Soil Organic Carbon Mapping Through Remote Sensing and In Situ Data with Random Forest by Using Google Earth Engine: A Case Study in Southern Africa

Javier Bravo-García (), Juan Mariano Camarillo-Naranjo, Francisco José Blanco-Velázquez and María Anaya-Romero
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Javier Bravo-García: Evenor-Tech, 41092 Seville, Spain
Juan Mariano Camarillo-Naranjo: Department of Physical Geography and Territorial Analysis, Universidad de Sevilla, 41004 Seville, Spain
Francisco José Blanco-Velázquez: Evenor-Tech, 41092 Seville, Spain
María Anaya-Romero: Evenor-Tech, 41092 Seville, Spain

Land, 2025, vol. 14, issue 7, 1-26

Abstract: This study, conducted within the SteamBioAfrica project, assessed the potential of Digital Soil Mapping (DSM) to estimate Soil Organic Carbon (SOC) across key regions of southern Africa: Otjozondjupa and Omusati (Namibia), Chobe (Botswana), and KwaZulu-Natal (South Africa). Random Forest (RF) models were implemented in the Google Earth Engine (GEE) environment, integrating multi-source datasets including real-time Sentinel-2 imagery, topographic variables, climatic data, and regional soil samples. Three model configurations were evaluated: (A) climatic, topographic, and spectral data; (B) topographic and spectral data; and (C) spectral data only. Model A achieved the highest overall accuracy (R 2 up to 0.78), particularly in Otjozondjupa, whereas Model B resulted in the lowest RMSE and MAE. Model C exhibited poorer performance, underscoring the importance of multi-source data integration. SOC variability was primarily influenced by elevation, precipitation, temperature, and Sentinel-2 bands B11 and B8. However, data scarcity and inconsistent sampling, especially in Chobe, reduced model reliability (R 2 : 0.62). The originality of this study lay in the scalable integration of real-time Sentinel-2 data with regional datasets in an open-access framework. The resulting SOC maps provided actionable insights for land-use planning and climate adaptation in savanna ecosystems.

Keywords: digital soil mapping; organic carbon soil; remote sensing; Google Earth Engine; random forest (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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