Estimation of the satellite-derived Leaf Area Index of spring wheat using machine learning approaches

The study focuses on the estimation of Leaf Area Index (LAI) for smallholder farms less than 1 acre in semi-arid regions, particularly in Bundelkhand, India. Accurate LAI estimation is crucial for optimizing crop management practices, enhancing yield predictions, and improving the sustainability of agricultural operations. This study evaluates the efficiency of different machine learning algorithms in deriving LAI from Sentinel-2 and Landsat-8 data, with a focus on spring wheat across two growing seasons (2020–2021 and 2021–2022) in six villages in the Bundelkhand region of India. Three machine learning approaches—Random Forest (RF), Support Vector Machine (SVM), and XGBoost—were employed for LAI estimation. Validation against ground-truth LAI measurements was carried out using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson’s correlation coefficient (R), and Multiplicative Bias (MBias). Results indicate that RF and SVM with Radial Basis Function (SVM-RBF) achieved the highest accuracy for both Sentinel-2 and Landsat-8 data. For Sentinel-2, RF and SVM-RBF both achieved an R-value of 0.94, with RMSE of 0.40 and MAE of 0.29 and 0.30, respectively. RF showed a slight overestimation (MBias = 1.02), while SVM-RBF had a perfect MBias of 1.00. XGBoost also performed well (R = 0.94), though with slightly higher RMSE (0.43) and MAE (0.33), and an MBias of 0.88, indicating slight underestimation. SVM linear had lower performance metrics (R = 0.84, RMSE = 0.62, MAE = 0.48, MBias = 1.02). For Landsat-8, RF and SVM-RBF also showed strong performance (R = 0.94), with RF achieving RMSE of 0.38 and MAE of 0.28, and SVM-RBF achieving the lowest RMSE of 0.37 and MAE of 0.29. Both had near-perfect MBias values (RF = 1.00, SVM-RBF = 0.99). XGBoost displayed a high R-value (0.93) but higher error metrics (RMSE = 0.40, MAE = 0.30, MBias = 1.01). SVM linear underperformed (R = 0.78, RMSE = 0.69, MAE = 0.53, MBias = 0.98). Overall, RF and SVM-RBF consistently outperformed SVM linear and XGBoost across both satellite datasets.