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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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Published in: | Sensors (Basel, Switzerland) Switzerland), 2023-08, Vol.23 (15), p.7003 |
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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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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.</description><identifier>ISSN: 1424-8220</identifier><identifier>EISSN: 1424-8220</identifier><identifier>DOI: 10.3390/s23157003</identifier><identifier>PMID: 37571787</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Agricultural production ; Cotton ; cotton fields ; Crop yields ; Crops ; Earth resources technology satellites ; Electric properties ; Electrical conductivity ; electromagnetic induction ; Food supply ; Irrigation ; Remote sensing ; Salinity ; Salt ; satellite remote sensing ; soil profiles ; soil salinization ; Soils, Salts in ; Vegetation ; Wavelet transforms</subject><ispartof>Sensors (Basel, Switzerland), 2023-08, Vol.23 (15), p.7003</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/). 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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.</description><subject>Agricultural production</subject><subject>Cotton</subject><subject>cotton fields</subject><subject>Crop yields</subject><subject>Crops</subject><subject>Earth resources technology satellites</subject><subject>Electric properties</subject><subject>Electrical conductivity</subject><subject>electromagnetic induction</subject><subject>Food supply</subject><subject>Irrigation</subject><subject>Remote sensing</subject><subject>Salinity</subject><subject>Salt</subject><subject>satellite remote sensing</subject><subject>soil profiles</subject><subject>soil salinization</subject><subject>Soils, Salts in</subject><subject>Vegetation</subject><subject>Wavelet transforms</subject><issn>1424-8220</issn><issn>1424-8220</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkl1rFDEUhgdRbK1e-Ack4I2CW_Mx-ZgrKWOrhQWFbcG7cGbmzG6W2aQm2YL99Wa7dWklFzkk7_ucc5JTVW8ZPRWioZ8TF0xqSsWz6pjVvJ4ZzunzR_FR9SqlNaVcCGFeVkdCS8200cfVbbuCCH3G6O6cX5JFcBP5GcPoJiQLmJx3d5Bd8MR50oacS3ThcBoSuU47wxz8kCATQ67cBmeLAsJEvkKGnWMRtnmF0ZNfzq8d-OUn0q6ch9fVixGmhG8e9pPq-uL8qv0-m__4dtmezWe9pE2eCWVgAEWpRmmkMoaioaB7zhpuOj5oLRRXna7HvpGMig71oNQgO91hraETJ9XlnjsEWNub6DYQ_9gAzt4fhLi0ELPrJ7QSdIPNyEemTPHWjWFSUhC8MYUJqrC-7Fk3226DQ48-R5ieQJ_eeLeyy3BrGa0558IUwocHQgy_t5iy3bjU4zSBx7BNlhtJBROcN0X6_j_pOmyjL29VVHXDqOH1rqTTvWoJpQPnx1AS92UNuHF98Lj7RnumFVVMU1UXw8e9oY8hpYjjoXxG7W6W7GGWivbd434Pyn_DI_4CRBHB5g</recordid><startdate>20230807</startdate><enddate>20230807</enddate><creator>Wang, Jiaqiang</creator><creator>Hu, Bifeng</creator><creator>Liu, Weiyang</creator><creator>Luo, Defang</creator><creator>Peng, Jie</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-0202-2046</orcidid><orcidid>https://orcid.org/0000-0001-9353-0307</orcidid></search><sort><creationdate>20230807</creationdate><title>Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China</title><author>Wang, Jiaqiang ; 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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.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>37571787</pmid><doi>10.3390/s23157003</doi><orcidid>https://orcid.org/0000-0003-0202-2046</orcidid><orcidid>https://orcid.org/0000-0001-9353-0307</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Agricultural production Cotton cotton fields Crop yields Crops Earth resources technology satellites Electric properties Electrical conductivity electromagnetic induction Food supply Irrigation Remote sensing Salinity Salt satellite remote sensing soil profiles soil salinization Soils, Salts in Vegetation Wavelet transforms |
title | Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China |
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