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.
A groundbreaking multi-task learning framework, DEMENTIA, has been developed by Prof. LI Hai and his team at the Hefei Institutes of Physical Science, to improve the early detection and assessment of Alzheimer's disease.
Researchers from the Aerospace Information Research Institute of the Chinese Academy of Sciences leveraged the advanced capabilities of SDGSAT-1's Glimmer Imager and Thermal Infrared Spectrometer to monitor gas flaring activities in the South China Sea.
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