Abstract Soil Organic Carbon (SOC) is a useful representative of soil fertility and an essential parameter in controlling the dynamics of various agrochemicals in soil. Soil texture is also used to calculate soil's ability to retain water for plant growth. SOC and soil texture are thus important parameters of agricultural soils and need to be regularly monitored. Optical satellite remote sensing offers the potential for frequent surveys over large areas. In addition, the recently-operated Sentinel-2 missions provide free imagery. This study compared the capabilities of Sentinel-2 for monitoring and mapping of SOC and soil texture (clay, silt and sand content) with those obtained from airborne hyperspectral (CASI/SASI sensors) and lab ASD FieldSpec spectroradiometer measurements at four agricultural sites in the Czech Republic. Combination of 10 extracted bands of the Sentinel-2 and 18 spectral indices, as independent variables, were used to train prediction models and then produce spatial distribution maps of the selected attributes. Results showed that the prediction accuracy based on lab spectroscopy, airborne and Sentinel-2 in the majority of the sites was adequate for SOC and fair for clay; however, Sentinel-2 imagery could not be used to detect and map variations in silt and sand. The SOC and clay maps derived from the airborne and spaceborne datasets showed similar trend, with both performing better where SOC levels were relatively high, though at the highest levels Sentinel-2 was able to create the SOC map more precisely than the airborne sensors. Taken across all SOC levels measured in the reference data, Sentinel-2 results were marginally lower than lab spectroscopy and airborne imagery, but this reduction in precision may be offset by the extensive geographical coverage and more frequent revisit characteristic of satellite observation. The increased temporal revisit and area are expected to be positive enhancements to the acquisition of high-quality information on variations in SOC and clay content of bare soils.

中文翻译：

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使用近端、机载和 Sentinel-2 光谱成像对土壤有机碳和质地进行检索和制图

摘要 土壤有机碳（SOC）是土壤肥力的有用代表，是控制土壤中各种农用化学品动态的重要参数。土壤质地也用于计算土壤为植物生长保水的能力。因此，SOC 和土壤质地是农业土壤的重要参数，需要定期监测。光学卫星遥感提供了在大范围内进行频繁调查的潜力。此外，最近运行的 Sentinel-2 任务提供免费图像。本研究比较了 Sentinel-2 监测和测绘 SOC 和土壤质地（粘土、淤泥和沙子含量）的能力与从空中高光谱（CASI/SASI 传感器）和实验室 ASD FieldSpec 光谱仪测量获得的能力，在四个农业地点捷克共和国。Sentinel-2 的 10 个提取波段和 18 个光谱指数的组合作为自变量用于训练预测模型，然后生成所选属性的空间分布图。结果表明，基于实验室光谱、机载和 Sentinel-2 的预测精度在大多数站点中对于 SOC 是足够的，对于粘土来说是公平的；然而，Sentinel-2 图像无法用于检测和绘制淤泥和沙子的变化。来自机载和星载数据集的 SOC 和粘土图显示出类似的趋势，两者在 SOC 水平相对较高的情况下表现更好，尽管在最高水平 Sentinel-2 能够比机载传感器更精确地创建 SOC 地图。在参考数据中测量的所有 SOC 水平中，Sentinel-2 的结果略低于实验室光谱和机载图像，但这种精度的降低可能会被广泛的地理覆盖范围和卫星观测更频繁的重访特征所抵消。预计时间重访和面积的增加将积极促进获取有关裸土 SOC 和粘土含量变化的高质量信息。