Open krsnapaudel opened 3 months ago
Here is the example code I wrote to compare soil data.
library(terra)
setwd("AgML/Data")
awc <- rast("WISE Soil/awc.tif")
maize_mask <- rast("crop_masks/crop_mask_maize_WC.tif")
eu_shapes <- vect("Shapefiles/shapefiles_EU/NUTS_RG_01M_2016_4326_LEVL_2.shp")
nl_shapes <- eu_shapes[eu_shapes$CNTR_CODE == "NL"]
awc <- crop(awc, nl_shapes)
maize_mask <- crop(maize_mask, nl_shapes)
maize_mask <- resample(maize_mask, awc, method="bilinear")
awc[maize_mask < 0 | maize_mask > 100] <- NA
awc <- mask(awc, nl_shapes)
soil_data <- read.csv("WISE Soil/soil_maize_NL.csv")
nl_shapes$awc <- soil_data$awc[soil_data$adm_id == nl_shapes$NUTS_ID]
# plot
min_awc <- min(minmax(awc)[1], min(nl_shapes$awc))
max_awc <- max(minmax(awc)[2], max(nl_shapes$awc))
par(mfrow = c(1, 2)) # reset plotting window
plot(nl_shapes, "awc", col=hcl.colors(100, palette="RdYlBu"), type="continuous", range=c(min_awc, max_awc))
plot(awc, col=hcl.colors(100, palette="RdYlBu"), type="continuous", range=c(min_awc, max_awc))
plot(nl_shapes, add=TRUE)
Results for CY-Bench data look good with AgML workshop validation. See #301. This means the data is mostly likely fine.
Check whether aggregated predictor data looks correct when compared to original rasters. Note aggregation uses crop masks. So comparing visually is probably tricky.