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

다국어 초록 (Multilingual 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 tailored 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 multispectral 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 outcomes 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-validation 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 attributed to its adjustment for tropical vegetation types. Future research should focus on comparing the MVLR model with other global LAI products to further validate and enhance its applicability.