A new paper led by Fujiang Ji demonstrates that satellite hyperspectral data can reliably track seasonal changes in plant physiology across U.S. forests. Using a framework that links PRISMA satellite imagery with NEON airborne hyperspectral trait maps, the team mapped chlorophyll, carotenoids, water content, and nitrogen across 11 NEON sites representing diverse forest types.
The approach produced high-accuracy trait estimates (R² up to 0.88) and revealed clear seasonal patterns—such as bell-shaped chlorophyll cycles in deciduous forests and site-specific water dynamics. Environmental analyses showed that solar radiation, temperature, and vapor pressure dominate seasonal changes depending on region, while soil properties explain most spatial variability.
This work shows that spaceborne hyperspectral imaging can deliver large-scale, time-series monitoring of plant functional traits, advancing our ability to study ecosystem responses to environmental change.
Citation: Ji et al., Remote Sensing of Environment (2026). https://doi.org/10.1016/j.rse.2025.115149