Spatial prediction of soil organic carbon stocks in Sudanese clay soils using regression kriging

Résumé

Soil organic carbon (SOC) stocks are a critical component of terrestrial carbon pools, influencing soil quality, agricultural productivity, and climate change mitigation. This study aimed to map and improve spatial estimation of SOC stocks in Sudan’s Blue Nile clay soils using regression kriging (RK). The model integrated 554 spatially unique soil profiles with nine environmental covariates: precipitation, temperature, relative humidity, normalized difference vegetation index (NDVI), land use/cover, bare soil index (BSI), digital elevation model (DEM), LS-factor, and aspect. Spectral indices were derived from Landsat 9 imagery (April 2024), while climate and terrain data were obtained from CHIRPS/WorldClim and SRTM (30 m). RK performance was robust, with spatial cross-validation R 2 = 0.72, RMSE = 8.4 Mg C ha −1 (29% of mean observed stock), and mean bias = −0.8 Mg C ha −1 . Predicted SOC stocks (0–30 cm) ranged from 12.4 to 51.2 Mg C ha −1 (mean 28.6 Mg C ha −1 ). NDVI, clay content, and topographic wetness index were the most influential predictors. Agricultural lands exhibited the highest stocks (51.2 Mg C ha −1 ), while bare lands had the lowest (14.2 Mg C ha −1 ). This study (1) applies spatially explicit validation for SOC mapping in Sudan’s Blue Nile region, (2) harmonizes legacy and contemporary soil data using equivalent soil mass correction, and (3) provides high-resolution SOC maps for climate-resilient agricultural planning. Findings support soil carbon management and climate mitigation in semi-arid regions.

Citer ce document

Accès au document

Texte intégral en lecture en ligne, réservé aux abonnés SPHAERO et aux membres de l'institution. Se connecter

Auteur(s)

Statistiques

Consultations : 1

Téléchargements : 0