A new remote-sensing framework has produced long-term, high-resolution maps of China's economic sustainability from 2001 to 2021. 

Trees shape climate, biodiversity, rural livelihoods, and the global bioeconomy, yet many remain invisible to today's large-scale monitoring systems.

A novel machine‑learning algorithm retrieves remote‑sensing reflectance (R_rs) from Himawari‑8 geostationary data at 10‑minute intervals – an unprecedented frequency for this meteorological satellite. 

Leaf functional traits—chlorophyll (CHL), carotenoid (CAR), equivalent water thickness (EWT), nitrogen (N), and leaf mass per area (LMA)—are essential for monitoring crop health and ecosystem productivity.