Multi-Scale Stereoscopic Hyperspectral Remote Sensing Estimation of Heavy Metal Contamination in Wheat Soil over a Large Area of Farmland

With the rapid development of China’s industrialization and urbanization, problem heavy metal pollution in soil has become increasingly prominent, seriously threatening safety ecosystem human health. The hyperspectral remote sensing technology provides possibility to achieve non-destructive monitoring contents. This study aimed fully explore potential ground satellite image spectra estimating We chose Xushe Town, Yixing City, Jiangsu Province as research area, collected samples from farmland over two different periods, measured contents metals Cd As laboratory. At same time, under field conditions, we also wheat leaves obtained HuanJing-1A HyperSpectral Imager (HJ-1A HSI) data. first performed various spectral transformation pre-processing techniques on leaf Then, used genetic algorithm (GA) optimized partial least squares regression (PLSR) establish an estimation model contents, while evaluating accuracy model. Finally, best models drew spatial distribution maps area. results showed following: (1) can highlight some hidden information spectra, including mathematical transformations such differentiation; (2) modeling, GA-PLSR higher than PLSR, using a GA for band selection improve model’s stability; (3) provide good ability estimate (relative percent difference (RPD) = 2.72) excellent (RPD 3.25); HJ-1A HSI only distinguishing high low values 1.87, RPD 1.91). Therefore, it is possible indirectly data, identify areas key pollution.