There are common sound speed errors resulting from insufficient density of sound speed profile (SSP) stations in a multibeam survey. Existing methods typically use empirical orthogonal function (EOF) analysis to create new SSPs that can mitigate sounding errors coursed by sound speed errors, but they are often inefficient due to their iterative nature. To address this issue, we propose a method for constructing SSPs with surface sound speeds (SSS) constraints to reduce the sounding errors. This method begins by standardizing the measured SSPs through unified stratification, followed by the clustering of the standardized SSPs (SSSPs). Next, the clustered SSSPs, along with highly accurate SSSs from the transducer, are utilized to estimate the parameters for the inverse distance weighting (IDW)-based spatiotemporal interpolation formula. Finally, the determined formula is employed in the depth direction to construct an interpolated SSP (ISSP), which is then used to correct sounding errors. Experiments verified the proposed method and the results show that the root mean squares (RMS) of the sounding errors improved by approximately 83%, decreasing from 1.169 m of the alternative SSP (ASSP) to 0.202 m of the ISSP. Additionally, the computational time of the ISSP was reduced by a factor of 67 compared to the EOF method.

The Surface Water and Ocean Topography (SWOT) mission offers significant potential in mapping sea surface height (SSH) for detecting mesoscale and submesoscale ocean signals. However, possible spurious signals caused by long-wavelength error (LWE) during SSH mapping pose a challenge in realizing the potential. We improved the widely used optimal interpolation method, to reduce the spurious mesoscale signal for SWOT. Since LWE remains in the SWOT SSH observations after cross-track calibration, the spatial differenced SSH observations instead of SSH observations were used as input for the mapping. The method was assessed using the Observing System Simulation Experiment (OSSE) and SWOT level 3 ocean products. The results show that LWE mainly has an effect on the mesoscale signal with wavelengths longer than 100 km, and the improved method can reduce spurious signals significantly compared to the standard optimal interpolation method. In addition, compared to the empirical optimal interpolation method commonly used in LWE reduction, the improved method has a comparable performance and no longer requires prior variance of LWE. For the uncorrected SWOT level 3 ocean products, the decimeter-level LWE can be reduced by the improved method and the mesoscale signal covered by it is successfully reconstructed. For the cross-track calibrated SWOT level 3 ocean products, residual centimeter-level LWE can also be reduced, and the SNR of mesoscale signals is improved by 62% for wavelengths longer than 100 km.

Sea level change is becoming increasingly complex in the context of global climate change. Accurate and reliable methods for monitoring water levels are essential for advancing the research of oceanic variations. The GNSS Interferometric Reflectometry (GNSS-IR) technique emerges as a complementary approach, leveraging signal-to-noise ratio (SNR) oscillations from reflected GNSS signals to estimate sea surface level. In GNSS-IR sea level retrieval, various factors including dynamic sea surface variations, surface roughness, and observation noise can introduce biases in reflector height (RH). These biases subsequently lead to phase errors in the SNR fitting process. A linear model is often employed to correct such phase deviations. The core challenge lies in the linear phase correction model’s inability to resolve phase deviations exceeding ±π, which introduces integer-cycle ambiguities. These errors, driven by the combined effects of multiple sources of uncertainty, result in RH residuals clustered around ±30–50 cm. To mitigate this issue, a two-stage correction method is developed: (1) a LightGBM (Light Gradient Boosting Machine) classifier identifies and corrects ±2π phase biases by analyzing SNR quality metrics (e.g., peak-to-noise ratio, full width at half maximum), environmental parameters (e.g., sea surface height change rate), and fitting residuals; (2) a sliding-window robust estimation refines RH values by dynamically compensating for residual outliers and tidal fluctuations. Validation across three coastal GNSS stations (SC02, CALC, FLCK) demonstrates significant improvements. The LightGBM model achieved 97.9~99.3% classification accuracy, effectively isolating and correcting 72–93% of ±2π deviations. Combined with robust estimation, the method reduced root mean square error (RMSE) by up to 48.6% compared to classical Lomb-Scargle Periodogram (LSP) results. Residual distributions transitioned from bimodal clusters to centralized peaks near zero, confirming the elimination of stratification artifacts.

Changes in lake water levels are closely related to climate change and can also reflect information about local human activities. Therefore, obtaining high temporal resolution time series of lake water levels is necessary for accurately analyzing hydrological changes. However, the existing methods mainly focus on the long-term changes in lake water levels, with less attention paid to short-term changes in lake water levels. In this article, we proposed a new method to construct high temporal resolution lake water level time series by fusing multisource altimetry satellite data based on Kalman filtering and using the MissForest algorithm to combine meteorological data Kalman Fusion-MissForest water level (KF-MFWL). The accuracy of KF-MFWL was validated using gauge data, as well as compared with HYDROWEB and DAHITI. Finally, a dataset of daily lake water level time series for the Qinghai-Tibet Plateau from 2019 to 2021 has been compiled, and the driving factors influencing water level changes were analyzed. Our result shows that the KF-MFWL time series is comparable to that of HYDROWEB and DAHITI, but with a much higher temporal resolution. The annual rate of water level change for 264 lakes in the Qinghai-Tibet Plateau is 0.021 m/y. Among them, the water level of 82 lakes has significantly increased with an average annual change rate of 0.171 m/y, while that of 55 lakes exhibits a remarkable decrease with an average annual change rate of −0.145 m/y. This study can provide an important data basis for water resource management in the Qinghai-Tibet Plateau region.

The global navigation satellite system (GNSS) refraction pattern technique makes use of two pairs of GNSS receivers and antennas to collect the refracted signal in the medium and the direct signal in the air, respectively. Due to the sensitive response of the refracted signal to variation of the dielectric constant, the technique is suitable for measuring medium dielectric constant-related parameters such as snowpack density, vegetation water content, and soil moisture. In this article, a forward model related to the power ratio of the refracted signal to the direct one is developed to elucidate the mechanism of soil moisture-induced refracted signal strength attenuation. By making use of the simulating results derived from the model, a second-order polynomial function is established to describe the relationship between the power ratio, soil temperature, and soil moisture. Based on the function, an inversion algorithm, which takes the GNSS carrier-to-noise (C/N0) observations under high elevation angles (50°–60°) and soil temperature as the inputs, is proposed for the near-surface soil moisture estimation. The proposed algorithm is validated through a dataset collected in an experimental campaign over two years. The results demonstrate that there exists a good agreement between the proposed method-derived soil moisture estimations and ground-truth ones; and the root-mean-square error (RMSE) of the proposed algorithm-derived soil moisture estimation is 0.009 cm3cm−3 when the ground-truth soil moisture is in the range from 0.150 to 0.550 cm3cm−3. Because the observations were collected by using consumer-grade GNSS chips and antennas, this study also provides a basis for the design and development of the low-cost GNSS soil moisture sensor in the future.

The great potential of NASA’s Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) to retrieve sea ice heights has been demonstrated. However, the presence of a significant number of noise photons in the ICESat-2 data makes accurate monitoring of sea ice changes challenging. This paper proposes an adaptive clustering and kernel density estimation-based (AC-KDE) method for estimating sea ice heights in ICESat-2 photon clouds. First, the adaptive clustering method effectively detects sea ice signal photons. The method’s input parameters are determined based on the ATLAS parameters and the LiDAR transmission equation. Then, the adaptive-count signal photon aggregates are used to estimate sea ice heights, and a variable along-track resolution is obtained using the kernel density estimation method. The AC-KDE method is applied to the MABEL and ICESat-2 data, and we compare it with other denoising algorithms, including the HBM, DBSCAN, OPTICS, UMD_RDA, DDM, and ILSM algorithms. The results indicate that the proposed method outperforms these algorithms in extracting signal photons with higher accuracy scores and F-scores, which are 0.97 & 0.97, 0.92 & 0.90, and 0.89 & 0.72 under high-medium-low signal-to-noise ratio conditions, respectively. Additionally, the retrieved sea ice heights are compared with the ATL07 heights. The AC-KDE heights show a significant correlation with coincident ATM heights, and have a lower RMSE value (0.066 m) compared to ATL07 heights (0.104 m). The AC-KDE method also demonstrates a vertical height precision of 0.01 m over flat leads. The proposed method can effectively extract signal photons and accurately estimate sea ice heights in polar regions.

Global Navigation Satellite System interferometric reflectometry is an emerging remote sensing technique that can be used to measure a wide range of geophysical parameters. In this letter, a low-cost navigational GNSS chip and RHCP antenna were used to receive and process the interference GNSS signal in a winter wheat farmland. By simplifying the wheat crop as multi-layer equivalent mediums (EMs), the characteristics of GNSS SNR observations recorded by the instrument were analyzed. The confidence level of the Lomb-Scargle spectral analysis result was used to identify the peak frequencies of the GNSS SNR series. Based on the peak frequencies, the EM heights can be calculated. The height estimations were used to compare with thein situwheat height measurements. The results show that the estimated EM height is inversely proportional to thein situwheat height in the wheat stem extension stage, with a correlation coefficient of −0.9939; and the estimation is very close to thein situones in wheat heading and ripening stages, with a root-mean-square error of 5.8 cm when the wheat height ranges between 40 and 75 cm.

In the last 20 years, the reflected signal of Global Navigation Satellite System (GNSS) has been used for remotely sensing a series of geophysical parameters, resulting in two GNSS based remotely sensing techniques: GNSS reflectometry (GNSS-R) and GNSS interferometric reflectometry (GNSS-IR). In this letter, the refracted GNSS signal is first proposed to estimate near-surface soil moisture (SM). Amplitude of the refracted GNSS signal will attenuate when penetrated into soil due to refraction and propagation of the signal in the soil. Amplitude attenuation degree of the refracted signal is quantified as the amplitude ratio (AR) of the direct GNSS signal to the refracted signal. Two low-cost navigational GNSS chips and right-hand circularly polarized (RHCP) antennas are used to collect the refracted and direct GNSS signal in an experimental campaign, respectively. To simplify the modeling, the AR at elevation angle of 20° is used to develop the model to describe the relationship between SM, AR, and soil temperature (ST) in the letter; and the AR and ST observation can be converted into SM accurately with a 2nd-order polynomial. The modeled SMs are strongly correlated with the sensor-based ones with correlation coefficient of 0.947 and root-mean-square error (RMSE) of 0.013 cm3/cm3(or, 1.3%) when SM is between 0.272 and 0.489 cm3/cm3. The study also suggests that, based on the proposed method, the low-cost GNSS instrument can be treated as a new type of sensor monitoring SM in a cost-effective way.

Soil moisture is fundamental to land surface hydrology, affecting flooding, groundwater recharge, and evapotranspiration. In this paper, a low-cost GNSS receiver is used to estimate soil moisture the first time. A new soil moisture estimation method based on the receiver SNR data is proposed. The relationship between amplitude attenuation factor (AAF) of SNR and soil moisture is investigated by using in-situ observations at first. Then, the retrieval method of SNR AAF is proposed. GPS data collected over a month in Chongqing, China was used to test the proposed method. Based on in-situ SNR and soil moisture observations, 1stand 2ndregression functions converting AAF into soil moisture were established by least squares method. The preliminary results show that there is a good agreement between the insitu soil moisture and the estimated one by the proposed method, with the RMSE smaller than 0.012 when soil moisture is in the range of 0.35 to 0.45.

In this paper we propose a new snow water equivalent (SWE) estimation method using GNSS-R method. The forward model is established to describe the relationship between antenna height (snow depth), snow density and multipath error by using combination of pseudorange and carrier-phase of GNSS dual-frequency signals. As the function of antenna height and snow density, the forward model is used to construct the fitness function based on least squares principle. Then, the problem of inversion of antenna height and snow density is transformed into a conventional multi-variable function optimization problem. The genetic algorithm is used to find the minimum of the fitness function to obtain the optimal snow depth and snow density estimates. The Galileo satellite navigation system data of an experimental campaign conducted in Harbin, China was used to test the proposed method. The preliminary results show that the proposed method can achieve SWE estimation accuracy of about 4cm.