The influence of solar altitude and azimuth angles makes shadows prevalent in remote sensing images of rugged terrains. Consequently, the normalized difference vegetation index (NDVI) of the shadow areas is much lower than that of sunlit areas—a phenomenon known as the NDVI topographic shadow effect. In this study, we developed an NDVI topographic shadow effect correction (NTSEC) model. The NTSEC is based on the difference in solar radiation between the sunlit and shadow areas, and introduces a variable factor that indicates the intensity of shadows to simulate the reflectance for direct solar light not received in the shadow areas. The simulated reflectance for direct solar light in the shadow areas is used as a compensation value to be summed with the original reflectance for calculating the corrected NDVI. Landsat 8 Operational Land Imager images from two different regions were used to test the NTSEC method and multiple strategies were employed to provide objective evaluative results. The model performance was compared with four commonly used topographic correction (TC) models: C-correction, Minnaert, variable empirical coefficient algorithm, and statistical empirical. The results show that NTSEC can suppress the overcorrection of the self shadow areas that are difficult to eliminate in the TC methods, while simultaneously improving the undercorrection of the cast shadow areas. The corrected NDVI of the self shadow and cast shadow areas were almost the same. The NDVI differences between shady and sunny slopes were significantly reduced after correction. In addition, the NTSEC method did not produce outliers, and the NDVI of different land cover types retained interclass stability. In summary, the NTSEC model can be used as a simple and robust method for correcting the NDVI in shadow areas.

We employed ascending and descending Sentinel-1A, optical image data, and field investigation methods to identify and monitor landslides in the Jinsha River Basin to overcome the difficulties associated with the use of a single method and its inaccuracies in identifying landslides in the alpine and canyon areas. Using distributed scatterer-synthetic aperture radar interferometry (DS-InSAR), Sentinel-1A ascending and descending data were integrated to obtain surface deformation information within the study area from July 2017 to May 2019. Thereafter, high-resolution optical image data were introduced to interpret landslides, and field investigations were conducted to validate landslides. These combined methods enabled the assessment of spatiotemporal evolutionary characteristics, and their accuracy in identifying typical landslides was verified. The results showed that the use of both ascending and descending data effectively avoided certain problems, such as the inability to identify certain landslide hazards or the retrieval of incomplete identification results due to geometric distortion associated with single-track SAR imaging. The combined use of these methods effectively improves the timeliness and verification of the accuracy of landslides. Fifteen landslides were identified in the study area, which had different degrees of tension cracks, vertical dislocations, and slip marks that were verified in the field. Of these, two landslides show serious deformation characteristics that currently pose a serious threat to lives and infrastructure. Follow-up monitoring of these landslides is essential. These findings will assist in obtaining comprehensive information about the distribution of landslides and their deformation developmental trends in the Ahai Reservoir area of the Jinsha River Basin and show that the combined methods can be employed to prevent and control landslides in this area.

The detection of potential rural mountain landslide displacements using time-series interferometric Synthetic Aperture Radar has been challenged by both atmospheric phase screens and decoherence noise. In this study, we propose the use of a combined distributed scatterer (DS) and the Prophet_ZTD-NEF model to rapidly map the landslide surface displacements in Diqing Tibetan Autonomous Prefecture, China. We conducted tests on 28 full-resolution SENTINEL-1A images to validate the effectiveness of our methods. The conclusions are as follows: (1) Under the same sample conditions, confidence interval estimation demonstrated higher performance in identifying SHPs compared to generalized likelihood ratio test. The density of DS points was approximately eight times and five times higher than persistent scatterer interferometry and small baseline subset methods, respectively. (2) The proposed Prophet_ZTD-NEF model considers the spatial and temporal variability properties of tropospheric delays, and the root mean square error of measured values was approximately 1.19 cm instead of 1.58 cm (PZTD-NEF). (3) The proposed Prophet_ZTD-NEF method reduced the mean standard deviation of the corrected interferograms from 1.88 to 1.62 cm and improved the accuracy of the deformation velocity solution by approximately 8.27% compared to Global Position System (GPS) measurements. Finally, we summarized the driving factors contributing to landslide instability.

Time-series interferometric synthetic aperture radar (TS-InSAR) is often affected by tropospheric artifacts caused by temporal and spatial variability in the atmospheric refractive index. Conventional temporal and spatial filtering cannot effectively distinguish topography-related stratified delays, leading to biased estimates of the deformation phases. Here, we propose a TS-InSAR atmospheric delay correction method based on ERA-5; the robustness and accuracy of ERA-5 data under the influence of different atmospheric delays were explored. Notably, (1) wet delay was the main factor affecting tropospheric delay within the interferogram; the higher spatial and temporal resolution of ERA-5 can capture the wet delay signal better than MERRA-2. (2) The proposed method can mitigate the atmospheric delay component in the interferogram; the average standard deviation (STD) reduction for the Radarsat-2 and Sentinel-1A interferograms were 19.68 and 14.75%, respectively. (3) Compared to the empirical linear model, the correlation between the stratified delays estimated by the two methods reached 0.73. We applied this method for the first time to a ground subsidence study in the Yuxi Basin and successfully detected three subsidence centers. We analyzed and discussed ground deformation causes based on rainfall and fault zones. Finally, we verified the accuracy of the proposed method by using leveling monitoring data.

Shadow indexes can effectively and easily detect shadows in remote sensing images. Although various shadow indexes are used, there is no consensus regarding the optimal one. The evaluation criteria used to determine the performance of shadow indexes are relatively homogeneous. A multi-criteria evaluation of widely used shadow indexes is necessary to analyse the application performance of each index in different scenarios. The eight most common shadow indexes were selected for this study. In addition to the standard visual inspection and accuracy assessment criteria, the cosine of solar incidence (cosi) was introduced to evaluate the sensitivity of the indexes to illumination intensity. Furthermore, in order to evaluate the separability of shadows and confusable objects, the interquartile range (IQR) of shadows and confusable objects was used to calculate the 1.5IQR separation ratio (1.5IQRSR). The multi-scale evaluation included mountain and urban scenarios, with the sensitivity to illumination intensity used to assess mountain scenarios and the capacity to separate confusable objects used to assess urban scenarios. The results obtained for each evaluation criterion were aggregated to form the multi-criteria evaluation score of the shadow indexes. The results show that the combinational shadow index (CSI), shadow detector index (SDI), and shadow index (SI) perform relatively well for each evaluation criterion. SI and CSI had the highest shadow detection accuracy in mountain and urban areas, respectively. The shadow enhancement index (SEI) and CSI had relatively high sensitivity to illumination intensity, especially CSI. In addition, CSI has the highest water separability but low separability for blue and dark impervious surfaces. SDI and SI have high separability for confusable objects, but they are ineffective in separating water bodies and less sensitive to illumination intensity. In summary, CSI, SDI, and SI excel in many application contexts, whereas the performances of the remaining five indexes were similar in all respects.

Improving the accuracy of zenith tropospheric delay (ZTD) models is an important task. However, the existing ZTD models still have limitations, such as a lack of appropriate vertical adjustment function and being unsuitable for China, which has a complex climate and great undulating terrain. A new approach that considers the time-varying vertical adjustment and delicate diurnal variations of ZTD was introduced to develop a new grid ZTD model (NGZTD). The NGZTD model employed the Gaussian function and considered the seasonal variations of Gaussian coefficients to express the vertical variations of ZTD. The effectiveness of vertical interpolation for the vertical adjustment model (NGZTD-H) was validated. The root mean squared errors (RMSE) of the NGZTD-H model improved by 58% and 22% compared to the global pressure and temperature 3 (GPT3) model using ERA5 and radiosonde data, respectively. The NGZTD model’s effectiveness for directly estimating the ZTD was validated. The NGZTD model improved by 22% and 31% compared to the GPT3 model using GNSS-derived ZTD and layered ZTD at radiosonde stations, respectively. Seasonal variations in Gaussian coefficients need to be considered. Using constant Gaussian coefficients will generate large errors. The NGZTD model exhibited outstanding advantages in capturing diurnal variations and adapting to undulating terrain. We analyzed and discussed the main error sources of the NGZTD model using validation of spatial interpolation accuracy. This new ZTD model has potential applications in enhancing the reliability of navigation, positioning, and interferometric synthetic aperture radar (InSAR) measurements and is recommended to promote the development of space geodesy techniques.