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arXiv · 2210.13752

Aboveground carbon biomass estimate with Physics-informed deep network

Juan Nathaniel·Levente J. Klein·Campbell D. Watson·Gabrielle Nyirjesy·Conrad M. Albrecht
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Abstract

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. Here, 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 SIF-based GPP. Validation results show that a masked variation of UNet has the lowest validation RMSE of 37.93 $\pm$ 1.36 Mg C/ha, as compared to 52.30 $\pm$ 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.

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Juan Nathaniel, Levente J. Klein, Campbell D. Watson, Gabrielle Nyirjesy, Conrad M. Albrecht. 2022-10-25. Aboveground carbon biomass estimate with Physics-informed deep network. https://arxiv.org/abs/2210.13752

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