Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote Sensing
Soil salinization is a severe soil degradation issue in arid and semiarid regions. The distribution of soil salinization can prove useful in mitigating soil degradation. Remote sensing monitoring technology is available for obtaining the distribution of soil salinization rapidly and nondestructively...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/18/4400 |
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| author | Xin Cui Wenting Han Yuxin Dong Xuedong Zhai Weitong Ma Liyuan Zhang Shenjin Huang |
| author_facet | Xin Cui Wenting Han Yuxin Dong Xuedong Zhai Weitong Ma Liyuan Zhang Shenjin Huang |
| author_sort | Xin Cui |
| collection | DOAJ |
| description | Soil salinization is a severe soil degradation issue in arid and semiarid regions. The distribution of soil salinization can prove useful in mitigating soil degradation. Remote sensing monitoring technology is available for obtaining the distribution of soil salinization rapidly and nondestructively. In this study, experimental data were collected from seven study areas of the Hetao Irrigation District from July to August in 2021 and 2022. The soil salt content (SSC) was considered at various soil depths, and the crop type and time series were considered as environmental factors. We analyzed the effects of various environmental factors on the sensitivity response of unmanned aerial vehicle (UAV)-derived spectral index variables to the SSC and assessed the accuracy of SSC estimations. The five indices with the highest correlation with the SSC under various environmental factors were the input parameters used in modeling based on three machine learning algorithms. The best model was subsequently used to derive prediction distribution maps of the SSC. The results revealed that the crop type and time series did not affect the relationship strength between the SSC and spectral indices, and that the classification of the crop type and time series can considerably enhance the accuracy of SSC estimation. The mask treatment of the soil pixels can improve the correlation between some spectral indices and the SSC. The accuracies of the ANN and RFR models were higher than SVR accuracy (optimal R<sup>2</sup> = 0.52–0.79), and the generalization ability of ANN was superior to that of RFR. In this study, considering environmental factors, a UAV remote sensing estimation and mapping method was proposed. The results of this study provide a reference for the high-precision prediction of soil salinization during the vegetation cover period. |
| first_indexed | 2024-03-10T22:06:47Z |
| format | Article |
| id | doaj.art-d969ff0496c54334b89a9f1037cac313 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T22:06:47Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d969ff0496c54334b89a9f1037cac3132023-11-19T12:47:10ZengMDPI AGRemote Sensing2072-42922023-09-011518440010.3390/rs15184400Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote SensingXin Cui0Wenting Han1Yuxin Dong2Xuedong Zhai3Weitong Ma4Liyuan Zhang5Shenjin Huang6College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, ChinaInstitute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, ChinaKey Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, ChinaComputer Science and Technology, Harbin Institute of Technology, Harbin 150001, ChinaSoil salinization is a severe soil degradation issue in arid and semiarid regions. The distribution of soil salinization can prove useful in mitigating soil degradation. Remote sensing monitoring technology is available for obtaining the distribution of soil salinization rapidly and nondestructively. In this study, experimental data were collected from seven study areas of the Hetao Irrigation District from July to August in 2021 and 2022. The soil salt content (SSC) was considered at various soil depths, and the crop type and time series were considered as environmental factors. We analyzed the effects of various environmental factors on the sensitivity response of unmanned aerial vehicle (UAV)-derived spectral index variables to the SSC and assessed the accuracy of SSC estimations. The five indices with the highest correlation with the SSC under various environmental factors were the input parameters used in modeling based on three machine learning algorithms. The best model was subsequently used to derive prediction distribution maps of the SSC. The results revealed that the crop type and time series did not affect the relationship strength between the SSC and spectral indices, and that the classification of the crop type and time series can considerably enhance the accuracy of SSC estimation. The mask treatment of the soil pixels can improve the correlation between some spectral indices and the SSC. The accuracies of the ANN and RFR models were higher than SVR accuracy (optimal R<sup>2</sup> = 0.52–0.79), and the generalization ability of ANN was superior to that of RFR. In this study, considering environmental factors, a UAV remote sensing estimation and mapping method was proposed. The results of this study provide a reference for the high-precision prediction of soil salinization during the vegetation cover period.https://www.mdpi.com/2072-4292/15/18/4400soil salt contentremote sensingunmanned aerial vehiclecrop typetime seriesdigital mapping |
| spellingShingle | Xin Cui Wenting Han Yuxin Dong Xuedong Zhai Weitong Ma Liyuan Zhang Shenjin Huang Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote Sensing Remote Sensing soil salt content remote sensing unmanned aerial vehicle crop type time series digital mapping |
| title | Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote Sensing |
| title_full | Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote Sensing |
| title_fullStr | Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote Sensing |
| title_full_unstemmed | Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote Sensing |
| title_short | Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote Sensing |
| title_sort | estimating and mapping soil salinity in multiple vegetation cover periods by using unmanned aerial vehicle remote sensing |
| topic | soil salt content remote sensing unmanned aerial vehicle crop type time series digital mapping |
| url | https://www.mdpi.com/2072-4292/15/18/4400 |
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