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.

The atmospheric weighted mean temperature (T_m) is a key parameter in global navigation satellite system (GNSS) water vapour retrieval and can convert the zenith wet delay (ZWD) into precipitable water vapour (PWV). However, there are some shortcomings in the existing T_m models, such as the detailed time-varying vertical adjustment rate not being considered. In addition, the spatiotemporal characteristics of T_m need to be further refined. Therefore, we developed a new global high-precision and high-spatiotemporal-resolution T_m model considering time-varying vertical adjustment rate using the latest European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) atmospheric reanalysis data. Firstly, a new global grid T_m vertical adjustment rate model (NGGTm-H) was developed using the sliding-window algorithm. Secondly, the daily variation characteristics of T_m and its relationships with geographical situations were investigated. Finally, a new global hybrid-grid T_m model (NGGTm) considering time-varying vertical adjustment rate was developed. To verify the effectiveness of the proposed model, the NGGTm model was compared with the Bevis and global pressure and temperature 3 (GPT3) models using the T_m data recorded at 378 radiosonde stations in 2017 and the surface gridded T_m data calculated from the ERA5 reanalysis data. The results show that taking the surface gridded T_m data of ERA5 as reference values, the average root-mean-square error (RMSE) value calculated by the NGGTm model was 2.84 K, which was lower with 0.50, 0.18 and 0.06 K than those of the Bevis, GPT3-5 and GPT3-1 models, respectively. Meanwhile, taking the T_m from the radiosonde stations as the reference values, the mean bias and RMSE of the NGGTm model were 0.10 and 3.30 K, respectively, which exhibit the best accuracy and stability among the Bevis, GPT3-5 and GPT3-1 models.

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.

Zenith tropospheric delay (ZTD) play an important role in the troposphere. This study proposed a new ZTD vertical profile grid model considering the height scale factor (NGZTD-H) based on the Gaussian function that adjusts the ZTD from the starting to the target height. To estimate the ZTD directly, a new NGZTD that considers the ZTD delicate diurnal variation was developed. The effectiveness of vertical and spatial interpolation for the NGZTD-H model was validated. Regarding vertical interpolation, the root mean squared error (RMSE) of the NGZTD-H model improved by 58% and 22%, compared to the global pressure and temperature 3 (GPT3) model. For spatial interpolation, the RMSE of the NGZTD-H model improved by 17% compared to that of the GPT3 model. 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. The NGZTD model exhibited outstanding advantages in capturing diurnal variations and adapting to undulating terrain. 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.

The atmospheric weighted mean temperature (T_m) is a key parameter in global navigation satellite system (GNSS) water vapor retrieval and can convert the zenith wet delay (ZWD) into precipitable water vapor (PWV). However, there are some shortcomings in the existing T_m models, such as the detailed time-varying lapse rate not being considered. Additionally, the spatiotemporal characteristics of T_m need to be further refined. Therefore, we developed a new global high-precision and high-spatiotemporal-resolution T_m model considering time-varying lapse rate using the latest European Centre for Medium-Range Weather Forecasts ReAnalysis 5 (ERA5) atmospheric reanalysis data. Firstly, a global multidimensional T_m lapse rate model (NGGTm-H model) was developed using the sliding window algorithm. Secondly, the daily variation characteristics of T_m and its relationships with geographical situation were investigated. Finally, a hybrid-grid global T_m model considering time-varying lapse rate (NGGTm model) was developed. To verify the effectiveness of the proposed model, the NGGTm model was compared with the Bevis and GPT3 models using the T_m data recorded at 378 radiosonde stations in 2017 and the surface grid T_m data calculated from the ERA5 reanalysis data. The results show that taking the surface grid T_m data of ERA5 as reference values, the average root mean square error (RMSE) value predicted by the NGGTm model was 2.84 K, which was higher with 0.50 K, 0.18 K and 0.06 K than those of the Bevis, GPT3-5 and GPT3-1 models, respectively. Meanwhile, taking the T_m data from the radiosonde stations as the reference values, the mean bias and RMSE of the NGGTm model were 0.10 K and 3.30 K, respectively, which exhibit the best accuracy and stability among the Bevis, GPT3-5 and GPT3-1 models.

This study addresses the stratified and turbulent tropospheric delays that impede interferometric synthetic aperture radar (InSAR) deformation monitoring in alpine canyon regions. We introduce a tropospheric delay model that incorporates time-varying Gaussian coefficients and coupled zenith wet delay (ZWD) by combining diverse multi-source data. This model was incorporated into StaMPS for InSAR processing. Evaluation results demonstrated that (1) the model accurately captured seasonal and diurnal tropospheric variations, achieving a root mean squared error (RMSE) of 2.01 cm relative to the GNSS reference data; (2) the model corrected stratified and turbulent delays and reduced interferometric phase standard deviation (STD) by 9.28% compared to the Generic Atmospheric Correction Online Service (GACOS); and (3) the deformation accuracy improved by 19.07% over GACOS. Discussion results indicate that accounting for time-varying Gaussian coefficients is essential and that coupling ZWD to rectify turbulent delays outperformed the filtering method. The observed negative interferogram corrections result from the random intensity of turbulent delays. These findings confirm the effectiveness of the proposed model for high-precision InSAR deformation monitoring in complex alpine terrains. The proposed model aims to enhance studies of tropospheric delay variations in alpine canyon regions and to mitigate such delays in InSAR-based geological hazard monitoring.