Quantifying aboveground biomass (AGB) is essential in the context of global climate change. Canopy height, which is related to AGB, can be mapped using machine learning models trained with multi-source spatial data and GEDI measurements. In this study, a comparative analysis of canopy height estimates derived from two models is presented: a U-Net deep learning model (CHNET) and a Random Forest algorithm (RFH). Both models were trained using GEDI lidar data and utilized multi-source inputs, including optical, radar, and environmental data. While CHNET can leverage its convolutional architecture to account for spatial correlations, we observed that it does not fully incorporate all the spatial autocorrelation present in GEDI canopy height measurements. By conducting a spatial analysis of the models' residuals, we also identified that GEDI data acquisition parameters, particularly the variability in laser beam energy combined with the azimuthal directions of the observation tracks, introduce spatial inconsistencies in the measurements in the form of periodic patterns. To address these anisotropies, we considered exclusively GEDI power beams, and we conducted our spatial autocorrelation analysis in the GEDI track azimuthal direction. Next, we employed the residual kriging (RK) spatial interpolation technique to account for the spatial autocorrelation of canopy heights and improve the accuracies of CHNET and RFH estimates. Adding RK corrections improved the performance of both CHNET and RFH, with more substantial gains observed for RFH. The corrections appeared to be localized around the GEDI sample points and the density of usable GEDI information is therefore an important factor in the effectiveness of spatial interpolation. Furthermore, our findings reveal that a Random Forest model combined with spatial interpolation can deliver performance comparable to that of a U-Net model alone.

翻译：在全球气候变化的背景下，量化地上生物量至关重要。与地上生物量相关的冠层高度，可通过利用多源空间数据和GEDI测量值训练的机器学习模型进行制图。本研究对两种模型得出的冠层高度估算结果进行了比较分析：U-Net深度学习模型（CHNET）和随机森林算法（RFH）。两种模型均使用GEDI激光雷达数据训练，并利用了包括光学、雷达和环境数据在内的多源输入。虽然CHNET能够利用其卷积架构来考虑空间相关性，但我们观察到它并未完全纳入GEDI冠层高度测量中存在的所有空间自相关。通过对模型残差进行空间分析，我们还发现GEDI数据采集参数，特别是激光束能量的变化结合观测轨道的方位角方向，以周期性模式的形式在测量中引入了空间不一致性。为了解决这些各向异性，我们仅考虑了GEDI功率波束，并在GEDI轨道方位角方向上进行了空间自相关分析。随后，我们采用残差克里金法空间插值技术来考虑冠层高度的空间自相关性，并提高CHNET和RFH估算的准确性。添加RK校正后，CHNET和RFH的性能均得到改善，其中RFH的改进更为显著。校正似乎集中在GEDI样本点附近，因此可用GEDI信息的密度是空间插值有效性的一个重要因素。此外，我们的研究结果表明，结合空间插值的随机森林模型可以达到与单独使用U-Net模型相媲美的性能。