Monitoring soil salinization in Arid cotton fields using Unmanned Aerial Vehicle hyperspectral imagery
Soil salinization is one of the main causes of low yield in cotton fields, leading to soil compaction and significantly affecting crop growth and nutrient absorption. There is an urgent need for rapid, non-destructive, and efficient methods to extract soil salinity spatial distribution information u...
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| Format: | Article |
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Elsevier
2025-06-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225002316 |
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| author | Jinming Zhang Jianli Ding Jiao Tan Jinjie Wang Zihan Zhang Zeyuan Wang Xiangyu Ge |
| author_facet | Jinming Zhang Jianli Ding Jiao Tan Jinjie Wang Zihan Zhang Zeyuan Wang Xiangyu Ge |
| author_sort | Jinming Zhang |
| collection | DOAJ |
| description | Soil salinization is one of the main causes of low yield in cotton fields, leading to soil compaction and significantly affecting crop growth and nutrient absorption. There is an urgent need for rapid, non-destructive, and efficient methods to extract soil salinity spatial distribution information under cover conditions. These methods are essential for optimizing irrigation techniques, controlling salinization, and promoting sustainable agriculture. To address these issues, this study proposes a method combining unmanned aerial vehicle (UAV) hyperspectral remote sensing data with a particle swarm optimization Gaussian process regression (PSO-GPR) model. Fractional-order differentiation (FOD) technology was used for spectral preprocessing, combined with the random frog algorithm (RF), uninformative variable elimination (UVE), and bootstrap soft shrinkage (BOSS) selection algorithms to optimize one-dimensional spectral bands. By integrating it with the optimal two-dimensional and three-dimensional indices, a PSO-GPR model will be developed to accurately predict soil salinity during the cotton emergence and squaring periods. The results indicate: (1) SG smoothing and FOD technology effectively enhanced the spectral characteristics of cotton field soils and crop canopies. (2) The soil salinity prediction model constructed using 0.7-order FOD combined with the BOSS algorithm yields the best performance, with an R2 of 0.92, RMSE of 0.15 dS m−1, and RPD of 3.54 on the test set. (3) The soil salinity distribution map generated from the optimal model clearly revealed the spatial distribution characteristics of salinity in the 0–10 cm topsoil layer, with significant differences observed under different cover conditions. This study presents a novel method for rapidly and accurately extracting soil salinity information in covered cotton fields. It supports scientific irrigation and salinity management, mitigates crop yield losses caused by salt accumulation, fosters sustainable development of the cotton industry, and provides a scientific foundation for soil salinity monitoring and management in precision agriculture. |
| format | Article |
| id | doaj-art-8bff674b38524f89bc9016cd667fdf97 |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-8bff674b38524f89bc9016cd667fdf972025-08-20T03:19:56ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-06-0114010458410.1016/j.jag.2025.104584Monitoring soil salinization in Arid cotton fields using Unmanned Aerial Vehicle hyperspectral imageryJinming Zhang0Jianli Ding1Jiao Tan2Jinjie Wang3Zihan Zhang4Zeyuan Wang5Xiangyu Ge6College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, ChinaXinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China; Xinjiang Institute of Technology, Aksu, China; Corresponding author at: College of Geography and Remote Sensing Science, Xinjiang University, No. 777 Huarui Street, Urumqi, Xinjiang 830017, China.College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, ChinaSoil salinization is one of the main causes of low yield in cotton fields, leading to soil compaction and significantly affecting crop growth and nutrient absorption. There is an urgent need for rapid, non-destructive, and efficient methods to extract soil salinity spatial distribution information under cover conditions. These methods are essential for optimizing irrigation techniques, controlling salinization, and promoting sustainable agriculture. To address these issues, this study proposes a method combining unmanned aerial vehicle (UAV) hyperspectral remote sensing data with a particle swarm optimization Gaussian process regression (PSO-GPR) model. Fractional-order differentiation (FOD) technology was used for spectral preprocessing, combined with the random frog algorithm (RF), uninformative variable elimination (UVE), and bootstrap soft shrinkage (BOSS) selection algorithms to optimize one-dimensional spectral bands. By integrating it with the optimal two-dimensional and three-dimensional indices, a PSO-GPR model will be developed to accurately predict soil salinity during the cotton emergence and squaring periods. The results indicate: (1) SG smoothing and FOD technology effectively enhanced the spectral characteristics of cotton field soils and crop canopies. (2) The soil salinity prediction model constructed using 0.7-order FOD combined with the BOSS algorithm yields the best performance, with an R2 of 0.92, RMSE of 0.15 dS m−1, and RPD of 3.54 on the test set. (3) The soil salinity distribution map generated from the optimal model clearly revealed the spatial distribution characteristics of salinity in the 0–10 cm topsoil layer, with significant differences observed under different cover conditions. This study presents a novel method for rapidly and accurately extracting soil salinity information in covered cotton fields. It supports scientific irrigation and salinity management, mitigates crop yield losses caused by salt accumulation, fosters sustainable development of the cotton industry, and provides a scientific foundation for soil salinity monitoring and management in precision agriculture.http://www.sciencedirect.com/science/article/pii/S1569843225002316UAV hyperspectralSoil salinizationMulched cotton fieldsFractional-order differentiation |
| spellingShingle | Jinming Zhang Jianli Ding Jiao Tan Jinjie Wang Zihan Zhang Zeyuan Wang Xiangyu Ge Monitoring soil salinization in Arid cotton fields using Unmanned Aerial Vehicle hyperspectral imagery International Journal of Applied Earth Observations and Geoinformation UAV hyperspectral Soil salinization Mulched cotton fields Fractional-order differentiation |
| title | Monitoring soil salinization in Arid cotton fields using Unmanned Aerial Vehicle hyperspectral imagery |
| title_full | Monitoring soil salinization in Arid cotton fields using Unmanned Aerial Vehicle hyperspectral imagery |
| title_fullStr | Monitoring soil salinization in Arid cotton fields using Unmanned Aerial Vehicle hyperspectral imagery |
| title_full_unstemmed | Monitoring soil salinization in Arid cotton fields using Unmanned Aerial Vehicle hyperspectral imagery |
| title_short | Monitoring soil salinization in Arid cotton fields using Unmanned Aerial Vehicle hyperspectral imagery |
| title_sort | monitoring soil salinization in arid cotton fields using unmanned aerial vehicle hyperspectral imagery |
| topic | UAV hyperspectral Soil salinization Mulched cotton fields Fractional-order differentiation |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225002316 |
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