National soil organic carbon (SOC) maps are essential to improve greenhouse gas accounting and support climate-smart agriculture. Large-scale SOC models based on wall-to-wall soil information from remote sensing remain a challenge due to the high diversity of natural soil conditions and the difficulty of accounting for the spatial location of the soil samples. In this study, we tested if the implementation of local ensemble models (LEM) can be used to improve the SOC predictions from Landsat-based soil reflectance composites (SRC) for Germany. For this, we divided the research area into 30 times 30 km tiles and calculated local generalized linear models (GLM) based on random, nearby observations. Based on the GLMs, local SOC maps were predicted and aggregated using a moving window approach. The local variable importance was analyzed to identify spatial dependencies in the correlation between the SRC and SOC. For the final SOC map, a Random Forest (RF) model was trained using the aggregated local SOC predictions, the SRC, and a full set of training samples from the agricultural soil inventory. The results show that the LEM was able to improve the accuracy (R = 0.68; RMSE = 5.6 g kg), compared to the maps based on a single, global model (R = 0.52; RMSE = 6.8 g kg). The local variable importance of the spectral bands showed clear spatial patterns throughout the research area. Differences can be explained by the local soil conditions, influencing the correlation between SOC and the spectral properties. Compared to the widely adopted integration of distance covariates such as geographical coordinates, the LEM was able the reduce the spatial autocorrelation to a greater extent and to improve the prediction accuracy, especially for underrepresented SOC values. The LEM presents a new method to integrate spatial information and increase the interpretability of DSM models.

中文翻译：

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使用局部集合模型和 Landsat 裸土复合材料绘制农田大比例土壤有机碳图

国家土壤有机碳 (SOC) 地图对于改善温室气体核算和支持气候智能型农业至关重要。由于自然土壤条件的高度多样性以及解释土壤样本空间位置的困难，基于遥感全面土壤信息的大规模 SOC 模型仍然是一个挑战。在本研究中，我们测试了是否可以使用局部集合模型 (LEM) 的实施来改进德国基于 Landsat 的土壤反射复合材料 (SRC) 的 SOC 预测。为此，我们将研究区域划分为 30 个 30 公里的区块，并根据随机的附近观测计算局部广义线性模型 (GLM)。基于 GLM，使用移动窗口方法预测和聚合局部 SOC 地图。分析局部变量重要性以确定 SRC 和 SOC 之间相关性的空间依赖性。对于最终的 SOC 地图，使用聚合的本地 SOC 预测、SRC 以及来自农业土壤清单的全套训练样本来训练随机森林 (RF) 模型。结果表明，与基于单一全局模型的地图（R = 0.52；RMSE = 6.8 g kg）相比，LEM 能够提高精度（R = 0.68；RMSE = 5.6 g kg）。光谱带的局部变量重要性在整个研究区域显示出清晰的空间模式。差异可以通过当地土壤条件来解释，影响 SOC 和光谱特性之间的相关性。与广泛采用的地理坐标等距离协变量的集成相比，LEM能够更大程度地降低空间自相关性并提高预测精度，特别是对于代表性不足的SOC值。 LEM 提出了一种整合空间信息并提高 DSM 模型可解释性的新方法。