Estimating Soil Organic Carbon Using Sentinel-2 Data under Zero Tillage Agriculture: A Machine Learning Approach

Soil organic carbon (SOC) is the main component of soil organic matter (SOM) and constitutes the crucial component of the soil. It supports key soil functions, stabilizes soil structure, aid in plant-nutrient retention and release, and promote water infiltration and storage. Predicting SOC using Sentinel-2 data integrated with machine learning algorithms under zero tillage practice is inadequately documented for developing countries like Zimbabwe. The purpose of this study is to evaluate the performance of support vector machine (SVM), artificial neural network (ANN), and partial least square regression (PLSR) algorithms from Sentinel-2 data for SOC estimation. The SVM, ANN and PLSR models were used with a cross-validation to estimate the SOC content based on 50 georeferenced calibration samples under a zero-tillage practice. The ANN model outperformed the other two models by delivering a coefficient of determination (R2) of between 55 and 60