Improving and validating the Hi-GLASS FVC product over mountainous regions in China and the United States using very-high-resolution satellite imagery

Fractional vegetation cover (FVC) is widely used to characterize vegetation conditions, yet its accuracy in mountainous regions remains highly uncertain due to complex terrain effects. Focusing on the Hi-GLASS FVC product, this study evaluates its performance in mountainous regions and proposes two improvement methods: a terrain-correction method (TC) and a multi-feature fusion method (MF). In the TC method, terrain-corrected surface reflectance is used as input to the Hi-GLASS FVC model. The MF method improves FVC estimation by incorporating multiple additional features, including observation geometry, topographic parameters, and vegetation indices. It is implemented as two models: a full-feature model (MF-ALL) and an optimized model using recursive feature elimination (MF-RFE). Using very high resolution (VHR) reference data, we quantitatively evaluated the accuracy of the two methods (TC and MF) over mountainous regions in China and the United States. The results reveal notable regional differences. In China, the MF-RFE model achieved the best performance, increasing R² by 62% relative to Hi-GLASS, slightly outperforming the MF-ALL model, while the TC method improved overall accuracy but reduced R² on sunny slopes by approximately 14%. In the United States, the MF-ALL model performed best, increasing R² by 42% over Hi-GLASS and slightly surpassing MF-RFE, whereas the TC method led to an overall accuracy decline. Further analysis showed that topography and vegetation type significantly influenced FVC estimation accuracy. Higher accuracy was generally observed on sunny slopes compared with shady slopes, with greater relative improvements on shady slopes; accuracy decreased with increasing slope; and forests exhibited larger improvements than non-forest vegetation types. Overall, the MF method substantially enhances the accuracy and robustness of mountainous FVC estimation compared with the TC method, providing a reliable framework for vegetation monitoring, carbon cycle assessment, and ecosystem management under complex terrain conditions.