Synergistic use of hyperspectral imagery, Sentinel-1 and LiDAR improves mapping of soil physical and geochemical properties at the farm-scale

摘要

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.