Integrating Active Learning and Regression Methods for Estimation of Grass Lai Over a Mountainous Region using Sentinel-2 Satellite Data

A comprehensive comparison and integration of retrieval methods is needed for accurately estimating vegetation biophysical variables such as leaf area index (LAI) over a multispecies grass canopy. This study tested the partial least squares regression (PLSR) and kernel ridge regression (KRR) for inversion of a radiative transfer model (RTM) to retrieve grass LAI in the Golden Gate Highlands National Park of South Africa during peak productivity. Furthermore, we constrained the inversion process using Active Learning techniques. Results show the most accurate LAI retrieval by KRR (over 34 sampled grass species) had a normalized root mean squared error of 19.28%. These findings have significant implications for the development of transferable rangeland monitoring systems in protected mountainous regions.