Estimation and Dynamic Analysis of Soil Salinity Based on UAV and Sentinel-2A Multispectral Imagery in the Coastal Area, China
An efficient, convenient, and accurate method for monitoring the distribution characteristics of soil salinity is required to effectively control the damage of saline soil to the land environment and maintain a virtuous cycle of the ecological environment. There are still problems with single-monito...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/11/12/2307 |
| _version_ | 1797456741360730112 |
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| author | Zixuan Zhang Beibei Niu Xinju Li Xingjian Kang Zhenqi Hu |
| author_facet | Zixuan Zhang Beibei Niu Xinju Li Xingjian Kang Zhenqi Hu |
| author_sort | Zixuan Zhang |
| collection | DOAJ |
| description | An efficient, convenient, and accurate method for monitoring the distribution characteristics of soil salinity is required to effectively control the damage of saline soil to the land environment and maintain a virtuous cycle of the ecological environment. There are still problems with single-monitoring data that cannot meet the requirements of different regional scales and accuracy, including inconsistent band reflectance between multi-source sensor data. This article proposes a monitoring method based on the multi-source data fusion of unmanned aerial vehicle (UAV) multispectral remote sensing, Sentinel-2A satellite remote sensing, and ground-measured salinity data. The research area and two experimental fields were located in the Yellow River Delta (YRD). The results show that the back-propagation neural network model (BPNN) in the comprehensive estimation model is the best prediction model for soil salinity (modeling accuracy R<sup>2</sup> reaches 0.769, verification accuracy R<sup>2</sup> reaches 0.774). There is a strong correlation between the satellite and UAV imagery, while the Sentinel-2A imagery after reflectivity correction has a superior estimation effect. In addition, the results of dynamic analysis show that the area of non-saline soil and mild-saline soil decreased, while the area of moderately and heavily saline soils and solonchak increased. Additionally, the average area share of different classes of saline soils distributed over the land use types varied in order, from unused land > grassland > forest land > arable land, where the area share of severe-saline soil distributed on unused land changed the most (89.142%). In this study, the results of estimation are close to the true values, which supports the feasibility of the multi-source data fusion method of UAV remote sensing satellite ground measurements. It not only achieves the estimation of soil salinity and monitoring of change patterns at different scales, but also achieve high accuracy of soil salinity prediction in ascending scale regions. It provides a theoretical scientific basis for the remediation of soil salinization, land use, and environmental protection policies in coastal areas. |
| first_indexed | 2024-03-09T16:12:14Z |
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| id | doaj.art-a1c6978202ce42208e41e12b5b3d0f47 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-09T16:12:14Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-a1c6978202ce42208e41e12b5b3d0f472023-11-24T16:08:58ZengMDPI AGLand2073-445X2022-12-011112230710.3390/land11122307Estimation and Dynamic Analysis of Soil Salinity Based on UAV and Sentinel-2A Multispectral Imagery in the Coastal Area, ChinaZixuan Zhang0Beibei Niu1Xinju Li2Xingjian Kang3Zhenqi Hu4School of Geographical Sciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, ChinaSchool of Resources and Environment, Shandong Agricultural University, Taian 271018, ChinaSchool of Resources and Environment, Shandong Agricultural University, Taian 271018, ChinaSchool of Geographical Sciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaAn efficient, convenient, and accurate method for monitoring the distribution characteristics of soil salinity is required to effectively control the damage of saline soil to the land environment and maintain a virtuous cycle of the ecological environment. There are still problems with single-monitoring data that cannot meet the requirements of different regional scales and accuracy, including inconsistent band reflectance between multi-source sensor data. This article proposes a monitoring method based on the multi-source data fusion of unmanned aerial vehicle (UAV) multispectral remote sensing, Sentinel-2A satellite remote sensing, and ground-measured salinity data. The research area and two experimental fields were located in the Yellow River Delta (YRD). The results show that the back-propagation neural network model (BPNN) in the comprehensive estimation model is the best prediction model for soil salinity (modeling accuracy R<sup>2</sup> reaches 0.769, verification accuracy R<sup>2</sup> reaches 0.774). There is a strong correlation between the satellite and UAV imagery, while the Sentinel-2A imagery after reflectivity correction has a superior estimation effect. In addition, the results of dynamic analysis show that the area of non-saline soil and mild-saline soil decreased, while the area of moderately and heavily saline soils and solonchak increased. Additionally, the average area share of different classes of saline soils distributed over the land use types varied in order, from unused land > grassland > forest land > arable land, where the area share of severe-saline soil distributed on unused land changed the most (89.142%). In this study, the results of estimation are close to the true values, which supports the feasibility of the multi-source data fusion method of UAV remote sensing satellite ground measurements. It not only achieves the estimation of soil salinity and monitoring of change patterns at different scales, but also achieve high accuracy of soil salinity prediction in ascending scale regions. It provides a theoretical scientific basis for the remediation of soil salinization, land use, and environmental protection policies in coastal areas.https://www.mdpi.com/2073-445X/11/12/2307soil salinizationland usemulti-source datapredictionBPNNYellow River Delta |
| spellingShingle | Zixuan Zhang Beibei Niu Xinju Li Xingjian Kang Zhenqi Hu Estimation and Dynamic Analysis of Soil Salinity Based on UAV and Sentinel-2A Multispectral Imagery in the Coastal Area, China Land soil salinization land use multi-source data prediction BPNN Yellow River Delta |
| title | Estimation and Dynamic Analysis of Soil Salinity Based on UAV and Sentinel-2A Multispectral Imagery in the Coastal Area, China |
| title_full | Estimation and Dynamic Analysis of Soil Salinity Based on UAV and Sentinel-2A Multispectral Imagery in the Coastal Area, China |
| title_fullStr | Estimation and Dynamic Analysis of Soil Salinity Based on UAV and Sentinel-2A Multispectral Imagery in the Coastal Area, China |
| title_full_unstemmed | Estimation and Dynamic Analysis of Soil Salinity Based on UAV and Sentinel-2A Multispectral Imagery in the Coastal Area, China |
| title_short | Estimation and Dynamic Analysis of Soil Salinity Based on UAV and Sentinel-2A Multispectral Imagery in the Coastal Area, China |
| title_sort | estimation and dynamic analysis of soil salinity based on uav and sentinel 2a multispectral imagery in the coastal area china |
| topic | soil salinization land use multi-source data prediction BPNN Yellow River Delta |
| url | https://www.mdpi.com/2073-445X/11/12/2307 |
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