First results SML sampling exercise
load("../output/first_test.RData")
stac500a = rast("~/serena/data/sml/europe/stack500.tiff")
sol <- rast("~/serena/data/sml/europe/soilRegion.tiff")
library(RColorBrewer)
JRC data
Here the maps of the input variables
The Soil Regions of the European Union and Adjacent Countries 1 : 5 000
000
nb.cols <- 20
mycolors <- colorRampPalette(brewer.pal(8, "Paired"))(nb.cols)
r2 = as.factor(sol)
plot(r2, col = mycolors)
## Warning: [barplot] a sample of 0.4% of the raster cells were used to estimate frequencies
To fill the JRC table
Here is the results of the different scenarios
cbind.data.frame(
scenario = c("A1","A2","A4","A5","A6",
"B1","B2","B3"),
nSites = c(testA1$NbSample,testA2$NbSample,testA4$NbSample,testA5$NbSample,
testA6$NbSample,
testB1$NbSample,testB2$NbSample,testB3$NbSample)
)
## scenario nSites
## 1 A1 4674
## 2 A2 3062
## 3 A4 627
## 4 A5 3932
## 5 A6 5517
## 6 B1 1820
## 7 B2 2818
## 8 B3 4936
Scenario A1 with with Lucas predictions of pH as
illustrative soil property
Scenario A5 with with Lucas predictions of pH as
illustrative soil property
Scenario A6 with with Lucas predictions of pH as
illustrative soil property
Scenario B2 with national predictions of pH as
illustrative soil property
comparison with 3 different sets of starting samples
Here we compare the computed required sample sizes using NUTS1 only as Domain and with different starting samples:
- A 5k sample selected by clhs based on 80k SI sample as proposed by JRC.
- A 80k SI sample
- A 20k SY sample
SI = random sampling SY = systematic sampling
The SY sample gave a larger required sample size.
There is a very large differences in computed required sample sizes.
## [1] "CLHS 5k sample" "879"
## [1] "A 80k SI sample" "1251"
## [1] "A 20k SY sample" "3038"
Here are the maps
For the 5K starting sample
For the 80K
starting sample
For a grid as
starting sample