A sweeping new review reveals how satellites are helping scientists track a quiet but widespread shift in global agriculture: the abandonment of cropland.

Satellite data used by archaeologists to find traces of ancient ruins hidden under dense forest canopies can also be used to improve the speed and accuracy to measure how much carbon is retained and released in forests. Understanding this carbon cycle is key to climate change research.

Researchers from Tsinghua University and Henan Normal University developed a dedicated data analysis framework for the Silicon Strip Detector Telescopes (SSDTs) of the Compact Spectrometer for Heavy-IoN Experiments (CSHINE). Based on a modular architecture design, the framework integrates core analysis steps—including detector calibration, particle identification, and track reconstruction—into a unified system through C++ classes, effectively addressing the technical challenges of processing complex SSDT signals. Its robust performance was demonstrated through the successful analysis of light-charged particles in the 25 MeV/u ⁸⁶Kr + ¹²⁴Sn experiment conducted at the first Radioactive Ion Beam Line in Lanzhou, allowing for precise extraction of physical observables, including energy, momentum, and particle type. Through the optimization of track recognition algorithms with the utilization of reconstructed physical data, including effective physical event counts and energy spectra, the research team significantly enhanced the track recognition efficiency, achieving a remarkable rate of approximately 90%. This framework provides a standardized and reusable technical solution for SSDT-based detector systems.