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SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings. [ABSTRACT FROM AUTHOR]

Soil water content is a critical environmental parameter in research and practice, though various technological and contextual constraints limit its estimation in arid areas with vegetation cover. This study combined the multitemporal remote sensing data of Sentinel-1 and Landsat 8 to conduct an inversion study on surface soil water content under low vegetation cover in Nagqu, central Tibetan Plateau. Four vegetation indices (NDVI, ARVI, EVI, and RVI) were extracted from optical remote sensing data. A water cloud model was used to eliminate the influence of the vegetation layer on the backscattering coefficient associated with vegetation cover, and a predictive model suitable for the Nagqu area was constructed. The water cloud model effectively incorporated a vegetation index instead of vegetation water content. We found that VV polarization was more suitable for soil water content inversion than VH polarization. Among the four vegetation indices, the soil water content inversion model constructed with RVI under VV polarization had the best fit (R2 = 0.8212; RMSE = 6.30). The second-best fit was observed for vegetation water content-NDVI (R2 = 0.8201). The soil water content inversion models all had an R2 > 0.6, regardless of the vegetation index used, though the RVI had the best fitting effect, indicating that this vegetation index is highly applicable in the water cloud model, as a substitute for vegetation water content, and is expected to perform well in similar study sites. [ABSTRACT FROM AUTHOR]

Zhaotong City in Yunnan Province is one of the largest apple growing bases in China. However, the terrain of Zhaotong City is complicated, and the rainy weather is more frequent, which brings difficulties to the identification of apple orchards by remote sensing. In this paper, an improved spectral angle feature is proposed by combining the Spectral Angle Mapper and Sentinel-1 data. Based on the Google Earth Engine and Sentinel image, a random forest classifier was used to extract apple orchards in the Ganhe Reservoir area, Zhaoyang District, Zhaotong City, which provides a theoretical basis for extracting the spatial distribution and sustainable development of the local apple industry. The classification results show that the improved spectral angle characteristics can improve the overall accuracy and F1 score of apple orchards. The RGB band combined with NDVI, GLCM, and improved spectral angle features obtained the most favorable results, and the F1 score and overall accuracy were 88.89% and 84.44%, respectively, which proved the reliability of the method in identifying apple orchards in Zhaotong City. [ABSTRACT FROM AUTHOR]

In order to prevent landslide and collapse in the mining area, it is essential not only to gain a full understanding of the surface deformation information about the open-pit mining area and surrounding dumps, but also to closely monitor the development of surface deformation. Small baseline set (SBAS) technology can be applied to extract the high-resolution spatial-temporal deformation information from the mining areas. Due to decoherence, however, it is difficult to obtain the effective information on deformation from some areas where the results are monitored. This paper proposes a kriging interpolation method that performs better in filling the missing values in the InSAR monitored results by integrating GPS measured data with InSAR monitored data. In the experimental process, 69 Sentinel-1 image data were collected from July 2018 to November 2020. The SBAS-InSAR technology was applied to monitor the surface deformation caused to the Kunyang phosphate mining area and surrounding dumping sites. It was found out that all the mining areas across the Kunyang Phosphorus Mine and the surrounding dumps suffered severe surface deformation, with the maximum line of sight (LOS) deformation rate of −132 mm/yr located in the dump in the north of the second mining area. The verification result shows that InSAR measurement results are consistent with the deformation estimated from GPS measurements. The root mean square error of most points is maintained within ±10 mm. The Kriging interpolation results of fusion GPS data are in good agreement with the GPS results. The root mean square errors at the 4 verification points are ±6.2 mm, ±4.6 mm, ±6.4 mm, ±7.8 mm. The method proposed in this paper is effective in filling in the missing values in the incoherent area, and can be applied to mine surface deformation monitoring with high reliability. The results of this paper serve as a scientific basis for the prevention and control of landslides, collapses and other hidden risks of geological disasters posed by the mining operations conducted in the Kunyang Phosphorus Mine. [ABSTRACT FROM AUTHOR]