Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China
Soil salinization is a major obstacle to land productivity, crop yield and crop quality in arid areas and directly affects food security. Soil profile salt data are key for accurately determining irrigation volumes. To explore the potential for using Landsat 8 time-series data to monitor soil salini...
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/15/7003 |
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| author | Jiaqiang Wang Bifeng Hu Weiyang Liu Defang Luo Jie Peng |
| author_facet | Jiaqiang Wang Bifeng Hu Weiyang Liu Defang Luo Jie Peng |
| author_sort | Jiaqiang Wang |
| collection | DOAJ |
| description | Soil salinization is a major obstacle to land productivity, crop yield and crop quality in arid areas and directly affects food security. Soil profile salt data are key for accurately determining irrigation volumes. To explore the potential for using Landsat 8 time-series data to monitor soil salinization, 172 Landsat 8 images from 2013 to 2019 were obtained from the Alar Reclamation Area of Xinjiang, northwest China. The multiyear extreme dataset was synthesized from the annual maximum or minimum values of 16 vegetation indices, which were combined with the soil conductivity of 540 samples from soil profiles at 0~0.375 m, 0~0.75 m and 0~1.00 m depths in 30 cotton fields with varying degrees of salinization as investigated by EM38-MK2. Three remote sensing monitoring models for soil conductivity at different depths were constructed using the Cubist method, and digital mapping was carried out. The results showed that the Cubist model of soil profile electrical conductivity from 0 to 0.375 m, 0 to 0.75 m and 0 to 1.00 m showed high prediction accuracy, and the determination coefficients of the prediction set were 0.80, 0.74 and 0.72, respectively. Therefore, it is feasible to use a multiyear extreme value for the vegetation index combined with a Cubist modeling method to monitor soil profile salinization at a regional scale. |
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| id | doaj.art-5a79e9f2f16847a4981550a3298d66a6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:16:26Z |
| publishDate | 2023-08-01 |
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| spelling | doaj.art-5a79e9f2f16847a4981550a3298d66a62023-11-18T23:37:24ZengMDPI AGSensors1424-82202023-08-012315700310.3390/s23157003Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, ChinaJiaqiang Wang0Bifeng Hu1Weiyang Liu2Defang Luo3Jie Peng4College of Agriculture, Tarim University, Alar 843300, ChinaDepartment of Land Resource Management, School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, ChinaCollege of Agriculture, Tarim University, Alar 843300, ChinaCollege of Agriculture, Tarim University, Alar 843300, ChinaCollege of Agriculture, Tarim University, Alar 843300, ChinaSoil salinization is a major obstacle to land productivity, crop yield and crop quality in arid areas and directly affects food security. Soil profile salt data are key for accurately determining irrigation volumes. To explore the potential for using Landsat 8 time-series data to monitor soil salinization, 172 Landsat 8 images from 2013 to 2019 were obtained from the Alar Reclamation Area of Xinjiang, northwest China. The multiyear extreme dataset was synthesized from the annual maximum or minimum values of 16 vegetation indices, which were combined with the soil conductivity of 540 samples from soil profiles at 0~0.375 m, 0~0.75 m and 0~1.00 m depths in 30 cotton fields with varying degrees of salinization as investigated by EM38-MK2. Three remote sensing monitoring models for soil conductivity at different depths were constructed using the Cubist method, and digital mapping was carried out. The results showed that the Cubist model of soil profile electrical conductivity from 0 to 0.375 m, 0 to 0.75 m and 0 to 1.00 m showed high prediction accuracy, and the determination coefficients of the prediction set were 0.80, 0.74 and 0.72, respectively. Therefore, it is feasible to use a multiyear extreme value for the vegetation index combined with a Cubist modeling method to monitor soil profile salinization at a regional scale.https://www.mdpi.com/1424-8220/23/15/7003soil salinizationelectromagnetic inductionsatellite remote sensingcotton fieldssoil profiles |
| spellingShingle | Jiaqiang Wang Bifeng Hu Weiyang Liu Defang Luo Jie Peng Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China Sensors soil salinization electromagnetic induction satellite remote sensing cotton fields soil profiles |
| title | Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China |
| title_full | Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China |
| title_fullStr | Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China |
| title_full_unstemmed | Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China |
| title_short | Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China |
| title_sort | characterizing soil profile salinization in cotton fields using landsat 8 time series data in southern xinjiang china |
| topic | soil salinization electromagnetic induction satellite remote sensing cotton fields soil profiles |
| url | https://www.mdpi.com/1424-8220/23/15/7003 |
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