# Aboveground carbon biomass estimate with Physics-informed deep network

arXiv: https://arxiv.org/abs/2210.13752 · HTML: https://mlsanity.com/paper/2210.13752/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A high-resolution canopy height model of the Earth](https://mlsanity.com/paper/2204.08322/) (2022)
- [Map Generation from Large Scale Incomplete and Inaccurate Data Labels](https://mlsanity.com/paper/2005.10053/) (2020)
- [Monitoring the Impact of Wildfires on Tree Species with Deep Learning](https://mlsanity.com/paper/2011.02514/) (2020)
- [Monitoring Urban Forests from Auto-Generated Segmentation Maps](https://mlsanity.com/paper/2206.06948/) (2022)
- [PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data](https://mlsanity.com/paper/2012.06907/) (2020)
- [Quantification of Carbon Sequestration in Urban Forests](https://mlsanity.com/paper/2106.00182/) (2021)
- [Wildfire risk forecast: An optimizable fire danger index](https://mlsanity.com/paper/2203.15558/) (2022)

## Built on by (most cited first, 4 of 4)

- [ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction](https://mlsanity.com/paper/2402.00712/) (2024)
- [Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation](https://mlsanity.com/paper/2406.19888/) (2024)
- [DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery](https://mlsanity.com/paper/2502.10060/) (2025)
- [A Survey of Physics-Informed AI for Complex Urban Systems](https://mlsanity.com/paper/2506.13777/) (2025)

## Abstract (arXiv)

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 ± 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.