Towards a new generation of Generic Atmospheric Correction Online Service for InSAR (GACOS 2.0)

Author(s):  
Chen Yu ◽  
Zhenhong Li

<div>The tremendous development of InSAR missions (e.g., Sentinel-1A/1B, ALOS-2, TerraSAR-X/TanDEM-X, COSMO-SkyMED, RADARSAT-2, and Gaofen-3) in recent years facilitates the study of smaller amplitude ground deformation using longer time series and over greater spatial scales. This poses new challenges for correcting interferograms for atmospheric (tropospheric) effects especially the dominant long wavelength effect and the spatial-temporal correlated topographic related effect, resulting the atmospheric effect being distance-dependent with larger interferograms experiencing greater contamination and preventing deformation mapping of large scales deformation phenomena such as inter-seismic tectonic strain accumulation, post-seismic relaxation of fault systems and Glacial Isostatic Adjustment (GIA). </div><div> </div><div>To overcome this, we have released the Generic Atmospheric Correction Online Service (GACOS) whose notable features comprise: (i) global coverage, (ii) all-weather, all-time usability, (iii) correction maps available in near real-time, and (iv) indicators to assess the correction performance and feasibility. The model applies operational high resolution ECMWF data (0.125-degree grid, 137 vertical levels, 6-hour interval) using an iterative tropospheric decomposition model and its performance for InSAR atmospheric correction was tested using globally-distributed interferograms, encompassing both flat and mountainous topographies, mid-latitude and near-polar regions, monsoon and oceanic climate systems, achieving a phase precision and displacement accuracy of approximately 1 cm for the corrected interferograms. Indicators describing the model’s performance including (i) ECMWF cross-RMS, (ii) phase-delay correlations, (iii) ECMWF time differences, and (iv) topography variations, were developed to provide quality control for subsequent automatic processing and provide insights of the confidence level with which the generated atmospheric correction maps may be applied. </div><div> </div><div>To further improve the performance of GACOS to better serve the InSAR community, a new generation (GACOS 2.0) is being developed by: (i) improving the temporal resolution by integrating the newly published 1-hour ERA-5 weather model and the 5-minute GPS tropospheric delay estimates; (ii) developing an API system to facilitate automatic data processing; and (iii) enhancing GACOS based on regional/local datasets (such as national weather model and regional GPS network). The ERA-5 product and global GPS tropospheric delay estimates are carefully validated in order to achieve a robust integration. Based on the globally distributed GPS network and the MODIS PWV product, the performance of GACOS 2.0 in different regions of the world is evaluated with its elevation and latitude dependency being concluded which could be served as another performance indicator. All these features will contribute to a simplified time series analysis method (i.e. relying less on spatial-temporal filters) to reduce the computational burden, provided that the majority of the atmospheric error has been mitigated by GACOS 2.0. </div><div> </div>

2020 ◽  
Author(s):  
Zhiguo Deng ◽  
Florian Zus ◽  
Kyriakos Balidakis ◽  
Wickert Jens ◽  
Harald Schuh

<p>During the last decade the stability of GNSS clocks has increased dramatically. New generation GNSS satellites are equipped with highly precise and stable clocks and the clock parameters can be predicted with even picoseconds accuracy for several hours. In this work we determined and predicted 90 days precise orbits and clocks of up to 115 satellites from GPS, GLO, GAL, BDS2/3 and QZSS. Based on the calculated and predicted orbit and clock products (SP3) we processed data from about 140 globally distributed stations using PPP in 24 hours static mode. The first 22 hours part uses the calculated satellite products and the last two hours part uses the predicted satellite products. The estimated parameters are daily station coordinates and 30 min tropospheric parameters (ZTD). To validate the last 2-hours of ZTD we generate a reference solution based on 24-hour calculated SP3 products. We also performed a statistical comparison with ECMWF weather model data which yields a root mean square deviation of about 12 mm. This initial comparison indicates that the ZTD estimated from predicted satellite orbit and clocks are sufficiently accurate for time critical meteorological applications.</p>


2020 ◽  
Author(s):  
Mohammad M.Aref ◽  
Bodo Bookhagen ◽  
Taylor T. Smith ◽  
Manfred R. Strecker

<p>The eastern Central Andes of northwestern Argentina is characterized by a steep topographic gradient with elevations ranging from 1000m in the foreland to more than 6000m in the eastern Andean Cordillera. This setting furthermore shows high topographic relief with deeply incised river valleys that are frequently impacted by strong rainfall events driven by the South American monsoon. Additionally, a strong vegetation cover contrast from dense coverage in the low elevation foreland to sparse coverage at high elevation defines the environmental gradient in this area. This area is impacted by several types of hillslope instabilities and landsliding: at some high elevations above 5000m hillslope instability are related to solifluction processes, whereas shallow and deep seated landsliding affect geologically preconditioned areas.</p><p>Here we use a combination of different radar sensors and wavelengths to describe the 3D deformation signal of instable hillslopes: TerraSAR-X, Sentinel-1, and ALOS2. To mitigate the tropospheric delay from InSAR measurements, phase-based and weather model approaches are applied to improve the spatial and temporal variations of displacement signals.  We use persistent and small baseline subsets (SBAS) category of distributed scatterer approaches to derive deformation fields and we invert for 3D deformation fields using several look angles in combination with GNSS data under different assumptions including that the horizontal component has a motion parallel to the downhill slope. We analyze Line-of-sight (LOS) time series and combine deformation fields with temperature and rainfall measurements to better understand driving forces of high-elevation hillslope instabilities We describe two deep-seated landslides with downslope velocities exceeding 5-10 cm/yr and we exploit image-cross correlation techniques of optical data to monitor seasonal and inter-annual changes. The periodic changes of InSAR deformation and temperature time series show freeze-thaw processes of the active layer thickness of the permafrost areas at elevations exceeding 5000m. We document that deep-seated, fast moving landslides are related to geologic preconditioning. The combination of SAR and optical approaches helps to describe hillslope regimes in steep and difficult to access terrain.</p>


2018 ◽  
Vol 36 (1) ◽  
pp. 31
Author(s):  
Fernando Paz Pellat

It is essential to minimize atmospheric effects on spectral information of remote sensors from space platforms to avoid under estimation of biophysical variables associated with satellite image data. In this paper, a generic algorithm was developed, based on sound theoretical arguments, to analyze time series ISVI spectral vegetation index (vegetation index based on iso-soil curves), thus avoiding the problems associated with the classic design of vegetation indices, where the spectral signal saturates quickly. The results, when applying the algorithm in pixel time series of AVHRR satellite images, showed that reduction and standardization of atmospheric effects in the ISVI was achieved. Using ISVI maximum values in time series (temporal window), a reasonable approximation to atmospheric conditions with minimum or standardized effects was obtained. In conclusion, although the scheme developed failed to eliminate the atmospheric effect on ISVI entirely, it was reduced to a minimum. The algorithm developed was simple enough for operational use, with regard to atmospheric correction methods using radiative model inversions.


2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.


2021 ◽  
Vol 13 (3) ◽  
pp. 409
Author(s):  
Howard Zebker

Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.


2017 ◽  
Vol 9 (11) ◽  
pp. 1095 ◽  
Author(s):  
Emmihenna Jääskeläinen ◽  
Terhikki Manninen ◽  
Johanna Tamminen ◽  
Marko Laine

2021 ◽  
Author(s):  
Milaa Murshan ◽  
Balaji Devaraju ◽  
Nagarajan Balasubramanian ◽  
Onkar Dikshit

<p>Satellite altimetry provides measurements of sea surface height of centimeter-level accuracy over open oceans. However, its accuracy reduces when approaching the coastal areas and over land regions. Despite this downside, altimetric measurements are still applied successfully in these areas through altimeter retracking processes. This study aims to calibrate and validate retracted sea level data of Envisat, ERS-2, Topex/Poseidon, Jason-1, 2, SARAL/AltiKa, Cryosat-2 altimetric missions near the Indian coastline. We assessed the reliability, quality, and performance of these missions by comparing eight tide gauge (TG) stations along the Indian coast. These are Okha, Mumbai, Karwar, and Cochin stations in the Arabian Sea, and Nagapattinam, Chennai, Visakhapatnam, and Paradip in the Bay of Bengal. To compare the satellite altimetry and TG sea level time series, both datasets are transformed to the same reference datum. Before the calculation of the bias between the altimetry and TG sea level time series, TG data are corrected for Inverted Barometer (IB) and Dynamic Atmospheric Correction (DAC). Since there are no prior VLM measurements in our study area, VLM is calculated from TG records using the same procedure as in the Technical Report NOS organization CO-OPS 065. </p><p>Keywords— Tide gauge, Sea level, North Indian ocean, satellite altimetry, Vertical land motion</p>


2021 ◽  
Author(s):  
Jānis Bikše ◽  
Inga Retike ◽  
Andis Kalvāns ◽  
Aija Dēliņa ◽  
Alise Babre ◽  
...  

<p>Groundwater level time series are the basis for various groundwater-related studies. The most valuable are long term, gapless and evenly spatially distributed datasets. However, most historical datasets have been acquired during a long-term period by various operators and database maintainers, using different data collection methods (manual measurements or automatic data loggers) and usually contain gaps and errors, that can originate both from measurement process and data processing. The easiest way is to eliminate the time series with obvious errors from further analysis, but then most of the valuable dataset may be lost, decreasing spatial and time coverage. Some gaps can be easily replaced by traditional methods (e.g. by mean values), but filling longer observation gaps (missing months, years) is complicated and often leads to false results. Thus, an effort should be made to retain as much as possible actual observation data.</p><p>In this study we present (1) most typical data errors found in long-term groundwater level monitoring datasets, (2) provide techniques to visually identify such errors and finally, (3) propose best ways of how to treat such errors. The approach also includes confidence levels for identification and decision-making process. The aim of the study was to pre-treat groundwater level time series obtained from the national monitoring network in Latvia for further use in groundwater drought modelling studies.</p><p>This research is funded by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165.</p>


2021 ◽  
Author(s):  
Vicky Jia Liu ◽  
Maaria Nordman ◽  
Nataliya Zubko

<p>Tropospheric delay is one of the major error sources for space geodetic techniques such as Very Long Baseline Interferometry (VLBI) and Global Navigation Satellite System (GNSS). In this study, we compared the agreement of tropospheric zenith wet delay (ZWD) seasonal variations derived from VLBI and GNSS observations at 8 stations that are located at all around the globe. We have analysed time series of 8 years, starting in 2012 until end of 2019. Results show that VLBI_ZWD present clear seasonal variations which depend on the location of each station, in the tropics the variability is more pronounced than in mid-latitudes or polar regions. Furthermore, the VLBI_ZWD also shows a reasonably good agreement with seasonal fit model. When comparing zenith wet delays derived from co-located GNSS and VLBI stations at  cut-off elevation angle, they agree quite well, which is proved by the high correlation coefficients, varying from 0.6 up to 0.95. The biases between the techniques are in mm level and standard errors of the whole time series are in few centimetres.</p>


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