  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.
