A Long-Term 30-m Fractional Vegetation Cover Dataset Downscaled From GLASS Products: Advancing Vegetation Monitoring at High Spatial Resolution

Fractional Vegetation Cover (FVC) is a key biophysical parameter for assessing vegetation condition and plays a critical role in ecosystem monitoring and Earth system modeling. However, most existing FVC products are constrained by medium to coarse spatial resolutions, limiting their application in fine-scale vegetation analyses over large regions. To address this issue, this study developed a extensible framework for downscaling the 500 m Global Land Surface Satellite (GLASS) FVC products to a 30 m resolution by synthesizing multi-source remote sensing datasets, thereby overcoming the limitations of coarse-resolution FVC products in fine-scale vegetation monitoring. The key highlight of this study lies in the integration of high-resolution spatial information with long-term FVC records through a robust and generalizable framework, enabling efficient and accurate vegetation monitoring across large regions. The framework was implemented on the Google Earth Engine (GEE) platform, facilitating the efficient generation of high-resolution, long-term FVC time series, termed GFVC30. By integrating the spatial detail of auxiliary data with the temporal continuity of GLASS FVC, the framework supported nationwide application across China. The GFVC30 product was validated using 157 independent ground measurements from eight global regions, yielding an R ² of 0.747 and an RMSE of 0.128. It preserved consistency with the spatial and temporal patterns of the original GLASS FVC while providing a more detailed representation of surface vegetation. Compared to existing FVC products (MultiVI 30 m, HXPT 250 m, MODIS 250 m, and GLASS 500 m), GFVC30 demonstrated superior spatial and temporal consistency, and better captured vegetation heterogeneity. This product enables refined and reliable vegetation monitoring over long time periods and broad geographic areas, supporting ecological assessment and climate response analysis from regional landscapes to localized communities. Overall, this study provides a robust and generalizable solution for producing fine-resolution, long-term FVC products to advance vegetation research and environmental monitoring.