Researchers at the Aerospace Information Research Institute (AIR) with the Chinese Academy of Sciences (CAS) proposed a novel approach to assess dynamics in photoprotective pigments （carotenoids, anthocyanins, and betalains）at the canopy level. This advancement reveals the climatic conditions under which these adjustments typically occur across various global land ecosystems, providing insights into plant adaptation to detrimental environments.

A study published in the Remote Sensing of Environment conducted a bibliometric analysis spanning from 1992 to 2022, which reviewed and described the transformative influence of a century of advancements in RSA.

Scientists at the Aerospace Information Research Institute (AIR) under the Chinese Academy of Sciences (CAS), in collaboration with their international collogues, have proposed a state-of-the-art deep-learning network, named EOLO, aiming to improve the detection of ocean eddies observed in C-band spaceborne synthetic aperture radar (SAR) imagery. This work has been published in Remote Sensing of Environment , which combines advanced AI algorithms with high-resolution spaceborne SAR data, providing a methodological basis for further studies of sub-mesoscale eddies.

A study published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing sheds light on how plants respond to stress, specifically in the case of cotton plants affected by verticillium wilt (VW). This disease can wreak havoc on cotton crops but understanding how it affects the plants at the photosynthetic physiological level has been a challenge.

In a recent effort, researchers from the Aerospace Information Research Institute (AIR) at the Chinese Academy of Sciences (CAS) have unveiled an unprecedentedly detailed dataset illuminating the complex network of water bodies sprawled across the Tibetan Plateau. Unlike previous studies, which primarily zoomed in on larger water bodies, this dataset paints a holistic picture by furnishing a high-resolution map of water bodies, pinpointing details down to a remarkable 2-meter precision for the year 2020.

Researchers from the State Key Laboratory of Transducer Technology with the Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS) have proposed an improved algorithm called Dynamic Quantum Particle Swarm Optimization (DQPSO) to improve the accuracy and reliability of pressure sensors used in tracking and monitoring wild migratory birds. This algorithm optimizes the performance of a Radial Basis Function (RBF) neural network, specifically designed for temperature compensation.