Near-surface air (Ta) and land surface (Ts) temperatures are essential parameters for research in the fields of agriculture, hydrology, and ecological changes, which require accurate datasets with different temporal and spatial resolutions. However, the sparse spatial distribution of meteorological stations in Northwest China may not effectively provide high-precision Ta data. And it is not clear whether it is necessary to improve the accuracy of Ts which has the most influence on Ta. In response to this situation, the main objective of this study is to estimate Ta for Northwest China using multiple linear regression models (MLR) and random forest (RF) algorithms, based on Landsat 8 images and auxiliary data collected from 2014 to 2019. Ts, NDVI (Normalized Difference Vegetation Index), surface albedo, elevation, wind speed, and Julian day were variables to be selected, then used to estimate the daily average Ta after analysis and adjustment. Also, the Radiative Transfer Equation (RTE) method for calculating Ts would be corrected by NDVI (RTE-NDVI). The results show that: 1) The accuracy of the surface temperature (Ts) was improved by using RTE-NDVI; 2) Both MLR and RF models are suitable for estimating Ta in areas with few meteorological stations; 3) Analyzing the temporal and spatial distribution of errors, it is found that the MLR model performs well in spring and summer, and is lower in autumn, and the accuracy is higher in plain areas away from mountains than in mountainous areas and nearby areas. This study shows that through appropriate selection and combination of variables, the accuracy of estimating the pixel-scale Ta from satellite remote sensing data can be improved in the area that has less meteorological data.

The shortage and uneven spatial and distribution of agricultural water resources has greatly restricted the sustainable development of regional society and economy. In this study, an improved five-element connection number set pair analysis model, which subdivides the same–different–opposite connection number in the set pair analysis theory to enhance the integrity and effectiveness of the original ternary connection numbers is constructed to evaluate the agricultural water resources carrying capacity (AWRCC) in the Haihe River Basin. Based on this evaluation result, an optimization model (AROL model) is proposed to optimize the effective irrigation area and groundwater exploitation to achieve a “better level” of AWRCC. The evaluation result shows that the current AWRCC of the Haihe River Basin is relatively low. The AWRCC in Shanxi, Inner Mongolia, and Liaoning is Level III and the current agricultural water resources are not overloaded but have little carrying potential. The AWRCC of Beijing, Tianjin, Hebei, Henan, and Shandong are rated IV and overloaded, among which Shandong has the lowest comprehensive score and the most serious overload. The optimization result shows that the extraction and conservation of groundwater in most areas of the Haihe River Basin is unbalanced and the effective irrigation area needs to be increased. With different current conditions in different areas, the groundwater exploitation and the effective irrigation area is adjusted correspondingly. Among the areas, the adjustment of groundwater exploitation and the effective irrigation area in Hebei are the most significant.

Due to population growth, environmental pollution and climate change, the lack of water resources has become a critical factor which threatens sustainable agricultural development. Reasonable irrigation scheduling strategies can reduce the waste of water and enhance agricultural water-use efficiency. In the present study, the decomposition-coordination theory was adopted to analyze the hierarchical canal system. A novel nonlinear multi-level multi-objective optimization model for complex canal systems was established, taking account of the multiple demands from decision makers and realistic factors of canal operation. An interactive method of the technique for order preference using similarity algorithm and genetic algorithm was proposed to solve the developed model. The developed model was successfully applied for the operational strategy making of a canal system located in the arid area of northwest China. The results indicated that the optimization model could help shorten the operational duration by two days, achieve about 26% reduction of irrigation water consumption, and improve the efficiency of water delivery from 0.566 to 0.687. That will be very favorable for the promotion of the agricultural water productivity, the relief of water shortage crisis and the sustainable development of agriculture. The outcomes can provide a wide range of support for decision making and make irrigation decision-making more scientific and systematic.

Optimizing regional crop water consumption is considered to be a significant approach for increasing yields and reducing water consumption. This paper proposes a single-objective linear programming model which couples the distributed water consumption model with crop suitability. The impacts of meteorological, topographic, and soil factors were taken into account in both the distributed water consumption model and the crop suitability. The developed model was applied to a real case study in the middle reaches of Heihe River basin, in the northwest of China. In the optimization model, the net benefit which combined the water consumption with crop suitability was regarded as the objective function, while the limits on available water and planting area were set as the constraints. Optimal results regarding crop distribution and water consumption were generated for dry, normal, and wet hydrological years. Two optimization strategies were analyzed, including one with a fixed area of each crop and the other with a fixed total planting area. Economic analyses showed that net income under both optimization strategies increased by 31% and 33%, respectively. Although water consumption increased slightly in both optimization scenarios, the unit water income and unit area income were much higher than in the pre-optimization conditions. The obtained results are valuable for supporting the adjustment of planting patterns and the identification of desired plans for sustainable irrigation water allocation.