Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 4; p. 876
• Main Authors: Broeg, Tom, Blaschek, Michael, Seitz, Steffen, Taghizadeh-Mehrjardi, Ruhollah, Zepp, Simone, Scholten, Thomas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Accuracy; Agricultural land; Algorithms; Carbon; Carbon content; climate; Climate effects; Climate models; Comparative analysis; cropland; data collection; Datasets; Digital Elevation Models; Digital mapping; digital soil mapping; Elevation; extrapolation; Germany; Land use; landscapes; Machine learning; Mapping; model transfer; Multispectral photography; Organic carbon; Organic soils; reflectance; Remote sensing; satellites; soil; Soil mapping; soil organic carbon; Soil properties; soil reflectance composite; Soils; Terrain; Vegetation; Germany; Bavaria Germany; Alps

Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate and food policies. In digital soil mapping (DSM), machine learning algorithms are used to predict soil properties from covariates derived from traditional soil m...