NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias.

NASA 的全球生态系统动力学调查 (GEDI) 是一项关键的气候任务，其目标是增进我们对森林在全球碳循环中作用的理解。虽然 GEDI 是第一个明确优化的天基激光雷达，用于测量预测地上生物量的垂直森林结构，但在广泛的观测和环境条件下准确解释大量波形数据具有挑战性。在这里，我们提出了一种新颖的监督机器学习方法来解释 GEDI 波形并在全球范围内回归树冠顶部高度。我们提出了一种基于深度卷积神经网络 (CNN) 集合的概率深度学习方法，以避免对未知效应（例如大气噪声）进行显式建模。该模型学习提取稳健的特征，这些特征可以泛化到看不见的地理区域，此外，还能产生对预测不确定性的可靠估计。最终，我们的模型产生的全球冠层顶部高度估计的预期 RMSE 为 2.7 m 低偏差。