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The increasing demand for food due to population growth and climate change poses significant challenges to achieve the Sustainable Development Goal of zero hunger by 2030. A key aspect in overcoming these challenges is to determine appropriate planting patterns for various crops, aimed at enhancing regional-scale crop water productivity despite the constraints of limited water and land resources. Remote sensing data and models provide the possibility for accurately estimating water productivity of different crops on a regional scale, but studies on remote sensing-based assessments of regional crop water productivity and its applications in agricultural management are still limited. In this study, we present a satellite-based integrated approach to assess crop planting suitability based on regional crop water productivity estimation. Focusing on the Hetao Irrigation District (HID) in the upper Yellow River basin, a representative irrigation district in arid region of Northwest China, we first use remote sensing data (HJ-1A/1B) to estimate water productivity for the two major crops, maize and sunflower, within the HID from evapotranspiration and yield estimates. Additionally, we introduce a novel crop planting suitability index based on the frequency distribution of crop water productivity, facilitating the determination of appropriate crop planting patterns. Our findings reveal that Dengkou, the periphery of Hangjinhouqi, and the southern part of Linhe are optimal for maize cultivation, while Wuyuan and the northern part of Linhe are ideal for sunflower cultivation. This is attributed to higher water productivity levels for maize in Dengkou (2.46 kg/m³) and Linhe (2.15 kg/m³), and for sunflower in Wuyuan (0.86 kg/m³). Following the optimization of crop planting distribution, the average water productivity for maize and sunflower increases by 7.6 % and 5.0 %, respectively. The proposed method can be generalized to other regions, and the results offer valuable insights for local governments in decision-making to regulate cropping pattern and maximize regional crop water productivity.
Yu et al. (Fri,) studied this question.