Fractional vegetation cover (FVC) is a critical biophysical parameter that quantifies the proportion of green vegetation projected vertically onto a unit ground area. It serves as a fundamental indicator for monitoring ecosystem health, modeling land surface processes, and assessing environmental changes such as desertification and soil erosion. While satellite remote sensing has become the dominant method for large-scale, high spatial resolution FVC monitoring, significant challenges persist in complex mountainous regions due to topographic effects and heterogeneous vegetation patterns, which complicate the validation of FVC products in these areas. This study derived high spatio-temporal resolution reference true-value products (RTVPs) for FVC in a typical mountain area through the synergistic integration of in-situ measurements, unmanned aerial vehicle (UAV) observations, and the Sentinel-2 constellation. The approach involved establishing a multi-temporal dataset of high-resolution UAV-based FVC true value data through space-air-ground synchronous observation experiments, developing a terrain-aware random forest regression model incorporating multi-dimensional features including surface reflectance, vegetation indices, topographic factors, observation geometry, and image texture, and constructing a spatio-temporal continuous FVC dataset through the harmonic modeling of Sentinel-2 like 10 m datasets. Validation showed that our UAV-scale FVC retrieval achieved an R² of 0.9623 and an RMSE of 0.0508 using the pixel dichotomy method. The mountain-specific FVC retrieval model demonstrated exceptional performance with an R² of 0.9406 and an RMSE of 0.0598 with the UAV reference maps. The resulting FVC RTVPs provide 10 m spatial resolution with a 5-d temporal resolution, effectively capturing fine-scale vegetation dynamics while maintaining temporal continuity. These RTVPs offer unprecedented accuracy for validating existing fine and coarse spatial resolution FVC products and serve as a benchmark for ecological modeling in complex terrain.

High spatial resolution Gross Primary Productivity (GPP) estimation makes it feasible to better understand the spatial heterogeneity of mountain vegetation photosynthesis. Spatial downscaling is a practical approach to obtaining high resolution GPP estimates, whereas there is almost no attention given to the downscaled biases resulted from the widely reported scaling errors in medium or coarse resolution GPP estimates. To fill the above gap, this study adopted an eco-hydrological model to obtain 960 m resolution distributed GPP estimates (not including scaling errors) and lumped GPP estimates (including scaling errors) over four mountainous watersheds. Then, the distributed and lumped estimates were downscaled from 960 m to 30 m, respectively. Finally, the contribution of reducing scaling errors was characterized by the agreement index (d), BIAS and Root-Mean-Square-Error (RMSE) values between downscaled GPP and referenced GPP (directly generated at the spatial resolution of 30 m). Results showed that a large difference existed between lumped and distributed GPP, with d, BIAS, and RMSE of 0.79, 212, and 334 gC m⁻² year⁻¹, demonstrating that the scaling errors should be given enough attention to current coarse resolution GPP estimates. Before considering the scaling errors, large uncertainties were observed in the GPP downscaled from lumped values, with d, BIAS, and RMSE of 0.68, 220, and 480 gC m⁻² year⁻¹. After considering the scaling errors, a significant improvement was achieved in the GPP downscaled from distributed values, with an increased d value of 0.81, a decreased BIAS value of 10 gC m⁻² year⁻¹, and a decreased RMSE value of 388 gC m⁻² year⁻¹, indicating that reducing the medium or coarse resolution scaling errors would effectively improve the spatial downscaling of mountain vegetation GPP. Our study highlights the effect of scaling errors on the spatial downscaling of mountain vegetation productivity, which should be given more attention in the future carbon modeling.