Performance of Remote Sensing in Scheduling Irrigation: A Review | ||||
| Egyptian Journal of Agronomy | ||||
| Volume 47, Issue 3, September 2025, Page 629-644 PDF (1.09 MB) | ||||
| Document Type: Review Article | ||||
| DOI: 10.21608/agro.2025.342640.1579 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ahmed M. Saad1; Hoda Soussa2; Abdelraouf Ramadan  3; Morad Abdelsalheen2
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| 1Master Student, Ain Shams Faculty of Engineering, Department of Irrigation and Water Resources, Ain Shams University, Egypt | ||||
| 2Ain Shams Faculty of Engineering, Department of Irrigation and Water Resources, Ain Shams University.Egypt | ||||
| 3Water Relation and Field Irrigation Dept., Agricultural & Biological Research Institute, National Research Centre, 33 EL Bohouth St., Dokki, Giza, Egypt, Postal Code: 12622 | ||||
| Abstract | ||||
| Irrigation management is crucial for sustainable agriculture, particularly in water-scarce regions such as arid and semi-arid zones. Soil moisture and evapotranspiration are critical parameters that need to be estimated with a high degree of uncertainty to enhance irrigation practices and sustainable water management. This review examines various remote sensing techniques, for assessing soil moisture and ET with special emphasis on their applicability in agricultural water management. We consider the necessity of ET in irrigation scheduling and explain its contribution, as well as the use of remote sensing for evaluating crop water demands. Key approaches reviewed include multispectral and radar remote sensing, in addition to models for instance the Penman-Monteith equation, surface energy balance algorithms (SEBAL), and vegetation index (NDVI) for monitoring crop health and water demand. This review also explores the issues related to ET estimation, which is based on remote sensing including calibration, temporal and spatial resolution variability, The findings are summarized to compare the remote sensing approaches to determining the volumetric water content of soils and irrigation management in arid regions. Hypotheses and the use of remote sensing, and real-world data augmentation superior to augmentation methods such as data-casting and deep learning techniques are discussed. The discussion covers hypotheses, the use of remote sensing, and real-world data augmentation, which proves superior to methods like data-casting and deep learning techniques. However, limitations such as ground truth difficulties, model calibration, and spatial resolution mismatches remain obstacles. Although this review presents a concise summary of the current knowledge on remote sensing | ||||
| Keywords | ||||
| Remote sensing; evapotranspiration; soil moisture; vegetation index; sustainable irrigation systems | ||||
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