论文标题
地上碳生物量通过物理信息深度网络估算
Aboveground carbon biomass estimate with Physics-informed deep network
论文作者
论文摘要
全球碳循环是了解我们的气候如何变化的关键过程。但是，难以监测动力学是很困难的，因为需要对关键状态参数（包括地上碳生物量（AGB））进行高分辨率的鲁棒测量。在这里，我们使用深层神经网络在美国大陆（CONUS）内生成AGB的壁墙图，该图具有2021年的30米空间分辨率。我们将雷达和光学高光谱图像与基于SIF的GPP的物理参数相结合。验证结果表明，UNET的蒙版变化的最低验证RMSE为37.93 $ \ pm $ 1.36 mg c/ha，而52.30 $ \ pm $ \ pm $ 0.03 mg c/ha对于随机森林算法。此外，除了雷达和光学图像外，还从基于SIF的GPP中学习的模型将验证RMSE降低了几乎10％，标准偏差降低了40％。最后，我们将模型应用于最近2021年加利福尼亚州Caldor Wildfire的AGB中的损失，并通过基于Sentinel的燃烧指数来验证我们的分析。
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.
