An international research team has developed the world's first 30-meter resolution dataset to track changes in land productivity, a crucial tool for fighting global land degradation.
A research team led by Prof. SHI Chong from the Aerospace Information Research Institute of the Chinese Academy of Sciences, in collaboration with Japanese scientists, has made a significant advancement in satellite-based air quality monitoring. They have developed AIRTrans, an innovative algorithm that greatly improves the accuracy and efficiency of retrieving key aerosol properties from multispectral satellite observations.
A recent study highlights how China's FengYun-3 (FY-3) meteorological satellites have improved global tracking of land surface temperature throughout daily cycles. Led by Prof. ZHAO Tianjie from the Aerospace Information Research Institute of the Chinese Academy of Sciences, the study utilizes the unique capabilities of the FY-3's Microwave Radiation Imagers (MWRI) to overcome the limitations of traditional satellite-based LST retrieval methods.
A research team, led by Prof. MENG Qingyan from the Aerospace Information Research Institute of the Chinese Academy of Sciences, has successfully developed the Global Spatiotemporal Fusion Model (GLOSTFM), a high-efficiency spatiotemporal fusion model that utilizes multi-source satellite data. By integrating thermal infrared and microwave observations from the Fengyun-3D satellite, GLOSTFM enhances the spatiotemporal resolution of land surface temperature data.
A research team from the Xinjiang Technical Institute of Physics and Chemistry of the Chinese Academy of Sciences, has made strides in the theoretical design of nonlinear optical (NLO) materials by leveraging machine learning techniques.
A research team from the Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences has introduced a novel feature selection method by removing noise entropy within mutual information.
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