Effects of hyperspectral data with different spectral resolutions on the estimation of soil heavy metal content: From ground-based and airborne data to satellite-simulated data

Abstract

Soil heavy metal distribution maps can provide decision-making information for pollution control and agricultural management. However, the estimation of heavy metals is sensitive to the resolution of the soil spectra due to their sparse content in soils. The purposes of this study were to test the sensitivity of Ni, Zn and Pb prediction results to variations in spectral resolution, then to map their spatial distributions over a large area. In addition, the effectiveness of spectral feature extraction was investigated. In total, 92 soil samples and corresponding field soil spectra were obtained from the Tongwei-Zhuanglang area in Gansu Province, China. Airborne HyMap hyperspectral image of this area was acquired simultaneously. Three satellite image spectra (AHSI_GF-5, Hyperion, AHSI_{ZY-1 02D}) were simulated using the field spectra which were measured under real environmental conditions rather than laboratory conditions. The combination of genetic algorithm and partial least squares regression (GA-PLSR) was used as prediction algorithm. The models calibrated by HyMap image full spectral bands had the highest accuracies (R_P² = 0.8558, 0.8002, and 0.8592 for Ni, Zn, and Pb, respectively) because of high consistency. For field spectra and three simulated satellite spectra, models calibrated by simulated AHSI_GF-5 spectra performed best because of appropriate resolution (5 nm in the visible near-infrared [VNIR] and 10 nm in the short-wave infrared [SWIR]). The spectral feature extraction method only improved prediction accuracy of the field spectra, indicating that this method benefited from higher spectral resolution. The mapping of the spatial distribution of soil heavy metals over a large area was realized based on HyMap image. According to the results of the satellite simulation spectra, this study proposes to use GF-5 hyperspectral image to estimate heavy metals content. The outcomes provide a reference for the utilization of aerial and satellite hyperspectral images in prediction of soil heavy metal concentrations.

Keywords: Digital soil mapping; Hyperspectral image; Soil heavy metal; Spectral features extraction; Spectral resolution.