GNSS Interferometry Reflectometry (GNSS-IR) technology achieves sensing of the surrounding environment using geodetic GNSS receivers. However, a distinctive type of GNSS-IR error manifests in the GPS L2P signal, characterized by a "double-peak" phenomenon in the signal-to-noise ratio (SNR) spectra, leading to inversion errors in GNSS-IR. Currently, the origins of this error are unclear, and corresponding correction strategies are lacking, thereby constraining the application of the GPS L2P signal. This study aims to delve into the causes of the double-peak error and propose a correction method. Firstly, according to the SNR forward model and the L2P tracking method, we simulated the L2P SNR arcs tracked through cross-correlation and Z-tracking methods. The simulated L2P SNR arcs exhibit oscillatory characteristics influenced by the L1 signal. Subsequently, various sites situated in marine, reservoir, or permafrost regions are chosen to analyze the spectral characteristics of L2P SNR data. The recorded L2P SNR sequences correspond well with the simulated L2P SNR sequences. We further scrutinize the impact of double-peak errors on L2P inversion results. Finally, we present a correction strategy for the "double-peak" error. The results indicate that the proposed error correction strategy effectively rectifies the L2P "double-peak" error, thereby enhancing the utility of GPS L2P signal inversion in GNSS-IR applications.

Phase decorrelation hampers the accuracy of distributed scatterer (DS) interferometry (DSI) in high-precision deformation monitoring. While several advanced phase linking (PL) techniques built upon the sample coherence matrix (SCM) have shown effectiveness in enhancing the signal-to-noise ratio (SNR), their performance significantly degrades under suboptimal SCM estimation, particularly in scenarios of fast decorrelation and near-zero coherence levels. This article presents an enhanced sequential phase estimator motivated by the degradation of theoretical accuracy due to pure noise-bearing interferograms in full-stack-exploiting PL schemes. In the proposed estimator, Bayesian ensemble theory is first employed to divide the full-stack data adaptively into ministacks based on the coherence pattern, rather than constant-size ministacks usually determined empirically. Building on this, the estimator introduces a nonconvex regularization-based sparse SCM by constraining the coherence matrix to have potential sparsity and low-rank (LR), which suppresses the influence of noisy interferometric pairs and improves SCM estimation. Moreover, we incorporate the deformation elasticity theory to provide additional spatial constraints on the reconstructed phase time series across adjacent pixels, realized by using the strain model to reduce phase discontinuity and further enhance the SNR. Experiments on the simulated and real Sentinel-1 images over a landslide-prone area in western Guizhou, China, demonstrate the effectiveness and superior performance of the new method.

The Fully-focused Synthetic Aperture Radar (FFSAR) altimetry technology has greatly enhanced the accuracy of coastal sea surface height (SSH). However, due to its pulse-limited mode in the cross-track direction, FFSAR is still susceptible to land interference. Additionally, the spatiotemporal resolution of in-situ data limits the evaluation of altimetric SSH in both coastal and open ocean regions. This study proposes a waveform interference detection and removal algorithm (WIDA), which utilizes waveform differencing and azimuthal constant false alarm rate techniques to identify and reject interference signals in island areas. Tide gauge data, particularly a seamless and high-resolution airborne gravimetric quasi-geoid model, are employed to validate the performance of WIDA, providing a new perspective for the continuous and consistent evaluation of SSH profiles. Validation of five years of data from six Sentinel-3A/B tracks over the Xisha Islands in the South China Sea demonstrates that FFSAR_WIDA (FFSAR waveforms retracked by WIDA), with 80 Hz sampling, significantly outperforms FFSAR_CORAL (Coastal Retracker for SAR Altimetry), UFSAR (Unfocused SAR), and UFSAR_WIDA within 5 km of the tide gauge station. The standard deviations (SD) for FFSAR_WIDA, FFSAR_CORAL, UFSAR, and UFSAR_WIDA are 0.153 m, 0.192 m, 0.367 m, and 0.173 m, respectively. Moreover, the evaluation with the quasi-geoid also reveals a marked improvement over FFSAR_CORAL and UFSAR within 5 km from the islands. The spatial distribution assessment shows that as the distance from the island decreases, the SD for FFSAR_CORAL (UFSAR) increases from 0.010 m (0.011 m) at 15 km to 0.061 m (0.089 m) at 3 km, while FFSAR_WIDA (UFSAR_WIDA) remains consistently below 0.011 m (0.014 m). Index Terms—FFSAR, Island Areas, Land Interference, Validation, Sea Surface Height.

Snow depth monitoring is of great significance for climate research and forecasting, natural resource management, ecological environmental protection, and disaster risk reduction and prevention. The existing in-situ snow sensor network has limited spatial coverage and resolution. With the continuous development of Global Navigation Satellite Systems (GNSS), a ground-based remote sensing technology, named GNSS-multipath reflectometry (GNSS-MR), based on GNSS geodetic receivers, has been proven to be capable of estimating snow depth. GNSS-MR infers the height of the reflecting surface beneath the antenna—referred to as reflector height (RH,RHcal)—by analyzing the frequency of multipath oscillations in the Signal-to-Noise Ratio (SNR) data, thereby enabling snow depth estimation. However, since snow typically accumulates on uneven terrain, terrain undulation becomes the primary source of error in snow depth retrieval. Currently, the principles and methodologies for correcting such terrain effects remain underdeveloped. To address this, the present study derives the geometric relationships under sloping terrain conditions and establishes a geometric model, including a tilt-induced error model. Building on this geometric model, the study further derives terrain correction methods for snow depth retrieval under undulating terrain and proposes a multi-GNSS combination approach that incorporates terrain correction. The P351 GNSS station was selected for experiments, with data analyzed from four snow seasons between 2019 and 2023. Meanwhile, DEM data around the site were collected to calculate parameters such as slope and aspect for terrain correction. To assess the impact of DEM resolution on terrain correction effectiveness, DEM data with 1 m and 12.5 m resolutions were used for comparison. The results indicate that using the 1 m resolution DEM improved accuracy by 42%, while the 12.5 m resolution DEM led to a 34% improvement. Notably, even with globally available low-resolution DEMs, the correction method still performs satisfactorily, demonstrating strong adaptability and practical utility.

The potential of using airborne gravity gradient tensor (GGT) for coastal quasi-geoid (QG) refinement is explored, and the contributions introduced from individual GGT components and their combinations are quantified and evaluated. High-resolution QGs, with a spatial resolution of ~0.5 km, are computed over St. George’s Bay in southwestern Newfoundland, Canada. The findings indicate that fully focused Synthetic Aperture Radar (FFSAR) and Surface Water and Ocean Topography (SWOT) altimetry data are effectively in differentiating the performance of various QGs in coastal areas. The application of the vertical gravity gradient obtains the highest quality QG when utilizing individual GGT components. The combination two or more components results in improved QGs compared to the results derived from individual components. The integration of full GGT yields the best QG, with standard deviation (SD) of misfits against Sentinel-3A FFSAR (SWOT) altimetry data being 1.13 (2.49) cm, representing reductions of 28.48–53.50% (5.32–15.02%) compared to results derived from individual GGT components. Comparisons of the QG computed by fusing full GGT with the Canadian gravimetric QG CGG2013 and high-degree global geopotential models further underscore the advantages of using GGT in QG modeling, revealing SD reductions of 55.16–65.02% (7.55–33.95%) against FFSAR (SWOT) altimetry data. These findings underscore the effectiveness of using airborne GGT in coastal QG modeling, particularly in recovering short-wavelength signals and addressing challenges in satellite altimetry over coastal environments. Additionally, this study highlights the superiority of using full GGT over individual components in QG modeling.

Bathymetry provides instrumental information for studying sedimentary processes, global climate change, and benthic morphologies. The advantages and applicabilities of different techniques for bathymetry detection vary. We propose a framework for bathymetry enhancement from multisource data based on spherical radial basis functions (SRBFs). A case study is conducted over the Paracel Islands in South China Sea (SCS), where Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) measurements, airborne gravimetric observations, echo soundings, and the reference model DTU18BAT are merged. Numerical results suggest that the fusion of ICESat-2 observations dramatically enhances the quality of the computed bathymetry model near the islands, the root-mean-squared error (RMSE) of which is reduced by 45.35%–67.95% compared to existing models when validated against the satellite-derived bathymetry (SDB) with decimeter-level accuracy. By additionally fusing the airborne gravimetric data, bathymetry is further enhanced by ~22.49%, particularly over islands with sparse ICESat-2 trajectories. Comparisons with surveyed airborne bathymetric lidar data over the northern Antelope Reef yielded results consistent with those obtained from the SDB, suggesting that SDB is possible to serve as control data in waters devoid of ground truth data. Further analysis reveals that the models constrained by echo soundings performed better than existing models in deep waters, with reductions of 17.79%–44.99% in terms of RMSE. By fusing airborne gravity data, bathymetry is improved by ~10%, highlighting the utilization of airborne gravimetry in both shallow and deep waters. The proposed SRBF approach offers an effective way to merge heterogeneous data for high-quality bathymetry determination.

The lack of marine gravimetric measurements and the presence of severely contaminated altimetry data pose multiple challenges in high-quality quasi-geoid (QG) and mean dynamic topography (MDT) determination over islands, where the application of nadir altimetry alone is inadequate. We explore the potential for regional enhancement using wide-swath data from the Surface Water and Ocean Topography (SWOT) mission. Numerical experiments over the Paracel Islands in South China Sea underscore the superiority of using SWOT data in QG/MDT computation. Comparisons with the airborne gravimetry-derived QG reveal that the Root Mean Square Errors (RMSEs) of QGs derived from the SWOT data are reduced by 38.24–60.90% compared to those derived solely from nadir altimetry. The QG profiles retrieved from the Sentinel-3A/B altimetry using the fully-focused SAR technology are effective in discriminating the quality of different QGs near islands. The RMSEs of SWOT-derived QGs constitute reductions of 6.27–17.52% compared to those computed from nadir altimetry alone. Notable improvements up to 4 cm are observed when Sentinel-3A/B tracks approached islands. The mutual comparison of the QGs computed from the SWOT gravity anomaly (GRA) and vertical gravity gradient (VGG) data suggests that the VGG-derived QG has improved quality, and the utilization of VGG recovers more small-scale signals. The SWOT-derived MDTs reduce the bubble-like errors up to several centimeters compared to those computed exclusively using nadir altimetry. Our findings highlight that utilizing SWOT data enables the acquisition of an accurate QG/MDT with an RMSE less than 1 cm compared to that derived from airborne gravimetry.

Global navigation satellite system (GNSS) real-time kinematic (RTK) positioning is highly appreciated for realizing high-accuracy navigation and positioning in many fields. Noteworthily, correct and reliable ambiguity resolution is a prerequisite of high-accuracy RTK positioning. However, refining the delicate handling of ambiguity resolution estimator and resolution form is still a tough issue, especially in urban environments. We proposed a new resilient ambiguity resolution strategy driven by batch best integer equivariant (BIE) estimator. The proposed strategy can resiliently adopt the integer rounding, bootstrapping, integer least squares (ILS), and BIE, which are paired with full ambiguity resolution (FAR) or partial ambiguity resolution. To validate the effectiveness of the proposed strategy, deformation monitoring and vehicle-borne experiments are carried out, including in typical urban scenarios. The results show that, in the monitoring experiment, the three-dimensional (3D) positioning accuracy of the proposed strategy is improved by 84.9% compared to the commonly used ILS of FAR. For the vehicle-borne experiment in urban environments, the positioning solutions of the proposed strategy perform the best with a decimeter-level positioning accuracy, which coincide well with the reference trajectory. The proposed strategy has the highest positioning availability with 83.3% and 91.9% epochs of positioning errors smaller than 0.2 m and 0.5 m in horizontal direction, respectively, which is promising for autonomous driving at Level 3 and high-accuracy lane-level navigation applications in urban environments. In conclusion, our proposed strategy exhibits the best positioning accuracy and availability, which is especially suitable for urban environments.

Significant wave height (SWH) is a crucial parameter that characterizes oceanic wave behavior, playing a pivotal role in oceanic research and disaster management strategies. Currently, the observation of SWH predominantly relies on both buoy and spaceborne microwave remote sensing techniques. However, these techniques face challenges when applied in coastal regions. Addressing this issue, this study introduces an innovative approach leveraging reflected signals to estimate SWH based on coastal Global Navigation Satellite System (GNSS) stations. We establish a model for wave height retrieval by examining the temporal variation in signal-to-noise ratio (SNR) data related to SWH, drawing upon the GNSS interferometric reflectometry (GNSS-IR) method for SWH retrieval. Experimental findings highlight the efficacy of the multisystem GNSS-IR SWH inversion. It demonstrates an accuracy of 12 cm, coupled with an average temporal resolution of 29 min, and exhibits a strong correlation coefficient of 0.95 when compared to ocean buoy measurements. The deployment of coastal GNSS stations emerges as a promising source for obtaining true and reliable data on coastal SWH.

How to improve the phase signal-to-noise ratio (SNR) of distributed scatterers (DSs) is a key topic in DS interferometry (DSI). Although some state-of-the-art phase linking (PL) estimators have been proposed, their performance is still limited by the accuracy of the estimated sample covariance matrix (SCM). The key challenges arise from the biased estimation of the near-zero coherence matrix (the magnitude matrix of SCM) under conditions of small sample sizes and heterogeneous samples. To overcome this limitation, we present a sparse regularization-based PL estimator that considers the potential sparsity structure of the inverse covariance matrix. In this new estimator, we first introduced the graphical lasso (GLasso) algorithm into the small samples estimation problem of SCM, which suppresses the biased estimation of the sparse inverse covariance matrix by introducingL1-norm regularization, significantly reducing the impact of weakly coherent interferograms in fast decorrelation scenarios. Furthermore, we also attempt to generalize this scheme to long-term coherence cases through the utilization ofL2-norm regularization. Both synthetic data tests and real Sentinel-1 data covering Changi Airport, Singapore, demonstrate the validity of the proposed approach.

With the development of Global Navigation Satellite Systems (GNSSs), it has been demonstrated that signal-to-noise ratio (SNR) observations from geodetic GNSS receivers can be used to estimate the parameters of reflecting surfaces, through a technique called GNSS-interferometry reflectometry (GNSS-IR). Recent studies have revealed that SNR-derived cutoff elevation and damping coefficient have the potential to estimate wind speed. However, the specific mathematical relationship between them and the wind speed remains undetermined. Consequently, this study, based on the existing approximate models for coherent and incoherent components of specular scattering, upgraded an existing SNR simulator to account for incoherent power of scattering signals. This upgraded simulator was used to conduct SNR simulations under varying wind speeds, allowing for the derivation of the relationship between damping coefficient and wind speed, as well as the relationship between cutoff elevation and wind speed. Moreover, an optimal design approach was introduced to estimate the damping coefficient of an SNR arc, preventing divergence in cases of insufficient matrix rank using the least-squares method. Data from GPS L5 signals recorded at the HKQT station during Super Typhoon “Mangkhut” were used. The results indicate that the simulated cutoff elevations and damping coefficients can reflect the trend of actual parameters varying with wind speed. However, there are still some differences between the simulated data and the actual data. At high wind speeds, the simulated cutoff elevation angles are lower than the actual extracted values, while the simulated damping coefficients are higher than the extracted coefficients.

Gravity anomaly over shallow waters is one of the fundamental data sources for studying sea level change, ocean currents, and water exchanges between coastal areas and open seas. However, the acquirement of gravity data over shallow waters faces multiple challenges due to the degraded quality of satellite altimetry data and scarcity of surveyed gravimetric observations. To alleviate this problem, we establish a framework for marine gravity anomaly refinement by using satellite-derived bathymetry (SDB). We use a cosine-tapered band pass filter to extract high-frequency gravity signals from the SDB data, which compensate for the unresolved signals in satellite altimetric gravity data. Numerical experiments over the Discovery Reef and an offshore region near the Port Hedland demonstrate that the utilization of SDB effectively strengths marine gravity anomaly. By combining the SDB data, the fits between the enhanced gravity anomaly models and surveyed airborne gravity data are improved, by 5.3–15.7% in comparison to an altimetric gravity model DTU21GRA. The SDB calculated from the linear band model has slightly better performances in gravity anomaly modeling than that computed from the band ratio model and physical-based approach, agreeing well with the SDB validation results. Our results verify the feasibility of using the SDB computed from the physical-based approach for gravity anomaly augmentation, which is of great value in areas devoid of ground truth depths. This study cements a way for the augmentation of marine gravity anomaly worldwide, especially in remote regions characterized by the scarcity of ground-based gravity data.

Phase Unwrapping (PU) based on spatial networks is a key procedure in time series synthetic aperture radar interferometry (TS-InSAR). Although the state-of-the-art techniques have shown good success in common cases, their performance remained uncertain in some challenging cases where the reliability of spatial network is difficult, e.g., reservoir areas with sparse points. In this context, this letter presents a new 3D PU method based on the fully connected spatiotemporal network (FCSN) to improve both accuracy and robustness of PU. The rationale behind is that we first implement a spatiotemporal network refinement including temporal interferogram pair selection and spatial network optimization. Based on the generated spatiotemporal network, we then establish a 3D PU mathematical framework by elaborating the 2D edgelist PU theory into the 3D domain. In this framework, all interferograms are unwrapped using integer linear programming method under the minimumL1-Norm criterion. The new feature of the proposed method is that after a single solution search, all interferograms are unwrapped with a relatively high accuracy. The experimental results on two real datasets confirm its effectiveness.

Tropospheric delays (TDs) still hinder the millimeter-scale measurement accuracy of interferometric synthetic aperture radar (InSAR). Towards the higher accuracy, this letter presents a new time series TDs correction method. The rationale behind the proposed method is that multi-view clustering (MvC) is introduced to identify the spatiotemporal TDs behaviors, particularly, in which one-pass multi-view clustering (OPMC) algorithm is employed to perform window segmentation rather than sticking to the commonly used boxcar windows. Next, a phase-elevation network correction model in each cluster is constructed by fully considering the spatiotemporal phase information. Besides, an iterative weighted scheme is designed to further enhance the robustness of the estimated model parameters. The Sentinel-1 datasets covering the southwest mountainous area, China, confirm the effectiveness of the new method.

Global navigation satellite system multipath reflectometry (GNSS-MR) has been validated for estimation of water level. More satellites and signals would aid the accuracy and sampling rate of GNSS-MR. GPS, GLONASS, Galileo, and BeiDou Navigation Satellite System-2 (BDS-2) have been studied for their GNSS-MR water-level retrieval performance. This study focuses on evaluating the retrieval performance of new BDS-3 signals. In addition, the BDS has a unique orbit design, including the geostationary orbit (GEO), inclined geosynchronous orbit (IGSO), and medium earth orbit (MEO). This study also examined the retrieval performance of signals from different orbits. The results showed that the signals from IGSO and MEO of BDS can be used to retrieve water level, whereas those from GEO cannot. The water-level retrievals of BDS-3 signals have similar root-mean-square errors (RMSEs) to the signals of the other three constellations, and lower RMSEs are related to lower BDS signal frequencies. The accuracy of GNSS-MR combined retrievals with the participation of BDS improved by 22.04% compared with that without BDS.

We study the role of airborne gravimetry for seamless bathymetry modeling over the Paracel Islands in northern South China Sea, and investigate the possibility of using ICESat-2 data and satellite-derived bathymetry (SDB) to evaluate bathymetry models over shallow waters. We use ICESat-2 data for training Sentinel-2 imagery and derive the SDB data with a Root Mean Squared Error (RMSE) of 0.29-0.50 m, which is lower than 10% of the maximum depths. The local bathymetry is modeled by using a modified version of S&S band-pass filter, and a partition-wise scheme is applied for determining the scaling factors. Numerical experiments verify the feasibility of using ICESat-2 and SDB data to assess bathymetry models. By utilizing the airborne gravity data, the fit between the computed bathymetry and the SDB data is significantly improved, by 18.7-58.0% over different shallow waters compared to recently released bathymetry models. The bathymetry predicted from the airborne data has also higher performance in deep water areas, which performs best in all these depth ranges from 500 to 3000 m. In comparison to the existing models, the RMSEs of the misfits between the computed bathymetry and the National Oceanic and Atmospheric Administration depths are reduced by tens to hundreds of meters in different depth ranges. Our study highlights that using airborne gravimetry for bathymetry modeling over island areas is advantageous, in both shallow and deep waters; and that ICESat-2 and SDB data can largely alleviate the lack of in-situ depths over shallow waters.

Orbital error, one of major error sources of InSAR observations, is characterized by long wavelength artifacts which can downgrade the monitoring accuracy, especially for wideswath SAR missions. In this paper, we present a novel approach for time series orbital error correction, with an emphasis on the computational and estimation efficiency of orbital error parameters over wide-area scale scenes. The proposed method combines the temporal triplet network and shrunken estimator, which integratesL2-Norm withL1-Norm regularization, also known as Lasso regularization. The rationale behind it is to first determine the initial orbital parameters by utilizing the traditional polynomial-based method in the spatial domain. Next, in order to weaken the interference of phase unwrapping errors and other undesired phase contributions, an additional correction procedure is implemented through building up redundant triplet network in the time domain and further a shrunken estimation method. Experiments on synthetic data and real Sentinel-1 datasets covering Eastern California confirm that the presented method can better balance the accuracy and computational efficiency. The proposed approach may therefore be useful for the processing of emerging big data InSAR.

This article analyzes the interferometric measurements of ground-based global navigation satellite systems (GNSSs) stations and proposes a novel method for sea surface states detection. The novel technique benefits from a cost-effective data collection from a large number of global GNSS stations. In this study, we extend a traditional GNSS interferometry reflectometry (GNSS-IR) model so that it can be applied to a multilayer surface by considering the surface roughness, total reflectivity, and penetration loss in multilayer situations. Based on this model, the wavelet analysis is used to perform parameterization on the interferometric observations represented by the signal to noise ratio (SNR). An integration factor and power curve are also proposed to characterize the surface state transition. One-year data from an Arctic geodetic GNSS station in the north of Canada are collected for analysis to validate the proposed approach in comparison with the existing methods based on the amplitude and damping factors. The results show that the new method demonstrates good usability and sensitivity to detect surface state transitions, e.g., icing, snowfall, and snow melting. However, the amplitude and damping factor-based methods derived from the single-layer model are only able to detect the pure ice surface and cannot respond to thick snow conditions. Finally, the high-resolution spaceborne images confirm the reliability of this method, exhibiting a great potential for long-term coastal sea surface detection based on the global geodetic GNSS stations and later being expected to be applied to sense cryosphere surface states.

The wet tropospheric correction (WTC) retrieved from the onboard calibration microwave radiometer (CMR) of Haiyang-2A (HY-2A) is critical in monitoring the global sea level. However, the CMR WTC became significantly biased from June 2017 due to the failure of the 18.7-GHz band, which caused massive errors in the sea surface height (SSH) measurements. We investigate the accuracy of the CMR WTC derived from the two remaining bands to address this problem. A comprehensive evaluation using multisource data demonstrates that the dual-band + backscattering coefficient (BC) algorithm achieves comparable accuracy to the three-band algorithm, and it does not suffer from any large errors when the equipment works well. Hence, we calibrated the HY-2A CMR data with the dual-band + BC algorithm when the 18.7-GHz band failed, and the accuracy of the CMR WTC is improved from 2.34 to 1.39 cm compared with European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 data. In addition, the SSH measurements are improved significantly by a maximum of 2 cm in mean value using the dual-band + BC WTC during the failure period of HY-2A CMR. Compared with Jason-3 SSH measurements, the HY-2A with dual-band + BC shows a slightly larger difference than HY-2A with three-band by 0.1 cm in rms. This method prolongs the operational lifetime of the HY-2A CMR and could be used in the reprocessing of HY-2A observations.

The low-level temperature inversions have significant impacts on Arctic climate change feedbacks. The Atmospheric Infrared Sounder (AIRS) can extract the inversions over both land and ocean, and it is, however, sensitive to the presence of clouds. In this paper, we evaluate the effect of cloud fraction (CF) on AIRS inversions over both land and ocean. First, the AIRS inversions under clear-sky conditions are compared with the results from the microwave-based Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) observations in 2007–2013. Results show that despite the COSMIC and AIRS inversions appearing to be deeper and stronger in winter than in autumn, spring, and summer, the former is generally shallower and stronger than the latter in all seasons over both land and ocean. Time-series analysis of the mean monthly inversions from COSMIC and AIRS observations in 2007–2013 under both clear-sky and cloudy conditions further indicates that their differences are systematic and can be effectively mitigated after calibration under all sky conditions. Taking the calibrated COSMIC inversions as references, the AIRS inversion depths can be estimated with a root mean square (rms) of less than about 86 and 135 m, and the AIRS inversion strength can be obtained with an rms of better than about 1.7 °C and 1.3 °C under cloudy conditions over land and ocean, respectively. Moreover, while the AIRS inversion depths are insensitive to CF variations over both land and ocean, the inversion strengths are more sensitive to the CF variations over land than ocean.

To acquire a 3-D deformation of Kilauea's south flank, measurements from GPS and differential interferometric synthetic aperture radar (DInSAR) were integrated based on ant colony optimization. Constraints from GPS and DInSAR measurements were used to establish an energy function based on the Gibbs equation. In this letter, projection vectors used in the energy function were refined depending on the elevation and location of each ground pixel. To achieve stable and fast convergence of the algorithm, the proposed method was tested with different parameters. An ant colony size of 80 with 20 generation loops was designed to derive the 3-D deformation by solving the energy function. Both the ascending and descending interferometric pairs, as well as the GPS observations, of Kilauea volcano, Hawaii, were used in this letter. The results show good consistency with the GPS checkpoints. Root mean squares of 1.40, 1.88, and 2.2 cm were achieved in the directions of north-south, east-west, and zenith, respectively. The derived 3-D deformation maps will allow a better understanding of the source geometry associated with volcanic and seismic activities that result in surface deformation.

Water vapor is one of the most variable atmospheric constituents. Knowledge of both the spatial and temporal variations of atmospheric water vapor is very important in forecasting regional weather and understanding the global climate system. The Moderate Resolution Imaging Spectroradiometer (MODIS) is the first space instrument to obtain precipitable water vapor (PWV) with near-infrared (nIR) bands and the traditional IR bands, which provides an opportunity to monitor PWV with wide coverage during both daytime and nighttime. However, the accuracy of PWV measurements obtained with IR bands is much lower than that with nIR bands. Moreover, seldom have studies been devoted to the calibrations of MODIS IR PWV. In this paper, the accuracy of MODIS IR water vapor product during the nighttime is assessed by ERA-Interim data, Global Positioning System, and radiosonde observations. Results reveal that the performance of MODIS IR water vapor product is much poorer than that from the other observations, and the MODIS IR PWV needs to be calibrated. As such, we propose a differential linear calibration model (DLCM) to calibrate the MODIS IR water vapor product during the nighttime. Case studies under both dry and moist atmosphere in midlatitude and equatorial regions are used to test and assess the performance of the DLCM. Results show that the DLCM can effectively enhance the accuracy of MODIS IR retrievals at nighttime. Furthermore, while the traditional least square model may over calibrate the MODIS IR PWV measurements occasionally, the DLCM can avoid that defect successfully.

Water vapor variations affect the interferometric synthetic aperture radar (InSAR) signal transmission and the accuracy of the InSAR measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) near infrared (nIR) water vapor product can correct InSAR atmospheric effects effectively, but it only works for the synthetic aperture radar (SAR) images acquired during the daytime. Although the MODIS infrared (IR) water vapor product owns poorer accuracy and spatial resolution than the nIR product, it is available for daytime as well as nighttime. In order to improve the accuracy of water vapor measurements from the MODIS IR product, a differential linear calibration model (DLCM) has been developed in this paper. The calibrated water vapor measurements from the IR product are then used for wet delay map production and nighttime overpass SAR interferogram atmospheric correction. Results show that the accuracy of the MODIS IR product can be improved effectively after calibration with the DLCM, and the derived wet delays are more suitable for InSAR atmospheric correction than original measurements from the IR product. Furthermore, a MODIS altitude-correlated turbulence model (MATM) is incorporated to correct the atmospheric effects from another descending ASAR interferogram. Results show that the MATM can reduce altitude-dependent water vapor artifacts more effectively than the traditional correction method without the need to incorporate the altitude information.

It can serve for the city planning scientifically and improve the people's knowledge about the detailed surroundings by evaluation of urban environment status from high resolution satellite images. Environmental factors such as vegetation type and coverage, water area and water pollution can be known from the images directly, the population density can be calculated from the building structure, the air pollution and the noise pollution come from the composition of the buildings and the roads. It takes IKONOS images to test the environmental quality of Nanjing in this paper. Firstly, different methods are used to extract the environmental factors information from the images, the statistical data of the environment protecting bureau and the government are used to test the precision; secondly, weight of each environmental factor is determined; finally, the environmental quality of each unit is calculated. The results accords with the standpoint of the inhabitant and the house price of the actual market.