Soil monitoring requires accurate and spatially explicit information on soil organic carbon (SOC) trends and changes over time. Spatiotemporal SOC models based on Earth Observation (EO) satellite data can support large‐scale SOC monitoring but often lack sufficient temporal validation based on long‐term soil data. In this study, we used repeated SOC samples from 1986 to 2022 and a time series of multispectral bare soil observations (Landsat and Sentinel‐2) to model high‐resolution cropland SOC trends for almost four decades. An in‐depth validation of the temporal model uncertainty and accuracy of the derived SOC trends was conducted based on a network of 100 long‐term monitoring sites that were continuously resampled every 5 years. While the general SOC prediction accuracy was high (R2 = 0.61; RMSE = 5.6 g kg−1), the direct validation of the derived SOC trends revealed a significantly greater uncertainty (R2 = 0.16; p < 0.0001), even though predicted and measured values showed similar distributions. Classifying the results into declining and increasing SOC trends, we found that 95% of all sites were either correctly identified or predicted as stable (p < 0.001), highlighting the potential of our findings. Increased accuracies for SOC trends were found in soils with higher SOC contents (R2 = 0.4) and sites with reduced tillage (R2 = 0.26). Based on the signal‐to‐noise ratio and temporal model uncertainty, we were able to show that the necessary time frame to detect SOC trends strongly depends on the absolute SOC changes present in the soils. Our findings highlight the potential to generate significant cropland SOC trend maps based on EO data and underline the necessity for direct validation with repeated soil samples and long‐term SOC measurements. This study marks an important step toward the usability and integration of EO‐based SOC maps for large‐scale soil carbon monitoring.

中文翻译：

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从太空看农田土壤有机碳变化的时空监测


土壤监测需要有关土壤有机碳 （SOC） 趋势和随时间变化的准确和空间明确信息。基于地球观测 （EO） 卫星数据的时空 SOC 模型可以支持大规模 SOC 监测，但通常缺乏基于长期土壤数据的足够时间验证。在这项研究中，我们使用了 1986 年至 2022 年的重复 SOC 样本和多光谱裸土观测的时间序列（Landsat 和 Sentinel-2）来模拟近四十年的高分辨率农田 SOC 趋势。基于 100 个长期监测点的网络对得出的 SOC 趋势的时间模型不确定性和准确性进行了深入验证，这些站点每 5 年连续重新采样一次。虽然一般 SOC 预测精度很高 （R2 = 0.61;RMSE = 5.6 g kg-1），对得出的 SOC 趋势的直接验证揭示了显着更大的不确定性 （R2 = 0.16;p < 0.0001），即使预测值和测量值显示相似的分布。将结果分为 SOC 下降和上升趋势，我们发现 95% 的站点被正确识别或预测为稳定 （p < 0.001），突出了我们研究结果的潜力。在 SOC 含量较高的土壤 （R2 = 0.4） 和耕作减少的土壤 （R2 = 0.26） 中发现 SOC 趋势的准确性有所提高。基于信噪比和时间模型不确定性，我们能够证明检测 SOC 趋势的必要时间框架在很大程度上取决于土壤中存在的绝对 SOC 变化。我们的研究结果强调了根据 EO 数据生成重要农田 SOC 趋势图的潜力，并强调了通过重复土壤样本和长期 SOC 测量进行直接验证的必要性。 这项研究标志着基于 EO 的 SOC 图在大规模土壤碳监测中的可用性和集成迈出了重要一步。