Researchers from the Aerospace Information Research Institute under the Chinese Academy of Sciences (AIRCAS) have developed a knowledge-guided artificial intelligence algorithm that significantly improves the resolution of global soil moisture observation.

A research team from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS), in collaboration with Changsha University of Science and Technology, and Tsinghua University, has proposed a systematic and unified framework that integrates Light Detection and Ranging (LiDAR) remote sensing with Weakly Supervised Learning (WSL). The study provides a comprehensive review that bridges the traditional gap between LiDAR data interpretation and large-scale parameter inversion, offering scalable solutions to reduce the high costs of manual data annotation in Earth observation.

A recent study published in the ISPRS Journal of Photogrammetry and Remote Sensing​, describes a physics-based framework that maps global subsurface soil temperature profiles by harnessing the multi-overpass capabilities of China's Fengyun (FY) meteorological satellites. This work was led by Prof. ZHAO Tianjie from the Laboratory of Earth Observation for Water at the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS).