Researchers from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS), in collaboration with the International Research Center of Big Data for Sustainable Development Goals and Aarhus University in Denmark, published a study in Nature Communications on 10 February, 2026. By integrating spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar observations with simulations from the dynamic vegetation model LPJ-GUESS, the team conducted the first nationwide assessment of the contributions of different categories of protected areas to aboveground forest carbon density. The study further projected future carbon gain potential under different climate scenarios and varying protection intensities.

A joint research team led by the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS) has conducted the first global, integrated assessment of disaster risks and human pressures affecting UNESCO World Heritage sites. The study found that 41% of these sites worldwide are exposed to significant natural hazards. Furthermore, it provides a systematic evaluation of global progress toward UN Sustainable Development Goal (SDG) 11.4, which aims to strengthen the protection of cultural and natural heritage.

Researchers from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS), in collaboration with Chongqing University of Posts and Telecommunications, have developed a high-resolution, daily atmospheric CO₂ dataset for China spanning 2016–2020. The dataset provides unprecedented insights into the spatiotemporal variations of column-averaged dry-air CO₂ mole fraction (XCO₂) and offers a robust data foundation for refined carbon monitoring and management.

The Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences has successfully conducted a business-oriented application experiment in satellite-to-ground laser communications with data rates exceeding 100 Gbps, achieving a peak transmission speed of 120 Gbps.

A new artificial intelligence–based approach is enabling more accurate and efficient monitoring of air pollution from space. By combining deep learning with physical radiative transfer modeling, researchers have developed a method that significantly improves the retrieval of atmospheric aerosol properties from complex satellite observations, supporting high-resolution, near-real-time monitoring of haze and dust events.

As a core component of the Three-North Shelter Forest Program in China's Three-North Region (TNR), farmland shelterbelts (FSs), also known as windbreaks, play a crucial role in reducing wind erosion and preventing soil degradation. However, the large-scale spatial patterns of FSs have remain poorly understood, limiting efforts to upgrade and optimize these systems.