4800
52 30739 30739 10679412 2011 2 0.2431185 0.1600052 -102.8800 36.8300
53 30740 30740 10689412 2011 2 0.3134275 NA -97.7600 34.5300
55 30742 30742 10719412 2011 2 0.3005504 NA -98.1100 36.3800
56 30743 30743 10729412 2011 2 0.2428468 0.2284873 -96.0000 34.3100
57 30744 30744 10749412 2011 2 0.1638405 NA -99.4200 34.8400
58 30745 30745 10759412 2011 2 0.2864296 0.2000756 -97.2100 36.0600
60 30747 30747 10779412 2011 2 0.2653727 NA -99.0100 36.9900
61 30748 30748 10789412 2011 2 0.2140846 NA -95.7800 34.8800
62 30749 30749 10809412 2011 2 0.2653454 0.1776427 -98.5700 34.7300
63 30750 30750 10819412 2011 2 0.4142421 NA -94.8400 36.8900
64 30751 30751 10829412 2011 2 0.3007821 NA -97.9600 35.2700
65 30752 30752 10859412 2011 2 0.4387112 0.1990605 -96.9100 36.9000
67 30754 30754 10899412 2011 2 0.3655561 NA -95.6100 36.7400
68 30755 30755 10919412 2011 2 0.2754514 0.2056588 -96.5000 36.0300
69 30756 30756 10959412 2011 2 0.3010093 0.2126226 -96.2600 35.4300
70 30757 30757 10969412 2011 2 0.3250596 NA -95.9100 35.5800
71 30758 30758 10979412 2011 2 0.3644982 NA -97.2300 34.7200
72 30759 30759 10989412 2011 2 0.3715582 NA -96.7700 36.3600
73 30760 30760 10999412 2011 2 0.2713482 0.2000756 -97.0500 36.0000
74 30761 30761 11009912 2011 2 0.2387571 0.1939471 -95.5600 35.8300
76 30763 30763 11029412 2011 2 0.4046039 NA -95.2700 36.3700
77 30764 30764 11039412 2011 2 0.2600746 NA -98.9600 35.9000
78 30765 30765 11049412 2011 2 0.3178882 0.1953398 -97.1500 36.3600
79 30766 30766 11069412 2011 2 0.2283450 0.1953839 -97.5900 34.1900
80 30767 30767 11089412 2011 2 0.3090513 NA -99.0400 36.1900
81 30768 30768 11099412 2011 2 0.3499464 NA -96.9500 35.3600
82 30769 30769 11109412 2011 2 0.2322625 NA -96.0400 36.4200
83 30770 30770 11119412 2011 2 0.1808669 0.1905241 -100.2600 36.6000
84 30771 30771 11129412 2011 2 0.2188482 0.1898242 -97.3400 35.5400
85 30772 30772 11139412 2011 2 0.3785246 NA -95.1800 35.2700
86 30773 30773 11149412 2011 2 0.3887450 0.2000756 -97.1000 36.1200
87 30774 30774 11159412 2011 2 0.1954525 NA -96.0700 34.8800
88 30775 30775 11179412 2011 2 0.3498904 NA -94.9900 35.9700
89 30776 30776 11189412 2011 2 0.4238400 NA -95.0100 34.7100
90 30777 30777 11199412 2011 2 0.2618586 0.1480726 -99.1400 34.4400
91 30778 30778 11209412 2011 2 0.3364304 0.2339444 -96.6800 34.3300
92 30779 30779 11229812 2011 2 0.2672518 NA -96.8400 34.7900
93 30780 30780 11239412 2011 2 0.3571061 0.2285453 -95.2200 36.7800
94 30781 30781 11241212 2011 2 0.3256629 0.1626039 -98.3200 34.3700
95 30782 30782 11269412 2011 2 0.2795525 NA -97.5200 34.9800
96 30783 30783 11279412 2011 2 0.2592818 0.1553605 -98.5300 35.8400
97 30784 30784 11289412 2011 2 0.2330579 NA -97.9900 34.1700
98 30785 30785 11299412 2011 2 0.2284118 NA -98.7800 35.5100
99 30786 30786 11329412 2011 2 0.3343371 NA -94.6400 36.0100
100 30787 30787 11339412 2011 2 0.3526150 0.2270108 -95.3500 34.9000
101 30788 30788 11349412 2011 2 0.3601271 NA -94.6900 34.9800
102 30789 30789 11359412 2011 2 0.2634812 0.1773577 -99.4200 36.4200
103 30790 30790 11369412 2011 2 0.3276068 NA -96.3400 36.5200
104 30791 30791 1900212 2011 2 0.3063157 0.1918712 -97.4600 35.2400
141 30828 30828 30070512 2011 2 0.0085600 0.1269383 -103.3800 33.3200
142 30829 30829 60079611 2011 2 0.3398168 NA -96.1800 37.3830
146 30833 30833 60119612 2011 2 0.2873734 NA -98.2850 36.8810
147 30834 30834 60129612 2011 2 0.3139968 0.2207670 -96.4270 36.8410
148 30835 30835 60139612 2011 2 0.2743646 NA -97.4850 36.6050
149 30836 30836 60169611 2011 2 0.3002286 NA -99.1340 36.0610
151 30838 30838 60189711 2011 2 0.3023986 NA -98.0170 35.5570
154 30841 30841 70480712 2011 2 0.4146116 0.2554975 -94.5829 37.4277
155 30842 30842 70780412 2011 2 0.1586265 NA -101.5950 36.5993
157 30844 30844 70800212 2011 2 0.3527068 0.2000756 -97.0914 36.1181
158 30845 30845 70810212 2011 2 0.2449762 0.2000756 -97.1082 36.1346
159 30846 30846 71000412 2011 2 0.1231812 NA -102.7740 33.9557

Kriging interpolation model

#100%
kable(validation_table_kriging_100, 
      caption = paste0('Validation Kriging (model vs ground-truth data) 100% 
                       valid data', 
                       layer_name_nasmd))
Validation Kriging (model vs ground-truth data) 100% valid data2011_02
X.1 X Station_ID Year Month mean_sm_depth_5cm extract_values Longitude Latitude
1 30688 30688 10019412 2011 2 0.2411175 0.1827023 -98.0200 34.8000
2 30689 30689 10029412 2011 2 0.2435521 0.2226087 -96.6600 34.7900
3 30690 30690 10039412 2011 2 0.3234629 0.1620981 -99.3400 34.5900
4 30691 30691 10059498 2011 2 0.3532900 0.1820831 -98.6700 36.7800
5 30692 30692 10061112 2011 2 0.1967957 0.2303229 -95.6700 34.2500
6 30693 30693 10089412 2011 2 0.1934604 0.1768817 -98.2900 34.9100
8 30695 30695 10119412 2011 2 0.2540407 0.1836900 -99.9000 36.0700
9 30696 30696 10139412 2011 2 0.2551487 0.1766267 -100.5300 36.8000
10 30697 30697 10159412 2011 2 0.2818268 0.1558909 -99.0600 35.4000
11 30698 30698 10169412 2011 2 0.2331411 0.2150093 -95.8700 35.9600
12 30699 30699 10179412 2011 2 0.2706664 0.2007533 -97.2500 36.7500
13 30700 30700 10189412 2011 2 0.3254380 0.1596870 -102.5000 36.6900
14 30701 30701 10199412 2011 2 0.2640646 0.2167469 -96.6300 35.1700
15 30702 30702 10209412 2011 2 0.3112093 0.1917167 -97.6900 36.4100
18 30705 30705 10239412 2011 2 0.2442550 0.1778016 -99.6400 36.8300
19 30706 30706 10259412 2011 2 0.1982011 0.2305486 -97.2700 33.8900
20 30707 30707 10269412 2011 2 0.3206943 0.1708896 -99.2700 35.5900
21 30708 30708 10279412 2011 2 0.1994179 0.2169288 -97.0000 34.8500
23 30710 30710 10299412 2011 2 0.2465456 0.1709209 -99.3500 36.0300
25 30712 30712 10329412 2011 2 0.2709046 0.2258731 -96.3300 34.6100
26 30713 30713 10339412 2011 2 0.2990518 0.1995597 -96.8000 35.6500
27 30714 30714 10349412 2011 2 0.2695300 0.1734024 -98.3600 36.7500
28 30715 30715 10359412 2011 2 0.2521746 0.1824787 -99.7300 35.5500
29 30716 30716 10399412 2011 2 0.3026357 0.2227294 -95.2500 34.2200
30 30717 30717 10419412 2011 2 0.3570429 0.2476003 -94.8500 35.6800
31 30718 30718 10429412 2011 2 0.3171425 0.2196423 -95.8900 36.9100
32 30719 30719 10439412 2011 2 0.2795500 0.2270675 -96.3200 33.9200
33 30720 30720 10449412 2011 2 0.3755546 0.1692720 -98.0400 35.5500
34 30721 30721 10459412 2011 2 0.1883393 0.1819592 -99.8000 35.2000
35 30722 30722 10469412 2011 2 0.3109550 0.2221975 -95.6600 35.3000
36 30723 30723 10479412 2011 2 0.2978971 0.1643291 -98.5000 36.2600
37 30724 30724 10499412 2011 2 0.2566993 0.2194418 -96.4300 36.8400
38 30725 30725 10509412 2011 2 0.2875494 0.1798514 -99.1400 36.7300
39 30726 30726 10519412 2011 2 0.2228443 0.1703392 -98.4700 35.1500
40 30727 30727 10529412 2011 2 0.2593462 0.1482820 -101.6000 36.6000
41 30728 30728 10549499 2011 2 0.3733143 0.1663970 -98.7400 34.2400
42 30729 30729 10559412 2011 2 0.2367632 0.1922952 -97.4800 35.8500
43 30730 30730 10569412 2011 2 0.3742504 0.2180979 -95.6400 35.7500
44 30731 30731 10579612 2011 2 0.2624818 0.2159912 -96.0000 35.8400
45 30732 30732 10589412 2011 2 0.2193125 0.1614368 -98.4800 35.4800
46 30733 30733 10599412 2011 2 0.0126611 0.1634906 -99.0500 34.9900
47 30734 30734 10619412 2011 2 0.3645396 0.1681250 -99.8300 34.6900
48 30735 30735 10629412 2011 2 0.2722127 0.1570497 -101.2300 36.8600
49 30736 30736 10639412 2011 2 0.2874546 0.2303229 -95.5400 34.0300
50 30737 30737 10649412 2011 2 0.4371421 0.2344666 -94.8800 33.8300
51 30738 30738 10669412 2011 2 0.3929139 0.2347081 -94.7800 36.4800
52 30739 30739 10679412 2011 2 0.2431185 0.1619551 -102.8800 36.8300
53 30740 30740 10689412 2011 2 0.3134275 0.1900536 -97.7600 34.5300
55 30742 30742 10719412 2011 2 0.3005504 0.1714961 -98.1100 36.3800
56 30743 30743 10729412 2011 2 0.2428468 0.2255354 -96.0000 34.3100
57 30744 30744 10749412 2011 2 0.1638405 0.1626737 -99.4200 34.8400
58 30745 30745 10759412 2011 2 0.2864296 0.1979105 -97.2100 36.0600
60 30747 30747 10779412 2011 2 0.2653727 0.1900837 -99.0100 36.9900
61 30748 30748 10789412 2011 2 0.2140846 0.2224800 -95.7800 34.8800
62 30749 30749 10809412 2011 2 0.2653454 0.1709871 -98.5700 34.7300
63 30750 30750 10819412 2011 2 0.4142421 0.2356938 -94.8400 36.8900
64 30751 30751 10829412 2011 2 0.3007821 0.1849897 -97.9600 35.2700
65 30752 30752 10859412 2011 2 0.4387112 0.2097431 -96.9100 36.9000
67 30754 30754 10899412 2011 2 0.3655561 0.2169445 -95.6100 36.7400
68 30755 30755 10919412 2011 2 0.2754514 0.2126190 -96.5000 36.0300
69 30756 30756 10959412 2011 2 0.3010093 0.2141069 -96.2600 35.4300
70 30757 30757 10969412 2011 2 0.3250596 0.2148069 -95.9100 35.5800
71 30758 30758 10979412 2011 2 0.3644982 0.2156619 -97.2300 34.7200
72 30759 30759 10989412 2011 2 0.3715582 0.2048352 -96.7700 36.3600
73 30760 30760 10999412 2011 2 0.2713482 0.1951926 -97.0500 36.0000
74 30761 30761 11009912 2011 2 0.2387571 0.2115628 -95.5600 35.8300
76 30763 30763 11029412 2011 2 0.4046039 0.2177174 -95.2700 36.3700
77 30764 30764 11039412 2011 2 0.2600746 0.1613958 -98.9600 35.9000
78 30765 30765 11049412 2011 2 0.3178882 0.1985068 -97.1500 36.3600
79 30766 30766 11069412 2011 2 0.2283450 0.2062896 -97.5900 34.1900
80 30767 30767 11089412 2011 2 0.3090513 0.1651388 -99.0400 36.1900
81 30768 30768 11099412 2011 2 0.3499464 0.2044429 -96.9500 35.3600
82 30769 30769 11109412 2011 2 0.2322625 0.2186628 -96.0400 36.4200
83 30770 30770 11119412 2011 2 0.1808669 0.1863493 -100.2600 36.6000
84 30771 30771 11129412 2011 2 0.2188482 0.1926424 -97.3400 35.5400
85 30772 30772 11139412 2011 2 0.3785246 0.2392363 -95.1800 35.2700
86 30773 30773 11149412 2011 2 0.3887450 0.1979105 -97.1000 36.1200
87 30774 30774 11159412 2011 2 0.1954525 0.2218645 -96.0700 34.8800
88 30775 30775 11179412 2011 2 0.3498904 0.2448455 -94.9900 35.9700
89 30776 30776 11189412 2011 2 0.4238400 0.2238967 -95.0100 34.7100
90 30777 30777 11199412 2011 2 0.2618586 0.1569521 -99.1400 34.4400
91 30778 30778 11209412 2011 2 0.3364304 0.2257139 -96.6800 34.3300
92 30779 30779 11229812 2011 2 0.2672518 0.2169288 -96.8400 34.7900
93 30780 30780 11239412 2011 2 0.3571061 0.2320458 -95.2200 36.7800
94 30781 30781 11241212 2011 2 0.3256629 0.1674273 -98.3200 34.3700
95 30782 30782 11269412 2011 2 0.2795525 0.1992546 -97.5200 34.9800
96 30783 30783 11279412 2011 2 0.2592818 0.1569453 -98.5300 35.8400
97 30784 30784 11289412 2011 2 0.2330579 0.1965552 -97.9900 34.1700
98 30785 30785 11299412 2011 2 0.2284118 0.1581108 -98.7800 35.5100
99 30786 30786 11329412 2011 2 0.3343371 0.2460457 -94.6400 36.0100
100 30787 30787 11339412 2011 2 0.3526150 0.2291403 -95.3500 34.9000
101 30788 30788 11349412 2011 2 0.3601271 0.2313518 -94.6900 34.9800
102 30789 30789 11359412 2011 2 0.2634812 0.1725601 -99.4200 36.4200
103 30790 30790 11369412 2011 2 0.3276068 0.2190988 -96.3400 36.5200
104 30791 30791 1900212 2011 2 0.3063157 0.1985738 -97.4600 35.2400
141 30828 30828 30070512 2011 2 0.0085600 0.1198175 -103.3800 33.3200
142 30829 30829 60079611 2011 2 0.3398168 0.2243551 -96.1800 37.3830
146 30833 30833 60119612 2011 2 0.2873734 0.1798862 -98.2850 36.8810
147 30834 30834 60129612 2011 2 0.3139968 0.2194418 -96.4270 36.8410
148 30835 30835 60139612 2011 2 0.2743646 0.1977338 -97.4850 36.6050
149 30836 30836 60169611 2011 2 0.3002286 0.1651388 -99.1340 36.0610
151 30838 30838 60189711 2011 2 0.3023986 0.1692720 -98.0170 35.5570
154 30841 30841 70480712 2011 2 0.4146116 0.2588817 -94.5829 37.4277
155 30842 30842 70780412 2011 2 0.1586265 0.1482820 -101.5950 36.5993
157 30844 30844 70800212 2011 2 0.3527068 0.1979105 -97.0914 36.1181
158 30845 30845 70810212 2011 2 0.2449762 0.1979105 -97.1082 36.1346
159 30846 30846 71000412 2011 2 0.1231812 0.1195770 -102.7740 33.9557
#75%
kable(validation_table_kriging_75, 
      caption = paste0('Validation Kriging (model vs ground-truth data) 75% 
                       valid data', 
                       layer_name_nasmd))
Validation Kriging (model vs ground-truth data) 75% valid data2011_02
X.1 X Station_ID Year Month mean_sm_depth_5cm extract_values Longitude Latitude
1 30688 30688 10019412 2011 2 0.2411175 0.1798075 -98.0200 34.8000
2 30689 30689 10029412 2011 2 0.2435521 0.2231225 -96.6600 34.7900
3 30690 30690 10039412 2011 2 0.3234629 0.1637044 -99.3400 34.5900
4 30691 30691 10059498 2011 2 0.3532900 0.1811143 -98.6700 36.7800
5 30692 30692 10061112 2011 2 0.1967957 0.2326744 -95.6700 34.2500
6 30693 30693 10089412 2011 2 0.1934604 0.1758427 -98.2900 34.9100
8 30695 30695 10119412 2011 2 0.2540407 0.1848502 -99.9000 36.0700
9 30696 30696 10139412 2011 2 0.2551487 0.1763895 -100.5300 36.8000
10 30697 30697 10159412 2011 2 0.2818268 0.1572313 -99.0600 35.4000
11 30698 30698 10169412 2011 2 0.2331411 0.2113064 -95.8700 35.9600
12 30699 30699 10179412 2011 2 0.2706664 0.2004545 -97.2500 36.7500
13 30700 30700 10189412 2011 2 0.3254380 0.1612489 -102.5000 36.6900
14 30701 30701 10199412 2011 2 0.2640646 0.2168460 -96.6300 35.1700
15 30702 30702 10209412 2011 2 0.3112093 0.1898686 -97.6900 36.4100
18 30705 30705 10239412 2011 2 0.2442550 0.1768935 -99.6400 36.8300
19 30706 30706 10259412 2011 2 0.1982011 0.2304075 -97.2700 33.8900
20 30707 30707 10269412 2011 2 0.3206943 0.1713838 -99.2700 35.5900
21 30708 30708 10279412 2011 2 0.1994179 0.2167264 -97.0000 34.8500
23 30710 30710 10299412 2011 2 0.2465456 0.1729799 -99.3500 36.0300
25 30712 30712 10329412 2011 2 0.2709046 0.2278446 -96.3300 34.6100
26 30713 30713 10339412 2011 2 0.2990518 0.2019946 -96.8000 35.6500
27 30714 30714 10349412 2011 2 0.2695300 0.1733726 -98.3600 36.7500
28 30715 30715 10359412 2011 2 0.2521746 0.1845160 -99.7300 35.5500
29 30716 30716 10399412 2011 2 0.3026357 0.2227032 -95.2500 34.2200
30 30717 30717 10419412 2011 2 0.3570429 0.2471947 -94.8500 35.6800
31 30718 30718 10429412 2011 2 0.3171425 0.2182568 -95.8900 36.9100
32 30719 30719 10439412 2011 2 0.2795500 0.2242923 -96.3200 33.9200
33 30720 30720 10449412 2011 2 0.3755546 0.1698166 -98.0400 35.5500
34 30721 30721 10459412 2011 2 0.1883393 0.1776797 -99.8000 35.2000
35 30722 30722 10469412 2011 2 0.3109550 0.2229933 -95.6600 35.3000
36 30723 30723 10479412 2011 2 0.2978971 0.1644139 -98.5000 36.2600
37 30724 30724 10499412 2011 2 0.2566993 0.2202393 -96.4300 36.8400
38 30725 30725 10509412 2011 2 0.2875494 0.1808152 -99.1400 36.7300
39 30726 30726 10519412 2011 2 0.2228443 0.1702996 -98.4700 35.1500
40 30727 30727 10529412 2011 2 0.2593462 0.1476905 -101.6000 36.6000
41 30728 30728 10549499 2011 2 0.3733143 0.1662315 -98.7400 34.2400
42 30729 30729 10559412 2011 2 0.2367632 0.1906722 -97.4800 35.8500
43 30730 30730 10569412 2011 2 0.3742504 0.2172078 -95.6400 35.7500
44 30731 30731 10579612 2011 2 0.2624818 0.2143675 -96.0000 35.8400
45 30732 30732 10589412 2011 2 0.2193125 0.1624952 -98.4800 35.4800
46 30733 30733 10599412 2011 2 0.0126611 0.1655217 -99.0500 34.9900
47 30734 30734 10619412 2011 2 0.3645396 0.1666076 -99.8300 34.6900
48 30735 30735 10629412 2011 2 0.2722127 0.1570084 -101.2300 36.8600
49 30736 30736 10639412 2011 2 0.2874546 0.2326744 -95.5400 34.0300
50 30737 30737 10649412 2011 2 0.4371421 0.2331208 -94.8800 33.8300
51 30738 30738 10669412 2011 2 0.3929139 0.2337496 -94.7800 36.4800
52 30739 30739 10679412 2011 2 0.2431185 0.1624001 -102.8800 36.8300
53 30740 30740 10689412 2011 2 0.3134275 0.1861127 -97.7600 34.5300
55 30742 30742 10719412 2011 2 0.3005504 0.1723205 -98.1100 36.3800
56 30743 30743 10729412 2011 2 0.2428468 0.2271344 -96.0000 34.3100
57 30744 30744 10749412 2011 2 0.1638405 0.1651558 -99.4200 34.8400
58 30745 30745 10759412 2011 2 0.2864296 0.1973443 -97.2100 36.0600
60 30747 30747 10779412 2011 2 0.2653727 0.1913626 -99.0100 36.9900
61 30748 30748 10789412 2011 2 0.2140846 0.2227469 -95.7800 34.8800
62 30749 30749 10809412 2011 2 0.2653454 0.1722627 -98.5700 34.7300
63 30750 30750 10819412 2011 2 0.4142421 0.2363778 -94.8400 36.8900
64 30751 30751 10829412 2011 2 0.3007821 0.1856314 -97.9600 35.2700
65 30752 30752 10859412 2011 2 0.4387112 0.2135460 -96.9100 36.9000
67 30754 30754 10899412 2011 2 0.3655561 0.2157632 -95.6100 36.7400
68 30755 30755 10919412 2011 2 0.2754514 0.2118120 -96.5000 36.0300
69 30756 30756 10959412 2011 2 0.3010093 0.2138663 -96.2600 35.4300
70 30757 30757 10969412 2011 2 0.3250596 0.2142573 -95.9100 35.5800
71 30758 30758 10979412 2011 2 0.3644982 0.2151466 -97.2300 34.7200
72 30759 30759 10989412 2011 2 0.3715582 0.2038457 -96.7700 36.3600
73 30760 30760 10999412 2011 2 0.2713482 0.1952277 -97.0500 36.0000
74 30761 30761 11009912 2011 2 0.2387571 0.2070379 -95.5600 35.8300
76 30763 30763 11029412 2011 2 0.4046039 0.2166042 -95.2700 36.3700
77 30764 30764 11039412 2011 2 0.2600746 0.1624499 -98.9600 35.9000
78 30765 30765 11049412 2011 2 0.3178882 0.1973876 -97.1500 36.3600
79 30766 30766 11069412 2011 2 0.2283450 0.2033376 -97.5900 34.1900
80 30767 30767 11089412 2011 2 0.3090513 0.1677526 -99.0400 36.1900
81 30768 30768 11099412 2011 2 0.3499464 0.2048233 -96.9500 35.3600
82 30769 30769 11109412 2011 2 0.2322625 0.2157803 -96.0400 36.4200
83 30770 30770 11119412 2011 2 0.1808669 0.1865300 -100.2600 36.6000
84 30771 30771 11129412 2011 2 0.2188482 0.1930467 -97.3400 35.5400
85 30772 30772 11139412 2011 2 0.3785246 0.2409803 -95.1800 35.2700
86 30773 30773 11149412 2011 2 0.3887450 0.1973443 -97.1000 36.1200
87 30774 30774 11159412 2011 2 0.1954525 0.2227046 -96.0700 34.8800
88 30775 30775 11179412 2011 2 0.3498904 0.2422423 -94.9900 35.9700
89 30776 30776 11189412 2011 2 0.4238400 0.2243303 -95.0100 34.7100
90 30777 30777 11199412 2011 2 0.2618586 0.1557993 -99.1400 34.4400
91 30778 30778 11209412 2011 2 0.3364304 0.2287306 -96.6800 34.3300
92 30779 30779 11229812 2011 2 0.2672518 0.2167264 -96.8400 34.7900
93 30780 30780 11239412 2011 2 0.3571061 0.2308501 -95.2200 36.7800
94 30781 30781 11241212 2011 2 0.3256629 0.1658372 -98.3200 34.3700
95 30782 30782 11269412 2011 2 0.2795525 0.1990392 -97.5200 34.9800
96 30783 30783 11279412 2011 2 0.2592818 0.1578457 -98.5300 35.8400
97 30784 30784 11289412 2011 2 0.2330579 0.1965365 -97.9900 34.1700
98 30785 30785 11299412 2011 2 0.2284118 0.1557731 -98.7800 35.5100
99 30786 30786 11329412 2011 2 0.3343371 0.2452958 -94.6400 36.0100
100 30787 30787 11339412 2011 2 0.3526150 0.2294212 -95.3500 34.9000
101 30788 30788 11349412 2011 2 0.3601271 0.2300249 -94.6900 34.9800
102 30789 30789 11359412 2011 2 0.2634812 0.1749874 -99.4200 36.4200
103 30790 30790 11369412 2011 2 0.3276068 0.2199297 -96.3400 36.5200
104 30791 30791 1900212 2011 2 0.3063157 0.1976691 -97.4600 35.2400
141 30828 30828 30070512 2011 2 0.0085600 0.1221779 -103.3800 33.3200
142 30829 30829 60079611 2011 2 0.3398168 0.2241846 -96.1800 37.3830
146 30833 30833 60119612 2011 2 0.2873734 0.1784590 -98.2850 36.8810
147 30834 30834 60129612 2011 2 0.3139968 0.2202393 -96.4270 36.8410
148 30835 30835 60139612 2011 2 0.2743646 0.1970787 -97.4850 36.6050
149 30836 30836 60169611 2011 2 0.3002286 0.1677526 -99.1340 36.0610
151 30838 30838 60189711 2011 2 0.3023986 0.1698166 -98.0170 35.5570
154 30841 30841 70480712 2011 2 0.4146116 0.2609000 -94.5829 37.4277
155 30842 30842 70780412 2011 2 0.1586265 0.1476905 -101.5950 36.5993
157 30844 30844 70800212 2011 2 0.3527068 0.1973443 -97.0914 36.1181
158 30845 30845 70810212 2011 2 0.2449762 0.1973443 -97.1082 36.1346
159 30846 30846 71000412 2011 2 0.1231812 0.1179052 -102.7740 33.9557
#50%
kable(validation_table_kriging_50, 
      caption = paste0('Validation Kriging (model vs ground-truth data) 50% 
                       valid data', 
                       layer_name_nasmd))
Validation Kriging (model vs ground-truth data) 50% valid data2011_02
X.1 X Station_ID Year Month mean_sm_depth_5cm extract_values Longitude Latitude
1 30688 30688 10019412 2011 2 0.2411175 0.1828822 -98.0200 34.8000
2 30689 30689 10029412 2011 2 0.2435521 0.2217434 -96.6600 34.7900
3 30690 30690 10039412 2011 2 0.3234629 0.1635225 -99.3400 34.5900
4 30691 30691 10059498 2011 2 0.3532900 0.1898325 -98.6700 36.7800
5 30692 30692 10061112 2011 2 0.1967957 0.2319934 -95.6700 34.2500
6 30693 30693 10089412 2011 2 0.1934604 0.1752065 -98.2900 34.9100
8 30695 30695 10119412 2011 2 0.2540407 0.1840571 -99.9000 36.0700
9 30696 30696 10139412 2011 2 0.2551487 0.1741098 -100.5300 36.8000
10 30697 30697 10159412 2011 2 0.2818268 0.1553869 -99.0600 35.4000
11 30698 30698 10169412 2011 2 0.2331411 0.2124528 -95.8700 35.9600
12 30699 30699 10179412 2011 2 0.2706664 0.2009548 -97.2500 36.7500
13 30700 30700 10189412 2011 2 0.3254380 0.1584623 -102.5000 36.6900
14 30701 30701 10199412 2011 2 0.2640646 0.2143308 -96.6300 35.1700
15 30702 30702 10209412 2011 2 0.3112093 0.1887784 -97.6900 36.4100
18 30705 30705 10239412 2011 2 0.2442550 0.1821724 -99.6400 36.8300
19 30706 30706 10259412 2011 2 0.1982011 0.2184131 -97.2700 33.8900
20 30707 30707 10269412 2011 2 0.3206943 0.1680601 -99.2700 35.5900
21 30708 30708 10279412 2011 2 0.1994179 0.2170856 -97.0000 34.8500
23 30710 30710 10299412 2011 2 0.2465456 0.1710311 -99.3500 36.0300
25 30712 30712 10329412 2011 2 0.2709046 0.2258016 -96.3300 34.6100
26 30713 30713 10339412 2011 2 0.2990518 0.1994191 -96.8000 35.6500
27 30714 30714 10349412 2011 2 0.2695300 0.1780720 -98.3600 36.7500
28 30715 30715 10359412 2011 2 0.2521746 0.1764612 -99.7300 35.5500
29 30716 30716 10399412 2011 2 0.3026357 0.2283171 -95.2500 34.2200
30 30717 30717 10419412 2011 2 0.3570429 0.2432900 -94.8500 35.6800
31 30718 30718 10429412 2011 2 0.3171425 0.2205341 -95.8900 36.9100
32 30719 30719 10439412 2011 2 0.2795500 0.2264199 -96.3200 33.9200
33 30720 30720 10449412 2011 2 0.3755546 0.1668797 -98.0400 35.5500
34 30721 30721 10459412 2011 2 0.1883393 0.1819619 -99.8000 35.2000
35 30722 30722 10469412 2011 2 0.3109550 0.2224328 -95.6600 35.3000
36 30723 30723 10479412 2011 2 0.2978971 0.1647601 -98.5000 36.2600
37 30724 30724 10499412 2011 2 0.2566993 0.2199847 -96.4300 36.8400
38 30725 30725 10509412 2011 2 0.2875494 0.1798521 -99.1400 36.7300
39 30726 30726 10519412 2011 2 0.2228443 0.1691901 -98.4700 35.1500
40 30727 30727 10529412 2011 2 0.2593462 0.1459370 -101.6000 36.6000
41 30728 30728 10549499 2011 2 0.3733143 0.1680321 -98.7400 34.2400
42 30729 30729 10559412 2011 2 0.2367632 0.1927331 -97.4800 35.8500
43 30730 30730 10569412 2011 2 0.3742504 0.2180521 -95.6400 35.7500
44 30731 30731 10579612 2011 2 0.2624818 0.2126655 -96.0000 35.8400
45 30732 30732 10589412 2011 2 0.2193125 0.1615533 -98.4800 35.4800
46 30733 30733 10599412 2011 2 0.0126611 0.1639840 -99.0500 34.9900
47 30734 30734 10619412 2011 2 0.3645396 0.1692660 -99.8300 34.6900
48 30735 30735 10629412 2011 2 0.2722127 0.1534442 -101.2300 36.8600
49 30736 30736 10639412 2011 2 0.2874546 0.2319934 -95.5400 34.0300
50 30737 30737 10649412 2011 2 0.4371421 0.2333317 -94.8800 33.8300
51 30738 30738 10669412 2011 2 0.3929139 0.2355757 -94.7800 36.4800
52 30739 30739 10679412 2011 2 0.2431185 0.1610667 -102.8800 36.8300
53 30740 30740 10689412 2011 2 0.3134275 0.1902457 -97.7600 34.5300
55 30742 30742 10719412 2011 2 0.3005504 0.1718295 -98.1100 36.3800
56 30743 30743 10729412 2011 2 0.2428468 0.2242978 -96.0000 34.3100
57 30744 30744 10749412 2011 2 0.1638405 0.1651153 -99.4200 34.8400
58 30745 30745 10759412 2011 2 0.2864296 0.1978866 -97.2100 36.0600
60 30747 30747 10779412 2011 2 0.2653727 0.1895295 -99.0100 36.9900
61 30748 30748 10789412 2011 2 0.2140846 0.2248429 -95.7800 34.8800
62 30749 30749 10809412 2011 2 0.2653454 0.1696371 -98.5700 34.7300
63 30750 30750 10819412 2011 2 0.4142421 0.2346410 -94.8400 36.8900
64 30751 30751 10829412 2011 2 0.3007821 0.1801911 -97.9600 35.2700
65 30752 30752 10859412 2011 2 0.4387112 0.2085168 -96.9100 36.9000
67 30754 30754 10899412 2011 2 0.3655561 0.2215305 -95.6100 36.7400
68 30755 30755 10919412 2011 2 0.2754514 0.2113313 -96.5000 36.0300
69 30756 30756 10959412 2011 2 0.3010093 0.2119218 -96.2600 35.4300
70 30757 30757 10969412 2011 2 0.3250596 0.2143583 -95.9100 35.5800
71 30758 30758 10979412 2011 2 0.3644982 0.2164808 -97.2300 34.7200
72 30759 30759 10989412 2011 2 0.3715582 0.2039229 -96.7700 36.3600
73 30760 30760 10999412 2011 2 0.2713482 0.1966835 -97.0500 36.0000
74 30761 30761 11009912 2011 2 0.2387571 0.2111886 -95.5600 35.8300
76 30763 30763 11029412 2011 2 0.4046039 0.2232752 -95.2700 36.3700
77 30764 30764 11039412 2011 2 0.2600746 0.1569991 -98.9600 35.9000
78 30765 30765 11049412 2011 2 0.3178882 0.1981556 -97.1500 36.3600
79 30766 30766 11069412 2011 2 0.2283450 0.2022780 -97.5900 34.1900
80 30767 30767 11089412 2011 2 0.3090513 0.1651669 -99.0400 36.1900
81 30768 30768 11099412 2011 2 0.3499464 0.2031621 -96.9500 35.3600
82 30769 30769 11109412 2011 2 0.2322625 0.2164088 -96.0400 36.4200
83 30770 30770 11119412 2011 2 0.1808669 0.1862934 -100.2600 36.6000
84 30771 30771 11129412 2011 2 0.2188482 0.1918827 -97.3400 35.5400
85 30772 30772 11139412 2011 2 0.3785246 0.2379455 -95.1800 35.2700
86 30773 30773 11149412 2011 2 0.3887450 0.1978866 -97.1000 36.1200
87 30774 30774 11159412 2011 2 0.1954525 0.2228129 -96.0700 34.8800
88 30775 30775 11179412 2011 2 0.3498904 0.2405855 -94.9900 35.9700
89 30776 30776 11189412 2011 2 0.4238400 0.2255577 -95.0100 34.7100
90 30777 30777 11199412 2011 2 0.2618586 0.1568900 -99.1400 34.4400
91 30778 30778 11209412 2011 2 0.3364304 0.2247598 -96.6800 34.3300
92 30779 30779 11229812 2011 2 0.2672518 0.2170856 -96.8400 34.7900
93 30780 30780 11239412 2011 2 0.3571061 0.2318473 -95.2200 36.7800
94 30781 30781 11241212 2011 2 0.3256629 0.1689945 -98.3200 34.3700
95 30782 30782 11269412 2011 2 0.2795525 0.1961836 -97.5200 34.9800
96 30783 30783 11279412 2011 2 0.2592818 0.1551468 -98.5300 35.8400
97 30784 30784 11289412 2011 2 0.2330579 0.1939797 -97.9900 34.1700
98 30785 30785 11299412 2011 2 0.2284118 0.1553948 -98.7800 35.5100
99 30786 30786 11329412 2011 2 0.3343371 0.2438428 -94.6400 36.0100
100 30787 30787 11339412 2011 2 0.3526150 0.2293695 -95.3500 34.9000
101 30788 30788 11349412 2011 2 0.3601271 0.2303876 -94.6900 34.9800
102 30789 30789 11359412 2011 2 0.2634812 0.1757044 -99.4200 36.4200
103 30790 30790 11369412 2011 2 0.3276068 0.2169231 -96.3400 36.5200
104 30791 30791 1900212 2011 2 0.3063157 0.1955523 -97.4600 35.2400
141 30828 30828 30070512 2011 2 0.0085600 0.1188894 -103.3800 33.3200
142 30829 30829 60079611 2011 2 0.3398168 0.2275701 -96.1800 37.3830
146 30833 30833 60119612 2011 2 0.2873734 0.1900799 -98.2850 36.8810
147 30834 30834 60129612 2011 2 0.3139968 0.2199847 -96.4270 36.8410
148 30835 30835 60139612 2011 2 0.2743646 0.1974921 -97.4850 36.6050
149 30836 30836 60169611 2011 2 0.3002286 0.1651669 -99.1340 36.0610
151 30838 30838 60189711 2011 2 0.3023986 0.1668797 -98.0170 35.5570
154 30841 30841 70480712 2011 2 0.4146116 0.2562036 -94.5829 37.4277
155 30842 30842 70780412 2011 2 0.1586265 0.1459370 -101.5950 36.5993
157 30844 30844 70800212 2011 2 0.3527068 0.1978866 -97.0914 36.1181
158 30845 30845 70810212 2011 2 0.2449762 0.1978866 -97.1082 36.1346
159 30846 30846 71000412 2011 2 0.1231812 0.1217617 -102.7740 33.9557

Linear regression models

#100%
kable(validation_table_linearmodel_100_10fold, 
      caption = paste0('Validation Linear Rregression 10 fold 
                       (model vs ground-truth data) 100% valid data', 
                       layer_name_nasmd))
Validation Linear Rregression 10 fold (model vs ground-truth data) 100% valid data2011_02
X.1 X Station_ID Year Month mean_sm_depth_5cm extract_values Longitude Latitude
1 30688 30688 10019412 2011 2 0.2411175 0.1884385 -98.0200 34.8000
2 30689 30689 10029412 2011 2 0.2435521 0.2184070 -96.6600 34.7900
3 30690 30690 10039412 2011 2 0.3234629 0.1791584 -99.3400 34.5900
4 30691 30691 10059498 2011 2 0.3532900 0.1720289 -98.6700 36.7800
5 30692 30692 10061112 2011 2 0.1967957 0.2165592 -95.6700 34.2500
6 30693 30693 10089412 2011 2 0.1934604 0.1937255 -98.2900 34.9100
8 30695 30695 10119412 2011 2 0.2540407 0.1695827 -99.9000 36.0700
9 30696 30696 10139412 2011 2 0.2551487 0.1600980 -100.5300 36.8000
10 30697 30697 10159412 2011 2 0.2818268 0.1770612 -99.0600 35.4000
11 30698 30698 10169412 2011 2 0.2331411 0.2395171 -95.8700 35.9600
12 30699 30699 10179412 2011 2 0.2706664 0.1963908 -97.2500 36.7500
13 30700 30700 10189412 2011 2 0.3254380 0.1576847 -102.5000 36.6900
14 30701 30701 10199412 2011 2 0.2640646 0.2117723 -96.6300 35.1700
15 30702 30702 10209412 2011 2 0.3112093 0.1894992 -97.6900 36.4100
18 30705 30705 10239412 2011 2 0.2442550 0.1591588 -99.6400 36.8300
19 30706 30706 10259412 2011 2 0.1982011 0.1972668 -97.2700 33.8900
20 30707 30707 10269412 2011 2 0.3206943 0.1705698 -99.2700 35.5900
21 30708 30708 10279412 2011 2 0.1994179 0.2206078 -97.0000 34.8500
23 30710 30710 10299412 2011 2 0.2465456 0.1719173 -99.3500 36.0300
25 30712 30712 10329412 2011 2 0.2709046 0.2147600 -96.3300 34.6100
26 30713 30713 10339412 2011 2 0.2990518 0.2064425 -96.8000 35.6500
27 30714 30714 10349412 2011 2 0.2695300 0.1736026 -98.3600 36.7500
28 30715 30715 10359412 2011 2 0.2521746 0.1673385 -99.7300 35.5500
29 30716 30716 10399412 2011 2 0.3026357 0.2124286 -95.2500 34.2200
30 30717 30717 10419412 2011 2 0.3570429 0.2170429 -94.8500 35.6800
31 30718 30718 10429412 2011 2 0.3171425 0.2245119 -95.8900 36.9100
32 30719 30719 10439412 2011 2 0.2795500 0.2003012 -96.3200 33.9200
33 30720 30720 10449412 2011 2 0.3755546 0.1853354 -98.0400 35.5500
34 30721 30721 10459412 2011 2 0.1883393 0.1703951 -99.8000 35.2000
35 30722 30722 10469412 2011 2 0.3109550 0.2076036 -95.6600 35.3000
36 30723 30723 10479412 2011 2 0.2978971 0.1727266 -98.5000 36.2600
37 30724 30724 10499412 2011 2 0.2566993 0.2111249 -96.4300 36.8400
38 30725 30725 10509412 2011 2 0.2875494 0.1636443 -99.1400 36.7300
39 30726 30726 10519412 2011 2 0.2228443 0.1897071 -98.4700 35.1500
40 30727 30727 10529412 2011 2 0.2593462 0.1546076 -101.6000 36.6000
41 30728 30728 10549499 2011 2 0.3733143 0.1868761 -98.7400 34.2400
42 30729 30729 10559412 2011 2 0.2367632 0.2104490 -97.4800 35.8500
43 30730 30730 10569412 2011 2 0.3742504 0.2091477 -95.6400 35.7500
44 30731 30731 10579612 2011 2 0.2624818 0.2223114 -96.0000 35.8400
45 30732 30732 10589412 2011 2 0.2193125 0.1916544 -98.4800 35.4800
46 30733 30733 10599412 2011 2 0.0126611 0.1820092 -99.0500 34.9900
47 30734 30734 10619412 2011 2 0.3645396 0.1757315 -99.8300 34.6900
48 30735 30735 10629412 2011 2 0.2722127 0.1570039 -101.2300 36.8600
49 30736 30736 10639412 2011 2 0.2874546 0.2165592 -95.5400 34.0300
50 30737 30737 10649412 2011 2 0.4371421 0.2245777 -94.8800 33.8300
51 30738 30738 10669412 2011 2 0.3929139 0.2549291 -94.7800 36.4800
52 30739 30739 10679412 2011 2 0.2431185 0.1576547 -102.8800 36.8300
53 30740 30740 10689412 2011 2 0.3134275 0.1967906 -97.7600 34.5300
55 30742 30742 10719412 2011 2 0.3005504 0.1779449 -98.1100 36.3800
56 30743 30743 10729412 2011 2 0.2428468 0.2070521 -96.0000 34.3100
57 30744 30744 10749412 2011 2 0.1638405 0.1734950 -99.4200 34.8400
58 30745 30745 10759412 2011 2 0.2864296 0.2047654 -97.2100 36.0600
60 30747 30747 10779412 2011 2 0.2653727 0.1599561 -99.0100 36.9900
61 30748 30748 10789412 2011 2 0.2140846 0.2041425 -95.7800 34.8800
62 30749 30749 10809412 2011 2 0.2653454 0.1868041 -98.5700 34.7300
63 30750 30750 10819412 2011 2 0.4142421 0.2337061 -94.8400 36.8900
64 30751 30751 10829412 2011 2 0.3007821 0.2076062 -97.9600 35.2700
65 30752 30752 10859412 2011 2 0.4387112 0.1986901 -96.9100 36.9000
67 30754 30754 10899412 2011 2 0.3655561 0.2302001 -95.6100 36.7400
68 30755 30755 10919412 2011 2 0.2754514 0.2170944 -96.5000 36.0300
69 30756 30756 10959412 2011 2 0.3010093 0.2054109 -96.2600 35.4300
70 30757 30757 10969412 2011 2 0.3250596 0.2346751 -95.9100 35.5800
71 30758 30758 10979412 2011 2 0.3644982 0.2305020 -97.2300 34.7200
72 30759 30759 10989412 2011 2 0.3715582 0.1986723 -96.7700 36.3600
73 30760 30760 10999412 2011 2 0.2713482 0.1954554 -97.0500 36.0000
74 30761 30761 11009912 2011 2 0.2387571 0.2290033 -95.5600 35.8300
76 30763 30763 11029412 2011 2 0.4046039 0.2327781 -95.2700 36.3700
77 30764 30764 11039412 2011 2 0.2600746 0.1739248 -98.9600 35.9000
78 30765 30765 11049412 2011 2 0.3178882 0.1908765 -97.1500 36.3600
79 30766 30766 11069412 2011 2 0.2283450 0.2004115 -97.5900 34.1900
80 30767 30767 11089412 2011 2 0.3090513 0.1691794 -99.0400 36.1900
81 30768 30768 11099412 2011 2 0.3499464 0.2103797 -96.9500 35.3600
82 30769 30769 11109412 2011 2 0.2322625 0.2247590 -96.0400 36.4200
83 30770 30770 11119412 2011 2 0.1808669 0.1576845 -100.2600 36.6000
84 30771 30771 11129412 2011 2 0.2188482 0.2315298 -97.3400 35.5400
85 30772 30772 11139412 2011 2 0.3785246 0.2090146 -95.1800 35.2700
86 30773 30773 11149412 2011 2 0.3887450 0.2047654 -97.1000 36.1200
87 30774 30774 11159412 2011 2 0.1954525 0.2043747 -96.0700 34.8800
88 30775 30775 11179412 2011 2 0.3498904 0.2299508 -94.9900 35.9700
89 30776 30776 11189412 2011 2 0.4238400 0.2276574 -95.0100 34.7100
90 30777 30777 11199412 2011 2 0.2618586 0.1823682 -99.1400 34.4400
91 30778 30778 11209412 2011 2 0.3364304 0.1971004 -96.6800 34.3300
92 30779 30779 11229812 2011 2 0.2672518 0.2206078 -96.8400 34.7900
93 30780 30780 11239412 2011 2 0.3571061 0.2553510 -95.2200 36.7800
94 30781 30781 11241212 2011 2 0.3256629 0.1844024 -98.3200 34.3700
95 30782 30782 11269412 2011 2 0.2795525 0.1990080 -97.5200 34.9800
96 30783 30783 11279412 2011 2 0.2592818 0.1753851 -98.5300 35.8400
97 30784 30784 11289412 2011 2 0.2330579 0.1985042 -97.9900 34.1700
98 30785 30785 11299412 2011 2 0.2284118 0.1770871 -98.7800 35.5100
99 30786 30786 11329412 2011 2 0.3343371 0.2350763 -94.6400 36.0100
100 30787 30787 11339412 2011 2 0.3526150 0.2310206 -95.3500 34.9000
101 30788 30788 11349412 2011 2 0.3601271 0.2294048 -94.6900 34.9800
102 30789 30789 11359412 2011 2 0.2634812 0.1673955 -99.4200 36.4200
103 30790 30790 11369412 2011 2 0.3276068 0.2288125 -96.3400 36.5200
104 30791 30791 1900212 2011 2 0.3063157 0.2088008 -97.4600 35.2400
141 30828 30828 30070512 2011 2 0.0085600 0.1655513 -103.3800 33.3200
142 30829 30829 60079611 2011 2 0.3398168 0.1982880 -96.1800 37.3830
146 30833 30833 60119612 2011 2 0.2873734 0.1760413 -98.2850 36.8810
147 30834 30834 60129612 2011 2 0.3139968 0.2111249 -96.4270 36.8410
148 30835 30835 60139612 2011 2 0.2743646 0.1872314 -97.4850 36.6050
149 30836 30836 60169611 2011 2 0.3002286 0.1691794 -99.1340 36.0610
151 30838 30838 60189711 2011 2 0.3023986 0.1853354 -98.0170 35.5570
154 30841 30841 70480712 2011 2 0.4146116 0.2424858 -94.5829 37.4277
155 30842 30842 70780412 2011 2 0.1586265 0.1546076 -101.5950 36.5993
157 30844 30844 70800212 2011 2 0.3527068 0.2047654 -97.0914 36.1181
158 30845 30845 70810212 2011 2 0.2449762 0.2047654 -97.1082 36.1346
159 30846 30846 71000412 2011 2 0.1231812 0.1658932 -102.7740 33.9557
#75%
kable(validation_table_linearmodel_75_10fold, 
      caption = paste0('Validation Linear Rregression 10 fold 
                       (model vs ground-truth data) 75% valid data', 
                       layer_name_nasmd))
Validation Linear Rregression 10 fold (model vs ground-truth data) 75% valid data2011_02
X.1 X Station_ID Year Month mean_sm_depth_5cm extract_values Longitude Latitude
1 30688 30688 10019412 2011 2 0.2411175 0.1875274 -98.0200 34.8000
2 30689 30689 10029412 2011 2 0.2435521 0.2167820 -96.6600 34.7900
3 30690 30690 10039412 2011 2 0.3234629 0.1785331 -99.3400 34.5900
4 30691 30691 10059498 2011 2 0.3532900 0.1721408 -98.6700 36.7800
5 30692 30692 10061112 2011 2 0.1967957 0.2148389 -95.6700 34.2500
6 30693 30693 10089412 2011 2 0.1934604 0.1927363 -98.2900 34.9100
8 30695 30695 10119412 2011 2 0.2540407 0.1698019 -99.9000 36.0700
9 30696 30696 10139412 2011 2 0.2551487 0.1606117 -100.5300 36.8000
10 30697 30697 10159412 2011 2 0.2818268 0.1766109 -99.0600 35.4000
11 30698 30698 10169412 2011 2 0.2331411 0.2377154 -95.8700 35.9600
12 30699 30699 10179412 2011 2 0.2706664 0.1959042 -97.2500 36.7500
13 30700 30700 10189412 2011 2 0.3254380 0.1582670 -102.5000 36.6900
14 30701 30701 10199412 2011 2 0.2640646 0.2102573 -96.6300 35.1700
15 30702 30702 10209412 2011 2 0.3112093 0.1891641 -97.6900 36.4100
18 30705 30705 10239412 2011 2 0.2442550 0.1596407 -99.6400 36.8300
19 30706 30706 10259412 2011 2 0.1982011 0.1959007 -97.2700 33.8900
20 30707 30707 10269412 2011 2 0.3206943 0.1704231 -99.2700 35.5900
21 30708 30708 10279412 2011 2 0.1994179 0.2189807 -97.0000 34.8500
23 30710 30710 10299412 2011 2 0.2465456 0.1720154 -99.3500 36.0300
25 30712 30712 10329412 2011 2 0.2709046 0.2131379 -96.3300 34.6100
26 30713 30713 10339412 2011 2 0.2990518 0.2052377 -96.8000 35.6500
27 30714 30714 10349412 2011 2 0.2695300 0.1736438 -98.3600 36.7500
28 30715 30715 10359412 2011 2 0.2521746 0.1673269 -99.7300 35.5500
29 30716 30716 10399412 2011 2 0.3026357 0.2107873 -95.2500 34.2200
30 30717 30717 10419412 2011 2 0.3570429 0.2155648 -94.8500 35.6800
31 30718 30718 10429412 2011 2 0.3171425 0.2235448 -95.8900 36.9100
32 30719 30719 10439412 2011 2 0.2795500 0.1986828 -96.3200 33.9200
33 30720 30720 10449412 2011 2 0.3755546 0.1847702 -98.0400 35.5500
34 30721 30721 10459412 2011 2 0.1883393 0.1702224 -99.8000 35.2000
35 30722 30722 10469412 2011 2 0.3109550 0.2061626 -95.6600 35.3000
36 30723 30723 10479412 2011 2 0.2978971 0.1726996 -98.5000 36.2600
37 30724 30724 10499412 2011 2 0.2566993 0.2104548 -96.4300 36.8400
38 30725 30725 10509412 2011 2 0.2875494 0.1639484 -99.1400 36.7300
39 30726 30726 10519412 2011 2 0.2228443 0.1888747 -98.4700 35.1500
40 30727 30727 10529412 2011 2 0.2593462 0.1552397 -101.6000 36.6000
41 30728 30728 10549499 2011 2 0.3733143 0.1858945 -98.7400 34.2400
42 30729 30729 10559412 2011 2 0.2367632 0.2093962 -97.4800 35.8500
43 30730 30730 10569412 2011 2 0.3742504 0.2077499 -95.6400 35.7500
44 30731 30731 10579612 2011 2 0.2624818 0.2208364 -96.0000 35.8400
45 30732 30732 10589412 2011 2 0.2193125 0.1908725 -98.4800 35.4800
46 30733 30733 10599412 2011 2 0.0126611 0.1813395 -99.0500 34.9900
47 30734 30734 10619412 2011 2 0.3645396 0.1752438 -99.8300 34.6900
48 30735 30735 10629412 2011 2 0.2722127 0.1576006 -101.2300 36.8600
49 30736 30736 10639412 2011 2 0.2874546 0.2148389 -95.5400 34.0300
50 30737 30737 10649412 2011 2 0.4371421 0.2226426 -94.8800 33.8300
51 30738 30738 10669412 2011 2 0.3929139 0.2530493 -94.7800 36.4800
52 30739 30739 10679412 2011 2 0.2431185 0.1581792 -102.8800 36.8300
53 30740 30740 10689412 2011 2 0.3134275 0.1956223 -97.7600 34.5300
55 30742 30742 10719412 2011 2 0.3005504 0.1778143 -98.1100 36.3800
56 30743 30743 10729412 2011 2 0.2428468 0.2055403 -96.0000 34.3100
57 30744 30744 10749412 2011 2 0.1638405 0.1730651 -99.4200 34.8400
58 30745 30745 10759412 2011 2 0.2864296 0.2038640 -97.2100 36.0600
60 30747 30747 10779412 2011 2 0.2653727 0.1603836 -99.0100 36.9900
61 30748 30748 10789412 2011 2 0.2140846 0.2026780 -95.7800 34.8800
62 30749 30749 10809412 2011 2 0.2653454 0.1858888 -98.5700 34.7300
63 30750 30750 10819412 2011 2 0.4142421 0.2324030 -94.8400 36.8900
64 30751 30751 10829412 2011 2 0.3007821 0.2064961 -97.9600 35.2700
65 30752 30752 10859412 2011 2 0.4387112 0.1982177 -96.9100 36.9000
67 30754 30754 10899412 2011 2 0.3655561 0.2289323 -95.6100 36.7400
68 30755 30755 10919412 2011 2 0.2754514 0.2159209 -96.5000 36.0300
69 30756 30756 10959412 2011 2 0.3010093 0.2038318 -96.2600 35.4300
70 30757 30757 10969412 2011 2 0.3250596 0.2328340 -95.9100 35.5800
71 30758 30758 10979412 2011 2 0.3644982 0.2286317 -97.2300 34.7200
72 30759 30759 10989412 2011 2 0.3715582 0.1979644 -96.7700 36.3600
73 30760 30760 10999412 2011 2 0.2713482 0.1946155 -97.0500 36.0000
74 30761 30761 11009912 2011 2 0.2387571 0.2273461 -95.5600 35.8300
76 30763 30763 11029412 2011 2 0.4046039 0.2312858 -95.2700 36.3700
77 30764 30764 11039412 2011 2 0.2600746 0.1737521 -98.9600 35.9000
78 30765 30765 11049412 2011 2 0.3178882 0.1903848 -97.1500 36.3600
79 30766 30766 11069412 2011 2 0.2283450 0.1990659 -97.5900 34.1900
80 30767 30767 11089412 2011 2 0.3090513 0.1692661 -99.0400 36.1900
81 30768 30768 11099412 2011 2 0.3499464 0.2090339 -96.9500 35.3600
82 30769 30769 11109412 2011 2 0.2322625 0.2235444 -96.0400 36.4200
83 30770 30770 11119412 2011 2 0.1808669 0.1582668 -100.2600 36.6000
84 30771 30771 11129412 2011 2 0.2188482 0.2299423 -97.3400 35.5400
85 30772 30772 11139412 2011 2 0.3785246 0.2075853 -95.1800 35.2700
86 30773 30773 11149412 2011 2 0.3887450 0.2038640 -97.1000 36.1200
87 30774 30774 11159412 2011 2 0.1954525 0.2029021 -96.0700 34.8800
88 30775 30775 11179412 2011 2 0.3498904 0.2282755 -94.9900 35.9700
89 30776 30776 11189412 2011 2 0.4238400 0.2258695 -95.0100 34.7100
90 30777 30777 11199412 2011 2 0.2618586 0.1815780 -99.1400 34.4400
91 30778 30778 11209412 2011 2 0.3364304 0.1958082 -96.6800 34.3300
92 30779 30779 11229812 2011 2 0.2672518 0.2189807 -96.8400 34.7900
93 30780 30780 11239412 2011 2 0.3571061 0.2537496 -95.2200 36.7800
94 30781 30781 11241212 2011 2 0.3256629 0.1834612 -98.3200 34.3700
95 30782 30782 11269412 2011 2 0.2795525 0.1978086 -97.5200 34.9800
96 30783 30783 11279412 2011 2 0.2592818 0.1751448 -98.5300 35.8400
97 30784 30784 11289412 2011 2 0.2330579 0.1972347 -97.9900 34.1700
98 30785 30785 11299412 2011 2 0.2284118 0.1766989 -98.7800 35.5100
99 30786 30786 11329412 2011 2 0.3343371 0.2334002 -94.6400 36.0100
100 30787 30787 11339412 2011 2 0.3526150 0.2291961 -95.3500 34.9000
101 30788 30788 11349412 2011 2 0.3601271 0.2276301 -94.6900 34.9800
102 30789 30789 11359412 2011 2 0.2634812 0.1676358 -99.4200 36.4200
103 30790 30790 11369412 2011 2 0.3276068 0.2277014 -96.3400 36.5200
104 30791 30791 1900212 2011 2 0.3063157 0.2074611 -97.4600 35.2400
141 30828 30828 30070512 2011 2 0.0085600 0.1656491 -103.3800 33.3200
142 30829 30829 60079611 2011 2 0.3398168 0.1979784 -96.1800 37.3830
146 30833 30833 60119612 2011 2 0.2873734 0.1760761 -98.2850 36.8810
147 30834 30834 60129612 2011 2 0.3139968 0.2104548 -96.4270 36.8410
148 30835 30835 60139612 2011 2 0.2743646 0.1869834 -97.4850 36.6050
149 30836 30836 60169611 2011 2 0.3002286 0.1692661 -99.1340 36.0610
151 30838 30838 60189711 2011 2 0.3023986 0.1847702 -98.0170 35.5570
154 30841 30841 70480712 2011 2 0.4146116 0.2411050 -94.5829 37.4277
155 30842 30842 70780412 2011 2 0.1586265 0.1552397 -101.5950 36.5993
157 30844 30844 70800212 2011 2 0.3527068 0.2038640 -97.0914 36.1181
158 30845 30845 70810212 2011 2 0.2449762 0.2038640 -97.1082 36.1346
159 30846 30846 71000412 2011 2 0.1231812 0.1660760 -102.7740 33.9557
#50%
kable(validation_table_linearmodel_50_10fold, 
      caption = paste0('Validation Linear Rregression 10 fold 
                       (model vs ground-truth data) 50% valid data', 
                       layer_name_nasmd))
Validation Linear Rregression 10 fold (model vs ground-truth data) 50% valid data2011_02
X.1 X Station_ID Year Month mean_sm_depth_5cm extract_values Longitude Latitude
1 30688 30688 10019412 2011 2 0.2411175 0.1882275 -98.0200 34.8000
2 30689 30689 10029412 2011 2 0.2435521 0.2191862 -96.6600 34.7900
3 30690 30690 10039412 2011 2 0.3234629 0.1787999 -99.3400 34.5900
4 30691 30691 10059498 2011 2 0.3532900 0.1728287 -98.6700 36.7800
5 30692 30692 10061112 2011 2 0.1967957 0.2169351 -95.6700 34.2500
6 30693 30693 10089412 2011 2 0.1934604 0.1938067 -98.2900 34.9100
8 30695 30695 10119412 2011 2 0.2540407 0.1704220 -99.9000 36.0700
9 30696 30696 10139412 2011 2 0.2551487 0.1607926 -100.5300 36.8000
10 30697 30697 10159412 2011 2 0.2818268 0.1769404 -99.0600 35.4000
11 30698 30698 10169412 2011 2 0.2331411 0.2417951 -95.8700 35.9600
12 30699 30699 10179412 2011 2 0.2706664 0.1979511 -97.2500 36.7500
13 30700 30700 10189412 2011 2 0.3254380 0.1583266 -102.5000 36.6900
14 30701 30701 10199412 2011 2 0.2640646 0.2122142 -96.6300 35.1700
15 30702 30702 10209412 2011 2 0.3112093 0.1908005 -97.6900 36.4100
18 30705 30705 10239412 2011 2 0.2442550 0.1596890 -99.6400 36.8300
19 30706 30706 10259412 2011 2 0.1982011 0.1967462 -97.2700 33.8900
20 30707 30707 10269412 2011 2 0.3206943 0.1706008 -99.2700 35.5900
21 30708 30708 10279412 2011 2 0.1994179 0.2215833 -97.0000 34.8500
23 30710 30710 10299412 2011 2 0.2465456 0.1726728 -99.3500 36.0300
25 30712 30712 10329412 2011 2 0.2709046 0.2152123 -96.3300 34.6100
26 30713 30713 10339412 2011 2 0.2990518 0.2071587 -96.8000 35.6500
27 30714 30714 10349412 2011 2 0.2695300 0.1743727 -98.3600 36.7500
28 30715 30715 10359412 2011 2 0.2521746 0.1674053 -99.7300 35.5500
29 30716 30716 10399412 2011 2 0.3026357 0.2126203 -95.2500 34.2200
30 30717 30717 10419412 2011 2 0.3570429 0.2180580 -94.8500 35.6800
31 30718 30718 10429412 2011 2 0.3171425 0.2274666 -95.8900 36.9100
32 30719 30719 10439412 2011 2 0.2795500 0.1994388 -96.3200 33.9200
33 30720 30720 10449412 2011 2 0.3755546 0.1856900 -98.0400 35.5500
34 30721 30721 10459412 2011 2 0.1883393 0.1703460 -99.8000 35.2000
35 30722 30722 10469412 2011 2 0.3109550 0.2078457 -95.6600 35.3000
36 30723 30723 10479412 2011 2 0.2978971 0.1732489 -98.5000 36.2600
37 30724 30724 10499412 2011 2 0.2566993 0.2135833 -96.4300 36.8400
38 30725 30725 10509412 2011 2 0.2875494 0.1641484 -99.1400 36.7300
39 30726 30726 10519412 2011 2 0.2228443 0.1898055 -98.4700 35.1500
40 30727 30727 10529412 2011 2 0.2593462 0.1550902 -101.6000 36.6000
41 30728 30728 10549499 2011 2 0.3733143 0.1863487 -98.7400 34.2400
42 30729 30729 10559412 2011 2 0.2367632 0.2119054 -97.4800 35.8500
43 30730 30730 10569412 2011 2 0.3742504 0.2096372 -95.6400 35.7500
44 30731 30731 10579612 2011 2 0.2624818 0.2238167 -96.0000 35.8400
45 30732 30732 10589412 2011 2 0.2193125 0.1920554 -98.4800 35.4800
46 30733 30733 10599412 2011 2 0.0126611 0.1818026 -99.0500 34.9900
47 30734 30734 10619412 2011 2 0.3645396 0.1753972 -99.8300 34.6900
48 30735 30735 10629412 2011 2 0.2722127 0.1576189 -101.2300 36.8600
49 30736 30736 10639412 2011 2 0.2874546 0.2169351 -95.5400 34.0300
50 30737 30737 10649412 2011 2 0.4371421 0.2251600 -94.8800 33.8300
51 30738 30738 10669412 2011 2 0.3929139 0.2584266 -94.7800 36.4800
52 30739 30739 10679412 2011 2 0.2431185 0.1581518 -102.8800 36.8300
53 30740 30740 10689412 2011 2 0.3134275 0.1967125 -97.7600 34.5300
55 30742 30742 10719412 2011 2 0.3005504 0.1786904 -98.1100 36.3800
56 30743 30743 10729412 2011 2 0.2428468 0.2070698 -96.0000 34.3100
57 30744 30744 10749412 2011 2 0.1638405 0.1730979 -99.4200 34.8400
58 30745 30745 10759412 2011 2 0.2864296 0.2060734 -97.2100 36.0600
60 30747 30747 10779412 2011 2 0.2653727 0.1604257 -99.0100 36.9900
61 30748 30748 10789412 2011 2 0.2140846 0.2040100 -95.7800 34.8800
62 30749 30749 10809412 2011 2 0.2653454 0.1864332 -98.5700 34.7300
63 30750 30750 10819412 2011 2 0.4142421 0.2366771 -94.8400 36.8900
64 30751 30751 10829412 2011 2 0.3007821 0.2086616 -97.9600 35.2700
65 30752 30752 10859412 2011 2 0.4387112 0.2004957 -96.9100 36.9000
67 30754 30754 10899412 2011 2 0.3655561 0.2329369 -95.6100 36.7400
68 30755 30755 10919412 2011 2 0.2754514 0.2188627 -96.5000 36.0300
69 30756 30756 10959412 2011 2 0.3010093 0.2051128 -96.2600 35.4300
70 30757 30757 10969412 2011 2 0.3250596 0.2364129 -95.9100 35.5800
71 30758 30758 10979412 2011 2 0.3644982 0.2317860 -97.2300 34.7200
72 30759 30759 10989412 2011 2 0.3715582 0.1998977 -96.7700 36.3600
73 30760 30760 10999412 2011 2 0.2713482 0.1960618 -97.0500 36.0000
74 30761 30761 11009912 2011 2 0.2387571 0.2306735 -95.5600 35.8300
76 30763 30763 11029412 2011 2 0.4046039 0.2351993 -95.2700 36.3700
77 30764 30764 11039412 2011 2 0.2600746 0.1741990 -98.9600 35.9000
78 30765 30765 11049412 2011 2 0.3178882 0.1919192 -97.1500 36.3600
79 30766 30766 11069412 2011 2 0.2283450 0.2002292 -97.5900 34.1900
80 30767 30767 11089412 2011 2 0.3090513 0.1696564 -99.0400 36.1900
81 30768 30768 11099412 2011 2 0.3499464 0.2111098 -96.9500 35.3600
82 30769 30769 11109412 2011 2 0.2322625 0.2271281 -96.0400 36.4200
83 30770 30770 11119412 2011 2 0.1808669 0.1583265 -100.2600 36.6000
84 30771 30771 11129412 2011 2 0.2188482 0.2336027 -97.3400 35.5400
85 30772 30772 11139412 2011 2 0.3785246 0.2094146 -95.1800 35.2700
86 30773 30773 11149412 2011 2 0.3887450 0.2060734 -97.1000 36.1200
87 30774 30774 11159412 2011 2 0.1954525 0.2042433 -96.0700 34.8800
88 30775 30775 11179412 2011 2 0.3498904 0.2316636 -94.9900 35.9700
89 30776 30776 11189412 2011 2 0.4238400 0.2288833 -95.0100 34.7100
90 30777 30777 11199412 2011 2 0.2618586 0.1818982 -99.1400 34.4400
91 30778 30778 11209412 2011 2 0.3364304 0.1967463 -96.6800 34.3300
92 30779 30779 11229812 2011 2 0.2672518 0.2215833 -96.8400 34.7900
93 30780 30780 11239412 2011 2 0.3571061 0.2595710 -95.2200 36.7800
94 30781 30781 11241212 2011 2 0.3256629 0.1837479 -98.3200 34.3700
95 30782 30782 11269412 2011 2 0.2795525 0.1990565 -97.5200 34.9800
96 30783 30783 11279412 2011 2 0.2592818 0.1756271 -98.5300 35.8400
97 30784 30784 11289412 2011 2 0.2330579 0.1983343 -97.9900 34.1700
98 30785 30785 11299412 2011 2 0.2284118 0.1771214 -98.7800 35.5100
99 30786 30786 11329412 2011 2 0.3343371 0.2372562 -94.6400 36.0100
100 30787 30787 11339412 2011 2 0.3526150 0.2324646 -95.3500 34.9000
101 30788 30788 11349412 2011 2 0.3601271 0.2308233 -94.6900 34.9800
102 30789 30789 11359412 2011 2 0.2634812 0.1680865 -99.4200 36.4200
103 30790 30790 11369412 2011 2 0.3276068 0.2318071 -96.3400 36.5200
104 30791 30791 1900212 2011 2 0.3063157 0.2094014 -97.4600 35.2400
141 30828 30828 30070512 2011 2 0.0085600 0.1657232 -103.3800 33.3200
142 30829 30829 60079611 2011 2 0.3398168 0.2004567 -96.1800 37.3830
146 30833 30833 60119612 2011 2 0.2873734 0.1770192 -98.2850 36.8810
147 30834 30834 60129612 2011 2 0.3139968 0.2135833 -96.4270 36.8410
148 30835 30835 60139612 2011 2 0.2743646 0.1885390 -97.4850 36.6050
149 30836 30836 60169611 2011 2 0.3002286 0.1696564 -99.1340 36.0610
151 30838 30838 60189711 2011 2 0.3023986 0.1856900 -98.0170 35.5570
154 30841 30841 70480712 2011 2 0.4146116 0.2460698 -94.5829 37.4277
155 30842 30842 70780412 2011 2 0.1586265 0.1550902 -101.5950 36.5993
157 30844 30844 70800212 2011 2 0.3527068 0.2060734 -97.0914 36.1181
158 30845 30845 70810212 2011 2 0.2449762 0.2060734 -97.1082 36.1346
159 30846 30846 71000412 2011 2 0.1231812 0.1663053 -102.7740 33.9557