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

plot(stac500a[[X]])

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)

barplot(r2)
## 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

load("../output/FillJRCTableResults.RData")
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 A1 with with Lucas predictions of pH as illustrative soil property
Scenario A5 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 A6 with with Lucas predictions of pH as illustrative soil property
Scenario B2 with national 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:

  1. A 5k sample selected by clhs based on 80k SI sample as proposed by JRC.
  2. A 80k SI sample
  3. 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.

# Results for 1
print(c( "CLHS 5k sample", resNUTS1Sallprop$NbSample))
## [1] "CLHS 5k sample" "879"
# Results for 2
print(c( "A 80k SI sample", resNUTS1allpropLargeSample$NbSample) )
## [1] "A 80k SI sample" "1251"
# Results for 3
print(c( "A 20k SY sample", resGRid10k$NbSample))
## [1] "A 20k SY sample" "3038"

Here are the maps

For the 5K starting sample

Sample5K For the 80K starting sample

test For a grid as starting sample

Sample5K
Sample5K

Prise en compte des sols