This repository contains the datasets used in Mirzadeh et al., 2022. It includes three InSAR time-series datasets from the Envisat descending orbit, ALOS-1 ascending orbit, and Sentinel-1A in ascending and descending orbits, acquired over the Abarkuh Plain, Iran, as well as the geological map of the study area and the GNSS and hydrogeological data used in this research. Dataset 1: Envisat descending track 292 Date: 06 Oct 2003 - 05 Sep 2005 (12 acquisitions) Processor: ISCE/stripmapStack + MintPy Displacement time-series (in HDF-EOS5 format): timeseries_LOD_tropHgt_ramp_demErr.h5 Mean LOS Velocity (in HDF-EOS5 format): velocity.h5 Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5 Geometry (in HDF-EOS5 format): geometryRadar.h5 Dataset 2: ALOS-1 ascending track 569 Date: 06 Dec 2006 - 17 Dec 2010 (14 acquisitions) Processor: ISCE/stripmapStack + MintPy Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5 Mean LOS Velocity (in HDF-EOS5 format): velocity.h5 Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5 Geometry (in HDF-EOS5 format): geometryRadar.h5 Dataset 2: Sentinel-1 ascending track 130 and descending track 137 Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 27 Oct 2014 - 29 Mar 2020 (114 descending acquisitions) Processor: ISCE/topsStack + MintPy Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5 Mean LOS Velocity (in HDF-EOS5 format): velocity.h5 Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5 Geometry (in HDF-EOS5 format): geometryRadar.h5 The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products and the MintPy commands/functions.
Planet Earth is a rotating flat ellipsoid and its dynamics oblateness (i.e. J 2 ) changes were mainly driven by the redistribution of Earth’s fluid mass and interaction of various spheres in the Earth system. Currently, the dynamics oblateness was determined from the satellite laser ranging (SLR) data. However, it was subject to the sparse SLR stations, uneven distribution in the Northern and Southern Hemispheres and non-continuous observation as well as dynamic models and constants in SLR data processing. Although the new generation of satellite gravity mission GRACE (gravity recovery and climate experiment) measurement has largely improved the lower-order coefficient estimates with one or two orders of magnitude, but the C 20 is not sensitive. In this paper, the high precise dynamics oblateness J 2 is derived from global continuous GPS loading displacements and GPS+OBP (ocean bottom pressure) as well as GPS+OBP+GRACE, respectively, which are analyzed and compared at multi-scales variations as well as their implications. It has shown that the annual variations of J 2 have a good agreement between GPS+OBP, GPS+OBP+GRACE, SLR and GRACE, while GPS alone has a smaller amplitude. For semi-annual variations, GRACE estimate is relatively worse due to the effect of about 161-day S 2 tide. Also the GPS+OBP and GPS+OBP+GRACE have a good correlation with SLR in intraseasonal and interannual J 2 variations, while GPS or GRACE alone is worse. Furthermore, the excitations of multi-scale J 2 variations are investigated and analyzed using geophysical models data. Results show that the variations of J 2 at seasonal, intraseasonal and interannual scales are mainly driven by the transfer and redistribution of Earth’s surface atmosphere, ocean and land water mass.
A new method, which integrates multi-variable consisting of Soil Moisture (SM) Active Passive (SMAP)-derived SM and vegetation optical depth, the water seasonality, geolocation, digital elevation model, slope, and biomass as inputs and adopts the technique of Bootstrap Aggregation of Regression Trees (BARTs) is proposed for retrieving monthly surface water fraction (SWF) at a spatial resolution of 0.025° from Cyclone Global Navigation Satellite System (CYGNSS) data. The model is trained using Surface Water Microwave Product Series (SWAMPS) data with a coarser resolution of 25 km and then applied to CYGNSS data with an enhanced resolution of 0.025° to generate high-resolution water maps. The resulting CYGNSS SWF (CSWF) maps are evaluated by comparing them with other water data sources, namely SWAMPS, Global Surface Water (GSW), and Global surface water dynamics (GLAD), as well as ground measurements. A quadruple collocation analysis indicates that the CSWF results exhibit the lowest error variance among the four SWF datasets. Furthermore, additional testing with water level measurements demonstrates a strong correlation with station data and clear seasonal patterns. Notably, the CSWF estimates significantly improve spatial coverage compared to both optical data (GSW and GLAD) with enhanced spatial resolution and the coarser SWAMPS data. This study underscores the effectiveness and efficiency of CSWF estimates, highlighting their potential as a valuable complement to existing microwave- and optical-based surface water products.
Big earthquakes often excite the acoustic resonance between the earth’s surface and the lower atmosphere. The perturbations can propagate upward into the ionosphere and trigger ionospheric anomalies detected by dual-frequency GPS observations, but coseismic ionospheric disturbance (CID) directivity and mechanism are not clear. In this paper, the ionospheric response to the Mw = 7.9 Alaska earthquake on 23 January 2018 is investigated from about 100 continuous GPS stations near the epicenter. The fourth-order zero-phase Butterworth band-pass filter with cutoffs of 2.2 mHz and 8 mHz is applied to obtain the ionospheric disturbances. Results show that the CIDs with an amplitude of up to 0.06 total electron content units (TECU) are detected about 10 min after the Alaska earthquake. The CIDs are as a result of the upward propagation acoustic waves triggered by the Rayleigh wave. The propagation velocities of TEC disturbances are around 2.6 km/s, which agree well with the wave propagation speed of 2.7 km/s detected by the bottom pressure records. Furthermore, the ionospheric disturbances following the 2018 Mw = 7.9 Alaska earthquake are inhomogeneous and directional which is rarely discussed. The magnitude of ionospheric disturbances in the western part of the epicenter is more obvious than in the eastern part. This phenomenon also corresponds to the data obtained from the seismographs and bottom pressure records (BPRs) at the eastern and western side of the epicenter.
The differential code bias (DCB) of the Global Navigation Satellite Systems (GNSS) receiver should be precisely corrected when conducting ionospheric remote sensing and precise point positioning. The DCBs can usually be estimated by the ground GNSS network based on the parameterization of the global ionosphere together with the global ionospheric map (GIM). In order to reduce the spatial-temporal complexities, various algorithms based on GIM and local ionospheric modeling are conducted, but rely on station selection. In this paper, we present a recursive method to estimate the DCBs of Global Positioning System (GPS) satellites based on a recursive filter and independent reference station selection procedure. The satellite and receiver DCBs are estimated once per local day and aligned with the DCB product provided by the Center for Orbit Determination in Europe (CODE). From the statistical analysis with CODE DCB products, the results show that the accuracy of GPS satellite DCB estimates obtained by the recursive method can reach about 0.10 ns under solar quiet condition. The influence of stations with bad performances on DCB estimation can be reduced through the independent iterative reference selection. The accuracy of local ionospheric modeling based on recursive filter is less than 2 Total Electron Content Unit (TECU) in the monthly median sense. The performance of the recursive method is also evaluated under different solar conditions and the results show that the local ionospheric modeling is sensitive to solar conditions. Moreover, the recursive method has the potential to be implemented in the near real-time DCB estimation and GNSS data quality check.
<p>Accurate estimate of the ice-sheet mass balance in Antarctic is very difficult due to complex ice sheet condition and sparse in situ measurements. In this paper, the low-degree gravity field coefficients of up to degree and order 5 derived from Satellite Laser Ranging (SLR) measurements are used to determine the ice mass variations in Antarctica for the period 1993–2011. Results show that the ice mass is losing with -36±13 Gt/y in Antarctica, -42±11 Gt/y in the West Antarctica and 6±10 Gt/y in the East Antarctica from 1993 to 2011. The ice mass variations from the SLR 5×5 have a good agreement with the GRACE 5×5, GRACE 5×5 (1&2) and GRACE (60×60) for the entire continent since 2003, but degree 5 from SLR is not sufficient to quantify ice losses in West and East Antarctica, respectively. The rate of ice loss in Antarctica is -28±17 Gt/y for 1993-2002 and -55±17 Gt/y for 2003-2011, indicating significant accelerated ice mass losses since 2003. Furthermore, the results from SLR are comparable with GRACE measurements.</p>
Economic development and climate change drive the land use and land cover (LULC) change globally. Annual robust maps of LULC are critical for studying climate change and land–climate interaction. However, the current existing methods for optimizing and expanding the publicly available China land cover data set (CLCD) are limited. In this article, 30-m annual LULC changes are obtained from 1990 to 2020 in the Yangtze River basin (YRB). The results show an overall accuracy rate of 82.66% and better performances on Geo-Wiki test samples when compared to similar products. Based on our 30-m annual LULC data set, the drastic LULC changes are found in YRB over a 30-year period, where impervious surface area more than tripled, cropland area decreased by 6.12%, and water area decreased by 6.09%. In addition, through the geographically and temporally weighted regression method, a fitting model with a goodness of fit of 0.91 well reveals that human activity plays a driving role in the LULC change of YRB.
A multi-frequency Global Navigation Satellite System (GNSS) provides greater opportunities for positioning and navigation applications, particularly the BeiDou Global Navigation Satellite System (BDS-3) satellites. However, multi-frequency signals import more pseudorange channels, which introduce more multi-channel Differential Code Biases (DCBs). The satellite and receiver DCBs from the new BDS-3 signals are not clear. In this study, 9 DCB types of the new BDS-3 signals from 30-days Multi-GNSS Experiment (MGEX) observations are estimated and investigated. Compared with the DCB values provided by the Chinese Academy of Science (CAS) products, the mean bias and root mean squares (RMS) error of new BDS-3 satellite DCBs are within ±0.20 and 0.30 ns, respectively. The satellite DCBs are mostly within ±0.40 ns with respect to the product of the Deutsches Zentrum für Luft- und Raumfahrt (DLR). The four sets of constructed closure errors and their mean values are within ±0.30 ns and ±0.15 ns, respectively. The mean standard deviation (STD) of the estimated satellite DCBs is less than 0.10 ns. In particular, our estimated satellite DCBs are more stable than DCB products provided by CAS and DLR. Unlike satellite DCBs, the receiver DCBs have poor compliance and show an obvious relationship with the geographic latitude when compared to the CAS products. The STDs of our estimated receiver DCBs are less than 1.00 ns. According to different types of receiver DCBs, the distribution of STDs indicates that the coefficient of the ionospheric correction has an influence on the stability of the receiver DCBs under the ionosphere with the same accuracy level. In addition, the type of receiver shows no regular effects on the stability of receiver DCBs.
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