The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China
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 show...
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MDPI AG
2021-11-01
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| Online Access: | https://www.mdpi.com/2073-445X/10/11/1227 |
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| author | Huihui Zhao Peijia Liu Baojin Qiao Kening Wu |
| author_facet | Huihui Zhao Peijia Liu Baojin Qiao Kening Wu |
| author_sort | Huihui Zhao |
| collection | DOAJ |
| description | 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 (<i>p</i> < 0.01), and of Hg was 0.318 (<i>p</i> < 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. |
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| language | English |
| last_indexed | 2024-03-10T05:21:02Z |
| publishDate | 2021-11-01 |
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| series | Land |
| spelling | doaj.art-bfe0e59e68d148629b46b853cd73f1df2023-11-23T00:02:05ZengMDPI AGLand2073-445X2021-11-011011122710.3390/land10111227The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of ChinaHuihui Zhao0Peijia Liu1Baojin Qiao2Kening Wu3School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Politics and Public Administration, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaSoil 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 (<i>p</i> < 0.01), and of Hg was 0.318 (<i>p</i> < 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.https://www.mdpi.com/2073-445X/10/11/1227anthrosolsBPNNmulti-spectralPLSRsoil heavy metalspatial distribution |
| spellingShingle | Huihui Zhao Peijia Liu Baojin Qiao Kening Wu The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China Land anthrosols BPNN multi-spectral PLSR soil heavy metal spatial distribution |
| title | The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China |
| title_full | The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China |
| title_fullStr | The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China |
| title_full_unstemmed | The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China |
| title_short | The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China |
| title_sort | spatial distribution and prediction of soil heavy metals based on measured samples and multi spectral images in tai lake of china |
| topic | anthrosols BPNN multi-spectral PLSR soil heavy metal spatial distribution |
| url | https://www.mdpi.com/2073-445X/10/11/1227 |
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