Radar remote sensing-based inversion model of soil salt content at different depths under vegetation
Excessive soil salt content (SSC) seriously affects the crop growth and economic benefits in the agricultural production area. Prior research mainly focused on estimating the salinity in the top bare soil rather than in deep soil that is vital to crop growth. For this end, an experiment was carried...
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PeerJ Inc.
2022-04-01
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| Online Access: | https://peerj.com/articles/13306.pdf |
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| author | Yinwen Chen Yuyan Du Haoyuan Yin Huiyun Wang Haiying Chen Xianwen Li Zhitao Zhang Junying Chen |
| author_facet | Yinwen Chen Yuyan Du Haoyuan Yin Huiyun Wang Haiying Chen Xianwen Li Zhitao Zhang Junying Chen |
| author_sort | Yinwen Chen |
| collection | DOAJ |
| description | Excessive soil salt content (SSC) seriously affects the crop growth and economic benefits in the agricultural production area. Prior research mainly focused on estimating the salinity in the top bare soil rather than in deep soil that is vital to crop growth. For this end, an experiment was carried out in the Hetao Irrigation District, Inner Mongolia, China. In the experiment, the SSC at different depths under vegetation was measured, and the Sentinel-1 radar images were obtained synchronously. The radar backscattering coefficients (VV and VH) were combined to construct multiple indices, whose sensitivity was then analyzed using the best subset selection (BSS). Meanwhile, four most commonly used algorithms, partial least squares regression (PLSR), quantile regression (QR), support vector machine (SVM), and extreme learning machine (ELM), were utilized to construct estimation models of salinity at the depths of 0–10, 10–20, 0–20, 20–40, 0–40, 40–60 and 0–60 cm before and after BSS, respectively. The results showed: (a) radar remote sensing can be used to estimate the salinity in the root zone of vegetation (0-30 cm); (b) after BSS, the correlation coefficients and estimation accuracy of the four monitoring models were all improved significantly; (c) the estimation accuracy of the four regression models was: SVM > QR > ELM > PLSR; and (d) among the seven sampling depths, 10–20 cm was the optimal inversion depth for all the four models, followed by 20–40 and 0–40 cm. Among the four models, SVM was higher in accuracy than the other three at 10–20 cm (RP2 = 0.67, RMSEP = 0.12%). These findings can provide valuable guidance for soil salinity monitoring and agricultural production in the arid or semi-arid areas under vegetation. |
| first_indexed | 2024-03-09T07:53:55Z |
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| language | English |
| last_indexed | 2024-03-09T07:53:55Z |
| publishDate | 2022-04-01 |
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| spelling | doaj.art-c0c1e3464d594b7f8b5a420c23ca27792023-12-03T01:19:55ZengPeerJ Inc.PeerJ2167-83592022-04-0110e1330610.7717/peerj.13306Radar remote sensing-based inversion model of soil salt content at different depths under vegetationYinwen Chen0Yuyan Du1Haoyuan Yin2Huiyun Wang3Haiying Chen4Xianwen Li5Zhitao Zhang6Junying Chen7College of Language and Culture, Northwest A&F University, Yangling, Shaanxi, ChinaGansu Water Conservancy & Hydro Power Survey & Design Research Institute, Lanzhou, Gansu, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, ChinaCollege of Language and Culture, Northwest A&F University, Yangling, Shaanxi, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, ChinaExcessive soil salt content (SSC) seriously affects the crop growth and economic benefits in the agricultural production area. Prior research mainly focused on estimating the salinity in the top bare soil rather than in deep soil that is vital to crop growth. For this end, an experiment was carried out in the Hetao Irrigation District, Inner Mongolia, China. In the experiment, the SSC at different depths under vegetation was measured, and the Sentinel-1 radar images were obtained synchronously. The radar backscattering coefficients (VV and VH) were combined to construct multiple indices, whose sensitivity was then analyzed using the best subset selection (BSS). Meanwhile, four most commonly used algorithms, partial least squares regression (PLSR), quantile regression (QR), support vector machine (SVM), and extreme learning machine (ELM), were utilized to construct estimation models of salinity at the depths of 0–10, 10–20, 0–20, 20–40, 0–40, 40–60 and 0–60 cm before and after BSS, respectively. The results showed: (a) radar remote sensing can be used to estimate the salinity in the root zone of vegetation (0-30 cm); (b) after BSS, the correlation coefficients and estimation accuracy of the four monitoring models were all improved significantly; (c) the estimation accuracy of the four regression models was: SVM > QR > ELM > PLSR; and (d) among the seven sampling depths, 10–20 cm was the optimal inversion depth for all the four models, followed by 20–40 and 0–40 cm. Among the four models, SVM was higher in accuracy than the other three at 10–20 cm (RP2 = 0.67, RMSEP = 0.12%). These findings can provide valuable guidance for soil salinity monitoring and agricultural production in the arid or semi-arid areas under vegetation.https://peerj.com/articles/13306.pdfvegetation coverageSoil salt contentRadar remote sensingSoil at different depthsBest subset selectionSupport vector machine |
| spellingShingle | Yinwen Chen Yuyan Du Haoyuan Yin Huiyun Wang Haiying Chen Xianwen Li Zhitao Zhang Junying Chen Radar remote sensing-based inversion model of soil salt content at different depths under vegetation PeerJ vegetation coverage Soil salt content Radar remote sensing Soil at different depths Best subset selection Support vector machine |
| title | Radar remote sensing-based inversion model of soil salt content at different depths under vegetation |
| title_full | Radar remote sensing-based inversion model of soil salt content at different depths under vegetation |
| title_fullStr | Radar remote sensing-based inversion model of soil salt content at different depths under vegetation |
| title_full_unstemmed | Radar remote sensing-based inversion model of soil salt content at different depths under vegetation |
| title_short | Radar remote sensing-based inversion model of soil salt content at different depths under vegetation |
| title_sort | radar remote sensing based inversion model of soil salt content at different depths under vegetation |
| topic | vegetation coverage Soil salt content Radar remote sensing Soil at different depths Best subset selection Support vector machine |
| url | https://peerj.com/articles/13306.pdf |
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