标题： 基于残差克里金法的区域尺度冠层高度制图：关于GEDI引起的各向异性和稀疏采样的见解 显示英文标题

标题： Residual Kriging for Regional-Scale Canopy Height Mapping: Insights into GEDI-Induced Anisotropies and Sparse Sampling

摘要： 量化地上生物量（AGB）在全球气候变化背景下至关重要。 树冠高度，与AGB相关，可以使用结合多源空间数据和GEDI测量值训练的机器学习模型进行映射。 在本研究中，呈现了两种模型生成的树冠高度估计值的比较分析：一种是U-Net深度学习模型（CHNET）和一种随机森林算法（RFH）。 这两个模型都使用GEDI激光雷达数据进行训练，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可以利用其卷积结构来考虑空间相关性，但我们发现它并未完全纳入GEDI树冠高度测量中的所有空间自相关性。 通过对手模型残差的空间分析，我们还发现GEDI数据采集参数，特别是激光束能量的变化以及观测轨迹的方位角方向，以周期性模式的形式在测量中引入了空间不一致。 为了解决这些各向异性，我们仅考虑了GEDI功率光束，并在GEDI轨迹方位角方向上进行了空间自相关分析。 接下来，我们采用了残差克里金（RK）空间插值技术来考虑树冠高度的空间自相关性，并提高CHNET和RFH估计的准确性。 添加RK校正提高了CHNET和RFH的性能，其中RFH的提升更为显著。 校正似乎集中在GEDI样本点周围，因此可用GEDI信息的密度是空间插值有效性的关键因素。 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U-Net模型相当的性能。 显示英文摘要

摘要： 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.