Soil Geostatistics and Mapping

Soil geostatistics and digital soil mapping use mathematical and computational methods to predict how soil properties vary across landscapes, combining ground measurements with satellite imagery, spectroscopic sensors, and machine learning to produce spatially continuous maps from sparse data. Accurate knowledge of where soils store carbon, hold water, or harbor contaminants is foundational to food security, climate modeling, and land management, yet traditional sampling methods are too slow and costly to keep pace with the scale at which soils are degrading globally. Researchers are actively working to improve the reliability of spatial interpolation in data-scarce regions, where training sets are thin and terrain is complex, and to reconcile predictions made at different spatial resolutions. A pressing open question is how well models trained on historical or regional data can generalize as land use and climate continue to shift the very soil conditions they were built to represent.

Works

67,843

Total citations

1,050,659

Keywords

Digital Soil MappingGeostatisticsRemote SensingSoil PropertiesSpectroscopySpatial Interpolation