How Earth Observation, Spectroscopy, and AI are changing soil use forever and how can we turn soil health research into thriving businesses? Key takeaways from the Soil Health Now! conference 2025

Prepared by: Tom Hengl (OpenGeoHub), Koen van Seijen (RegenEarth), Naeem Lakhani (The First Thirty), and Ichsani Wheeler (OpenGeoHub) Soil Health Now! was held at the Wageningen International…

Surface soil moisture for Europe 2014-2024 at 1 km annual and quarterly aggregates

Copernicus Land Monitoring Services provides Surface Soil Moisture 2014-present (raster 1 km), Europe, daily – version 1. Each day covers only 5 to 10% of European land mask and shows lines of scenes (obvious artifacts). This is the long-term aggregates of daily images of soil moisture (0–100%) based on two types of aggregation: Long-term quarterly (winter, spring, summer and autumn), Annual quantiles P.05, P.50 and P.95, The soil moisture rasters are based on Sentinel 1 and described in detail in:  Bauer-Marschallinger, B. ; Freeman, V. ; Cao, S. ; Paulik, C. ; Schaufler, S. ; Stachl, T. ; Modanesi, S. ; Massari, C. ; Ciabatta, L. ; Brocca, L. ; Wagner, W. Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles. IEEE Transactions on Geoscience and Remote Sensing 2019, 1 - 20. DOI 10.1109/TGRS.2018.2858004 Aggregation has been generated using the terra package in R in combination with the matrixStats::rowQuantiles function. library(terra) library(matrixStats) g1 = terra::vect("/mnt/inca/EU_landmask/tilling_filter/eu_ard2_final_status.gpkg") ## 1254 tiles tile = g1[534] nc.lst = list.files('/mnt/landmark/SM1km/ssm_1km_v1_daily_netcdf/', pattern = glob2rx("*.nc$"), full.names=TRUE) ## 3726 ## test it #r = terra::rast(nc.lst[100:210]) agg_tile = function(r, tile, pv=c(0.05,0.5,0.95), out.year="2015.annual"){ bb = paste(as.vector(ext(tile)), collapse = ".") out.tif = paste0("./eu_tmp/", out.year, "/sm1km_", pv, "_", out.year, "_", bb, ".tif") if(any(!file.exists(out.tif))){ r.t = terra::crop(r, ext(tile)) ## each tile is 100x100 pixels 365 days r.t = as.data.frame(r.t, xy=TRUE, na.rm=FALSE) sel.c = grep(glob2rx("ssm$"), colnames(r.t)) ## remove everything outside the range t1s = cbind(data.frame(matrixStats::rowQuantiles(as.matrix(r.t[,sel.c]), probs = pv, na.rm=TRUE)), data.frame(x=r.t$x, y=r.t$y)) #str(t1s) ## write to GeoTIFFs r.o = terra::rast(t1s[,c("x","y","X5.","X50.","X95.")], type="xyz", crs="+proj=longlat +datum=WGS84 +no_defs") for(k in 1:length(pv)){ terra::writeRaster(r.o[[k]], filename=out.tif[k], gdal=c("COMPRESS=DEFLATE"), datatype='INT2U', NAflag=32768, overwrite=FALSE) } rm(r.t); gc() tmpFiles(remove=TRUE) } } ## quarterly values: lA = data.frame(filename=nc.lst) library(lubridate) lA$Date = ymd(sapply(lA$filename, function(i){substr(strsplit(basename(i), "_")[[1]][4], 1, 8)})) #summary(is.na(lA$Date)) #hist(lA$Date, breaks=60) lA$quarter = quarter(lA$Date, fiscal_start = 11) summary(as.factor(lA$quarter)) for(qr in 1:4){ #qr=1 pth = paste0("A.q", qr) rs = terra::rast(lA$filename[lA$quarter==qr]) #agg_tile(rs, tile, out.year=pth) x = parallel::mclapply(sample(1:length(g1)), function(i){try( agg_tile(rs, tile=g1[i], out.year=pth) )}, mc.cores=20) for(type in c(0.05,0.5,0.95)){ x <- list.files(path=paste0("./eu_tmp/", pth), pattern=glob2rx(paste0("sm1km_", type, "_*.tif$")), full.names=TRUE) out.tmp <- paste0(pth, ".", type, ".sm1km_eu.txt") vrt.tmp <- paste0(pth, ".", type, ".sm1km_eu.vrt") cat(x, sep="\n", file=out.tmp) system(paste0('gdalbuildvrt -input_file_list ', out.tmp, ' ', vrt.tmp)) system(paste0('gdal_translate ', vrt.tmp, ' ./cogs/soil.moisture_s1.clms.qr.', qr, '.p', type, '_m_1km_20140101_20241231_eu_epsg4326_v20250206.tif -ot "Byte" -r "near" --config GDAL_CACHEMAX 9216 -co BIGTIFF=YES -co NUM_THREADS=80 -co COMPRESS=DEFLATE -of COG -projwin -32 72 45 27')) } } ## per year ---- for(year in 2015:2023){ l.lst = nc.lst[grep(year, basename(nc.lst))] r = terra::rast(l.lst) ## test it: pth = paste0(year, ".annual") x = parallel::mclapply(sample(1:length(g1)), function(i){try( agg_tile(r, tile=g1[i], out.year=pth) )}, mc.cores=40) ## Mosaics: for(type in c(0.05,0.5,0.95)){ x <- list.files(path=paste0("./eu_tmp/", pth), pattern=glob2rx(paste0("sm1km_", type, "_*.tif$")), full.names=TRUE) out.tmp <- paste0(pth, ".", type, ".sm1km_eu.txt") vrt.tmp <- paste0(pth, ".", type, ".sm1km_eu.vrt") cat(x, sep="\n", file=out.tmp) system(paste0('gdalbuildvrt -input_file_list ', out.tmp, ' ', vrt.tmp)) system(paste0('gdal_translate ', vrt.tmp, ' ./cogs/soil.moisture_s1.clms.annual.', type, '_m_1km_', year, '0101_', year, '1231_eu_epsg4326_v20250206.tif -ot "Byte" -r "near" --config GDAL_CACHEMAX 9216 -co BIGTIFF=YES -co NUM_THREADS=80 -co COMPRESS=DEFLATE -of COG -projwin -32 72 45 27')) } }

Post-doctoral researcher: dynamic soil-landscape modeling – OpenGeoHub Foundation: Connect | Create | Share | Repeat

Spatiotemporal prediction of soil organic carbon density for Europe (2000--2022) in 3D+T based on Landsat-based spectral indices time-series

The paper describes a comprehensive framework for soil organic carbon density (SOCD) (kg/m3) modeling and mapping, based on spatiotemporal Random Forest (RF) and Quantile Regression Forests (QRF). 22,428 SOCD measurements and a wide range of covariate layers—particularly the 30m Landsat-based ...

AgriLLM: Harnessing Transformers for Farmer Queries

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types.

Communication officer - OpenGeoHub Foundation: Connect | Create | Share | Repeat

Application deadline: 1st of July 2024Possible start date: 1st of August 2024Duration: 2+ yearsJob location: Waldeck Pyrmontlaan 14, 6965HK Doorwerth, The

GitHub - AI4SoilHealth/SoilHealthDataCube: Soil Health Data Cube for Europe

Soil Health Data Cube for Europe. Contribute to AI4SoilHealth/SoilHealthDataCube development by creating an account on GitHub.

ESA EO4SoilProtection 2024: Predicting SOC Density

In this hackathon, participants are invited to develop innovative models for predicting SOC density in space and time.

World Soil Day | United Nations

World Soil Day (WSD) is held annually on 5 December as a means to focus attention on the importance of healthy soil and advocating for the sustainable management of soil resources.