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The rapid growth of industrialization and urbanization in China has led to an increase in soil heavy metal pollution, which poses a serious threat to ecosystem safety and human health. The advancement of spectral technology offers a way to rapidly and non-destructively monitor soil heavy metal content. In order to explore the potential of rice leaf spectra to indirectly estimate soil heavy metal content. We collected farmland soil samples and measured rice leaf spectra in Xushe Town, Yixing City, Jiangsu Province, China. In the laboratory, the heavy metals Cd and As were determined. In order to establish an estimation model between the pre-processed spectra and the soil heavy metals Cd and As content, a genetic algorithm (GA) was used to optimise the partial least squares regression (PLSR). The model's accuracy was evaluated and the best estimation model was obtained. The results showed that spectral pre-processing techniques can extract hidden information from the spectra. The first-order derivative of absorbance was more effective in extracting spectral sensitive information from rice leaf spectra. The GA-PLSR model selects only about 10% of the bands and has better accuracy in spectral modeling than the PLSR model. The spectral reflectance of rice leaves has the capacity to estimate Cd content in the soil (relative percent difference [RPD] = 2.09) and a good capacity to estimate As content in the soil (RPD = 2.97). Therefore, the content of the heavy metals Cd and As in the soil can be estimated indirectly from the spectral data of rice leaves. This study provides a reference for future remote sensing monitoring of soil heavy metal pollution in farmland that is quantitative, dynamic, and non-destructive over a large area. [ABSTRACT FROM AUTHOR]

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

In recent years, heavy metal contamination in agricultural soils in Jiangsu Province has attracted more and more attention. However, most studies have been characterized by their small scale, few samples, and short-term monitoring. The overall status and temporal accumulation characteristics of heavy metals have not been fully reflected. Therefore, this paper attempted to provide a comprehensive analysis of the current status of heavy metals and provide accurate information for soil pollution management in Jiangsu Province. This paper collected available data in the literature (1993–2021) on heavy metal-polluted agricultural soils in Jiangsu Province. Based on these available data, the weighted mean values of each heavy metal were obtained by meta-analysis. Then, the ecological risks in soils were evaluated and spatiotemporal variations in the accumulation of heavy metals were explored. In addition, suggestions for pollution prevention and control were made by predicting future heavy metal concentrations. The results showed that Cd and Hg were the major polluting elements in Jiangsu Province. The spatial enrichment of heavy metals followed the pattern of southern > northern > central. Heavy metal concentrations in Nanjing, Suzhou, and Xuzhou should be paid special attention. The ecological risk level of heavy metals in agricultural soils in Jiangsu Province was high, predominantly contributed by Hg and Cd. The accumulation of most heavy metals gradually decreased after 2010, while the opposite happened with Cd. Jiangsu Province should continue to take active pollution control measures in order to maintain the decreasing trend of heavy metal concentrations in farmland soils. This study could provide a scientific and theoretical basis for the development of pollution control and soil remediation measures. [ABSTRACT FROM AUTHOR]