Sea ice thickness retrieval using Global Navigation Satellite System-Reflectometry (GNSS-R) is a challenging problem in sea ice remote sensing, especially for sea ice thicknesses over 1 m, which is still in the blank stage. In this paper, a seamless stacking-based retrieval method for sea ice thickness is proposed, which reduces the RMSE for thicknesses below 1 m while ensuring the accuracy of sea ice thickness retrieval for thicknesses above 1 m.Principal component analysis (PCA) was used to extract delayed Doppler map (DDM) features, while the scattering coefficient and incidence angle were calculated using TechDemoSat-1 (TDS-1) data. Sea ice salinity and temperature were derived from soil moisture and ocean salinity (SMOS) data and used as inputs to the model along with other features. The performance of four machine learning algorithms - Decision Tree (DT), K Nearest Neighbors (KNN), Support Vector Regression (SVR), and Random Forest (RF) - was compared, and the stacking model was constructed using these four algorithms as the base learner to improve performance. Validation using SMOS data for thicknesses up to 1 m showed that the stacking algorithm significantly improved retrieval accuracy, reducing the RMSE from 7 cm to 0.4 cm and improving the correlation coefficient (r) from 0.94 to 0.99. For thicknesses greater than 1 m, validation using Cryosat-2 data also showed strong performance. In addition, the effect of sea ice parameters on retrieval accuracy and sources of error was analyzed.

The Global Navigation Satellite System interferometric reflectometry (GNSS-IR) method with high spatial and temporal resolution is used to derive snow depth as a complement to existing snow products because of its ease of implementation. GNSS-IR snow depth retrieval accuracy is affected by the land cover and terrain irregularities on the reflecting surface. The number of full waveforms of the signal-to-noise ratio (SNR) is a reliable indicator of reflector height (RH). More importantly, this feature can be extracted in real time. Considering the presence of noise in the received signal, a fitting process is essential. In this article, we propose to use the particle swarm optimization (PSO) algorithm to fit the SNR oscillatory term and extract the number of fit peaks (NFP) to describe the number of full waveforms. Based on signal optimization, retrieval results are enhanced by exploiting the relationship between the good NFP (G-NFP) derived from the historical data and snow depth, and the operation is devoid of a priori constraints. The validation experiment used GNSS data from the P351 station of the EarthScope Plate Boundary Observatory (PBO) network and in situ snow depth measurements from the Snowdrift Telemetry (SNOTEL) network for 2020–2022. Snow depth retrieval results from 2020 to 2021 were used as historical data to derive the G-NFP distribution statistically. Statistically, each NFP corresponds to roughly 25 cm of snow depth change. The G-NFP distribution was then used in the snow depth retrieval process for 2022. The experimental results show that the root-mean-square errors (RMSEs) for global positioning system (GPS)-S1C, GLONASS-S1C, beidou navigation satellite system (BDS)-S2I, and Galileo-S1C based on the PSO-NFP method are 10, 13, 11, and 12 cm, respectively. Compared to the conventional method (CM), the accuracies have improved by approximately 38%, 43%, 35%, and 33%. Moreover, during the snow-free state, the retrieval accuracies based on the PSO-NFP method are improved by approximately 60% compared to the CM. The results show that the proposed method is very suitable for GNSS stations with large snow depth and terrain fluctuations and improves the retrieval results in the snow-free state. Moreover, NFP does not require prior data and can be extracted in real time, indicating its strong generality and potential to serve as a fundamental metric for other snow depth retrieval methods.

The Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technique based on signal-to-noise ratio (SNR) data is widely used for snow depth retrieval. Since snow depth retrieval in a snow-free state is very important for meteorological monitoring and since many corrections are post-processed to improve the retrieval accuracy, we propose a GNSS-IR snow depth retrieval model based on a back-propagation neural network optimized by a genetic algorithm to detect the snow state and predict snow depth using the frequency, amplitude and phase of the multipath oscillation term as input features. GPS data collected from the P351 station of the PBO network and measured snow depth from the SNOTEL network were used to conduct the experiments. The accuracy of daily snow state detection for the experimental station exceeded 96%. Combined with the snow state detection results for snow depth regression prediction, the experimental results show that the root mean square error of the snow depth retrieval results for P351 station is 12.09 cm. Compared with the traditional model, the retrieval accuracy is improved by 29.1%, and the correlation coefficient also reaches 0.97, indicating that the proposed snow depth retrieval model not only has high accuracy but also has strong stability. In this study, snow state detection is proposed to improve the retrieval accuracy in snow-free conditions, and the possibility of snow depth retrieval without antenna height is provided.

Global Navigation Satellite System-Reflectometry (GNSS-R) is one of the main technologies used for sea ice remote sensing detection, and is based on the multipath interference effect of satellite signals. To improve the GNSS-R sea ice detection performance in terms of accuracy, robustness to noise, and data utilization, a linear discriminant analysis (LDA)-based method was proposed in this paper. Delay-Doppler maps (DDMs) collected from TechDemoSat-1 (TDS-1) were employed as input and classified into different types based on the signal-to noise ratio (SNR) related to the noise effect. For low-effect-noise DDMs, the LDA-based sea-ice detection method presented an accuracy of 95.03%, verifying the feasibility of LDA-based GNSS-R sea-ice detection. For the middle noise effect and high noise effect DDMs, the LDA-based method is more robust to noise effects than the convolutional neural network (CNN) method. Although the detection accuracy decreased when the SNR decreased or integral delay waveform average (IDWA) increased, the LDA-based method was more robust than the CNN-based one. The data utilization and melting period were also analyzed to account for variations in detection accuracy. The LDA-based method used 67.82% more data than previous experiments with threshold IDWA≤58210.32 and SNR>-17.48dB. The melting periods were analyzed based on the noise, SNR, surface reflectivity, and permittivity. When the status of sea ice changes, outliers of surface reflectivity appear, the average permittivity varies in [10, 60], and the detection accuracy decreases during the melting period of sea ice. The results show that the correlation coefficient with the National Oceanic and Atmospheric Administration (NOAA) data is up to 0.93, with different threshold IDWA or IDWA. The LDA-based method predicted results that greatly matched the sea ice distribution from the NOAA data.

Snow depth monitoring is meaningful for climate analysis, hydrological research and snow disaster prevention. Global Navigation Satellite System-Reflectometry (GNSS-R) technology uses the relationship between the modulation frequency of the signal-to-noise ratio (SNR) and reflector height to monitor snow depth. Existing research on single constellation has made good progress and is gradually developing towards multi-constellation combined inversion. Aiming at the accuracy of snow depth inversion, this paper introduces the variational mode decomposition (VMD) algorithm with the characteristics of an adaptive high-pass filter to detrend the SNR data. The experimental results of KIRU station and P351 station show that VMD algorithm is suitable for different constellations and has better signal separation effect. The snow depth inversion results for both stations are in high agreement with the in-situ snow depths provided by the Swedish Meteorological and Hydrological Institute (SMHI) and the SNOTEL network, respectively. The root mean square error (RMSE) of the inversion results is reduced by 20-40% compared to the least squares fitting (LSF) algorithm, and the correlation coefficients are also greatly improved. Moreover, considering that there is no overlap between the climate station and the inversion area, this paper introduces the maximum spectral amplitude as another reference data source and obtains basically consistent experimental conclusions. On this basis, the maximum spectral amplitude is used as the input variable of the entropy method, and the feasibility of the combination strategy is studied. The results show that the combined strategy reduces a little inversion error and improves the temporal resolution of snow depth monitoring. It is of great significance for more accurate and rapid monitoring of snow depth changes and disaster warnings, and provides an important reference for further research on GNSS-R technology.

The characterization of global mean sea level is important to predict floods and to quantify water resources for human use and irrigation, especially in coastal regions. Recently, the application of global navigation satellite system reflectometry (GNSS-R) for water level monitoring has been successfully demonstrated. This paper focuses on the retrieval of sea surface height within a field experiment that was conducted at the Onsala Space Observatory (OSO) using the phase-based altimetry method. A continuous phase tracking algorithm, which relies on the GNSS amplitude and phase observations is proposed and works even under rough sea conditions at OSO's coast. Factors impacting the phase-based altimetry model, i.e., atmospheric propagation effects of the GNSS signals and influence of the GNSS-R observation instrument, are discussed. The relationship between the yield of coherent GNSS-R compared to the overall recorded events and the wind speed is investigated in detail. Ground-based sea-level measurements from June 10 to July 3, 2015 demonstrate that altimetric information about the reflecting water surface can be obtained with a root mean square error of 4.37 cm with respect to a reference tide gauge (TG) data set. The sea surface changes, derived from our field experiment and the reference TG, are highly correlated with a correlation coefficient of 0.93. The altimetric information can be retrieved even when the sea surface is very rough, corresponding to wind speeds up to 13 m/s. Moreover, the use of inexpensive conventional GNSS antennas shows that the system is useful for future large-scale sea level monitoring applications including numerous low-cost coastal ground stations.