RESEARCH ON NDVI NORMALIZATION METHOD BASED ON GF IMAGES

This study proposes a global-to-local approach for estimating forest height by fusing repeat-pass synthetic aperture radar interferometry and Global Ecosystem Dynamics Investigation (GEDI) data. Using Advanced Land Observing Satellite (ALOS-1) data and a twofold strategy to address temporal gaps, the method produced 30 m gridded forest height mosaics for the northeastern United States and China, demonstrating promising accuracies and offering potential for fusing data from future missions.

Existing L-band vegetation optical depth (L-VOD) products suffer from data gaps and coarse resolution of historical data. Therefore, it is necessary to integrate multi-temporal and multisource L-VOD products. 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.  

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.