Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin
Accurate assessment of the soil salinization is an important step for mitigation of agricultural land degradation. Remote sensing (RS) is widely used for salinity assessment, but knowledge on prediction precision is lacking. A RS-based salinity assessment in Khorezm allows for modest reliable predic...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Kazakh-German University
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/108474 |
| _version_ | 1855519615263703040 |
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| author | Ibrakhimov, M. Awan, U. K. Sultanov, M. Akramkhanov, A. Djumaboev, Kakhramon Conrad, C. Lamers, J. |
| author_browse | Akramkhanov, A. Awan, U. K. Conrad, C. Djumaboev, Kakhramon Ibrakhimov, M. Lamers, J. Sultanov, M. |
| author_facet | Ibrakhimov, M. Awan, U. K. Sultanov, M. Akramkhanov, A. Djumaboev, Kakhramon Conrad, C. Lamers, J. |
| author_sort | Ibrakhimov, M. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Accurate assessment of the soil salinization is an important step for mitigation of agricultural land degradation. Remote sensing (RS) is widely used for salinity assessment, but knowledge on prediction precision is lacking. A RS-based salinity assessment in Khorezm allows for modest reliable prediction with weak (R2=0.15–0.29) relationship of the salinity maps produced with RS and interpolation of electromagnetic EM38 during growth periods and more reliable (R2=0.35–0.56) beyond irrigation periods. Modeling with HYDRUS-1D at slightly, moderately and highly saline sites at various depths showed that irrigation forces salts to move to deeper layers: salts reappear in the upper profile during dry periods. Beyond irrigation events, salts gradually accumulated in the upper soil layers without fluctuations. Coupling RS techniques with numerical modeling provided better insight into salinity dynamics than any of these approaches alone. This should be of interest to farmers and policy makers since the combination of methods will allow for better planning and management. |
| format | Journal Article |
| id | CGSpace108474 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Kazakh-German University |
| publisherStr | Kazakh-German University |
| record_format | dspace |
| spelling | CGSpace1084742025-03-11T09:50:20Z Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin Ibrakhimov, M. Awan, U. K. Sultanov, M. Akramkhanov, A. Djumaboev, Kakhramon Conrad, C. Lamers, J. soil salinization irrigated land remote sensing modelling forecasting techniques soil profiles groundwater irrigated farming cotton case studies Accurate assessment of the soil salinization is an important step for mitigation of agricultural land degradation. Remote sensing (RS) is widely used for salinity assessment, but knowledge on prediction precision is lacking. A RS-based salinity assessment in Khorezm allows for modest reliable prediction with weak (R2=0.15–0.29) relationship of the salinity maps produced with RS and interpolation of electromagnetic EM38 during growth periods and more reliable (R2=0.35–0.56) beyond irrigation periods. Modeling with HYDRUS-1D at slightly, moderately and highly saline sites at various depths showed that irrigation forces salts to move to deeper layers: salts reappear in the upper profile during dry periods. Beyond irrigation events, salts gradually accumulated in the upper soil layers without fluctuations. Coupling RS techniques with numerical modeling provided better insight into salinity dynamics than any of these approaches alone. This should be of interest to farmers and policy makers since the combination of methods will allow for better planning and management. 2020-05-27 2020-06-15T05:36:58Z 2020-06-15T05:36:58Z Journal Article https://hdl.handle.net/10568/108474 en Open Access Kazakh-German University Ibrakhimov, M.; Awan, U. K.; Sultanov, M.; Akramkhanov, A.; Djumaboev, Kakhramon; Conrad, C.; Lamers, J. 2019. Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin. Central Asian Journal of Water Research, 5(2):100-116. [doi: https://doi.org/10.29258/CAJWR/2019-R1.v5-2/64-81eng] |
| spellingShingle | soil salinization irrigated land remote sensing modelling forecasting techniques soil profiles groundwater irrigated farming cotton case studies Ibrakhimov, M. Awan, U. K. Sultanov, M. Akramkhanov, A. Djumaboev, Kakhramon Conrad, C. Lamers, J. Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin |
| title | Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin |
| title_full | Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin |
| title_fullStr | Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin |
| title_full_unstemmed | Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin |
| title_short | Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin |
| title_sort | combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the aral sea basin |
| topic | soil salinization irrigated land remote sensing modelling forecasting techniques soil profiles groundwater irrigated farming cotton case studies |
| url | https://hdl.handle.net/10568/108474 |
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