Several deficiencies exist in the present evaluation of land reclamation quality in mining areas. These include the absence of an established set of evaluation index systems and standard acceptance criteria, as well as the use of traditional sampling techniques, which are costly and in efficiency, and time-consuming. Compared with the traditional sampling survey methods, remote sensing has the advantages of a wide detection range, diverse information collection, multiple data-acquiring strategies, high speed, and short cycle. In this study, we used the Xinzhuang coal mining field in Yongcheng, Henan Province as an example to extract information and invert surface parameters using remote sensing techniques, based on national and local reclamation regulations and standards. Subsequently, using remote sensing, we constructed an index system for evaluating land reclamation quality in three aspects: reclaiming project quality, soil quality, and ecological benefits. Through the grading standards of evaluation indicators and quantitative remote sensing models, we determined the extracted information on the area of indicators, roads, ditches, soil moisture, organic matter, and ecological benefits after reclamation. Based on this, we established a quality evaluation model for mining land reclamation using an improved index and method. The evaluation units were divided, and the weight of the evaluation index was determined using the analytic hierarchy process and data envelopment analysis (AHP–DEA) method. The land reclamation quality in the study area was comprehensively evaluated, field accuracy was verified, and the results were analyzed. The results show that, except for the removal of roads, houses, and fishponds in the study area, all 13 evaluation units achieved a score of 60 points or higher. The quality of reclamation met the standards, and the evaluation results were consistent with the conclusions of the field investigation and project acceptance report, demonstrating the reliability and feasibility of the method developed in this study. The research results will provide technical support for the scientific evaluation of land reclamation quality.

The Middle Route of the South-to-North Water Diversion Project is a critical infrastructure that ensures optimal water resource distribution across river basins and safeguards the livelihood of people in China. This study investigated its effects on the land surface temperature (LST) and fractional vegetation coverage (FVC) in the Danjiang River Basin. Moreover, it examined the spatial and temporal patterns of this project, providing a scientific basis for the safe supply of water and ecological preservation. We used the improved interpolation of mean anomaly (IMA) method based on the digital elevation model (DEM) to reconstruct LST while FVC was estimated using the image element dichotomous model. Our findings indicated a general increase in the average LST in the Danjiang River Basin post-project implementation. During both wet and dry seasons, the cooling effect was primarily observed in the south-central region during the daytime, with extreme values of 6.1 °C and 5.9 °C. Conversely, during the nighttime, the cooling effect was more prevalent in the northern region, with extreme values of 3.0 °C and 2.3 °C. In contrast, the warming effect during both seasons was predominantly located in the northern region during the daytime, with extreme values of 5.3 °C and 5.5 °C. At night, the warming effect was chiefly observed in the south-central region, with extreme values of 5.8 °C and 5.9 °C. FVC displayed a seasonal trend, with higher values in the wet season and overall improvement over time. Statistical analysis revealed a negative correlation between vegetation change and daytime temperature variations in both periods (r = −0.184, r = −0.195). Furthermore, a significant positive correlation existed between vegetation change and nighttime temperature changes (r = 0.315, r = 0.328). Overall, the project contributed to regulating LST, fostering FVC development, and enhancing ecological stability in the Danjiang River Basin.

Surface temperature is a key parameter in scientific studies, encompassing areas such as resource environment, climate change, and terrestrial ecosystems. The moderate resolution imaging spectroradiometer land surface temperature (MODIS LST) products play a critical role in research related to land surface temperature. However, these products are often plagued with data loss or distortion, attributable to atmospheric conditions or technical impediments. Unfortunately, there is a shortage of fast LST reconstruction methods that consider both the temporal relationships between close images and the LST variation characteristics in complex, heterogeneous terrain. To address this, the present study proposed a novel method, the improved interpolation of the mean anomalies based on the digital elevation model (DEM-IMA), seeking to fill in missing temperature values. The model suggested in this research was evaluated by comparing it to conventional methods such as interpolation of mean anomalies (IMA) and gapfill (GF) using a combination of simulation data and actual satellite data. The results suggest that the DEM-IMA model enhances land surface temperature reconstruction, particularly for heterogeneous landscapes. The approach effectively restored missing data, displaying a remarkable level of accuracy overall. It surpassed both the IMA and GF methods in the task of filling small, medium, and large cloud gaps in day and night LST data. It reduced the root mean square error (RMSE) by 17%, with its accuracy higher at night than during the day. The findings of this study have the potential to provide valuable technical support for enhancing the utilization of MODIS LST products and for conducting quantitative analysis and assessment of regional climate resources with greater effectiveness.