Pilot studies have demonstrated the potential of remote sensing for soil organic carbon (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non‑photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building image composites. These composites tend to minimize the disturbing effects by applying sets of criteria. Here, we aim to develop a robust method that allows selecting Sentinel-2 (S‑2) pixels with minimal influence of the following disturbing factors: crop residues, surface roughness and soil moisture. We selected all S-2 cloud-free images covering the Belgian Loam Belt from January 2019 to December 2020 (in total 36 images). We then built nine exposed soil composites based on four sets of criteria: (1) lowest Normalized Burn Ratio (NBR2), (2) Normalized Difference Vegetation Index (NDVI) < 0.25, (3–5) NDVI < 0.25 and NBR2 < threshold, (6) the ‘greening-up’ period of a crop and (7–9) the ‘greening-up’ period of a crop and NBR2 < threshold. The ‘greening-up’ period was selected based on the NDVI timeline, where ‘greening-up’ is considered as the last date of acquisition where the soil is exposed (NDVI < 0.25) before the crop develops (NDVI > 0.25). We then built a partial least square regression (PLSR) model with 10-fold cross-validation to estimate the SOC content based on 137 georeferenced calibration samples on the nine composites. We obtained non-satisfactory results (R² < 0.30, RMSE > 2.50 g C kg^–1, and RPD < 1.4, n > 68) for all composites except for the composite in the ‘greening-up’ stage with a NBR2 < 0.07 (R² = 0.54 ± 0.12, RPD = 1.68 ± 0.45 and RMSE = 2.09 ± 0.39 g C kg^–1, n = 49). Hence, the ‘greening-up’ method combined with a strict NBR2 threshold allows selecting the purest exposed soil pixels suitable for SOC prediction. The limit of this method might be its coverage of the total cropland area, which in a two-year period reached 62%, compared to 95% coverage if only the NDVI threshold is applied.

中文翻译：

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Sentinel-2暴露土壤复合物用于土壤有机碳预测

初步研究表明，遥感技术可用于裸露农田中土壤有机碳（SOC）的制图。但是，由于土壤表面的干扰因素（例如光合活性植被和非光合活性植被），土壤湿度或表面粗糙度的变化，通常阻碍了将遥感用于SOC预测。随着免费提供的卫星数据数量的增加，最近的研究集中在通过构建图像合成物来稳定土壤反射率上。这些复合材料倾向于通过应用一系列标准来最小化干扰影响。在这里，我们旨在开发一种鲁棒的方法，该方法允许选择Sentinel-2（S‑2）像素而对以下干扰因素的影响最小：农作物残渣，表面粗糙度和土壤湿度。我们选择了2019年1月至2020年12月覆盖比利时壤土带的所有S-2无云图像（总共36张图像）。然后，我们基于四组标准构建了九个裸露的土壤复合材料：（1）最低归一化燃烧比（NBR2），（2）归一化植被指数（NDVI）<0.25，（3-5）NDVI <0.25和NBR2 <阈值，（6）作物的“绿化”时期，（7-9）作物的“绿化”时期，且NBR2 <阈值。基于NDVI时间轴选择“绿化”时期，其中“绿化”被认为是在作物生长之前（NDVI> 0.25）土壤暴露（NDVI <0.25）的最后收购日期。然后，我们建立了具有10倍交叉验证的偏最小二乘回归（PLSR）模型，以基于九种复合材料上的137个地理参考校准样本估算SOC含量。我们获得了不令人满意的结果（R²<0.30，RMSE> 2.50 g C kg^–1，并且所有复合材料的RPD <1.4，n> 68），除了处于“绿化”阶段且NBR2 <0.07（R²= 0.54±0.12，RPD = 1.68±0.45和RMSE = 2.09±0.39 ）的复合材料之外g C kg ^–1，n = 49）。因此，结合严格的NBR2阈值的“绿化”方法可以选择适合SOC预测的最纯净的裸露土壤像素。此方法的局限性可能是其对整个农田的覆盖率，两年内达到了62％，而仅应用NDVI阈值则覆盖率为95％。