ArticleSensors (Basel, Switzerland)2025
A Comparative Assessment of Sentinel-2 and UAV-Based Imagery for Soil Organic Carbon Estimations Using Machine Learning Models.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
As the largest carbon reservoir in terrestrial ecosystems, soil organic carbon (SOC) plays a critical role in the global carbon cycle and climate change mitigation. A promising approach to swiftly procuring geographically dispersed SOC data is the amalgamation of UAV-based multispectral imagery at the local scale and Sentinel-2 satellite imagery at the regional scale. This integrated approach is particularly well-suited for precision agriculture and real-time monitoring. In this study, we evaluated the performance of UAVs and Sentinel-2 imagery in predicting SOC using four machine-learning models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANNs). UAV imagery outperformed Sentinel-2, achieving more accurate detection of local SOC variability thanks to its finer spatial resolution (5-10 cm versus 10-20 m). Among the models tested, the Random Forest algorithm achieved the highest accuracy, with an R
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