Chinese scientists have developed a novel method to detect and monitor polluted water in rural areas—a critical step toward tackling harmful algal blooms and contaminated ponds. By combining high-resolution satellite imagery with deep learning methods, researchers from the Aerospace Information Research Institute (AIR) under the Chinese Academy of Sciences (CAS) can now pinpoint ponds overrun by duckweed or algae with remarkable accuracy.

A study published in the Journal of Remote Sensing by a research team from the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences (CAS) evaluates three algorithms—Band Shape Fitting (BSF), Three-band Fraunhofer Line Discrimination (3FLD), and Singular Vector Decomposition (SVD)—designed to measure solar-induced chlorophyll fluorescence (SIF) in plants. These algorithms aim to improve the accuracy of SIF data, which is essential for monitoring photosynthesis in both ecological and agricultural research.

A team of researchers from the Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, has developed a new framework to evaluate the spatial representativeness of soil moisture stations and its influencing factors globally. By revealing that around 63% of current soil moisture observation stations reliably reflect soil moisture conditions at the satellite pixel scale, this study addresses key challenges in validating satellite-based soil moisture products. Published in IEEE Transactions on Geoscience and Remote Sensing, the research provides valuable references for the rational deployment of soil moisture sites and the robust validation of satellite products.