The Buzhaoba open-pit mine is an important lignite production base in Yunnan Province, China. As mining activities have continued to progress, varying degrees of deformation have occurred in different areas of the Buzhaoba open-pit mine, threatening normal coal production and mine safety. To comprehensively investigate the characteristics of surface deformation and its influencing factors at the Buzhaoba open-pit mine, this study employed the following methods: first, the SBAS-InSAR technique was used to process 86 Sentinel-1A ascending and descending orbit remote sensing images from 2020 to 2023, obtaining LOS surface deformation information for the mining area; second, leveling observation data were used to validate the accuracy of the SBAS-InSAR results, and based on the principle of two-dimensional deformation decomposition, the east–west and vertical surface deformation information of the mining area was obtained; finally, the temporal variation characteristics and influencing factors of the Buzhaoba open-pit mine were analyzed. The study results indicate that (1) the maximum LOS surface deformation rates in the ascending and descending orbits of the mining area were −42.1 mm/a and −114.0 mm/a, respectively; (2) the correlation coefficient between the SBAS-InSAR monitoring results and the leveling observation results was 0.938, confirming the reliability of the SBAS-InSAR monitoring results; (3) the maximum east–west and vertical deformation rates obtained from the two-dimensional deformation decomposition were −103.4 mm/a and −189.2 mm/a, respectively, with the surface deformation in the east–west direction being more pronounced; (4) internal factors such as stratigraphic lithology and geological structures, as well as atmospheric rainfall, have a certain degree of influence on the surface deformation of the Buzhaoba open-pit mine. Therefore, the research results of this study can provide important data support and theoretical references for safety management and disaster prevention in the mining area.

The starch content in rice grains is a key factor in determining their quality. An optimal starch level not only ensures grain fullness, improving storage stability, but also enhances the stickiness and viscosity of cooked rice, thereby boosting its palatability and nutritional value. However, traditional methods for monitoring starch content are expensive and lack the capability to provide rapid spatial distribution information across large areas. To address this limitation, this study focuses on mature rice grains in the Yingjiang region, leveraging multispectral data from the Sentinel-2 satellite. First and second derivative transformations were applied to the multispectral reflectance data, followed by the use of three feature selection algorithms to identify key spectral bands. BP neural networks and ELM neural network regression models were then integrated to quantitatively estimate starch content across the study area. As a result, high-precision spatial distribution maps of starch content were generated, providing a novel and efficient method for large-scale rapid monitoring. The results demonstrate that, compared to full-band data, the use of SPA feature selection significantly improved the predictive accuracy of both BP and ELM models, despite a slight increase in the models’ MSE. Similarly, CARS feature selection also contributed substantially to enhancing the accuracy of the BP and ELM models. In contrast, UVE feature selection significantly reduced the MSE of the BP model, improving predictive precision, with the model achieving an R² of 0.8061 and an MSE of 0.3896. This study highlights that the inversion method, which combines feature selection algorithms with machine learning models, can effectively enhance the predictive accuracy of starch content estimation. Among the tested approaches, the combination of UVE feature selection and BP neural networks delivered the best performance. These findings confirm the feasibility of utilizing Sentinel-2 satellite multispectral data for the quantitative inversion of agronomic parameters across large agricultural areas, providing robust technical support for precision agriculture.