## 1 Introduction  
Soil provides basic habitats and natural resources for the survival of humans, plants, and animals [1; 2]. However, as a result of metal mining and the associated dumping of tailings, soil contamination by heavy metals has become a serious problem [3; 4]. Heavy metals become enriched in soil, leading to deleterious effects on food safety via the food chain [5], as well as indirect concentration and other harmful impacts on human health [6]. At present, many reclamation methods have been applied in areas affected by mining, of which phytoremediation is a common method for the remediation of heavy-metal-contaminated soil. Some research has shown that local plants could achieve ecological rehabilitation of soils contaminated by heavy metals [7; 8]. Therefore, the rapid determination of heavy metals in soil is crucial for the monitoring and assessment of remediation. In terms of the detection of heavy metal concentration, the traditional method relies on laboratory analysis and field sampling to determine the concentration and distribution of heavy metals. This is a time-consuming and expensive process [9; 10; 11; 12]. In order to quickly determine heavy metal contamination in soil, the spectroscopic method has been widely used in soil monitoring [13; 14]. Compared with traditional methods, the spectroscopic method has more advantages, such as less damage to the ecological environment, lower cost, and accurate reflection of the biological, chemical, and physical characteristics of objects [15]. This technology is convenient for obtaining the content of heavy metals and its dynamic evolution in soil [15; 16; 17; 18]. Many researchers have found that soil organic matter (SOM), iron oxide, water, and other active ingredients of soil have a good correlation with visible-near-infrared spectroscopy [19] and have used reflectance spectroscopy to effectively estimate soil properties. Based on the relationship between heavy metals and soil active components, soil heavy metal concentrations can be indirectly predicted by spectroscopy [20; 21]. Based on the significant relationship between soil composition and spectral characteristics, [PERSON] successfully predicted the Cd and Zn contents in the soil of the Rhine Basin [22]. Recently, the study of estimating heavy metal concentration by visible and near-infrared spectroscopy (VNIRS) has mainly focused on soils with medium and high heavy metal concentrations [23; 24; 25; 26]. For the detection of changes in pollutant content, more studies focused on measuring vegetation reflectance as a proxy to soil contamination [27; 28; 29]. However, in the process of soil reclamation, vegetation is sometimes absent. Therefore, in this study, we still directly used the soil reflectance to evaluate and monitor soil remediation effects. Because the content of heavy metals in soil under remediation varies widely and considerably, the relationship between heavy metal content and spectral reflectance is ambiguous, and it requires a more suitable model for estimation. In terms of the methods developed to detect heavy metals based on hyperspectral spectroscopy, various models have been established in combination with statistical theory to predict the concentration of heavy metals in soil. Partial least squares regression (PLSR) [30], stepwise multiple linear regression (SMLR) [31], support vector machine (SVM) [32], the random forest method (RF) [33], and the back propagation neural network (BPNN) [34; 35; 36; 37] are some commonly used technical methods. A promising approach is to use the mind evolutionary algorithm back propagation neural network (MEA-BPNN) to estimate heavy metal concentrations in soil. In 1998, [PERSON] and others proposed the multilateral environmental agreement [38] to explain the principles of the MEA. The MEA-BPNN model uses an evolutionary algorithm to optimize back propagation (BP) neural networks, combining BPNN's local search capabilities with MEA's global search capabilities.