Predicting Dynamics of Soil Salinity and Sodicity Using Remote Sensing Techniques: A Landscape-Scale Assessment in the Northeastern Egypt
Traditional mapping of salt affected soils (SAS) is very costly and cannot precisely depict the space–time dynamics of soil salts over landscapes. Therefore, we tested the capacity of Landsat 8 Operational Land Imager (OLI) data to retrieve soil salinity and sodicity during the wet and dry seasons i...
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description | Traditional mapping of salt affected soils (SAS) is very costly and cannot precisely depict the space–time dynamics of soil salts over landscapes. Therefore, we tested the capacity of Landsat 8 Operational Land Imager (OLI) data to retrieve soil salinity and sodicity during the wet and dry seasons in an arid landscape. Seventy geo-referenced soil samples (0–30 cm) were collected during March (wet period) and September to be analyzed for pH, electrical conductivity (EC), and exchangeable sodium percentage (ESP). Using 70% of soil and band reflectance data, stepwise linear regression models were constructed to estimate soil pH, EC, and ESP. The models were validated using the remaining 30% in terms of the determination coefficient (R2) and residual prediction deviation (RPD). Results revealed the weak variability of soil pH, while EC and ESP had large variabilities. The three indicators (pH, EC, and ESP) increased from the wet to dry period. During the two seasons, the OLI bands had weak associations with soil pH, while the near-infrared (NIR) band could effectively discriminate soil salinity and sodicity levels. The EC and ESP predictive models in the wet period were developed with the NIR band, achieving adequate outcomes (an R2 of 0.65 and 0.61 and an RPD of 1.44 and 1.43, respectively). In the dry period, the best-fitted models were constructed with deep blue and NIR bands, yielding an R2 of 0.59 and 0.60 and an RPD of 1.49 and 1.50, respectively. The SAS covered 50% of the study area during the wet period, of which 14 and 36% were saline and saline-sodic soils, respectively. The extent increased up to 59% during the dry period, including saline soils (12%) and saline-sodic soils (47%). Our findings would facilitate precise, rapid, and cost-effective monitoring of soil salinity and sodicity over large areas. |
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Therefore, we tested the capacity of Landsat 8 Operational Land Imager (OLI) data to retrieve soil salinity and sodicity during the wet and dry seasons in an arid landscape. Seventy geo-referenced soil samples (0–30 cm) were collected during March (wet period) and September to be analyzed for pH, electrical conductivity (EC), and exchangeable sodium percentage (ESP). Using 70% of soil and band reflectance data, stepwise linear regression models were constructed to estimate soil pH, EC, and ESP. The models were validated using the remaining 30% in terms of the determination coefficient (R2) and residual prediction deviation (RPD). Results revealed the weak variability of soil pH, while EC and ESP had large variabilities. The three indicators (pH, EC, and ESP) increased from the wet to dry period. During the two seasons, the OLI bands had weak associations with soil pH, while the near-infrared (NIR) band could effectively discriminate soil salinity and sodicity levels. The EC and ESP predictive models in the wet period were developed with the NIR band, achieving adequate outcomes (an R2 of 0.65 and 0.61 and an RPD of 1.44 and 1.43, respectively). In the dry period, the best-fitted models were constructed with deep blue and NIR bands, yielding an R2 of 0.59 and 0.60 and an RPD of 1.49 and 1.50, respectively. The SAS covered 50% of the study area during the wet period, of which 14 and 36% were saline and saline-sodic soils, respectively. The extent increased up to 59% during the dry period, including saline soils (12%) and saline-sodic soils (47%). Our findings would facilitate precise, rapid, and cost-effective monitoring of soil salinity and sodicity over large areas.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su15129440</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Arid environments ; Arid regions ; Dry season ; Electrical conductivity ; Electrical resistivity ; Environmental monitoring ; Geology ; Global positioning systems ; GPS ; Laboratories ; Landsat ; Machine learning ; Methods ; Near infrared radiation ; Prediction models ; Product information ; Rainy season ; Regression analysis ; Remote sensing ; Saline soils ; Salinity ; Salinity effects ; Salt ; Sea-water ; Sodic soils ; Soil dynamics ; Soil pH ; Soil salinity ; Soils, Salts in ; Sustainability</subject><ispartof>Sustainability, 2023-06, Vol.15 (12), p.9440</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-c9ed001b0e3421719b81ff7dc3edde46d78cca43e15d0fbb78e91326aefb08953</citedby><cites>FETCH-LOGICAL-c368t-c9ed001b0e3421719b81ff7dc3edde46d78cca43e15d0fbb78e91326aefb08953</cites><orcidid>0000-0002-5212-609X ; 0000-0002-1627-6250 ; 0000-0003-0328-7679 ; 0000-0002-8621-6595 ; 0000-0001-5703-4621</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Abuzaid, Ahmed S</creatorcontrib><creatorcontrib>El-Komy, Mostafa S</creatorcontrib><creatorcontrib>Shokr, Mohamed S</creatorcontrib><creatorcontrib>El Baroudy, Ahmed A</creatorcontrib><creatorcontrib>Mohamed, Elsayed Said</creatorcontrib><creatorcontrib>Rebouh, Nazih Y</creatorcontrib><creatorcontrib>Abdel-Hai, Mohamed S</creatorcontrib><title>Predicting Dynamics of Soil Salinity and Sodicity Using Remote Sensing Techniques: A Landscape-Scale Assessment in the Northeastern Egypt</title><title>Sustainability</title><description>Traditional mapping of salt affected soils (SAS) is very costly and cannot precisely depict the space–time dynamics of soil salts over landscapes. Therefore, we tested the capacity of Landsat 8 Operational Land Imager (OLI) data to retrieve soil salinity and sodicity during the wet and dry seasons in an arid landscape. Seventy geo-referenced soil samples (0–30 cm) were collected during March (wet period) and September to be analyzed for pH, electrical conductivity (EC), and exchangeable sodium percentage (ESP). Using 70% of soil and band reflectance data, stepwise linear regression models were constructed to estimate soil pH, EC, and ESP. The models were validated using the remaining 30% in terms of the determination coefficient (R2) and residual prediction deviation (RPD). Results revealed the weak variability of soil pH, while EC and ESP had large variabilities. The three indicators (pH, EC, and ESP) increased from the wet to dry period. During the two seasons, the OLI bands had weak associations with soil pH, while the near-infrared (NIR) band could effectively discriminate soil salinity and sodicity levels. The EC and ESP predictive models in the wet period were developed with the NIR band, achieving adequate outcomes (an R2 of 0.65 and 0.61 and an RPD of 1.44 and 1.43, respectively). In the dry period, the best-fitted models were constructed with deep blue and NIR bands, yielding an R2 of 0.59 and 0.60 and an RPD of 1.49 and 1.50, respectively. The SAS covered 50% of the study area during the wet period, of which 14 and 36% were saline and saline-sodic soils, respectively. The extent increased up to 59% during the dry period, including saline soils (12%) and saline-sodic soils (47%). Our findings would facilitate precise, rapid, and cost-effective monitoring of soil salinity and sodicity over large areas.</description><subject>Accuracy</subject><subject>Arid environments</subject><subject>Arid regions</subject><subject>Dry season</subject><subject>Electrical conductivity</subject><subject>Electrical resistivity</subject><subject>Environmental monitoring</subject><subject>Geology</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Laboratories</subject><subject>Landsat</subject><subject>Machine learning</subject><subject>Methods</subject><subject>Near infrared radiation</subject><subject>Prediction models</subject><subject>Product information</subject><subject>Rainy season</subject><subject>Regression analysis</subject><subject>Remote sensing</subject><subject>Saline soils</subject><subject>Salinity</subject><subject>Salinity effects</subject><subject>Salt</subject><subject>Sea-water</subject><subject>Sodic soils</subject><subject>Soil dynamics</subject><subject>Soil pH</subject><subject>Soil salinity</subject><subject>Soils, Salts in</subject><subject>Sustainability</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpVkc1KAzEQxxdRUGovPkHAk8JqstntZr0VrR9QVFw9L9lkUiNtUjMp2EfwrU2toGYO88HvPzNksuyI0TPOG3qOK1axoilLupMdFLRmOaMV3f0T72dDxDeaHuesYaOD7PMxgLYqWjcjV2snF1Yh8Ya03s5JK-fW2bgm0ulUSdwmecEN_AQLH4G04L7TZ1Cvzr6vAC_ImEyTAJVcQt4qOQcyRgTEBbhIrCPxFci9D8lJjBAcmczWy3iY7Rk5Rxj--EH2cj15vrzNpw83d5fjaa74SMRcNaApZT0FXhasZk0vmDG1Vhy0hnKka6GULDmwSlPT97WAhvFiJMH0VDQVH2TH277L4Df7xu7Nr4JLI7tCFI0QrBQiUWdbapb276wzPgapkmlIX-QdGJvq47oSvCrrskiCk3-CxET4iDO5Quzu2qf_7OmWVcEjBjDdMtiFDOuO0W5zyu73lPwLZteRWg</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Abuzaid, Ahmed S</creator><creator>El-Komy, Mostafa S</creator><creator>Shokr, Mohamed S</creator><creator>El Baroudy, Ahmed A</creator><creator>Mohamed, Elsayed Said</creator><creator>Rebouh, Nazih Y</creator><creator>Abdel-Hai, Mohamed S</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0002-5212-609X</orcidid><orcidid>https://orcid.org/0000-0002-1627-6250</orcidid><orcidid>https://orcid.org/0000-0003-0328-7679</orcidid><orcidid>https://orcid.org/0000-0002-8621-6595</orcidid><orcidid>https://orcid.org/0000-0001-5703-4621</orcidid></search><sort><creationdate>20230601</creationdate><title>Predicting Dynamics of Soil Salinity and Sodicity Using Remote Sensing Techniques: A Landscape-Scale Assessment in the Northeastern Egypt</title><author>Abuzaid, Ahmed S ; El-Komy, Mostafa S ; Shokr, Mohamed S ; El Baroudy, Ahmed A ; Mohamed, Elsayed Said ; Rebouh, Nazih Y ; Abdel-Hai, Mohamed S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-c9ed001b0e3421719b81ff7dc3edde46d78cca43e15d0fbb78e91326aefb08953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Arid environments</topic><topic>Arid regions</topic><topic>Dry season</topic><topic>Electrical conductivity</topic><topic>Electrical resistivity</topic><topic>Environmental monitoring</topic><topic>Geology</topic><topic>Global positioning systems</topic><topic>GPS</topic><topic>Laboratories</topic><topic>Landsat</topic><topic>Machine learning</topic><topic>Methods</topic><topic>Near infrared radiation</topic><topic>Prediction models</topic><topic>Product information</topic><topic>Rainy season</topic><topic>Regression analysis</topic><topic>Remote sensing</topic><topic>Saline soils</topic><topic>Salinity</topic><topic>Salinity effects</topic><topic>Salt</topic><topic>Sea-water</topic><topic>Sodic soils</topic><topic>Soil dynamics</topic><topic>Soil pH</topic><topic>Soil salinity</topic><topic>Soils, Salts in</topic><topic>Sustainability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Abuzaid, Ahmed S</creatorcontrib><creatorcontrib>El-Komy, Mostafa S</creatorcontrib><creatorcontrib>Shokr, Mohamed S</creatorcontrib><creatorcontrib>El Baroudy, Ahmed A</creatorcontrib><creatorcontrib>Mohamed, Elsayed Said</creatorcontrib><creatorcontrib>Rebouh, Nazih Y</creatorcontrib><creatorcontrib>Abdel-Hai, Mohamed S</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abuzaid, Ahmed S</au><au>El-Komy, Mostafa S</au><au>Shokr, Mohamed S</au><au>El Baroudy, Ahmed A</au><au>Mohamed, Elsayed Said</au><au>Rebouh, Nazih Y</au><au>Abdel-Hai, Mohamed S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting Dynamics of Soil Salinity and Sodicity Using Remote Sensing Techniques: A Landscape-Scale Assessment in the Northeastern Egypt</atitle><jtitle>Sustainability</jtitle><date>2023-06-01</date><risdate>2023</risdate><volume>15</volume><issue>12</issue><spage>9440</spage><pages>9440-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Traditional mapping of salt affected soils (SAS) is very costly and cannot precisely depict the space–time dynamics of soil salts over landscapes. Therefore, we tested the capacity of Landsat 8 Operational Land Imager (OLI) data to retrieve soil salinity and sodicity during the wet and dry seasons in an arid landscape. Seventy geo-referenced soil samples (0–30 cm) were collected during March (wet period) and September to be analyzed for pH, electrical conductivity (EC), and exchangeable sodium percentage (ESP). Using 70% of soil and band reflectance data, stepwise linear regression models were constructed to estimate soil pH, EC, and ESP. The models were validated using the remaining 30% in terms of the determination coefficient (R2) and residual prediction deviation (RPD). Results revealed the weak variability of soil pH, while EC and ESP had large variabilities. The three indicators (pH, EC, and ESP) increased from the wet to dry period. During the two seasons, the OLI bands had weak associations with soil pH, while the near-infrared (NIR) band could effectively discriminate soil salinity and sodicity levels. The EC and ESP predictive models in the wet period were developed with the NIR band, achieving adequate outcomes (an R2 of 0.65 and 0.61 and an RPD of 1.44 and 1.43, respectively). In the dry period, the best-fitted models were constructed with deep blue and NIR bands, yielding an R2 of 0.59 and 0.60 and an RPD of 1.49 and 1.50, respectively. The SAS covered 50% of the study area during the wet period, of which 14 and 36% were saline and saline-sodic soils, respectively. The extent increased up to 59% during the dry period, including saline soils (12%) and saline-sodic soils (47%). Our findings would facilitate precise, rapid, and cost-effective monitoring of soil salinity and sodicity over large areas.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su15129440</doi><orcidid>https://orcid.org/0000-0002-5212-609X</orcidid><orcidid>https://orcid.org/0000-0002-1627-6250</orcidid><orcidid>https://orcid.org/0000-0003-0328-7679</orcidid><orcidid>https://orcid.org/0000-0002-8621-6595</orcidid><orcidid>https://orcid.org/0000-0001-5703-4621</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Arid environments Arid regions Dry season Electrical conductivity Electrical resistivity Environmental monitoring Geology Global positioning systems GPS Laboratories Landsat Machine learning Methods Near infrared radiation Prediction models Product information Rainy season Regression analysis Remote sensing Saline soils Salinity Salinity effects Salt Sea-water Sodic soils Soil dynamics Soil pH Soil salinity Soils, Salts in Sustainability |
title | Predicting Dynamics of Soil Salinity and Sodicity Using Remote Sensing Techniques: A Landscape-Scale Assessment in the Northeastern Egypt |
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