Grid-scale agricultural land and water management: A remote-sensing-based multiobjective approach

Abstract

This paper developed a remote-sensing-based multiobjective (RSM) approach to formulate sustainable agricultural land and water resources management strategies at a grid scale. To meet the spatial resolution and accuracy need of agricultural management, downscaled precipitation data sets were obtained with the help of global precipitation measurement (GPM) data and other spatial information. Spatial crop water requirement information were obtained via the combination use of the Penman-Monteith method, remote sensing information (MOD16/PET) and virtual water theory. Through integrating these spatial data and considering the impact of different spatial environments on crop growth, a grid-based integer multiobjective programming (GIMP) model was developed to determine best suitable crop planting types at all grids. GIMP can simultaneously consider several conflicting objectives: crop growth suitability, crop spatial water requirements, and ecosystem service value. Further, GIMP results were inputted into a grid-based nonlinear fractional multiobjective programming (GNFMP) model with three objectives: maximize economic benefits, maximize water productivity, and minimize blue water utilization, to optimize irrigation-water allocation. To verify the validity of the proposed approach, a real-world application in the middle reaches of Heihe River Basin, northwest China was conducted. Results show that the proposed method can improve the ecosystem service value by 0.36 × 108 CNY, the economic benefit by 21.85%, the irrigation-water productivity by 25.92%, and reduce blue water utilization rate by 24.32% comparing with status quo.