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Downscaling Chlorophyll-a (Chl-a) concentration derived from satellite image is crucial for refined applications such as water quality monitoring. However, the precision of downscaling is usually constrained by various environmental and geophysical factors. In this paper, we develop a downscaling method for Chl-a concentration to improve precision, especially for inland lakes with different nutrient status and surrounding environment. The method downscales the Sentinel-3 Chl-a concentration from 300 m to 30 m, based on an integration of the multivariate analysis (MVA) and the gradient boosting decision tree (GBDT) model. Firstly, we analyzed 21 Chl-a concentration related indices derived from Landsat-8 TIRS, Sentinel-1 SAR, and Sentinel-2 MSI images, to identify optimal factors for Chl-a concentration variability. Secondly, a GBDT regression model integrated the optimal factors and Sentinel-3 Chl-a concentrations at coarse resolution, is constructed to convey the nonlinear relations between them. Finally, fine-resolution Chl-a concentrations were produced by employing the GBDT regression model to auxiliary factors at fine scale for 12 large inland lakes across China. The results indicated that the proposed MVA-GBDT method effectively inferred the spatial variability of Chl-a concentration with a mean RMSE of 4.505 mg&#x002F;m 3 , an improvement of 5&#x0025; &#x223C; 39&#x0025; over other downscaling methods. Furthermore, for lakes with large water quality heterogeneity, the method led to a cross validation RMSE and a difference in accuracy of 5.371 mg&#x002F;m 3  and 0.866 mg&#x002F;m 3 , respectively. In addition, this study examined the significance of the auxiliary factors and found that the NDCI (normalized difference chlorophyll index) and WST (water surface temperature) were the two most important factors for MVA-GBDT to detect coarse spatial resolution of Chl-a concentration, particularly for NDCI in lakes with high nutrient contrasts. These findings contribute to the generation of fine-scale Chl-a concentrations in lakes and support related applications.

Downscaling Sentinel-3 Chlorophyll-a Concentration for Inland Lakes based on Multivariate Analysis and Gradient Boosting Decision Trees Regression