High-spatiotemporal-resolution land surface temperature (LST) is a crucial parameter in various environmental monitoring. However, due to the limitation of sensor trade-off between the spatial and temporal resolutions, such data are still unavailable. Therefore, the generation and verification of such data are of great value. The spatiotemporal fusion algorithm, which can be used to improve the spatiotemporal resolution, is widely used in Landsat and MODIS data to generate Landsat-like images, but there is less exploration of combining long-time series MODIS LST and Landsat 8 LST product to generate Landsat 8-like LST. The purpose of this study is to evaluate the accuracy of the long-time series Landsat 8 LST product and the Landsat 8-like LST generated by spatiotemporal fusion. In this study, based on the Landsat 8 LST product and MODIS LST product, Landsat 8-like LST is generated using Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced STARFM (ESTARFM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm, and tested and verified in the research area located in Gansu Province, China. In this process, Landsat 8 LST product was verified based on ground measurements, and the fusion results were comprehensively evaluated based on ground measurements and actual Landsat 8 LST images. Ground measurements verification indicated that Landsat 8 LST product was highly consistent with ground measurements. The Root Mean Square Error (RMSE) was 2.862 K, and the coefficient of determination R² was 0.952 at All stations. Good fusion results can be obtained for the three spatiotemporal algorithms, and the ground measurements verified at All stations show that R² was more significant than 0.911. ESTARFM had the best fusion result (R² = 0.915, RMSE = 3.661 K), which was better than STARFM (R² = 0.911, RMSE = 3.746 K) and FSDAF (R² = 0.912, RMSE = 3.786 K). Based on the actual Landsat 8 LST images verification, the fusion images were highly consistent with actual Landsat 8 LST images. The average RMSE of fusion images about STARFM, ESTARFM, and FSDAF were 2.608 K, 2.245 K, and 2.565 K, respectively, and ESTARFM is better than STARFM and FSDAF in most cases. Combining the above verification, the fusion results of the three algorithms were reliable and ESTARFM had the highest fusion accuracy.

The DSSAT model, integrated the calibrated Hargreaves ET model and dynamic crop coefficient, was run with the generated weather data by SDSM4.2 and CanESM2 to predict the potential yield and crop water requirement (ET_C) of winter wheat in the Huang-Huai-Hai Plain in China under RCP4.5 and RCP8.5 scenarios. The results showed that the spatial distribution of potential yield in the future under RCP4.5 and RCP8.5 were similar, characterized by an increasing trend from the northwest inland to the southeast coast. The spatial distribution of ET_Cdecreased gradually from the Shandong Peninsula to the surrounding area, and the minimum ET_Cwas observed in the southern part of Huang-Huai-Hai Plain. The potential yield, ET_C,and effective precipitation during winter wheat growing seasons might increase in the future under RCP4.5, while irrigation water requirements (IWR) would decrease. Under RCP8.5, the effective precipitation during the wheat growing seasons decreased first and then increased. However, the potential yield, ET_C, and IWR of winter wheat increased first and then decreased. This study can provide some scientific evidence to mitigate the negative effects of climate change on agricultural production and water use in the Huang-Huai-Hai Plain.

The development and utilization of unconventional water resources has become a strategy to alleviate the agricultural water crisis in many countries and regions. To understand the research progress, hot spots, and future trends in the field of unconventional water agricultural irrigation (UWAI), this paper systematically analyzes 6738 publications based on the core database of Web of Science 1990–2023 using the scientific bibliometric analysis software CiteSpace, VOSviewer, and Scimago Graphica. The results showed that the research on UWAI is always rapidly developing. Soil science, crop science, and bioengineering are the main disciplines involved. Most research on WUAI has occurred in China and the United States. Countries with higher levels of development tend to have more influence. Collaboration among authors is fragmented, and collaboration between authors and states needs to be strengthened. Through keyword analysis, the research hotspots are summarized as follows: (1) The effects of traditional and emerging pollutants brought by unconventional water irrigation on soil physicochemical properties, crop growth, and groundwater quality; (2) the health threats caused by pollutants entering the food chain and groundwater; (3) unconventional water utilization technologies, including rainwater harvesting agriculture, precision agriculture, and urban agriculture. Future research hotspots will focus on the mechanisms of pollutant solute transport and transformation in the water–soil–crop system under non-conventional water irrigation conditions and crop physiological responses. We suggest that the research on traditional and emerging pollutants in unconventional water should be strengthened in the future, and the risk control system of unconventional water irrigation should be improved. International cooperation should be strengthened, especially with poor countries in arid regions, to promote the formation of unified international standards and guidelines for non-conventional water irrigation in agriculture.

This research provides a comprehensive analysis of the spatiotemporal evolution of the regional cropping structure and its influencing factors. Using Landsat satellite images, field surveys, and yearbook data, we developed a planting structure extraction model employing the classification regression tree algorithm to obtain data on the major crop cultivation and structural characteristics of Alar reclamation from 1990 to 2023. A dynamic model and transfer matrix were used to analyze temporal changes, and a centroid migration model was used to study spatial changes in the cropping structure. Nonparametric mutation tests and through-traffic coefficient analysis were utilized to quantify the main driving factors influencing the cropping structure. During the period of 1990–2023, the cotton area in the Alar reclamation region expanded by 722.08 km², while the jujube exhibited an initial increase followed by a decrease in the same period. The primary reasons are linked to the cost of purchase, agricultural mechanization, and crop compatibility. In the Alar reclamation area, cotton, chili, and jujube are the primary cultivated crops. Cotton is mainly grown on the southern side of the Tarim River, while chili cultivation is concentrated on the northern bank of the river. Over the years, there has been a noticeable spatial complementarity in the distribution and density of rice and cotton crops in this region. In the Alar reclamation, the main factors influencing the change in cultivated land area are cotton price, agricultural machinery gross power, and population. Consequently, implementing measures such as providing planting subsidies and other policy incentives to enhance planting income can effectively stimulate farmers’ willingness to engage in planting activities.