Assessment of PlanetScope Spectral Data for Estimation of Peanut Leaf Area Index Using Machine Learning and Statistical Methods

This study estimated peanut (Arachis hypogaea L.) leaf area index (LAI), a critical vegetation parameter, using spectral bands and vegetation indices (VIs) derived from PlanetScope (~3m) imagery by comparing Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Partial Least Squares Regression (PLSR) algorithms. Most VIs exhibited strong relationships with LAI but showed saturation when LAI reached 3 m²/m². Thirteen VIs were individually evaluated for estimating LAI using the aforementioned machine learning and statistical algorithms, and the results showed that the best single predictors of LAI are: SR and RTVIcore (RF, R2 = 0.84, RMSE = 0.62 m2/m2); RTVIcore (XGBoost, R2 = 0.88, RMSE = 0.52 m2/m2); and RTVIcore and MSAVI (PLSR, R2 = 0.61, RMSE = 0.96 m2/m2). The top six ranked VIs were selected to calibrate the RF, XGBoost, and PLSR algorithms. The validation of the algorithms showed that the RF achieved the highest prediction accuracy (R2 = 0.844, RMSE = 0.858 m²/m², RRMSE = 25.17%), followed by XGBoost (R2 = 0.808, RMSE = 0.92 m²/m², RRMSE = 26.99%), while the PLSR showed relatively lower model accuracy (R2 = 0.76, RMSE = 0.983 m²/m², RRMSE = 28.85%). Further results demonstrate that VIs derived from spectral bands provide superior model accuracy in estimating peanut LAI compared to the use of spectral bands alone. Overall, the presented results are significant for future crop monitoring using RF to reduce overreliance on multiple models for peanut LAI.