文献类型：期刊文献

英文题名：Integrating RS data with fuzzy decision systems for innovative crop water needs assessment

作者：Hashemi, Faezeh Sadat[1,2];Zoej, Mohammad Javad Valadan[2];Youssefi, Fahimeh[1,2];Li, Huxiong[1];Shafian, Sanaz[3];Farnaghi, Mahdi[4];Pirasteh, Saied[1]

机构：[1]Shaoxing Univ, Inst Artificial Intelligence, 508 West Huancheng Rd, Shaoxing 312000, Zhejiang, Peoples R China;[2]KN Toosi Univ Technol, Fac Geodesy & Geomat Engn, Dept Photogrammetry & Remote Sensing, Tehran, Iran;[3]Virginia Polytech Inst & State Univ, Sch Plant & Environm Sci, Blacksburg, VA 24061 USA;[4]Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Dept Geoinformat Proc GIP, Drienerlolaan 5, NL-7522 NB Enschede, Netherlands

年份：2025

卷号：136

外文期刊名：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

收录：SCI-EXPANDED(收录号：WOS:001413446100001)、、Scopus(收录号：2-s2.0-85213283968)、WOS

语种：英文

外文关键词：Food Security; Irrigation; Evapotranspiration; Metric Model; Water Stress; Fuzzy Decision-Making System

外文摘要：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 decisionmaking systems in promoting sustainable agriculture.