Generating dense time-series of spatially continuous 30m fractional vegetation cover for the Qinghai-Tibetan Plateau based on Google Earth Engine

The Qinghai-Tibetan Plateau (QTP) is one of the most sensitive and vulnerable regions under global climate change. Vegetation is the key component of the QTP ecosystem and is closely related to the ecological vulnerability. Fractional vegetation cover (FVC) is an important parameter to characterize vegetation conditions in the horizontal direction. Therefore, dense time-series of spatially continuous FVC at high spatial resolution is essential for understanding the detailed spatiotemporal dynamic changes in vegetation across QTP. Landsat is an ideal remote sensing data source for high spatial resolution FVC monitoring. However, frequent cloud cover during the growing season on QTP makes it challenging to observe FVC constantly only using Landsat. Spatiotemporal fusion methods integrating the advantages of Landsat and MODIS have been widely developed. Currently, most spatiotemporal fusion methods assume that the relationship between Landsat and MODIS is fixed over the prediction period. For regions with strong heterogeneity and large temporal variations, the relationship between Landsat and MODIS is variable along time. In addition, most methods fuse the reflectance bands separately without considering the interrelationship between bands. Therefore, a method blending Landsat and MODIS reflectance to generate FVC with 30m spatial resolution and 8-day interval based on Google Earth Engine (GEE) is proposed in this study. This method considers the dynamic relationship between MODIS and Landsat by analyzing the time-series data collected from multiple years. And a novel two-band simultaneous smoothing strategy is developed in this method, which can generate smoothed and consistent time-series of red and near-infrared bands simultaneously. Compared with three previous typical methods in two challenging QTP regions with rapid vegetation change, it can be found that the synthesized 30m reflectance data generated by the proposed method can get more accurate FVC. The validation results compared with the field-measured FVC further confirm the validity of the proposed method. The generated FVC products across QTP exhibit spatial continuity and reasonable time-series profiles. The proposed method is thus expected to provide high-quality FVC time-series with high spatiotemporal resolution over multiple years for QTP and other regions with frequent data missing based on GEE.