Broeg Tom, Don Axel, Wiesmeier Martin, Scholten Thomas, Erasmi Stefan
Thünen Earth Observation (ThEO), Thünen Institute of Farm Economics, Braunschweig, Germany.
Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, Tübingen, Germany.
Glob Chang Biol. 2024 Dec;30(12):e17608. doi: 10.1111/gcb.17608.
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 (R = 0.61; RMSE = 5.6 g kg), the direct validation of the derived SOC trends revealed a significantly greater uncertainty (R = 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 (R = 0.4) and sites with reduced tillage (R = 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.
土壤监测需要有关土壤有机碳（SOC）随时间变化的趋势和变化的准确且具有空间明确性的信息。基于地球观测（EO）卫星数据的时空SOC模型可以支持大规模的SOC监测，但往往缺乏基于长期土壤数据的充分时间验证。在本研究中，我们使用了1986年至2022年的重复SOC样本以及多光谱裸土观测的时间序列（陆地卫星和哨兵 - 2）来模拟近四十年来高分辨率农田SOC趋势。基于一个每5年持续重新采样的100个长期监测站点网络，对导出的SOC趋势的时间模型不确定性和准确性进行了深入验证。虽然一般SOC预测精度较高（R = 0.61；RMSE = 5.6 g/kg），但对导出的SOC趋势的直接验证显示出显著更大的不确定性（R = 0.16；p < 0.0001），尽管预测值和测量值显示出相似的分布。将结果分为SOC下降和上升趋势，我们发现所有站点中有95%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（R = 0.4）和少耕的站点（R = 0.26）中发现了更高的SOC趋势精度。基于信噪比和时间模型不确定性，我们能够表明检测SOC趋势所需的时间框架强烈取决于土壤中存在的绝对SOC变化。我们的研究结果突出了基于EO数据生成重要农田SOC趋势图的潜力，并强调了用重复土壤样本和长期SOC测量进行直接验证的必要性。这项研究标志着朝着基于EO的SOC地图在大规模土壤碳监测中的可用性和整合迈出了重要一步。
