Abstract: Chlorophyll content is a crucial parameter for evaluating forest health and vegetation growth. It is an urgent to accurately estimate chlorophyll content at the regional scale with low cost by using remote sensing techno-logy. In this study, we took Xinping County, Yuxi City, Yunnan Province, as the research area, and used GEDI data as the main information source. Based on the empirical Bayesian Kriging regression prediction (EBKRP) method, we accurately obtained the continuous distribution of the spot characteristic parameters in the unknown space of the study area. Combined with measured data of 52 plots, we used Pearson correlation, random forest (RF) and gradient boosting regression tree (GBRT) to screen the optimal combination parameters. We further established the best estimation model of chlorophyll content of Dendrocalamus giganteus at regional scale by Random forest regression (RFR) and GBRT models. The results showed that EBKRP demonstrated high prediction accuracy and reliability, with R² values ranging from 0.34 to 0.99, RMSE from 0.012 to 3134.005, rRMSE from 0.011 to 0.854, and CRPS from 965.492 to 1626.887. Different parameter optimization methods yielded slightly different optimal para-meter combinations. Different remote sensing modeling methods showed varying accuracy levels. The GBRT model (R²=0.94, RMSE=0.132, P=91.2%) outperformed the RFR model (R²=0.89, RMSE=0.192, P=89.3%). Using the GBRT model for estimating and mapping the spatial distribution of D. giganteus chlorophyll content, which ranged from 0.22 to 2.32 g·m^-2, with an average of 1.36 g·m^-2. These results aligned with the actual D. giganteus distribution in the study area, indicating that the GBRT model using GEDI data optimized by EBKRP could be feasible and reliable for estimating forest biochemical parameters, thereby providing effective support for forest health monitoring.

Key words: GEDI, EBKRP, combination parameter optimization, machine learning, chlorophyll content, estimation