Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments
This study addresses the critical issues of water scarcity and soil salinization impacting cotton production in South Xinjiang, China. It introduces an innovative framework for assessing regional cotton crop suitability by integrating ground-measured soil water and salt data with UAV multispectral a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377424005511 |
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| author | Jianqiang He Yonglin Jia Yi Li Asim Biswas Hao Feng Qiang Yu Shufang Wu Guang Yang Kadambot.H.M. Siddique |
| author_facet | Jianqiang He Yonglin Jia Yi Li Asim Biswas Hao Feng Qiang Yu Shufang Wu Guang Yang Kadambot.H.M. Siddique |
| author_sort | Jianqiang He |
| collection | DOAJ |
| description | This study addresses the critical issues of water scarcity and soil salinization impacting cotton production in South Xinjiang, China. It introduces an innovative framework for assessing regional cotton crop suitability by integrating ground-measured soil water and salt data with UAV multispectral and Sentinel-2A satellite imagery from the 2022 cotton growing season. An optimized set of vegetation indices was identified through multicollinearity analysis and full subset selection. Six advanced machine learning methods, including Random Forest (RF), were used alongside the ratio mean method to effectively upscale soil water and salt content models from the field to the regional level. A newly developed cotton suitability index was created to categorize soil water and salt conditions, resulting in detailed suitability maps for 2022 and 2023. Key findings include: (1) Model Performance: The RF model outperformed others in predicting soil water and salt content, with R² values ranging from 0.763 to 0.846 for soil moisture and 0.703–0.843 for soil salinity. It showed greater accuracy at 0–10 cm depth than 10–20 cm depth. (2) Imagery Correlation: A significant correlation was observed between UAV and Sentinel-2A imagery (R² = 0.498–0.745). Reflectivity corrections in Sentinel-2A data notably improved RF model inversion accuracy (R² gains of 0.114–0.384). (3) Suitability Analysis: The cotton suitability index maps for 2022 and 2023 indicated that most fields in Tumushuke (TMSK) were moderately suitable for cotton growth, although some areas were unsuitable. This highlights the need for additional irrigation and targeted soil water and salt management to meet cotton requirements and reduce salinity risks. Overall, this study enhances precision agriculture techniques for arid environments and provides valuable insights for managing soil salinity, supporting sustainable cotton production in challenging climates. |
| format | Article |
| id | doaj-art-99a10b6d00dd45b4807374f946b0502c |
| institution | Kabale University |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-99a10b6d00dd45b4807374f946b0502c2025-01-07T04:16:44ZengElsevierAgricultural Water Management1873-22832025-02-01307109215Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environmentsJianqiang He0Yonglin Jia1Yi Li2Asim Biswas3Hao Feng4Qiang Yu5Shufang Wu6Guang Yang7Kadambot.H.M. Siddique8College of Water Resources and Architectural Engineering at Northwest Agriculture and Forestry University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas at Ministry of Education, Northwest A&F University, Yangling, Shaanxi 712100, PR ChinaCollege of Water Resources and Architectural Engineering at Northwest Agriculture and Forestry University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas at Ministry of Education, Northwest A&F University, Yangling, Shaanxi 712100, PR ChinaCollege of Water Resources and Architectural Engineering at Northwest Agriculture and Forestry University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas at Ministry of Education, Northwest A&F University, Yangling, Shaanxi 712100, PR China; Institute of Soil Fertilizer and Agricultural Water Saving, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, PR China; College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang 832003, PR China; Corresponding author at: College of Water Resources and Architectural Engineering at Northwest Agriculture and Forestry University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas at Ministry of Education, Northwest A&F University, Yangling, Shaanxi 712100, PR China.School of Environmental Sciences, University of Guelph, Guelph, Ontario N1G 2W1, CanadaState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi 712100, PR ChinaState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi 712100, PR ChinaCollege of Water Resources and Architectural Engineering at Northwest Agriculture and Forestry University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas at Ministry of Education, Northwest A&F University, Yangling, Shaanxi 712100, PR ChinaCollege of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang 832003, PR ChinaThe UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001, AustraliaThis study addresses the critical issues of water scarcity and soil salinization impacting cotton production in South Xinjiang, China. It introduces an innovative framework for assessing regional cotton crop suitability by integrating ground-measured soil water and salt data with UAV multispectral and Sentinel-2A satellite imagery from the 2022 cotton growing season. An optimized set of vegetation indices was identified through multicollinearity analysis and full subset selection. Six advanced machine learning methods, including Random Forest (RF), were used alongside the ratio mean method to effectively upscale soil water and salt content models from the field to the regional level. A newly developed cotton suitability index was created to categorize soil water and salt conditions, resulting in detailed suitability maps for 2022 and 2023. Key findings include: (1) Model Performance: The RF model outperformed others in predicting soil water and salt content, with R² values ranging from 0.763 to 0.846 for soil moisture and 0.703–0.843 for soil salinity. It showed greater accuracy at 0–10 cm depth than 10–20 cm depth. (2) Imagery Correlation: A significant correlation was observed between UAV and Sentinel-2A imagery (R² = 0.498–0.745). Reflectivity corrections in Sentinel-2A data notably improved RF model inversion accuracy (R² gains of 0.114–0.384). (3) Suitability Analysis: The cotton suitability index maps for 2022 and 2023 indicated that most fields in Tumushuke (TMSK) were moderately suitable for cotton growth, although some areas were unsuitable. This highlights the need for additional irrigation and targeted soil water and salt management to meet cotton requirements and reduce salinity risks. Overall, this study enhances precision agriculture techniques for arid environments and provides valuable insights for managing soil salinity, supporting sustainable cotton production in challenging climates.http://www.sciencedirect.com/science/article/pii/S0378377424005511Arid environmentRegional-scaleSoil water contentSoil salt contentSuitability indexCotton |
| spellingShingle | Jianqiang He Yonglin Jia Yi Li Asim Biswas Hao Feng Qiang Yu Shufang Wu Guang Yang Kadambot.H.M. Siddique Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments Agricultural Water Management Arid environment Regional-scale Soil water content Soil salt content Suitability index Cotton |
| title | Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments |
| title_full | Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments |
| title_fullStr | Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments |
| title_full_unstemmed | Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments |
| title_short | Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments |
| title_sort | regional scale precision mapping of cotton suitability using uav and satellite data in arid environments |
| topic | Arid environment Regional-scale Soil water content Soil salt content Suitability index Cotton |
| url | http://www.sciencedirect.com/science/article/pii/S0378377424005511 |
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