Enhancing leaf area index estimation in tropical vegetation: a comparative study of multivariate linear regression and Sentinel Application Platform-derived leaf area index

Abstract

Background: The leaf area index (LAI) quantifies the total one-sided green leaf area per unit of soil area, making it a crucial parameter in models that simulate carbon, nutrient, water, and energy fluxes within forest ecosystems. This study enhances LAI estimation techniques by employing a multivariate linear regression (MVLR) approach specifically tai-lored to tropical vegetation. We integrated field-collected LAI data with spectral indices and multispectral bands to develop a robust predictive empirical model. The LAI estimates derived from the MVLR approach are rigorously compared with those obtained from the Sentinel Application Platform (SNAP), a widely utilized tool for remote sensing analysis. Results: In developing the MVLR model, nine multispectral bands, seven vegetation indices (VIs), and two biophysical variables derived from Sentinel-2 multispectral image were tested to identify efficient predictors for LAI estimation. To determine significant multi-spectral bands and VIs (ensuring no multicollinearity, high coefficient of determination (R2), low root mean square error (RMSE), and a p-value < 0.05) for the best representative model, stepwise multiple linear regression (SMLR) was employed. Multispectral bands 7 and 8, along with the VIs soil adjusted vegetation index and normalized difference vegetation index, and the fraction of vegetation cover biophysical variable, produced superior out-comes and serve as strong predictor variables for LAI. The accuracy of the MVLR model was validated using 17 directly measured LAI sample plots with the leave-one-out cross-vali-dation method. The estimated LAI using the MVLR model achieved higher accuracy, with an R2 of 0.94, compared to the SNAP toolbox (R2 = 0.71). The RMSE and bias of the MVLR model were 0.18 and 0.006, respectively, while for SNAP-derived LAI, the RMSE and bias were 0.53 and 0.31, respectively. Conclusions: The improved accuracy and reduced error of the MVLR model are attribut-ed to its adjustment for tropical vegetation types. Future research should focus on com-paring the MVLR model with other global LAI products to further validate and enhance its applicability.