RESEARCH ON NDVI NORMALIZATION METHOD BASED ON GF IMAGES

The existing L-band Vegetation Optical Depth (VOD) products suffer from data gaps and coarse resolution of historical data. Our study begins with the reconstruction of missing data and then develops a spatiotemporal fusion model to generate global daily seamless 9-km L-VOD products from 2010 to 2021, which are crucial for understanding the global carbon cycle. The dataset is freely accessible for use in environmental monitoring.

Although there are various XCO₂ products, they are all limited by the spatial resolution or spatiotemporal coverage. In this study, the first global 0.05° XCO₂ product (GCXCO₂) for 21 years is generated by combining the OCO-2 satellite observations and models simulations. The dynamic normalization strategy is applied to enhance the temporal expansibility of stacking learning model, and the product is superior than the model simulations showing similar characteristic with OCO-2 observations.

Investigations in central and western China show that tree ring inverted latewood intensity expresses a strong positive relationship with growing-season temperatures, indicating exciting potential for regions south of 30° N that are traditionally not targeted for temperature reconstructions. Earlywood BI also shows good potential to reconstruct hydroclimate parameters in some humid areas and will enhance ring-width-based hydroclimate reconstructions in the future.

Snow variation is a vital factor in global climate change. Satellite-based approaches are effective for large-scale environmental monitoring. Nevertheless, the high cloud fraction seriously impedes the remote-sensed investigation. Therefore, a recent 20-year cloud-free snow cover collection in China is generated for the first time. This collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues.

This study generated the first global 1-km continuous NDVI product (STFLNDVI) for 4-decades by fusing multi-source satellite products. Simulated and real-data assessments confirmed the satisfactory and stable accuracy of STFLNDVI regarding spatial details and temporal variations. STFLNDVI is an ideal solution to the trade-off between spatial resolution and time coverage in current NDVI products, which of great significance for long-term regional and global vegetation and climate change studies.