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.