Assessing Soil Organic Carbon in Semi-Arid Agricultural Soils Using UAVs and Machine Learning: A Pathway to Sustainable Water and Soil Resource Management

The global effort to combat climate change highlights the critical role of storing organic carbon in soil to reduce greenhouse gas emissions. Traditional methods of mapping soil organic carbon (SOC) have been labour-intensive and costly, relying on extensive laboratory analyses. Recent advancements in unmanned aerial vehicles (UAVs) offer a promising alternative for efficiently and affordably mapping SOC at the field level. This study focused on developing a method to accurately predict topsoil SOC at high resolution using spectral data from low-altitude UAV multispectral imagery, complemented by laboratory data from the Nogalte farm in Murcia, Spain, as part of the LIFE AMDRYC4 project. To attain this objective, Python version 3.10 was used to implement several machine learning techniques, including partial least squares (PLS) regression, random forest (RF), and support vector machine (SVM). Among these, the random forest algorithm demonstrated superior performance, achieving an R 2 value of 0.92, RMSE of 0.22, MAE of 0.19, MSE of 0.05, and EVE of 0.71 in estimating SOC. The results of the RF model were then visualised spatially using GIS and compared with simple spatial interpolations of soil analyses. The findings suggest that a multispectral sensor UAV-based modelling and mapping of SOC can provide valuable insights for farmers, offering a practical means to monitor SOC levels and enhance precision agriculture systems. This innovative approach reduces the time and cost associated with traditional SOC mapping methods and supports sustainable agricultural practices by enabling more precise management of soil resources.