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).
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.
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.
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$YYMM <- as.character()
layer_name_sm <- substr(sm_files[i], 20,26)
print(layer_name_sm)
layer_name_prcp <- substr(prcp_files[i], 13,19)
print(layer_name_prcp)
layer_name_tmin <- substr(tmin_files[i], 13,19)
print(layer_name_tmin)
layer_name_nasmd <- substr(sm_files[i], 20,26)
print(layer_name_nasmd)
df[1,1] <- layer_name_sm
accuracy_report_100 <- df
accuracy_report_75 <- df
accuracy_report_50 <- df
This section runs Kriging Spatial Interpolation method using 100%, 75% and 50% of available valid pixels in the soil moisture monthly layer. The number of valid pixels varies according to every monthly layer.
#generation of a model to fit variogram
model_100 <- autofitVariogram(soil_moist~1, input_data = sm_raster_krig, verbose= FALSE,
GLS.model=TRUE)
#application of kriging spatial interpolation using the previous fittef model
sm_kriging_100 <- autoKrige(soil_moist~1, input_data = sm_raster_krig, GLS.model = TRUE, new_data = raster_reference_pixels)
#save the R file, which contains the variogram model
saveRDS(model_100, file = paste0('variogram_model_100_', layer_name_sm))
write.csv(model_100$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)
#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$krige_output
sm_kriging_75_output <- spTransform(sm_kriging_75_output, wgs84)
pred_75 <- rasterize(sm_kriging_75_output, raster_reference, 'var1.pred')
std_err_75 <- rasterize(sm_kriging_75_output, raster_reference, 'var1.stdev', na.rm = FALSE)
#export of outputs to raster files
writeRaster(stack(pred_75, std_err_75), file=paste0('ordKriging_', layer_name_sm,
'_pred_error_sm_75.tif'), overwrite = TRUE)
#slecction of training data, 50% of valid available pixels
trainIndex50 <- createDataPartition(sm_raster[,3], p = .5, list = FALSE, times = 1)
Train50 <- sm_raster[ trainIndex50,]
Test50 <- sm_raster[-trainIndex50,]
coordinates(Train50) =~ x+y
projection(Train50) <- wgs84
Train50 <- spTransform(Train50, CRSobj = albers_proj)
#generation of a model to fit variogram
model_50 <- autofitVariogram(soil_moist~1, input_data = Train50, verbose= FALSE,
GLS.model=TRUE)
#application of kriging spatial interpolation using the previous fittef model
sm_kriging_50 <- autoKrige(soil_moist~1, input_data = Train50, GLS.model = TRUE, new_data = raster_reference_pixels)
saveRDS(model_50, file = paste0('variogram_model_50_', layer_name_sm))
write.csv(model_50$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)
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.
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$var1.pred)
,3)
sm_kriging_cv_75_10fold <- autoKrige.cv(soil_moist~1, input_data = Train75, nfold = 10)
acc_report_75_krig <- accuracy_report_75[, -c(4,5,7)]
accuracy_report_75[1,6] <- 10
accuracy_report_75[1,2] <- round(cor(sm_kriging_cv_75_10fold$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)
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$var1.pred)
,3)
This section brings together the Kriging models generated by autokrige function, as well as the semivariogram explaining the spatial distribution of the actual data and the parameters to the best fitted model. Finally, cross validation is reported for each cases using different percentages fin put data for kriging model generation.
#Kriging Model 100% of available data
plot(sm_kriging_100, col=colorRampPalette(c("tan4", "tan2", "yellow1", "yellowgreen",
"green", "springgreen2", "steelblue1", "steelblue4"))(20))
#Kriging Model 75% of available data
plot(sm_kriging_75, col=colorRampPalette(c("tan4", "tan2", "yellow1", "yellowgreen",
"green", "springgreen2", "steelblue1", "steelblue4"))(20))
#Kriging Model 50% of available data
plot(sm_kriging_50, col=colorRampPalette(c("tan4", "tan2", "yellow1", "yellowgreen",
"green", "springgreen2", "steelblue1", "steelblue4"))(20))
#Semivariogram model using 100% of available data
kable(model_100$exp_var, caption = 'Explained Variance', digits = 5)
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$var_model, caption = 'Variance Model', digits = 5)
model | psill | range | kappa | ang1 | ang2 | ang3 | anis1 | anis2 |
---|---|---|---|---|---|---|---|---|
Nug | 0.00006 | 0 | 0.0 | 0 | 0 | 0 | 1 | 1 |
Ste | 0.00654 | 2685935 | 0.6 | 0 | 0 | 0 | 1 | 1 |
#Semivariogram model using 75% of available data
kable(model_75$exp_var, caption = 'Explained Variance', digits = 5)
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$var_model, caption = 'Variance Model', digits = 5)
model | psill | range | kappa | ang1 | ang2 | ang3 | anis1 | anis2 |
---|---|---|---|---|---|---|---|---|
Nug | 0.00003 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
Sph | 0.02056 | 12941523 | 0 | 0 | 0 | 0 | 1 | 1 |
#Semivariogram model using 50% of available data
kable(model_50$exp_var, caption = 'Explained Variance', digits = 5)
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)
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 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')
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')
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')
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'))
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.
Training subsets with different percentage of valid data. These are the inputs for the multiple regression model.
#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])
#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])
#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'
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'))
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))
kable(accuracy_report_100, caption = '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')
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')
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 |
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$Layer <- layer_name_sm
satellite_field_correlation_100 <- satellite_field_correlation
satellite_field_correlation_75 <- satellite_field_correlation
satellite_field_correlation_50 <- satellite_field_correlation
validation_csv_values_100 <- na.omit(read.csv(nasmdb_files[[i]]))
if(length(validation_csv_values_100$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$extract_values))
satellite_field_correlation_100[1, 2] <- layer_name_nasmd
satellite_field_correlation_100[1, 3] <- correlation
satellite_field_correlation_100[1, 4] <- RMSE
satellite_field_correlation_100[1, 5] <- no_stations
satellite_field_correlation_100[1, 6] <- no_points
#Correlation output
kable(satellite_field_correlation_100,
caption = paste0('Monthly Satellite 100% valid data vs Field Data correlation '
, layer_name_nasmd))
ID | Layer | Correlation | RMSE | Number_Stations | Number_Points |
---|---|---|---|---|---|
134 | 2011_02 | 0.4441071 | 0.1197464 | 107 | 106 |
write.csv(satellite_field_correlation_100, file = paste0('satellite_nasmdb_100_validation_', layer_name_nasmd, '.csv'))
validation_csv_values_75 <- na.omit(read.csv(nasmdb_files[[i]]))
if(length(validation_csv_values_75$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$extract_values))
satellite_field_correlation_75[1, 2] <- layer_name_nasmd
satellite_field_correlation_75[1, 3] <- correlation
satellite_field_correlation_75[1, 4] <- RMSE
satellite_field_correlation_75[1, 5] <- no_stations
satellite_field_correlation_75[1, 6] <- no_points
#Correlation output
kable(satellite_field_correlation_75,
caption = paste0('Monthly Satellite 75% valid data vs Field Data correlation ',
layer_name_nasmd))
ID | Layer | Correlation | RMSE | Number_Stations | Number_Points |
---|---|---|---|---|---|
134 | 2011_02 | 0.3931272 | 0.11936 | 107 | 74 |
write.csv(satellite_field_correlation_75, file = paste0('satellite_nasmdb_75_validation_', layer_name_nasmd, '.csv'))
validation_csv_values_50 <- na.omit(read.csv(nasmdb_files[[i]]))
if(length(validation_csv_values_50$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))
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'))
Correlation between kriging model outputs (100%, 75%, 50%) and field data from the North American Soil Moisture Database.
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$extract_values))
kriging_field_correlation_100 <- satellite_field_correlation
kriging_field_correlation_100[1, 2] <- layer_name_nasmd
kriging_field_correlation_100[1, 3] <- correlation
kriging_field_correlation_100[1, 4] <- RMSE
kriging_field_correlation_100[1, 5] <- no_stations
kriging_field_correlation_100[1, 6] <- no_points
kable(kriging_field_correlation_100,
caption = paste0('Monthly Kriging 100% valid data vs Field Data correlation ',
layer_name_nasmd))
ID | Layer | Correlation | RMSE | Number_Stations | Number_Points |
---|---|---|---|---|---|
134 | 2011_02 | 0.462877 | 0.1107679 | 107 | 107 |
write.csv(kriging_field_correlation_100, file = paste0('kriging_nasmdb_100_validation_', layer_name_nasmd, '.csv'))
validation_csv_values_75 <- na.omit(read.csv(nasmdb_files[[i]]))
if(length(validation_csv_values_75$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$extract_values))
kriging_field_correlation_75 <- satellite_field_correlation
kriging_field_correlation_75[1, 2] <- layer_name_nasmd
kriging_field_correlation_75[1, 3] <- correlation
kriging_field_correlation_75[1, 4] <- RMSE
kriging_field_correlation_75[1, 5] <- no_stations
kriging_field_correlation_75[1, 6] <- no_points
kable(kriging_field_correlation_75,
caption = paste0('Monthly Kriging 75% valid data vs Field Data correlation ',
layer_name_nasmd))
ID | Layer | Correlation | RMSE | Number_Stations | Number_Points |
---|---|---|---|---|---|
134 | 2011_02 | 0.4636105 | 0.1108116 | 107 | 107 |
write.csv(kriging_field_correlation_75, file = paste0('kriging_nasmdb_75_validation_', layer_name_nasmd, '.csv'))
validation_csv_values_50 <- na.omit(read.csv(nasmdb_files[[i]]))
if(length(validation_csv_values_50$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$extract_values))
kriging_field_correlation_50 <- satellite_field_correlation
kriging_field_correlation_50[1, 2] <- layer_name_nasmd
kriging_field_correlation_50[1, 3] <- correlation
kriging_field_correlation_50[1, 4] <- RMSE
kriging_field_correlation_50[1, 5] <- no_stations
kriging_field_correlation_50[1, 6] <- no_points
kable(kriging_field_correlation_50,
caption = paste0('Monthly Kriging 50% valid data vs Field Data correlation ',
layer_name_nasmd))
ID | Layer | Correlation | RMSE | Number_Stations | Number_Points |
---|---|---|---|---|---|
134 | 2011_02 | 0.4727591 | 0.1108978 | 107 | 107 |
write.csv(kriging_field_correlation_50, file = paste0('kriging_nasmdb_50_validation_', layer_name_nasmd, '.csv'))
#Export of validation tables from kriging models to CSV files, as well as correlation results
#100
write.csv(validation_table_kriging_100, file = paste0('validation_reference_kriging_100',
layer_name_nasmd, '.csv'))
#75%
write.csv(validation_table_kriging_75, file = paste0('validation_reference_kriging_75',
layer_name_nasmd, '.csv'))
#50%
write.csv(validation_table_kriging_50, file = paste0('validation_reference_kriging_50',
layer_name_nasmd, '.csv'))
Correlation between linear model outputs (100%, 75%, 50%) and field data from the North American Soil Moisture Database
validation_csv_values_100 <- na.omit(read.csv(nasmdb_files[[i]]))
if(length(validation_csv_values_100$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$extract_values))
lm_field_correlation_100_10fold <- satellite_field_correlation
lm_field_correlation_100_10fold[1, 2] <- layer_name_nasmd
lm_field_correlation_100_10fold[1, 3] <- correlation
lm_field_correlation_100_10fold[1, 4] <- RMSE
lm_field_correlation_100_10fold[1, 5] <- no_stations
lm_field_correlation_100_10fold[1, 6] <- no_points
kable(lm_field_correlation_100_10fold,
caption = paste0('Monthly Linear Model 100% valid data 10 fold vs
Field Data correlation ',
layer_name_nasmd))
ID | Layer | Correlation | RMSE | Number_Stations | Number_Points |
---|---|---|---|---|---|
134 | 2011_02 | 0.4034743 | 0.1108907 | 107 | 107 |
write.csv(lm_field_correlation_100_10fold, file = paste0('glm10fold_nasmdb_100_validation_', layer_name_nasmd, '.csv'))
validation_csv_values_75 <- na.omit(read.csv(nasmdb_files[[i]]))
if(length(validation_csv_values_75$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$extract_values))
lm_field_correlation_75_10fold <- satellite_field_correlation
lm_field_correlation_75_10fold[1, 2] <- layer_name_nasmd
lm_field_correlation_75_10fold[1, 3] <- correlation
lm_field_correlation_75_10fold[1, 4] <- RMSE
lm_field_correlation_75_10fold[1, 5] <- no_stations
lm_field_correlation_75_10fold[1, 6] <- no_points
kable(lm_field_correlation_75_10fold,
caption = paste0('Monthly Linear Model 75% valid data 10 fold vs
Field Data correlation ',
layer_name_nasmd))
ID | Layer | Correlation | RMSE | Number_Stations | Number_Points |
---|---|---|---|---|---|
134 | 2011_02 | 0.4056848 | 0.111548 | 107 | 107 |
write.csv(lm_field_correlation_75_10fold, file = paste0('glm10fold_nasmdb_75_validation_', layer_name_nasmd, '.csv'))
validation_csv_values_50 <- na.omit(read.csv(nasmdb_files[[i]]))
if(length(validation_csv_values_50$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))
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 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'))
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.
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)
#100%
kable(validation_table_satellite_100,
caption = paste0('Validation Reference (satellite vs ground-truth data) 100%
valid data',
layer_name_nasmd))
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))
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))
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 |
#100%
kable(validation_table_kriging_100,
caption = paste0('Validation Kriging (model vs ground-truth data) 100%
valid data',
layer_name_nasmd))
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))
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))
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 |
#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))
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))
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))
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 |