A collaborative effort involving China’s procuratorate agencies tasked with environmental litigations at national, municipal, and district levels, in conjunction with the Key Laboratory of Electromagnetic Radiation and Detection Technology at the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences (CAS), witnessed the application of electromagnetic technology in the investigation of an environmental public interest litigation case, marking the pioneering use of its kind in the country.

In the vast and unforgiving expanse of Antarctica, where human activities are on the rise, researchers from the Key Laboratory of Digital Earth Science with the Aerospace Information Research Institute (AIR) under the Chinese Academy of Sciences (CAS) have introduced a new tool to navigate the challenges posed by the harsh environment. The newly defined Human Activity Suitability (HAS) index aims to provide essential macro-scale information for decision-makers when selecting sites for human activities on Antarctic ice shelves.

In a recent survey conducted by the International Research Center of Big Data for Sustainable Development Goals (CBAS), 51 experts from various scientific fields worldwide shared their insights on the role of Digital Public Goods (DPGs) in advancing the United Nations Sustainable Development Goals (SDGs). The survey aimed to shed light on defining DPGs and addressing challenges associated with their practical implementation in supporting the SDGs.

Ground-penetrating radar (GPR) has long been a pivotal tool in estimating the permittivity of various mediums. A significant technique used in this estimation is the radar echo amplitude method, which crucially derives permittivity magnitude through the correlation between reflection coefficients and permittivity. Building upon this principle, a recent study published in Remote Sensing on Oct. 31 introduces a full-wave inversion real-time permittivity estimation method specifically tailored for stepped-frequency GPR (SFGPR).

Scientists from the National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace information Research Institute (AIR), Chinese Academy of Sciences (CAS), together with collaborators from Shihezi University (Xinjiang, China), harnessed solar-induced chlorophyll fluorescence as a potent indicator for predicting crop yields, and achieved significant advancements in the realm of mid-season cotton yield forecast in Xinjiang.

In a recent editorial published in the Innovation Geoscience, researchers from the Aerospace Information Research Institute (AIR) with the Chinese Academy of Sciences (CAS) highlight the significant advances in multimodal AI foundation models in Earth observation techniques, particularly in unlocking the power of remote sensing big data, and their profound impact on our understanding of the planet's various facets.