New paper led by Shaoyu Wang hashtag
#RSE š¢
We developed a deep-learning based GPP model,
#GPPnet, which only requires multi-channel
#Sentinel2 reflectance and incoming light. No land cover, no VPD, no temperature.
This simple model predicted canopy photosynthesis for both C3 and C4 (without separate models), captured drought/heatwave effects, light use efficiency and interannual variability.
Why and how multi-channel reflectance can explain variations in canopy photosynthesis remains an open questionāand an exciting avenue that deserves future study.
Because all input data are available in real time on
#GEE, we envision monitoring canopy photosynthesis any place, any time.
Ultimately, integrating data-driven (GPP-net), semi-empirical (NIRvP), and process-based models (BESS) will enable a wide range of applications, from near-real-time crop monitoring to global estimates of photosynthesis.
@DechantBenjamin @HelinZhang11301 @FengHuaize @IjeonghoN @ChanghyunChoi13
@snucals1 @SeoulNatlUni
GPP-net: a robust high-resolution GPP estimation network for Sentinel-...
sciencedirect.com/science/arā¦