Mapping Soil Organic Carbon in Degraded Ecosystems Through Upscaled Multispectral Unmanned Aerial Vehicle–Satellite Imagery

Lorena Salgado, Lidia Moriano González, José Luis R. Gallego, Carlos A. López-Sánchez, Arturo Colina and Rubén Forján ()
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Lorena Salgado: Environmental Biogeochemistry & Raw Materials Group, Institute of Natural Resources and Territorial Planning (INDUROT), University of Oviedo, 33600 Mieres, Spain
Lidia Moriano González: Environmental Biogeochemistry & Raw Materials Group, Institute of Natural Resources and Territorial Planning (INDUROT), University of Oviedo, 33600 Mieres, Spain
José Luis R. Gallego: Environmental Biogeochemistry & Raw Materials Group, Institute of Natural Resources and Territorial Planning (INDUROT), University of Oviedo, 33600 Mieres, Spain
Carlos A. López-Sánchez: SMartForest Group, Department of Organisms and Systems Biology, University of Oviedo, 33600 Mieres, Spain
Arturo Colina: Environmental Biogeochemistry & Raw Materials Group, Institute of Natural Resources and Territorial Planning (INDUROT), University of Oviedo, 33600 Mieres, Spain
Rubén Forján: Environmental Biogeochemistry & Raw Materials Group, Institute of Natural Resources and Territorial Planning (INDUROT), University of Oviedo, 33600 Mieres, Spain

Land, 2025, vol. 14, issue 2, 1-26

Abstract: Soil organic carbon (SOC) is essential for maintaining ecosystem health, and its depletion is widely recognized as a key indicator of soil degradation. Activities such as mining and wildfire disturbances significantly intensify soil degradation, leading to quantitative and qualitative declines in SOC. Accurate SOC monitoring is critical, yet traditional methods are often costly and time-intensive. Advances in technologies like Unmanned Aerial Vehicles (UAVs) and satellite remote sensing (SRS) now offer efficient and scalable alternatives. Combining UAV and satellite data through machine learning (ML) techniques can improve the accuracy and spatial resolution of SOC monitoring, facilitating better soil management strategies. In this context, this study proposes a methodology that integrates geochemical data (SOC) with UAV-derived information, upscaling the UAV data to satellite platforms (GEOSAT-2 and SENTINEL-2) using ML techniques, specifically random forest (RF) algorithms. The research was conducted in two distinct environments: a reclaimed open-pit coal mine, representing a severely degraded ecosystem, and a high-altitude region prone to recurrent wildfires, both characterized by extreme environmental conditions and diverse soil properties. These scenarios provide valuable opportunities to evaluate the effects of soil degradation on SOC quality and to assess the effectiveness of advanced monitoring approaches. The RF algorithm, optimized with cross-validation (CV) techniques, consistently outperformed other models. The highest performance was achieved during the UAV-to-SENTINEL-2 upscaling, with an R 2 of 0.761 and an rRMSE of 8.6%. Cross-validation mitigated overfitting and enhanced the robustness and generalizability of the models. UAV data offered high-resolution insights for localized SOC assessments, while SENTINEL-2 imagery enabled broader-scale evaluations, albeit with a smoothing effect. These findings underscore the potential of integrating UAV and satellite data with ML approaches, providing a cost-effective and scalable framework for SOC monitoring, soil management, and climate change mitigation efforts.

Keywords: soil degradation; remote sensing; random forest; wildfire recurrence; mining (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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