A research team led by Prof. LI Xiangxian from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed a new deep learning model that significantly enhances both the accuracy and interpretability of roadside air pollutant forecasts.
A research team from the Aerospace Information Research Institute of the Chinese Academy of Sciences has developed a new method combining deep learning with physical radiative transfer modeling to improve the retrieval of atmospheric aerosol properties from complex satellite observations, supporting high-resolution, near-real-time monitoring of haze and dust events.
A recent satellite-based study has uncovered alarming declines in groundwater storage across High Mountain Asia, widely known as the "Asian Water Tower". This critical water source, which sustains agricultural irrigation, urban water supplies and ecological security for hundreds of millions of people in more than a dozen downstream countries, is depleting at a staggering rate of approximately 24.2 billion tonnes per year.
A research team from the Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences, in collaboration with researchers from the University of Liverpool, has developed an adaptive jerk control (AJC) method based on a biased sliding surface (BSS) design.
A research team from the National Time Service Center systematically examined how uncalibrated code biases in low Earth orbit (LEO) satellites affect LEO-augmented precise point positioning. This study brings the benefits of perfect assumptions back to reality by addressing the impact of LEO satellites.
A research team led by Profs. LI Jing and LIU Qinhuo from the Aerospace Information Research Institute of the Chinese Academy of Sciences has developed the world's first global, high-resolution map of leaf chlorophyll content (LCC), providing a new method for closely tracking plant health and ecosystem productivity worldwide.
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