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Aboveground carbon biomass estimate with Physics-informed deep network
Juan Nathaniel · · Campbell Watson · Gabrielle Nyirjesy · Conrad Albrecht
Event URL: https://www.climatechange.ai/papers/neurips2022/9 »

The global carbon cycle is a key process to understand how our climate is changing. However, monitoring the dynamics is difficult because a high-resolution robust measurement of key state parameters including the aboveground carbon biomass (AGB) is required. We use deep neural network to generate a wall-to-wall map of AGB within the Continental USA (CONUS) with 30-meter spatial resolution for the year 2021. We combine radar and optical hyperspectral imagery, with a physical climate parameter of solar-induced chlorophyll fluorescence (SIF)-based gross primary productivity (GPP). Validation results show that a masked variation of UNet has the lowest validation RMSE of 37.93 ± 1.36 Mg C/ha, as compared to 52.30 ± 0.03 Mg C/ha for random forest algorithm. Furthermore, models that learn from SIF-based GPP in addition to radar and optical imagery reduce validation RMSE by almost 10% and the standard deviation by 40%. Finally, we apply our model to measure losses in AGB from the recent 2021 Caldor wildfire in California, and validate our analysis with Sentinel-based burn index.

Author Information

Juan Nathaniel (Columbia University)
Campbell Watson (IBM Research)

I'm an atmospheric scientist at IBM Research where my research spans climate, weather and water. I was a postdoc at Yale University with Prof. Ron Smith, and completed a PhD at the University of Melbourne with Prof. Todd Lane. Currently leading AI for Climate initiatives with the Future of Climate at IBM Research.

Gabrielle Nyirjesy (Columbia University)

Gabby Nyirjesy is a Master’s in Data Science student at Columbia University. Her prior education includes a B.S. in Materials Science and Engineering from Johns Hopkins University in 2016. Gabby has 5 years of experience implementing machine learning models for her clients at Accenture.

Conrad Albrecht (IBM Research)

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