Soil Geostatistics and Mapping

Soil geostatistics and digital soil mapping use spatial statistics, remote sensing, and machine learning to predict how soil properties—texture, carbon content, nutrient levels—vary continuously across landscapes from a limited number of physical samples. Accurate, high-resolution soil maps underpin decisions in agriculture, land management, and climate modeling, making them a practical prerequisite for what researchers call soil security: ensuring soils can sustain food production and ecosystem services into the future. Active work is pushing toward global-scale mapping frameworks that integrate spectroscopic measurements and satellite imagery to reduce the cost and time of traditional field surveys. Open challenges include quantifying uncertainty in predictions across undersampled regions and developing models that remain reliable as land use and climate alter soil conditions faster than maps can be updated.

Works

69,358

Total citations

1,068,126

Keywords

Digital Soil MappingGeostatisticsRemote SensingSoil PropertiesSpectroscopySpatial Interpolation