Water resource management using remote sensing and coyote optimization algorithms

This paper proposes a new methodology for investigating water management options in agricultural irrigation that accounts for the heterogeneity of irrigation system characteristics and limitations in existing water resources. The process uses a random data matching method to obtain operational management methods and system features using remote sensing data and water resource management optimization to evaluate different management methods. Regional modelling was performed, using the SWAP model under deterministic–stochastic conditions. Inputs such as sowing dates, irrigation procedures, soil characteristics, groundwater depth and water quality were treated as distributed data. To estimate these data, residual minimization was used between the field-scale evapotranspiration distributions modelled in the SWAP model and two Landsat 8 ETM+ images, as well as the Surface Energy Balance Algorithm for Land (SEBAL). The investigation of water management methods using distributed data as input was performed, and optimization of water management and data assimilation was achieved by applying the improved coyote algorithm. The case study was conducted in Mashhad during the dry season of 2018–2019. The results suggest that simultaneous consideration of crop and water management methods, rather than an independent evaluation, can lead to further improvement in regional wheat yield under water shortage conditions.