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New paper led by Shaoyu Wang hashtag #RSE 📢
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 question—and 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/articl…
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