Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China
Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiote...
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| Published in | Geoderma Vol. 353; pp. 172 - 187 |
|---|---|
| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.11.2019
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiotemporal dynamics. The salt-affected soil is predominant in the Ebinur Lake region in the Northwestern China. However, detailed local soil salinity information is ambiguous at the best due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping of soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations and MSI data and MSI data-derived spectral indices using the partial least square regression (PLSR) approach will yield fairly accurate regional salinity map. Based on electrical conductivity of 1:5 soil:water extract (EC) of 72 ground-truth measurements (out of 116 sample sites) and various spectral parameters, such as satellite band reflectance, published satellite salinity indices, red-edge indices, newly constructed two-band indices, and three-band indices from MSI data, we built a few inversion models in an attempt to produce the regional salinity maps. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results suggest that both the newly proposed normalized difference index (NDI) [(B12 − B7) / (B12 + B7)] and three-band index (TBI4) [(B12 − B3) / (B3 − B11)] show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements. The RF-PLSR model performed the best across the five models with R2V, RMSEV, and RPD of 0.92, 7.58 dS m−1, and 2.36, respectively. The result from this model was then used to map the soil salinity over the study area. Our analyses suggest that soil salinization changes quite significantly in different seasons. Specifically, soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. We contend that the results from the study will be useful for soil salinization monitoring and land reclamation in arid or semi-arid regions outside the current study area.
•The introduction of red-edge bands can enhance the sensitivities of the indices to soil salinity.•Three-band index [(B12 − B3) / (B3 − B11)] shows a best correlation (r = 0.544) with measured EC.•RF-PLSR model was proved a suitable method for soil salinity estimating and mapping.•The study shows a large variability in soil salinity in dry and wet seasons. |
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| AbstractList | Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiotemporal dynamics. The salt-affected soil is predominant in the Ebinur Lake region in the Northwestern China. However, detailed local soil salinity information is ambiguous at the best due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping of soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations and MSI data and MSI data-derived spectral indices using the partial least square regression (PLSR) approach will yield fairly accurate regional salinity map. Based on electrical conductivity of 1:5 soil:water extract (EC) of 72 ground-truth measurements (out of 116 sample sites) and various spectral parameters, such as satellite band reflectance, published satellite salinity indices, red-edge indices, newly constructed two-band indices, and three-band indices from MSI data, we built a few inversion models in an attempt to produce the regional salinity maps. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results suggest that both the newly proposed normalized difference index (NDI) [(B12 − B7) / (B12 + B7)] and three-band index (TBI4) [(B12 − B3) / (B3 − B11)] show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements. The RF-PLSR model performed the best across the five models with R2V, RMSEV, and RPD of 0.92, 7.58 dS m−1, and 2.36, respectively. The result from this model was then used to map the soil salinity over the study area. Our analyses suggest that soil salinization changes quite significantly in different seasons. Specifically, soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. We contend that the results from the study will be useful for soil salinization monitoring and land reclamation in arid or semi-arid regions outside the current study area.
•The introduction of red-edge bands can enhance the sensitivities of the indices to soil salinity.•Three-band index [(B12 − B3) / (B3 − B11)] shows a best correlation (r = 0.544) with measured EC.•RF-PLSR model was proved a suitable method for soil salinity estimating and mapping.•The study shows a large variability in soil salinity in dry and wet seasons. Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiotemporal dynamics. The salt-affected soil is predominant in the Ebinur Lake region in the Northwestern China. However, detailed local soil salinity information is ambiguous at the best due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping of soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations and MSI data and MSI data-derived spectral indices using the partial least square regression (PLSR) approach will yield fairly accurate regional salinity map. Based on electrical conductivity of 1:5 soil:water extract (EC) of 72 ground-truth measurements (out of 116 sample sites) and various spectral parameters, such as satellite band reflectance, published satellite salinity indices, red-edge indices, newly constructed two-band indices, and three-band indices from MSI data, we built a few inversion models in an attempt to produce the regional salinity maps. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results suggest that both the newly proposed normalized difference index (NDI) [(B12 − B7) / (B12 + B7)] and three-band index (TBI4) [(B12 − B3) / (B3 − B11)] show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements. The RF-PLSR model performed the best across the five models with R2V, RMSEV, and RPD of 0.92, 7.58 dS m−1, and 2.36, respectively. The result from this model was then used to map the soil salinity over the study area. Our analyses suggest that soil salinization changes quite significantly in different seasons. Specifically, soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. We contend that the results from the study will be useful for soil salinization monitoring and land reclamation in arid or semi-arid regions outside the current study area. |
| Author | Wang, Jingzhe Li, Xiaohang Teng, Dexiong Lizaga, Ivan Chen, Xiangyue Yu, Danlin Ding, Jianli Yuan, Lin Guo, Yahui Zhang, Zipeng Ge, Xiangyu Liang, Jing Ma, Xuankai |
| Author_xml | – sequence: 1 givenname: Jingzhe surname: Wang fullname: Wang, Jingzhe organization: Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 800046, China – sequence: 2 givenname: Jianli surname: Ding fullname: Ding, Jianli organization: Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 800046, China – sequence: 3 givenname: Danlin surname: Yu fullname: Yu, Danlin organization: School of Sociology and Population Studies, Renmin University of China, Beijing, 100872, China – sequence: 4 givenname: Xuankai surname: Ma fullname: Ma, Xuankai organization: Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 800046, China – sequence: 5 givenname: Zipeng surname: Zhang fullname: Zhang, Zipeng organization: Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 800046, China – sequence: 6 givenname: Xiangyu surname: Ge fullname: Ge, Xiangyu organization: Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 800046, China – sequence: 7 givenname: Dexiong surname: Teng fullname: Teng, Dexiong organization: Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 800046, China – sequence: 8 givenname: Xiaohang surname: Li fullname: Li, Xiaohang organization: Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 800046, China – sequence: 9 givenname: Jing surname: Liang fullname: Liang, Jing organization: Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 800046, China – sequence: 10 givenname: Ivan surname: Lizaga fullname: Lizaga, Ivan organization: Department of Soil and Water, Estación Experimental de Aula Dei (EEAD-CSIC), Avda. Montañana 1005, 50059 Zaragoza, Spain – sequence: 11 givenname: Xiangyue surname: Chen fullname: Chen, Xiangyue organization: Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 800046, China – sequence: 12 givenname: Lin surname: Yuan fullname: Yuan, Lin organization: School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China – sequence: 13 givenname: Yahui surname: Guo fullname: Guo, Yahui organization: Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China |
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| SubjectTerms | algorithms arid lands China desertification dry season electrical conductivity farming systems lakes land degradation land restoration least squares monitoring Red-edge reflectance Remote sensing salinity satellites semiarid zones Sentinel-2 Soil salinity soil salinization Spectral indices sustainable development water quality wet season |
| Title | Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China |
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