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.

Exploring the coordination of agricultural water resources (W), cultivated land (L), and the ecoenvironment (E) system is crucial for sustainable agriculture in the North China Plain (NCP). However, the synergistic effects of this composite system remain unclear. Coupling coordination degrees (CCDs) of 53 cities in the NCP for the years 2011, 2015, and 2020 were evaluated using the TOPSIS model, and the coupling coordination model, combined with the analytic hierarchy process and entropy weight method. The evaluation results were further analyzed to identify obstacle factors. The findings reveal the following: (1) The comprehensive development level showed a fluctuating upward trend, with closeness values ranging from 0.418 to 0.574 in 2020, indicating an improvement of 14.6–52.3% compared to 2011. The coefficient of variation (CV) for each province rose from 12.65% in 2011 to 13.64% and subsequently declined to 9.12% by 2020. (2) Between 2011 and 2020, CCDs of the W–L–E composite system exhibited a consistent upward trend. In 2020, regions with intermediate or better coordination accounted for 34.0%, and were primarily located in Jiangsu Province, the southern part of Anhui Province, the northwestern part of Shandong Province, and the municipalities of Beijing and Tianjin. (3) In 2011 and 2015, significant obstacle factors included the water quality compliance rate and the per capita disposable income of rural residents, although these were not primary obstacles in 2020. The water supply modulus and multiple cropping index were major obstacle factors in 2011, 2015, and 2020. Developing water-appropriate cropping patterns based on regional water resource endowment is the essential path for the sustainable and coordinated development of water, land, and ecology in the NCP.

Tracking winter wheat–summer maize distribution is crucial for the management of agricultural water resources in the water-scarce North China Plain (NCP). However, the spatio-temporal change in planting structure that has occurred during the last 20 years remains unclear. Therefore, winter wheat–summer maize distribution between 2001 and 2020 was determined via the maximum likelihood algorithm of supervised classification and a threshold method using the MODIS NDVI product MOD13Q1 and Landsat 5/7 images. The results reveal that dividing distributions into six sample categories—winter wheat–summer maize, winter wheat–rice, spring maize, cotton, other double-cropping systems, and fruit trees—proved to be an efficient way to discriminate winter wheat–summer maize distribution, with R² and RMSE values ranging from 0.738 to 0.901 and from 179.05 to 215.72 km², respectively. From 2001 to 2020, the planting area continually expanded, experiencing a significant growth of 3.32 × 10⁴ km² (23.44%). Specifically, the planting area decreased by 2982.13 km² (10.06%) in the northern part of the NCP, including the Beijing–Tianjin–Hebei region, while it increased by 3.62 × 10⁴ km² (32.30%) in the middle and southern parts, encompassing Shandong, Henan, Anhui, and Jiangsu provinces. The stable growing region was primarily concentrated in the middle of the Hebei Plain, along the Yellow River irrigation areas and humid zones of the southwest, accounting for 75–85% of the total NCP planting area. Our results can provide references for adjusting agricultural planting structures, formulating food security strategies, and optimizing the management of water resources in the NCP.