A team of researchers at the Aerospace Information Research Institute (AIR) under the Chinese Academy of Sciences (CAS) has developed a novel model that could reshape how we monitor and understand the Earth's surface temperature, particularly in complex mountainous regions. This new model, called the Thermal Equivalent Slope Kernel-Driven (TESKD) model, offers unprecedented accuracy in estimating land surface temperature (LST) from thermal remote sensing data, overcoming challenges that have stymied scientists for years.

Recent research utilizing data from the Chinese Fengyun-3D satellite has unveiled a comprehensive global inventory of emissions from open biomass burning (OBB). This study, published in the Earth System Science Data on August 2，was conducted by a team of scientists led by SHI Yusheng, a researcher with the Aerospace Information Research Institute (AIR) under the Chinese Academy of Sciences (CAS), providing crucial insights into the impact of OBB on air quality, climate, and human health.

Scientists have developed a method to measure forest heights using advanced satellite technology, significantly reducing the need for labor-intensive fieldwork. A recent study published on July 29 in the Journal of Remote Sensing by a team of international researchers leverages high-resolution satellite imagery from China's GF-7 satellite and historical images from Google Earth to achieve highly precise forest height measurements.

A recent study published in the Journal of Hydrology has highlighted the dramatic changes in river ice coverage for the Yenisei River basin over the past two decades. Researchers found that the ice coverage on this major Siberian river has significantly decreased from 2002 to 2021, with the trend closely linked to rising winter air temperatures. This decline has profound implications for the Arctic environment and climate systems.

In a recent study published in ACS Nano, a group of scientists led by Prof. CAI Xinxia from the Aerospace Information Research Institute (AIR) under the Chinese Academy of Sciences (CAS), in collaboration with international collogues, have developed an innovative approach to investigating the dynamics of neuronal networks. Utilizing three-dimensional gold microelectrode arrays, the scientists have significantly improved the ability to monitor and analyze communication within neuronal networks.

Researchers led by Professor HUSI Letu at the Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), have developed a novel algorithm for measuring cloud property using neutral networks. The algorithm, known as CRANN (Cloud Retrieval Algorithm based on Neural Networks), focusing on retrieving cloud fraction and cloud-top pressure from hyperspectral measurements in the O2-O2 band. The findings were published in the journal Remote Sensing of Environment.