Multi-satellite and Multi-Signal-to-Noise Ratio (SNR) types provide more basic data for the monitoring of sea level height by Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology. Few studies predict sea level height with high temporal resolution. This study proposes a theory based on GNSS-IR technology and Auto Regressive Integrated Moving Average (ARIMA) model. Taking the MAYG station as an example, the process of sea level height prediction and the determination of the best prediction scheme are shown. First, the sea level height monitored by the GNSS-IR technology is averaged hour by hour as the sea level height at the instantaneous moment. A linear interpolation method is used to compensate for the missing sea level time series. Then, the number of days to participate in the prediction of GNSS-IR sea level height and its forecast are selected for the experiment of predicting tide level. In the tide level prediction study from DOY125 to DOY153, the results of predicting the future 24 h, 48 h, and 72 h from one tide level period (29 days) illustrate that the accuracy of predicting the sea level height at 24 h is the best (R: 0.967, RMSE: 0.270 m, ME: −0.151 m), and the predicted tide level is highly consistent with the tide level measured at the tide gauge station. In addition, experiments reveal that the prediction accuracy gradually decreases with the increase of prediction time. This study verifies the feasibility of the hour-by-hour sea level prediction theory combined with the GNSS-IR technology and the ARIMA model. It proves that the theory can carry out continuous prediction of sea level, further promoting the breakthrough and transformation of GNSS-IR technology to the application level.

As an essential part of the global water cycle, the effective detection of snow depth is of great significance to the study of climate change. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology emerged as a new ground-based snow depth monitoring method. The principle is to invert the height of the reflection surface according to the SNR at a low elevation angle. However, the topographic relief in the reflection area of GNSS stations often dramatically impacts the accuracy of snow depth inversion. This study proposes a Terrain Tilt Correction (TTC) algorithm based on different azimuth intervals to address it. Taking the P351 and P676 stations in the United States as examples, the snow depth inversion research of different systems are carried out. In the P351 station with gentle terrain, the TTC algorithm can maintain stable accuracy. In the P676 station with steep terrain, the TTC algorithm can effectively compensate for the error caused by the landscape and significantly improve the accuracy of snow depth inversion. The experimental results of the two stations show that the snow depth inversion results of different systems after TTC are consistent with the in situ snow depth provided by the respective meteorological stations. Subsequently, a Helmert Variance Component Estimation (HVCE) weighting algorithm is introduced for multi-system combinations based on different systems with different snow depth inversion accuracies, and the feasibility and stability of the combination strategy are investigated. Compared with the in situ snow depth, the correlation coefficient (R) of the combined snow depth at the P351 station is 0.91, the Root Mean Square Error (RMSE) is 19.7 cm, and the Mean Error (ME) is -12.2 cm. The snow depth obtained by the combination of the multi-system at the P676 station has an R of 0.95, an RMSE of 9.9 cm, and an ME of -4.9 cm. Compared with HVCE weighting before TTC, the RMSE is reduced by 53.3%, and the absolute value of ME is reduced by 74.2%. This study is not only of great significance for high-precision snow depth monitoring, but also provides a necessary technical reference for further research on multi-system combined.

Multisatellite systems and multi‐signal‐to‐noise ratio types provide a more prosperous data basis for the inversion of sea level by GNSS‐IR technology. However, there are few studies on data reconstruction of the retrieved sea level height. This study takes the SPBY station as an example and introduces the least squares learning cross‐checking method with the Gaussian kernel model (GKM) L2 constraint. Furthermore, research on hourly sea level reconstruction based on GNSS‐IR is carried out. Compared with the measured sea level, the sea level height in 2021 obtained after the fusion of multisource data has an R of 0.905 and an RMSE of 0.144 m. And the result obtained after data reconstruction has an R of 0.958 and an RMSE of 0.090 m. Compared with the multisource data fusion results before reconstruction, R is increased by 5.9%, and RMSE is decreased by 37.5%. Finally, using the reconstructed sea level data based on Long Short‐Term Memory (LSTM) artificial neural network to carry out the research on sea level prediction, which verifies the conclusion that more reliable forecast values can be obtained based on 5 months of training data. Among them, the R from 1 to 24 hr is 0.905, and the RMSE is 0.145 m. Compared with the inversion accuracy of GNSS‐IR, the R is increased by 2.0%, and the RMSE is decreased by 21.4%. This study demonstrates the feasibility of GNSS‐IR technology and L2‐constrained least squares learning cross‐checking method based on the GKM to reconstruct sea level data with high temporal resolution and high accuracy. The reliability of the sea level prediction based on GKM reconstruction and LSTM is verified in the sea level forecast of the next 24 epochs, which has essential applications in sea level data recovery and forecasting.

The Tianmu-1 satellites (TM-1) is China’s first commercial BDS/Global Navigation Satellite System (GNSS) remote sensing and detection constellation that is compatible with the four major navigation systems, namely BDS, GPS, GLONASS, and Galileo. It could conduct three-dimensional and integrated operational detection of the global ocean, atmosphere, and ionosphere around the clock. The Cyclone Global Navigation Satellite System (CYGNSS) is an Earth System Science Pathfinder mission of the National Aeronautics and Space Administration (NASA) in the United States. This mission represents the first frequent space-based measurements of surface wind speeds in the inner cores of tropical cyclones. Comprising eight microsatellites, it is capable of providing near-seamless coverage of the Earth. This paper utilizes the observation reflection data of the TM-1 and CYGNSS in 2023. It respectively studies the capabilities of these two satellite constellations in estimating soil moisture (SM, 0–5 cm). This research opens broader prospects for obtaining high-precision SM products using satellite-based GNSS reflection data. Using Global Navigation Satellite System-reflectometry (GNSS-R) signal data in conjunction with the Normalized Difference Vegetation Index (NDVI), precipitation, Digital Elevation Model (DEM), slope, and geographic coordinates, the SM data from the Soil Moisture Active Passive (SMAP) satellite serves as the benchmark for developing a model based on the Random Forest (RF) method, facilitating SM value estimation in the Sichuan-Tibet transportation corridor. Research results indicate that the generated SM Pearson correlation coefficient (R) is 0.806, with a root mean square error (RMSE) of 0.054 cm³ cm⁻³. Based on this, the extrapolated monthly SM values were obtained, with the maximum RMSE of the monthly retrieval results not exceeding 0.065 cm³ cm⁻³, and the absolute value of the bias not exceeding 0.037 cm³ cm⁻³. Utilizing the CYGNSS, the R soil moisture in the study area is 0.765, with an RMSE of 0.066 cm³ cm⁻³. The extrapolated monthly SM values indicate that the maximum RMSE for the monthly retrieval results does not exceed 0.088 cm³ cm⁻³, while the absolute value of the bias remains within 0.068 cm³ cm⁻³. Overall, although the SM results of the two satellites are underestimated in the study area, the SM retrieval effect achieved by TM is better than that of the CYGNSS satellite. Simultaneously, it is proved that using TM to realize spaceborne GNSS-R SM retrieval is feasible, providing a new data source for studying spaceborne GNSS-R SM retrieval.

As a critical indicator of atmospheric water vapor content, precipitable water vapor (PWV) holds significant scientific value in global water cycle studies, climate change research, and extreme weather monitoring. Current PWV observation techniques have limitations in terms of balance accuracy, spatiotemporal resolution, and data continuity. This study addresses the high data loss rate and systematic bias issues in the FY-4A PWV product by proposing a dual-scale spatiotemporal fusion model based on machine learning, achieving high spatiotemporal resolution reconstruction of FY-4A PWV over China. The constructed dual-scale reconstruction framework consists of two key stages: the first stage integrates FY-4A PWV and ERA5 data to generate PWV products at a 0.25° resolution hourly; the second stage introduces GNSS PWV, land cover type and normalized difference vegetation index to further downscale and produce a refined PWV dataset at a 4 km resolution hourly. The 10-fold cross-validation of the two-scale model had an R ² of 0.98 and a root mean square error (RMSE) of 2.06 mm. The independent tests based on radiosonde PWV had an R ² of 0.98 and an RMSE of 2.53 mm. The spatial distribution analysis shows that the reconstructed PWV data can not only accurately reflect the macro trend of regional water vapor distribution in China, but also show fine spatial heterogeneity in small areas of complex terrain. This study provides a novel method for optimizing the FY-4A PWV product and offers high-precision data support for regional climate research and extreme weather monitoring.