A research team from Peking University and the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences (CAS) has developed a new photonic clock chip that offers a key advancement for future ultra-fast computing for AI development, 6G networks, autonomous vehicles, and remote sensing.

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.