Airborne imaging spectroscopy data provide soil and vegetation information over relatively large areas at high spatial resolutions (<5 m). We combined airborne hyperspectral data with space-borne data (LiDAR DEM and Sentinel-1) to map soil properties and investigate the contributions of the different sensor data to the mapping accuracy. The study was conducted on a 330-ha farm in south-central Wisconsin, USA, where soils are relatively young and soil variation is high. Seventy-three soil samples (0–10 cm depth) were taken from cropped fields before planting. The soil data were used with remote sensing data for mapping clay, silt, sand, total carbon (TC), Mg, Al, Si, Fe, Ca, Ti, Mn and Zr. Three types of variables were compared: (a) DEM + Sentinel-1 & 2, as these are easy-to-obtain, (b) hyperspectral data with high-spatial resolution, and (c) hyperspectral data + DEM + Sentinel-1, to evaluate if the prediction can be improved by combining hyperspectral data with DEM + Sentinel-1, and if combining DEM + Sentinel-1 with hyperspectral data had higher prediction accuracy than combination with Sentinel-2 data. The partial least square regression (PLSR) model was used for establishing relationships between soil and remote sensing data. It was found that airborne hyperspectral imaging can accurately map the spatial distributions of soil clay content, Si and Fe concentrations. Combining hyperspectral data with DEM and Sentinel-1 improved the performance of models for mapping a range of soil properties (e.g., clay, silt, sand, Al, Ti, Mn and Zr). Total C, Mg and Ca concentrations cannot be predicted from the combination of hyperspectral data, Sentinel-1 and terrain attributes. In a highly heterogeneous landscape, surface soil properties can be accurately mapped combining LiDAR DEM, Sentinel-1 and hyperspectral data.

中文翻译：

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高光谱图像、Sentinel-1 和 LiDAR 的协同使用改进了农场尺度土壤物理和地球化学特性的绘图

机载成像光谱数据以高空间分辨率（<5 m）提供相对较大区域的土壤和植被信息。我们将机载高光谱数据与星载数据（LiDAR DEM 和 Sentinel-1）相结合来绘制土壤特性图，并研究不同传感器数据对制图精度的贡献。该研究是在美国威斯康星州中南部一个 330 公顷的农场进行的，那里的土壤相对年轻，土壤变异很大。种植前从农田中采集了 73 个土壤样品（0-10 厘米深度）。土壤数据与遥感数据一起用于绘制粘土、淤泥、沙子、总碳 (TC)、镁、铝、硅、铁、钙、钛、锰和锆。比较了三种类型的变量：(a) DEM + Sentinel-1 & 2，因为它们很容易获得，(b) 具有高空间分辨率的高光谱数据，(c) 高光谱数据 + DEM + Sentinel-1，以评估是否可以通过将高光谱数据与 DEM + Sentinel-1 组合来改进预测，以及是否将 DEM + Sentinel-1 与高光谱数据组合具有比组合更高的预测精度Sentinel-2 数据。偏最小二乘回归（PLSR）模型用于建立土壤和遥感数据之间的关系。结果表明，机载高光谱成像可以准确地绘制土壤粘土含量、Si 和 Fe 浓度的空间分布图。将高光谱数据与 DEM 和 Sentinel-1 相结合，提高了绘制一系列土壤特性（例如粘土、淤泥、沙子、Al、Ti、Mn 和 Zr）的模型的性能。无法从高光谱数据、Sentinel-1 和地形属性的组合中预测总 C、Mg 和 Ca 浓度。