Accurate road extraction from remote sensing images is of great importance, yet the complex backgrounds, significant scale differences, and detailed characteristics of road information pose a tremendous challenge to the semantic modeling capabilities of deep learning methods. Recent studies have shown impressive success with diffusion-based methods in semantic segmentation. However, when applied to road extraction tasks, the remarkable performance can be diminished by intricate terrains and unreasonable feature fusion. In this article, we reframe road extraction as a mask-generating task, progressively refining road mask predictions through a timestep-by-timestep denoising process. Specifically, we propose a novel adaptive frequency-aware latent diffusion framework named AFLDiff. The pretrained VQ-VAE encoder with coupled perceptual compression suppresses semantic features that are weakly associated with roads in remote sensing images, reconstructing the image into a low-dimensional latent space for training. The adaptive frequency-aware module filters out redundant noise from the noisy features and enhances useful high- and low-frequency components, bridging the gap between noisy and semantic features for better fusion. Extensive experimental results demonstrate that our method achieves state-of-the-art (SOTA) performance on the DeepGlobe, Massachusetts, and CHN6-CUG datasets, with cross-dataset validation further confirming its robustness and generalization capabilities.

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.

Exact quantifications of the interannual and transient ice mass balance of the Greenland ice-sheet (GrIS) are important for unraveling its complex dynamic processes under an increasingly warmer earth. In this study, we aim to analyze the transient mass variations of the GrIS over the past two decades, from April 2002 to April 2020. To achieve this, we integrate densely sampled time series data from various sources, including global positioning system (GPS) coordinates, gravity recovery and climate experiment (GRACE) and GRACE follow-on (GRACE-FO) gravimetry, and surface mass balance (SMB) model. The principal component analysis (PCA) method is applied to improve the characterization of the GPS common-mode components (CMCs) associated with the interannual oscillation. We found that there was an abnormal subsidence of the bedrock surrounding Jakobshavn Isbræ in West GrIS during 2016 to 2019, which could be primarily attributed to the elastic mass loading effects caused by the deceleration of glaciers linking to the pronounced decrease of ice discharge. To improve the accuracy of the elastic loading signals estimated by GRACE/GRACE-FO around the margins of Greenland, we proposed a GPS-based scale factor method and yielded a scale factor of 2.58 ± 0.15. After the analysis of multisource loading signals, we suggest that the current transient dynamic process of the GrIS is predominantly influenced by the runoff of meltwater, which is triggered by warm and dry atmospheric circulations characterizing by interannual climate episodes.

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.

Using the global navigation satellite system (GNSS) data from the Hong Kong region, this study comprehensively investigates the ionospheric irregularities responses to strong geomagnetic storms over the past two solar cycles 2001–2020. Based on the geomagnetic index Dst, a total of 64 strong storms are confirmed during 2001–2020. Statistical results indicate that for the total 64 strong storms, only 20 storms are considered to trigger irregular occurrences. When the occurrence local time (LT) of the minimum dDst (dDst$_{\mathrm {min}}$) is in 10:00–14:00 LT, no ionospheric irregularities occurred at nighttime although there is a total of 14 strong storms, while that of dDst min is in the nighttime of 18:00–21:00 LT, ionospheric irregularities are detected in ten out of 12 strong storms. For the two special storms on 19 April 2002 (dDst min occurred at 21:00 LT) and 23 May 2002 (dDst min occurred at 20:00 LT), they did not trigger the generation of ionospheric irregularities although their dDst min occurred in 18:00–21:00 LT. Based on vertical total electron content (VTEC) derived from global positioning system (GPS) measurements, it is found that the westward electric fields during two storms should play a vital role to inhibit the nighttime ionospheric irregularities (NIIs) occurrence. This study suggests that caution should be taken when the dDst min determined LT is used to decide the occurrence of nighttime irregularities.

Glacier mass changes in high mountainous areas are in an unbalanced status due to global climate warming. In this study, we use the Gravity Recovery and Climate Experiment as well as its Follow-on mission (GRACE/GRACE-FO) data to constrain the spatiotemporal variations of glacier mass in the high mountain of Tibetan Plateau (TP) during 2002 and 2020. The total glacier mass balance shows a descending trend over the TP and its surroundings with an overall melting rate of ~21 Gt/yr in the past two-decades. The significant glacier mass loss of spatial pattern is mainly concentrated in the periphery of the TP, such as the Himalayas, Hindu Kush and Tianshan, while the Kunlun Mountains demonstrates an increasing trend of glacier mass balance. The annual variation of glaciers in 22 sub-regions shows significant spatial differences in the amplitudes and phases, which is the direct feedback of regional climate change. In addition, based on the wavelet spectrum analysis, we find that the abnormal glacier mass changes correlate well with the interannual oscillations of precipitation and temperature. This corresponds to the extreme climate events, such as 2010-2012 La Niña event and 2015-2016 El Niño event. All these results validate the climate process in response to the interannual variation of glaciers on the TP, thus is of great significance to the study of systematic balance of regional water cycle.

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.

An improved inversion method with additional constraints for surface mass load is developed for the sparse distribution of GNSS stations. Two synthetic mass load models from a checkerboard mass distribution and a more realistic distribution derived from GLDAS model were applied to test the method. We find that applying boundary constraints and the a priori reference model in regions with sparse sites significantly improves the inversion results by 88% and 60%, respectively. Synthetic tests demonstrate that the improved inversion method can identify the errors well and extract useful signal for inversion. Compared with previous method, the potential improvement of our method to mass inversion with a sparse distribution of sites is about 13.3% to 38.3%. The method was then applied to the Yunnan with real GNSS vertical motion for terrestrial water storage (TWS) inversion. The inverted results show good agreement with GLDAS and precipitation data, with a correlation coefficient of 0.85 and 0.61, respectively. Our spatiotemporal patterns resulted from GNSS manifest that the TWS in Yunnan shows a gradual decreasing slope distribution from the southwest mountains to the northeast plains, with an annual EWH amplitudes of 317mm during 2017-2019. Moreover, the GNSS inverted TWS show that Yunnan received abundant rainfall in 2017 and 2018, while experienced a severe drought event in 2019. All these results indicate that for GNSS network with sparse distribution over large areas, our improved inversion method with additional constraints can be a useful and reliable method for monitoring mass load change and identifying climate extremes.

In global navigation satellite system reflectometry (GNSS-R) applications, water level variations could be determined by analyzing one of three types of data: signal-to-noise ratios (SNRs), combinations of carrier-phase observations and combinations of pseudorange and carrier-phase observations. In this study, a new GNSS-R method based on the analysis of a posteriori residual of undifferenced and uncombined (UC) Precise Point Positioning (PPP) and ionosphere free (IF) PPP was proposed. Double peaks were found in the Lomb-Scargle periodogram (LSP) estimates of dual-frequency carrier-phase residuals generated with UC PPP, the pseudorange and carrier-phase residuals generated with IF PPP and the detrended S2 time series. To solve this problem, we present a strict quality control strategy to screen the correct retrievals. Two experimental datasets from a dam and a bridge-deformation monitoring system, respectively, were used to evaluate the performance of this new method in water-level retrieval. The results show that the water level retrievals from the pseudorange and carrier-phase residuals generated by the UC PPP and IF PPP positioning method show a good agreement with the in-situ references, with correlation coefficient exceeding 0.95 and 0.97 in the two experiments, which is comparable to the SNR method. The retrievals showed a root mean square error (RMSE) of 6-7cm in the reservoir experiment and 13-16cm in the Ganjiang river experiment. Meanwhile, the inter-frequency bias for about 0.2m was found in the Xilongchi reservoir water level retrieval experiment, however, it is not the case for the Ganjiang River water level retrieval experiment. The new method enables the simultaneous determination of displacements and water-level variation and is thus applicable for in-situ displacement monitoring of civil engineering structures, such as dam, bridge etc.