Hyperspectral imagery reveals large spatial variations of heavy metal content in agricultural soil - A case study of remote-sensing inversion based on Orbita Hyperspectral Satellites (OHS) imagery

The widespread heavy metal contamination in soil induced by extensive human disturbance has been a global significant issue because of its chronic toxic effects on human health. However, the establishment of effective monitoring and assessing methods for heavy metal content in the soil remain a long-term challenge due to the intrinsic limitation of the current multi-band remote sensing technology and field measurement methods. Prompted by this significant technique gap, we represent an implementation of remote-sensing inversion models based on hyperspectral imagery to reconstruct soil heavy metal contents within an experimental farmland located in Mianzhu city, Sichuan, China. We collected soil samples at the pre-defined sites, and measured soil heavy metal contents and soil spectrum in the laboratory condition. Meanwhile, we obtained Orbita Hyperspectral Satellites (OHS) imagery and quantified the associated vegetation indexes. The measured soil spectrum, bands of OHS, and the generated vegetation indexes were mathematically transformed to better represent their relations with soil heavy metal contents. Consequently, the most sensitive variables were selected as potent predictors of soil heavy metal contents in the inversion models. After evaluating the optimal inversion models for each heavy metal element, we implemented them to reconstruct the spatial patterns of soil heavy metal contents over the study landscape. We found obvious benefits of the remote sensing inversion model in predicting the spatial heterogeneity of heavy metal content within this small landscape patch. Specifically, the inversion model unraveled a normal distribution of heavy metal contents within the landscape, while the traditional spatial interpolation based on field measurements may suggest a largely skewed distribution. Compared with airborne-based studies, this study represents an application of spaceborne satellite data, which can be easily applied to a large spatial scale and long-term monitoring.