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.

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.

Reasonable evaluation of evapotranspiration (ET) is crucial for optimizing agricultural water resource management. In the study, we utilized the Data Mining Sharpener (DMS) model; the Landsat thermal infrared images were sharpened from a spatial resolution of 100 m to 30 m. We then used the Surface Energy Balance System (SEBS) to estimate daily ET during the winter wheat growing season in the People’s Victory Irrigation District in Henan, China. It was concluded that the spatiotemporal patterns of land surface temperature and daily evapotranspiration remained consistent before and after sharpening. Results showed that the R² value between the ET of 30 m spatial resolution and the value by eddy covariance method reached 0.814, with an RMSE of 0.516 mm and an MAE of 0.245 mm. All of these were higher than those of 100 m spatial resolution (R² was 0.802, the RMSE was 0.534 mm, and the MAE was 0.253 mm). Furthermore, the daily ET image with a 30 m spatial resolution exhibited clear texture and distinct boundaries, without any noticeable mosaic effects. The changes in surface temperature and ET were more consistent in complex subsurface environments. The daily evapotranspiration of winter wheat was significantly higher in areas with intricate drainage systems compared to other regions. During the early growth stage, daily evapotranspiration decreased steadily until the overwintering stage. After the greening and jointing stages, it began to increase and peaked during the sizing period. The correlation between net solar radiation and temperature with ET was significant, while relative humidity and soil moisture were negatively correlated with ET. Throughout the growth period, net solar radiation had the greatest effect on ET.