Generating high spatio-temporal fractional vegetation cover reference product for the Wanglang mountain area via space-air-ground integration approach

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.