Effective mitigation of atmospheric phase screen (APS) is crucial for synthetic aperture radar (SAR) interferometry in coastal regions. However, existing methods suffer from various limitations in estimating atmospheric delays, especially turbulent mixing components. As a solution, we propose a sparse-convolution-based attention deep residual U-shaped network (sARU-Net), a submanifold sparse convolutional neural network (SSCNN), for adaptive estimation of APS from InSAR coherent pixels. Moreover, we propose a straightforward and effective iterative processing strategy for generating sample datasets for model training. The results obtained from the TerraSAR-X and Sentinel-1 datasets in two coastal regions of China demonstrate a reduction in the standard deviation of the interferograms by 77.7% and 68.3%, respectively, after applying our correction method. In addition, the InSAR results align more closely with the leveling data. On the same dataset, our method shows superior performance compared with both the generic atmospheric correction online service (GACOS) for InSAR method and the linear model method. When compared with previous convolutional neural network (CNN), (i.e., attention-based deep residual U-shaped network, ARU-Net) method, our approach exhibits better accuracy and more detail and is 21% more computationally efficient for training, while requiring 24% less graphics processing unit (GPU) memory. The effectiveness of the proposed method shows that sparse convolution (SC) has great potential in mitigating the effects of APS in InSAR coherent pixels.

Retrieval of glacier surface heights from ICESat-2 photon-counting data is significant to monitor glacier surface morphologies (e.g., crevasses) and their changes. However, existing methods are susceptible to complex glacier surfaces and diverse signal-to-noise ratios (SNRs). Therefore, we propose a robust density estimation method for glacier-height retrieval from ICESat-2 photon-counting data. A multi-scale random sample consensus (RANSAC) strategy is firstly employed to obtain the best elliptical neighborhood for each photon, to improve the consistency of neighboring photons. Then, a hybrid weighted density estimation method is developed to robustly describe the differences in the spatial distribution patterns between signal and noise photons. High-quality extraction of signal photons is finally implemented using adaptive thresholds in the local along-track segments. To test its performance, four datasets with strong and weak beams from the Jakobshavn Isbræ Glacier, western Greenland, were selected. Results showed that the proposed method achieved excellent performances in glacier-height retrieval in various glacier surface morphologies and diverse SNRs, due to discriminative densities between noise and signal photons were obtained. The average percentages of along-track segments with signal densities larger than noise densities for weak and strong beams were 92.0% and 99.9%. The averageF1-scoresreached 0.927 and 0.968 for the weak and strong beams, even theF1-scorereached 0.892 for the heavily crevassed complex surface with a low SNR. Moreover, comparison experiments with existing methods demonstrated that the proposed method showed superior performances in density estimation and glacier-height retrieval, and improved more than 17.33% and 16.10% in the most complex surface, respectively.

The fidelity of Inter-Campaign Biases (ICBs) estimates for Ice, Cloud, and land Elevation Satellite (ICESat) ice elevation products is critical for ice sheet mass balance research. Prior studies have confirmed that ICBs are significantly dependent on data products, released versions, and surface types, leading to the hypothesis that ICBs for Level 2 Geosciences Laser Altimeter (GLA12) newly released products remain poorly determined. Here, we propose a new method to estimate ICBs using altimetry crossovers relative to a known stable ice surface, Dome A summit, East Antarctica, and a multi-time reference framework, which maximizes the number of crossovers. The ICBs of GLA12 release 634 product range from -8.1±1.1 cm (L3A) to 19.3±0.8 cm (L2C), and most of them range from -5 cm to 10 cm. However, the ICBs for a few campaigns show significant campaign-dependent variations (L2B and L3D). They exhibit insignificant trend (-0.9±0.8 cm/yr) during entire ICESat mission, but there is a clear trend at -3.0±0.3 cm/yr since February 2006 (L3E). We conclude that the time-variable ICBs should be carefully considered on ice sheet mass balance estimate, especially for specific campaigns.

Terrestrial laser scanning (TLS) is an important means to monitor landslides, and the layout is the key to guarantee captured point clouds with a high quality and low cost. Nevertheless, TLS layout in landslide monitoring is currently determined by user’s subjective experience, and shows a poor performance in reliability and point accuracy. Therefore, we propose multi-objective optimization based TLS layout planning for landslide monitoring. Three-dimensional (3D) scanning simulation of candidate scan positions is first carried out. A multi-objective optimization problem (MOP) is then constructed according to requirements of landslide TLS monitoring in the coverage, cost, and point accuracy. Finally, the combination of an improved non-dominated sorting genetic algorithm Ⅱ (NSGA-II) and the Pareto-Edgeworth-Grierson (PEG) algorithm is introduced to solve the MOP and obtain the optimal layout. To test the proposed method, experiments were performed in two landslide sites in Wuhan, China. Results showed that the planned TLS layouts reached a trade-off among coverage, point accuracy, and the number of TLS scan positions. Comparison experiments based on simulation demonstrated that the constraints fused into NSGA-II dramatically improved the reliability of the planned TLS layout, and the proposed method improved the coverage and point accuracy by 11.62% and 1.52 times compared to the user’s experience. Real scanning demonstrated the proposed method improved the coverage by 10.48%, and reduced the deformation error by 0.78 mm~7.66 mm compared to the user’s experience. In summary, the proposed method is an efficient approach for TLS layout planning, which can improve the ability of landslide TLS monitoring.

Atmospheric phase screen (APS) is a very critical issue for the application of interferometric synthetic aperture radar (InSAR) techniques. The spatial-temporal variations of APS are the dominant error source in interferograms and may completely mask displacement signals. Many external meteorological data-based methods and phase-based methods have been developed in the past decades, but all have their inherent limitations. In this article, we propose a deep learning-based method, which is based on an attention-based deep residual U-shaped network (ARU-Net), to mitigate atmospheric artifacts. With this approach, APS patches and clean interferogram patches are sampled from InSAR interferograms to train the network. After training, the network can be used to mitigate the APS for individual interferograms. Compared with the generic atmospheric correction model (GACOS) and the advanced time-series InSAR method distributed scatterer interferometry (DSI), the key advantage of our method is that atmospheric delay can be effectively learned and removed from individual high-resolution interferometric phase itself without external data. Accuracy was validated by using individual and stacked interferograms from TerraSAR-X data over the Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP) sites. The results showed that our method consistently delivered greater standard deviation (SD) reduction after APS correction than the GACOS method. Moreover, the time-series results were in agreement with the DSI and leveling measurements. The effectiveness of the proposed ARU-Net to remove APS effects from interferograms shows great potential for the development of a new set of deep learning-based APS reduction methods.

Global warming results in the rapid melting of glacial lakes in the Himalayas. In this paper, we took the Karakoram range in the Himalayas as the study area, and we derived the glacial lake area in 2001, 2014, and 2017 with semantic segmentation algorithm. Firstly, five high-resolution images in Mount Lucania were collected from Google Earth. Secondly, each image was labelled, and 80000 training images with the size of 256×256 pixels were derived using augmentation approach. Thirdly, a model of the stacking network of U-Net and SegNet based on these training images to extract the glacial lake was trained. Then, another five images of Karakoram range were derived from Google Earth as a testing dataset to demonstrate the performance of training model. Finally, the glacial lakes were extracted from the images of Karakoram range in the same month in 2001, 2014 and 2017. Our result showed that the area of the glacial lake in Karakoram range increased rapidly from 2001 to 2014, but decreased from 2014 to 2017.

With the development of the Moon exploration in recent years, the concept of using the Moon as an Earth observation platform is gaining increased attention. Due to the ultra-wide imaging swath, Moon-based synthetic aperture radar (SAR) / interferometric (InSAR) would be capable of monitoring global-scale phenomena. Here we propose a set of possible system parameters concerning the antenna size and the integration time. The lunar ephemeris DE430 is employed to calculate the spatial baseline of Moon-based repeat-pass InSAR pairs. The spatial baseline decorrelation is analyzed by using the statistic of the number of usable InSAR pairs, whose spatial baseline meets the criterion defined in this paper, with different temporal baselines and lunar declinations. Our result shows that the repeat-pass acquisitions with temporal baseline of 1, 26 and 27 cycles and the maximum lunar declination provide most of the usable InSAR pairs.

The Moon-based Synthetic Aperture Radar (Moon-based SAR) has a great potential to offer ultra-wide imaging swath with a high revisit cycle for Macro-scale Solid Earth Observation (EO). Theoretically, we propose a set of system parameters of Moon-based SAR concerning the antenna size and Pulse Repetition Frequency (PRF). In contrast to traditional spaceborne SAR missions, Moon-based SAR system works relying on the relative motion between the earth target and the system. Therefore, the actual relative motion of the Moon-Earth system is considered during the parameters simulation. In addition, the performance of Moon-Based SAR imaging is taken into account. Our results show that Moon-Based SAR would be capable of monitoring thousands of kilometers scale phenomena. It may form an imaging swath of 5000 km by using multi-beam SAR techniques. The results would support the system design of Moon-based SAR for global-scale EO.

Deriving shallow-water depth from multispectral satellite imageries appears very promising in near-shore bathymetric mapping for its high efficiency and low costs. Lyzenga's linear band method, which currently is the most widely used algorithm in the field, performs a linear regression between some known water depths and image-derived input variables. In real-world situations, the linear assumption in Lyzenga's method cannot be exactly satisfied because of complex environmental factors, and its performance is, therefore, often limited. In this letter, we improve Lyzenga's method by introducing generalized additive models to describe the nonlinear relationship between water depths and image-derived regressors. The performance of the new method was evaluated by using Landsat-8 multispectral data over the coastal areas of Oahu Island, Hawaii. The experimental results of two independent validation regions show that the depth-retrieval accuracy of the proposed algorithm was about 20% better than that of Lyzenga's method, and the performance improvement was most pronounced in the depth interval of 5-10 m.

In this study, we obtain ground subsidence result in Shangyu District, a coastal city of China, using DS-InSAR approach with 24 COSMO-SkyMed images. The causes of ground subsidence were analyzed with geology data and field investigation preliminary. The result indicates that urban construction, groundwater overpumping, compaction of fill materials and overloads induced by constructions in land reclamation zone were responsible for ground subsidence. The InSAR result was validated with levelling measurements, achieving an average error and a standard deviation with-0.8 mm/year and ±4.9 mm/year which could reach the standard of urban subsidence monitoring.

Although the precision of the surface parameters calculated from satellite data gets higher and higher, it is still difficult to guarantee the temporal consistency and spatial continuity for large scale geoscience phenomena. Developing new earth observation platforms is a feasible way to improve the consistency and the continuity. As the only natural satellite of the Earth, the Moon has special advantages as a platform for earth observation. This paper mainly discusses the advantages and the potential applications of moon-based earth observation, as well as the train of thoughts of further researches.

In this study, an advanced InSAR time-series analysis technique was applied to obtain the ground subsidence result in Hong Kong International Airport with 29 TerraSAR-X SpotLight images. In oder to improve the accuracy of monitoring points and the quality of both interferogram and coherence, the NL-InSAR filter is adopted in this processing framework. The result demonstrates the great prospects of NL-InSAR filter in InSAR time series analysis technique.

High-precision glacier Digital Elevation Model (DEM) and accurate glacier elevation changes is very important for the research of glacier mass balance and glacial dynamics over mountain glaciers. Here, we present a bi-static InSAR processing algorithm for DEM generation with TanDEMX-X(TDX)/TerraSAR-X(TSX) data and SRTM DEM in the western Qilian Mountains. First of all, a 10 m resolution of TDX DEM is calculated and evaluated as 1.493 ± 0.747 m. Moreover, the glacier elevation changes of the western Qilian Mountains between 2000 yr and 2013 yr are estimated, which are in the negative state as a whole from 2000 yr to 2013 yr. This study demonstrates the great potential of the TanDEM-X bi-static InSAR technology in mapping glacier surface topography and estimating glacier elevation changes over mountain glaciers.

Geladandong Ice Field is one of the largest ice fields in central Qinghai-Tibetan Plateau, also the water source of Yangtze River, Selin Co Lake and Chibozhang Co Lake. In this study, we aim to monitor the glacier flow rates and its change in 1990s and 2000s by Differential SAR Interferometry and offset tracking. We obtained SAR images acquired by ERS-1/2 and Envisat/ASAR in 1990s and 2000s then processed them with offset-tracking method, also validated with D-InSAR method. The result indicates that most glaciers in Geladandong Ice Fields kept stable flow rates at 15-30m/a during 1990s and 2000s. Some glaciers are identified as surge type. We selected two glaciers and studied the time series of flow velocity profiles. During the surging period, flow rate could be 3 to 10 time as normal years. These two glaciers forwarded their terminus during their surging period by Landsat optical monitoring. We concluded that during 1990s and 2000s most glaciers kept a stable flow velocity and some glacier surged during the study period therefore terminus forwarding cannot equal to mass gaining.

As global warming intensifies, most of mountain glaciers are in retreat in the worldwide. However, glaciers in Karakoram have been observed to be slightly thickening and advancing. Because the Karakoram Mountains are one of the most remote and less accessible mountain ranges in the world, there are very few in situ observations of glacier dynamics in the Karakoram. In this study, we use 3 ENVISAT ASAR images acquired during the period 2004-2006 to detect surface velocity of the Baltoro glacier. Glacier surface velocity is derived from the SAR offset tracking method which is widely used to estimate surface displacements. Our results indicate that the glacier surface velocity is relatively stable during 2004 to 2006. Moreover, we discuss ice dynamics mechanisms by using transverse velocity profiles.

Asian High Mountain glaciers play an important role in climate change and water cycle on both a global and regional scale. The Puruogangri is the largest modern ice field in the Tibetan Plateau with an area of 400 km2in total, where the information on ice topography and glacier velocity are scarce due to the difficulty to reach. In this study, the glacier surface elevation and velocity in the Puruogangri were measured using satellite SAR interferometry applied to a pair of ERS-1/2 SAR Tandem images acquired during 1998. A maximum surface velocity of 0.12 m/day was observed in glacier tongues of the eastern portion of the Puruogangri, with an averaged velocity of 0.07 m/day. The results are consistent to the filed observations obtained in 2002. The elevation of the Puruogangri estimated by the Tandem InSAR is ranged from 5200 m to 6200 m which is comparable to the SRTM DEM.

A new integrated SAR signal processor, geo-coding and interferometric processing commercial software (GeoWatch software) has been developed. It supports high speed and large data processing based on ERS-1&2, ENVISAT, ALOS PALSAR, JERS-1, RADARSAT-1&2, TerraSAT and CSK SAR data, with user friendly GUI and parallel processing on the latest 64 bit Windows and UNIX/Linux systems and both personal computers and powerful multicore CPU+GPU cluster platforms. It has been applied to the DSM estimation in a mountain area and subsidence estimation at an airport using its application-oriented step-by-step pipeline processing, and the results showed the following key features: 1. Deformation monitoring coverage expansion from coherent points to distributed scatters; 2. Deformation monitoring period reduction and accuracy improving by multi-track SAR interferometric processing; 3. Truly ortho-rectified interferogram, image, digital surface model (DSM) and deformation map production.

The TerraSAR-X SAR system provides high spatial resolution and geometric accuracy imagery which supports well the mapping of the 3-D structure of large-scale civil infrastructure and its motions (4-D). In this work, we investigated the potential of TerraSAR-X observations for monitoring rapid land subsidence induced by reclamation activities. A case study was conducted in Hong Kong Disneyland Theme Park (DTP) at the Penny's Bay, one of the largest land reclamation projects worldwide. A total of 16 TerraSAR-X scenes were used in a small-baseline PSI method for retrieving residual reclamation settlement in the study area. The preliminary results indicate that a remarkably high density of high coherent point targets (>2,500 PS point/km2) was identified in the reclamation area and a large land subsidence rate on the order of 35 cm/yr could be detected in the center of reclaimed land. The high detail level of deformation filed as well as the high sensitivity regarding rapid residual settlement makes TerraSAR-X interferometry a remarkable potential Earth Observation technique to enable the detection of ground deformation related to the large-scale infrastructure development in reclaimed land.

The Xianshuihe fault of Sichuan province, southwest China, is a highly active strike-slip fault approximately 350 km long. Previous studies have described up to 10-17 mm/yr of left-lateral slip on the Xianshuihe fault during the last decades, as indicated from geological criteria and GPS observations. The satellite InSAR observation is an alternative technique effectively used to measure the crustal deformation on the active fault. However, the conventional strip mode SAR imagery (i.e. ENVISAT/ASAR IM image) covers usually a 100 km wide stripe and will therefore not be sufficient for such large-scale deformation study. In this paper, we presented ScanSAR interferometry applied to ENVISAT/ASAR WSM data to produce about 400×400 km2 deformation map over the Xianshuihe fault zone and the Garzê-Yushu fault zone. The preliminary stacked deformation results of six WS-WS interferograms indicated a northeast-southwest deformation trend nearly perpendicular to the two fault zones and demonstrated the potential to monitor such wide-stretched deformation using ENVISAT WSS data.

Results of ocean surface wind speed retrieval from C-band ENVISAT ASAR images using a physics wind model are shown. The physics model is based on the radar backscatter theory in which the radar cross section is calculated considering the contribution of both Bragg scattering or resonance and specular reflection from the sea surface. The wind speeds retrieved from VV- or HH-polarized ASAR images using the physics model were compared to both buoy measurements in Hong Kong coastal waters and that retrieved using the empirical C-band algorithms (CMOD4, CMOD5, CMOD_IRR2). The results show the feasibility of the physics model for ocean surface wind speed retrieval from both VV and HH-polarized ASAR images at moderate wind condition.

An innovative constant false alarm rate (CFAR) algorithm was studied for ship detection using synthetic aperture radar (SAR) images of the sea. Two advances were achieved. An alpha-stable distribution rather than a traditional Weibull or$K$-distribution was used to model the distribution of sea clutter. The distribution of sea clutter in a SAR image was typically heterogeneous, caused mainly by variable wind and current conditions. Image segmentation was carried out to improve the homogeneity of the distribution in each subimage or region. In comparison with ship detection using the CFAR algorithms based on the Weibull or$K$-distribution, our algorithm detected the most number of ships with the smallest number of false alarms.