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).

A research team led by Prof. WANG Shudong at the State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), has made significant progress in reconstructing long-term global surface soil moisture variations and improving future projections through observation-constrained climate modeling. The study, Quantifying Historical and Future Surface Soil Moisture Drying Using Deep Learning and Remote Sensing, was recently published in Earth's Future .

In a study published in Earth System Science Data​, an international research team has created the first high-resolution global dataset of forest disturbance types, ---the Global Forest Disturbance Type Dataset (GFD), showing that 31% of the world's forest area, approximately 1.247 billion hectares, experienced disturbances between 2000 and 2020.

A new SAR interpretation method helps reveal the specific sources on three-dimensional targets that correspond to strong radar scattering in SAR images. By combining a customized differentiable Synthetic aperture radar (SAR) simulator with inverse optimization, the approach links bright scattering patterns in 2-dimensional (2D) SAR images to specific 3-dimensional (3D) structures. The method could improve target analysis, image interpretation, and physically grounded SAR simulation in remote sensing.

A research team led by Professor ZENG Jiangyuan from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS) has developed a new algorithm that significantly improves global soil moisture observation, offering a valuable tool for agriculture, water management, and disaster early warning.