Integrating RS data with fuzzy decision systems for innovative crop water needs assessment
Irrigation is a critical component of global water usage, accounting for approximately 70 % of water consumption. As the world’s population continues to grow, the demand for food will increase, making it essential to improve irrigation management by reducing water waste and increasing efficiency. Th...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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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/S1569843224006964 |
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| author | Faezeh Sadat Hashemi Mohammad Javad Valadan Zoej Fahimeh Youssefi Huxiong Li Sanaz Shafian Mahdi Farnaghi Saied Pirasteh |
| author_facet | Faezeh Sadat Hashemi Mohammad Javad Valadan Zoej Fahimeh Youssefi Huxiong Li Sanaz Shafian Mahdi Farnaghi Saied Pirasteh |
| author_sort | Faezeh Sadat Hashemi |
| collection | DOAJ |
| description | Irrigation is a critical component of global water usage, accounting for approximately 70 % of water consumption. As the world’s population continues to grow, the demand for food will increase, making it essential to improve irrigation management by reducing water waste and increasing efficiency. This study aims to develop and validate a fuzzy decision-making system that determines crop irrigation needs based on parameters that affect plant water requirements. These parameters can be monitored using Remote sensing (RS) satellites, enabling large-scale agricultural irrigation monitoring. The study utilized Landsat-8 satellite data and meteorological data. It also employed a fuzzy decision system with inputs of estimated evapotranspiration, Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST), Crop Water Stress Index (CWSI), Stress Index (SI), and Soil Moisture (SM). The output of the fuzzy model is a map that effectively determines the irrigation requirements for agricultural land relatively. The system was tested on six Landsat images of winter wheat crops in Tehran University’s agricultural fields. The estimated evapotranspiration was compared to Reference Evapotranspiration (ETr) obtained from the FAO-Penman-Monteith equation, resulting in a root mean square error of 0.33 mm. The fuzzy decision system was evaluated by comparing its results with Vegetation Water Content (VWC) measurements during satellite overpass time. The NDVI, CWSI, SI, and SM variables had the highest R2 with VWC data (0.71––0.92) on all six dates. This approach has significant implications for improving irrigation management practices, reducing water waste, and increasing crop yields, which can contribute to global food security. The study highlights the potential of RS technology and fuzzy decision-making systems in promoting sustainable agriculture. |
| format | Article |
| id | doaj-art-cecd43babba0408988ede1ffab0a2ac4 |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-cecd43babba0408988ede1ffab0a2ac42025-08-20T02:15:33ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-0113610433810.1016/j.jag.2024.104338Integrating RS data with fuzzy decision systems for innovative crop water needs assessmentFaezeh Sadat Hashemi0Mohammad Javad Valadan Zoej1Fahimeh Youssefi2Huxiong Li3Sanaz Shafian4Mahdi Farnaghi5Saied Pirasteh6Institute of Artificial Intelligence, Shaoxing University, 508 West Huancheng Road, Yuecheng District, Shaoxing, Zhejiang Province Postal Code 312000, China; Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology, Tehran, IranDepartment of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology, Tehran, IranInstitute of Artificial Intelligence, Shaoxing University, 508 West Huancheng Road, Yuecheng District, Shaoxing, Zhejiang Province Postal Code 312000, China; Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology, Tehran, IranInstitute of Artificial Intelligence, Shaoxing University, 508 West Huancheng Road, Yuecheng District, Shaoxing, Zhejiang Province Postal Code 312000, China; Corresponding author.School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USADepartment of Geo-Information Processing (GIP), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Drienerlolaan 5, 7522 NB Enschede, the NetherlandsInstitute of Artificial Intelligence, Shaoxing University, 508 West Huancheng Road, Yuecheng District, Shaoxing, Zhejiang Province Postal Code 312000, ChinaIrrigation is a critical component of global water usage, accounting for approximately 70 % of water consumption. As the world’s population continues to grow, the demand for food will increase, making it essential to improve irrigation management by reducing water waste and increasing efficiency. This study aims to develop and validate a fuzzy decision-making system that determines crop irrigation needs based on parameters that affect plant water requirements. These parameters can be monitored using Remote sensing (RS) satellites, enabling large-scale agricultural irrigation monitoring. The study utilized Landsat-8 satellite data and meteorological data. It also employed a fuzzy decision system with inputs of estimated evapotranspiration, Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST), Crop Water Stress Index (CWSI), Stress Index (SI), and Soil Moisture (SM). The output of the fuzzy model is a map that effectively determines the irrigation requirements for agricultural land relatively. The system was tested on six Landsat images of winter wheat crops in Tehran University’s agricultural fields. The estimated evapotranspiration was compared to Reference Evapotranspiration (ETr) obtained from the FAO-Penman-Monteith equation, resulting in a root mean square error of 0.33 mm. The fuzzy decision system was evaluated by comparing its results with Vegetation Water Content (VWC) measurements during satellite overpass time. The NDVI, CWSI, SI, and SM variables had the highest R2 with VWC data (0.71––0.92) on all six dates. This approach has significant implications for improving irrigation management practices, reducing water waste, and increasing crop yields, which can contribute to global food security. The study highlights the potential of RS technology and fuzzy decision-making systems in promoting sustainable agriculture.http://www.sciencedirect.com/science/article/pii/S1569843224006964Food SecurityIrrigationEvapotranspirationMetric ModelWater StressFuzzy Decision-Making System |
| spellingShingle | Faezeh Sadat Hashemi Mohammad Javad Valadan Zoej Fahimeh Youssefi Huxiong Li Sanaz Shafian Mahdi Farnaghi Saied Pirasteh Integrating RS data with fuzzy decision systems for innovative crop water needs assessment International Journal of Applied Earth Observations and Geoinformation Food Security Irrigation Evapotranspiration Metric Model Water Stress Fuzzy Decision-Making System |
| title | Integrating RS data with fuzzy decision systems for innovative crop water needs assessment |
| title_full | Integrating RS data with fuzzy decision systems for innovative crop water needs assessment |
| title_fullStr | Integrating RS data with fuzzy decision systems for innovative crop water needs assessment |
| title_full_unstemmed | Integrating RS data with fuzzy decision systems for innovative crop water needs assessment |
| title_short | Integrating RS data with fuzzy decision systems for innovative crop water needs assessment |
| title_sort | integrating rs data with fuzzy decision systems for innovative crop water needs assessment |
| topic | Food Security Irrigation Evapotranspiration Metric Model Water Stress Fuzzy Decision-Making System |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224006964 |
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