Abstract
Soil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has shown advantages in the field of monitoring heavy metals. Based on 971 heavy metal samples and Sentinel-2 multi-spectral images in Tai Lake, China, we analyzed the correlation between six heavy metals (Cd, Hg, As, Pb, Cu, Zn) and spectral factors, and selected As and Hg as the input factors of inversion model. The correlation coefficient of the best model of As was 0.53 (p < 0.01), and of Hg was 0.318 (p < 0.01). We used the methods of partial least squares regression (PLSR) and back propagation neural network (BPNN) to establish inversion models with different combinations of spectral factors by using 649 measured samples. In addition, 322 measured samples were used for accuracy evaluation. Compared with the PLSR model, the BP neural network builds the model with higher accuracy, and B1-B4 combined with LnB1-LnB4 builds the model with the highest accuracy. The accuracy of the best model was verified, with an average error of 19% for As and 45% for Hg. Analyzing the spatial distribution of heavy metals by using the interpolation method of Kriging and IDW. The overall distribution trend of the two interpolations is similar. The concentration of As elements tends to increase from north to south, and the relatively high value of Hg elements is distributed in the east and west of the study area. The factories in the study area are distributed along rivers and lakes, which is consistent with the spatial distribution of heavy metal enrichment areas. The relatively high-value areas of heavy metal elements are related to the distribution of metal products factories, refractory porcelain factories, tile factories, factories and mining enterprises, etc., indicating that factory pollution is the main reason for the enrichment of heavy metals.
Highlights
Heavy metal pollution is exacerbated by metal based industrial activities, and heavy metals can enter the human body through contaminated food, inhaled through the atmosphere, drunk via contaminated water, through skin contact from agriculture, etc
Liu et al (2019) established the PSO-back propagation neural network (BPNN) model to invert the content of Cd, Hg, and As elements, which improved the prediction accuracy of the heavy metal inversion model greatly, and indicated that machine learning methods had great potential to estimate the content of soil heavy metals accurately [16]
Statistical analysis of six heavy metals in 971 soil samples in the study area showed in Jiangsu Province, indicating that the content of heavy metal elements in the soil had been affected by human activities
Summary
Used spectral data to establish machine learning models to invert heavy metal concentrations [15]. Liu et al (2019) established the PSO-BPNN model to invert the content of Cd, Hg, and As elements, which improved the prediction accuracy of the heavy metal inversion model greatly, and indicated that machine learning methods had great potential to estimate the content of soil heavy metals accurately [16]. The spectral data of Sentinel-2 is mathematically transformed to reduce the spectral characteristics of non-heavy metals and highlight the spectral characteristics of soil heavy metals. Accuracy heavy metal prediction, partial least square regression gression andofback propagation neural and network (BPNN). We selected the target heavy metals with high correlation and established inversion models by combining spectral data from Sentinel-2 images.
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