RESUMEN
To explore the potential of using the mineral alteration information extracted by remote sensing technology to indirectly estimate the heavy metal content of salinized soil, 23 sampling points were uniformly set up in the town of Gudao in the Yellow River Delta as the research area in 2022. The concentrations of seven heavy metals, Cr, Cu, Pb, Zn, As, Mn and Ni, at the sampling points were determined in laboratory tests. Spectral derivative indices, topographic factors, and mineral alteration information (iron staining, hydroxyl, and carbonate ions) were extracted and screened as modeling factors using Sentinel 2 imagery. An inverse model of heavy metal content was constructed using the random forest algorithm, and the model accuracy was evaluated using the cross-validation method. The results of the study show that: (1) Hydroxyl and carbonate ion alteration can be effectively used for the inversion of soil As and Ni content in this study area. Iron-stained alteration can be used as a modeling factor in the inversion of Cr, Cu, Pb, Zn, and Mn concentrations. (2) The inclusion of alteration information improves the accuracy of heavy metal content inversion. The Cu concentration was verified to be the best predictor, with an RMSE of 3.309, MAPE of 11.072%, and R2 of 0.904, followed by As, Ni, and Zn; the predictive value of Mn, Cr and Pb was average. (3) Based on the results of concentration inversion, the high concentration areas of As, Ni, and Mn are primarily distributed on both sides of the river and around lakes and ponds. The high-concentration areas of Zn were mainly distributed in the farmland areas on both sides of the river. Areas with high concentrations of Cu were mainly distributed in the eastern oil extraction area, both sides of the rivers, and around lakes.
RESUMEN
In the study of the inversion of soil multi-species heavy metal element concentrations using hyperspectral techniques, the selection of feature bands is very important. However, interactions among soil elements can lead to redundancy and instability of spectral features. In this study, heavy metal elements (Pb, Zn, Mn, and As) in entisols around a mining area in Harbin, Heilongjiang Province, China, were studied. To optimise the combination of spectral indices and their weights, radar plots of characteristic-band Pearson coefficients (RCBP) were used to screen three-band spectral index combinations of Pb, Zn, Mn, and As elements, while the Catboost algorithm was used to invert the concentrations of each element. The correlations of Fe with the four heavy metals were analysed from both concentration and characteristic band perspectives, while the effect of spectral inversion was further evaluated via spatial analysis. It was found that the regression model for the inversion of the Zn elemental concentration based on the optimised spectral index combinations had the best fit, with R2 = 0.8786 for the test set, followed by Mn (R2 = 0.8576), As (R2 = 0.7916), and Pb (R2 = 0.6022). As far as the characteristic bands are concerned, the best correlations of Fe with the Pb, Zn, Mn and As elements were 0.837, 0.711, 0.542 and 0.303, respectively. The spatial distribution and correlation of the spectral inversion concentrations of the As and Mn elements with the measured concentrations were consistent, and there were some differences in the results for Zn and Pb. Therefore, hyperspectral techniques and analysis of Fe elements have potential applications in the inversion of entisols heavy metal concentrations and can improve the quality monitoring efficiency of these soils.
RESUMEN
Monitoring heavy metal stress in crops via hyperspectral remote sensing technology is an effective way. A new approach, namely the ratio difference of autocorrelation function first derivative (RDA), is proposed to extract characteristic regions of maize leaves spectra for the initially identification on contaminated category of copper (Cu) and lead (Pb). Simultaneously, empirical mode decomposition (EMD) and power spectral density (PSD) are integrated to construct EMD-PSD to visually discrimination on Cu and Pb stress from frequency domain perspective. In our work, pot experiment contaminated by Cu and Pb were designed and carried out, and corresponding chemical data, chlorophyll and spectra of maize leaves were collected. Based on acquired spectra, RDA is used to obtain indicators and characteristic intervals of spectra, and then EMD-PSD is applied to obtain intrinsic mode functions (IMFs) from spectra and PSD maps. Through experimental analysis, the following conclusions are drawn: 1) the red edge and red shoulder region of spectra can be used as candidate on indicator to find the characteristic regions of spectra, and integrated intersection spectral intervals are considerable to distinguish Cu and Pb; 2) PSD maps extracted by EMD-PSD significantly can discriminate stress of Cu and Pb with three-dimensional visualization. This study takes the combination of spectral domain and frequency domain as the exploration point, the stress of Cu and Pb was distinguished by mutual verification based on spectra (group I and group II and 2014 experiment). In summary, the proposed method can identify the weak differences of spectra and distinguish the stress of Cu and Pb qualitatively, which provides a new perspective for the identification of heavy metal stress categories.