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First-Ever Satellite Monitoring of Leaf Photoprotective Pigment Regulation in Global Vegetation
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.
Jun 07, 2024
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Uncovering Ancient Mysteries from Space: How Remote Sensing is Transforming Archaeology
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.
Jun 05, 2024
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Most Detailed Map of Water Bodies Across Tibetan Plateau Produced
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.
May 28, 2024
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Study Unravels Dynamics of Plant Stress Using Solar-Induced Chlorophyll Fluorescence
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.
May 27, 2024
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AI Contributes to Ocean Eddy Detection by Synthetic Aperture Radar
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.
May 20, 2024
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