基于物理信息深度网络的地下碳生物量估算
Aboveground carbon biomass estimate with Physics-informed deep network
摘要：
全球碳循环是了解气候变化的关键过程。然而，监测的动态是困难的，因为需要一个高分辨率的强大的测量关键状态参数，包括地上碳生物量（AGB）。在这里，我们使用深度神经网络来生成2021年美国大陆（CONUS）内AGB的墙到墙的地图，空间分辨率为30米。我们结合雷达和光学高光谱图像，与物理气候参数的SIF为基础的GPP。验证结果表明，UNet的掩蔽变化具有最低的验证RMSE，为37.93 $\pm$ 1.36 Mg C/ha，而随机森林算法为52.30 $\pm$ 0.03 Mg C/ha。此外，除了雷达和光学图像之外，还从基于SIF的GPP学习的模型将验证RMSE降低了近10%，标准差降低了40%。最后，我们应用我们的模型来衡量最近2021年加州Caldor野火中AGB的损失，并使用基于Sentinel的燃烧指数验证我们的分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
