Global Navigation Satellite System (GNSS) offer all-weather and real-time capabilities, enabling the real-time monitoring of precipitable water vapor (PWV). Traditional method for obtaining high-precision GNSS PWV require meteorological parameters such as pressure and the atmospheric weighted mean temperature, which are inconvenient to obtain at GNSS stations, especially in real-time PWV monitoring. To address these challenges, a deep neural network (DNN)-based model, namely D-PWV, has been established to directly convert zenith total delay (ZTD) derived from GNSS signals into PWV without relying on meteorological parameters. The performance of the D-PWV model is comprehensively assessed using GNSS PWV, the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), radiosonde (RS), and data from the 2018 typhoon event Mangkhut in the Guangxi region of China, respectively. The results reveal that the D-PWV model exhibits a bias of -0.25 mm, 0.09 mm, and 0.28 mm when compared to GNSS PWV, RS PWV, and ERA5 PWV, respectively. The corresponding RMSE values are 2.00 mm, 3.37 mm, and 2.74 mm. Notably, when compared to the backpropagation neural network (BP), the D-PWV model demonstrates a smaller range of variation in both Bias and RMSE across the study area and shows greater stability in the latitudinal direction. Besides, the D-PWV model performs stably during Typhoon Mangkhut, with a correlation coefficient of 0.96 with the RS PWV, and the changes on GNSS stations are also highly consistent with the ERA5 PWV, effectively reflecting the PWV variations in the Guangxi region during the typhoon. It shows that the model constructed in this paper can provide real-time PWV values in the absence of measured meteorological parameters.

The atmospheric weighted mean temperature (Tm) is an important parameter in calculating the precipitable water vapor from Global Navigation Satellite System (GNSS) signals. As both GNSS positioning and GNSS precipitable water vapor detection require high-precision spatial and temporal resolutions for calculating Tm, high-precision modeling of Tm has gained widespread attention in recent years. The previous models for calculating Tm have the limitation of too many model parameters or single grid data. Therefore, this study presents a global high-precision Tm model (GGTm-H model) developed from the latest MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version-2) atmospheric reanalysis data provided by United States National Aeronautics and Space Administration (NASA). The accuracy of GGTm-H model was verified by combining the MERRA-2 surface Tm data and 319 radiosonde data. The results highlighted that: (1) When the MERRA-2 Tm data was used as a reference value, the mean annual RMSE of the GGTm-H model was observed to be 2.72K. When compared with the Bevis model, GPT2w-5 and GPT2w-1 models, GGTm-H model showed an improvement of 1.5K, 0.33K, and 0.21K, respectively. (2) When the radiosonde data was used as a reference value, the mean bias and RMSE of the GGTm-H model were $-0.41$ K and 3.82K, respectively. Compared with the other models, the GGTm-H model had the lowest mean annual bias and RMSE. The developed model does not consider any meteorological parameters while calculating Tm. Therefore, it has important applications in the real-time and high-precision monitoring of precipitable water vapor from GNSS signal.

In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega ($\tau -w$) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m², respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m², respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring.

The spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) has been shown its capacity for global soil moisture (SM) retrieval. However, the traditional SM retrieval method with introduction of redundant auxiliary parameters may lead to increased dependence on auxiliary data sources. Conversely, relying solely on spaceborne GNSS-R reflectivity for global SM retrieval limits accuracy and overlooks the influence of complex surface conditions. To address these issues, a bias correction model for SM product of Cyclone GNSS (CYGNSS), a spaceborne GNSS-R mission, is proposed. In this model, the bias of CYGNSS SM product is refer to soil moisture active passive (SMAP) SM. To capture a small range of the bias changes, a sliding window algorithm is introduced to divide the global region into regular windows of equally sized. The bias corrected results are verified with SM derived from SMAP and the International SM Network (ISMN). The results show that, after bias correction, the root mean square error (RMSE) was 0.059 cm3/cm3, representing a 21.33% improvement, and the correlation coefficient R increased from 0.801 to 0.876, improving 8.56%. The validation with ISMN stations showed that the average RMSE decreased from 0.088 to 0.070 cm3/cm3 with a reduction of 20.45%, while the correlation coefficient R improved by 29.37% as well as the unbiased RMSE (ubRMSE) decreased by 11.76%. The result demonstrates that the proposed model further strengthens the capability of spaceborne GNSS-R in SM monitoring.

Cyclone Global Navigation Satellite System (CYGNSS) has emerged as an effective technique for inland water body detection due to its high sensitivity to inland waters. However, existing methods for inland water body detection using CYGNSS are limited by the difficulty in balancing high spatiotemporal resolution with strong generalization capability. Moreover, the limited spatial redundancy in short-term CYGNSS data restricts its capacity for high-precision inland water detection on its own. To address these issues, this study proposed a novel dual-branch model, termed STRUE. The model integrated a Swin Transformer and ResNet within a U-Net-enhanced student-teacher framework. This framework was developed through the fusion of multi-source data, including CYGNSS, SMAP, FABDEM, MODIS, and GSWE. The results showed that, for inland water body detection, the model attained a spatial resolution of 0.01° and a temporal resolution of 7 days. In terms of performance, it achieved an F1-score (F1) of 0.914, a mean Intersection over Union (mIoU) of 0.880, a Matthews Correlation Coefficient (MCC) of 0.873, and a Recall (R) of 0.963. Additionally, compared with traditional methods and models, the proposed model demonstrated a better performance in spatial continuity, structural integrity, and detail recovery, while mitigating common limitations such as cloud obscuration, spatial incoherence, and overestimation artifacts. These results further enhance the capacity of spaceborne GNSS-R for inland water body detection.