Scientists from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS), in collaboration with Shihezi University, have developed a novel method to identify cotton fields in Xinjiang during the plastic-mulching stage. The study, titled "Advancing the earliest identifiable timing of cotton fields by the unique mechanized plastic-mulching practices in Xinjiang," was published in Remote Sensing of Environment.

A study published in Remote Sensing of Environment has generated the first 10-meter dynamic water dataset (DWH10) for the Hindu Kush Himalaya (HKH) region, revealing that existing global water products may substantially underestimate surface water extent in mountainous areas.

A research team led by Prof. CAI Xinxia, Assoc. Prof. LUO Jinping and Assoc. Prof. WANG Mixia from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS) has demonstrated that in vitro biological neuronal networks can achieve both low-power consumption and high-speed communication through predictable electrical stimulation, providing a promising new route for next-generation brain-inspired computing systems. The study shows that regular stimulation increased neural communication speed by about 1.79 times without adding extra energy cost.

A UNESCO report released on 21 April 2026, People and Nature in UNESCO-designated Sites: Global and Local Contributions, delivers the first global assessment of over 2,260 World Heritage sites, Biosphere Reserves and Global Geoparks. The study finds that more than one in four sites could reach critical ecological tipping points by 2050 if pressures intensify, while also showing that monitored wildlife populations within these sites have remained stable on average, contrasting sharply with a 73% global decline since 1970.

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.