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Remote Sensing in Space Geodesy and Cartography Methods II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 10111

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Special Issue Editors

Prof. Dr. Jinyun Guo

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College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China
Interests: space geodesy; marine geodesy; physical geodesy
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Cheinway Hwang

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Department of Civil Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
Interests: satellite geodesy; oceanography; theory of parameter estimation
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Dr. Yu Sun

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Academy of Digital China, Fuzhou University, Fuzhou, China
Interests: space geodesy; physical geodesy
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Dr. Tzu-pang Tseng

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Department of Civil Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
Interests: satellite geodesy; GNSS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, the massive amounts of remote sensing data obtained from space geodetic techniques, such as satellite gravimetry, satellite geodesy, GNSS, InSAR, and LiDAR, have greatly advanced the broad research field of space geodesy, and have also facilitated innovation in data mining and cartography methods. As new space platforms are continuously developed and novel measurements obtained, space geodesy and cartography are faced with unprecedented challenges and opportunities; these include the accurate determination of Earth’s shape and gravity field, better visualization of multisource data, and the construction of the digital Earth. All of these fields require more advanced and sophisticated remote sensing methods and applications.

This Special Issue will highlight remote sensing methods and applications in space geodesy and cartography, embracing the scope of the Satellite Missions for Earth and Planetary Exploration section of Remote Sensing.

This Special Issue aims to publish studies covering all aspects of satellite gravimetry, satellite altimetry, satellite optical/multispectral/hyperspectral/SAR remote sensing, GNSS, LiDAR, deep space detection, space geodetic theory and techniques, the space environment, and the digital Earth and planet; additionally, we are interested in theory, methods, techniques, algorithms, data validation, scientific products, and applications. Review articles are also welcome. Articles may address, but are not limited to:

Digital Earth;
Topography and thematic mapping;
Earth shape and gravity field modeling;
Co-ordinate reference frame and deformation monitoring;
Planet geodesy and cartography;
Space environment and deep space detection.

Prof. Dr. Jinyun Guo
Prof. Dr. Cheinway Hwang
Dr. Yu Sun
Dr. Tzu-pang Tseng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

GNSS
LiDAR
satellite gravimetry
satellite altimetry
optical/multispectral/hyperspectral/SAR remote sensing
space geodetic technique
deep space detection

Published Papers (10 papers)

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Research

21 pages, 15023 KiB

Open AccessArticle

Expected Precision of Gravity Gradient Recovered from Ka-Band Radar Interferometer Observations and Impact of Instrument Errors

by Hengyang Guo, Xiaoyun Wan, Fei Wang and Song Tian

Remote Sens. 2024, 16(3), 576; https://doi.org/10.3390/rs16030576 - 02 Feb 2024

Viewed by 738

Abstract

Full tensor of gravity gradients contains extremely large amounts of information, which is one of the most important sources for research on recovery seafloor topography and underwater matching navigation. The calculation and accuracy of the full tensor of gravity gradients are worth studying. The Ka-band interferometric radar altimeter (KaRIn) of surface water and ocean topography (SWOT) mission enables high spatial resolution of sea surface height (SSH), which would be beneficial for the calculation of gravity gradients. However, there are no clear accuracy results for the gravity gradients (the gravity gradient tensor represents the second-order derivative of the gravity potential) recovered based on SWOT data. This study evaluated the possible precision of gravity gradients using the discretization method based on simulated SWOT wide-swath data and investigated the impact of instrument errors. The data are simulated based on the sea level anomaly data provided by the European Space Agency. The instrument errors are simulated based on the power spectrum data provided in the SWOT error budget document. Firstly, the full tensor of gravity gradients (SWOT_GGT) is calculated based on deflections of the vertical and gravity anomaly. The distinctions of instrument errors on the ascending and descending orbits are also taken into account in the calculation. The precision of the T_zz component is evaluated by the vertical gravity gradient model provided by the Scripps Institution of Oceanography. All components of SWOT_GGT are validated by the gravity gradients model, which is calculated by the open-source software GrafLab based on spherical harmonic. The T_zz component has the poorest precision among all the components. The reason for the worst accuracy of the T_zz component may be that it is derived by T_xx and T_yy, T_zz would have a larger error than T_xx and T_yy. The precision of all components is better than 6 E. Among the various errors, the effect of phase error and KaRIn error (random error caused by interferometric radar) on the results is greater than 2 E. The effect of the other four errors on the results is about 0.5 E. Utilizing multi-cycle data for the full tensor of gravity gradients recovery can suppress the effect of errors. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)

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12 pages, 5013 KiB

Open AccessCommunication

SISRE of BDS-3 MEO: Evolution as Well as Comparison between D1 and B-CNAV (B-CNAV1, B-CNAV2) Navigation Messages

by Zhenghua Dong and Songlin Zhang

Remote Sens. 2024, 16(3), 484; https://doi.org/10.3390/rs16030484 - 26 Jan 2024

Viewed by 571

Abstract

The signal-in-space range error (SISRE) has a direct impact on the performance of global navigation satellite systems (GNSSs). It is an important indicator of navigation satellite space server performance. The new B-CNAV navigation messages (B-CNAV1 and B-CNAV2) are broadcast on the satellites of the Beidou Global Navigation Satellite System (BDS-3), and they are different from D1 navigation messages in satellite orbit parameters. The orbit accuracy of B-CNAV navigation messages lacks analyses and comparisons with D1. The accuracy and stability of the new hydrogen and rubidium clocks on BDS-3 satellites need annual analyses of long time series, which will affect the service quality of this system. Based on precise ephemeris products from the Center for Orbit Determination in Europe (COD), the orbit error, clock error, and SISRE of 24 medium Earth orbit (MEO) satellite D1 and B-CNAV navigation messages of BDS-3 were computed, analyzed, and compared. Their annual evolution processes for the entire year of 2022 were studied. Thanks to the use of inter-satellite links (ISLs) adopted by BDS-3 MEO satellites, the ages of the ephemeris are accurate and the percent of ages of data, ephemerides (AODEs), and ages of data and clocks (AODCs) shorter than 12 h were 99.95% and 99.96%, respectively. In addition, the broadcast orbit performance was also improved by ISLs. The root mean square (RMS) values of the BDS-3 MEO broadcast ephemeris orbit error were 0.067 m, 0.273 m, and 0.297 m in the radial, cross, and along directions, respectively. Moreover, the 3D RMS value was 0.450 m. Thanks to the use of new orbit parameters in the B-CNAV navigation messages of BDS-3 MEO, its satellite orbit accuracy was obviously better than that of D1 in the radial direction. Its improved accuracy can reach up to about 1.2 cm, and the percentage of its accuracy improvement was about 19.06%. With respect to clock errors, the timescale differences between the two clock products were eliminated to assess the accuracy of broadcasting ephemeris clock errors. A standard deviation value of 0.256 m shows good performances as a result of the use of the two new types of atomic clocks, although the RMS value was 0.541 m due to a nonzero mean bias. Overall, the accuracy of atomic clocks was good. For the new hydrogen and rubidium atomic clocks, their RMS and standard deviation were 0.563 m and 0.231 m and 0.519 m and 0.281 m, respectively. The stability of the former was better than that of the latter. However, due to the nonzero mean bias the latter was better than the former in accuracy. The RMS value of the SISRE of BDS-3 MEO’s broadcast ephemeris was 0.556 m, and the value was 0.920 m when it had a 95% confidence level. In contrast, after deducting the influence of the clock error, the value of SISRE_ORB was 0.092 m. Since the satellite clock error was substantially larger than the orbit radial error, the SISRE was mainly affected by the clock error, and their annual evolutions were consistent. Because of the improvement to the B-CNAV’s navigation message with respect to orbit radial accuracy, SISRE_ORB has improved in accuracy. Compared to D1, it had a significant effect on improving the accuracy of SISRE_ORB, and the percentage of the accuracy improvement was 8.40%. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)

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17 pages, 6661 KiB

Open AccessArticle

Coseismic Slip Distribution and Coulomb Stress Change of the 2023 M_W 7.8 Pazarcik and M_W 7.5 Elbistan Earthquakes in Turkey

by Xiaofeng Dai, Xin Liu, Rui Liu, Menghao Song, Guangbin Zhu, Xiaotao Chang and Jinyun Guo

Remote Sens. 2024, 16(2), 240; https://doi.org/10.3390/rs16020240 - 08 Jan 2024

Viewed by 890

Abstract

On 6 February 2023, the M_W 7.8 Pazarcik and the M_W 7.5 Elbistan earthquakes occurred in southeastern Turkey, close to the Syrian border, causing many deaths and a great deal of property destruction. The Pazarcik earthquake mainly damaged the East Anatolian Fault Zone (EAFZ). The Elbistan earthquake mainly damaged the Cardak fault (CF) and the Doğanşehir fault (DF). In this study, Sentinel-1A ascending (ASC) and descending (DES) orbit image data and pixel offset tracking (POT) were used to derive surface deformation fields in the range and azimuth directions induced by the Pazarcik and Elbistan earthquakes (hereinafter referred to as the Turkey double earthquakes). Utilizing GPS coordinate sequence data, we computed the three-dimensional surface deformation resulting from the Turkey double earthquakes. The surface deformation InSAR and GPS results were combined to invert the coseismic slip distribution of the EAFZ, CF, and DF using a layered earth model. The results show that the coseismic ruptures of the Turkey double earthquakes were dominated by left-lateral strike-slips. The maximum slip was 7.76 m on the EAFZ and about 8.2 m on the CF. Both the earthquakes ruptured the surface. The Coulomb failure stress (CFS) was computed based on the fault slip distribution and the geometric parameters of all the active faults within 300 km of the M_W 7.8 Pazarcik earthquake’s epicenter. The CFS change resulting from the Pazarcik earthquake suggests that the subsequent Elbistan earthquake was triggered by the Pazarcik earthquake. The Antakya fault experienced an increase in CFS of 8.4 bars during this double-earthquake event. Therefore, the M_W 6.3 Uzunbağ earthquake on 20 February 2023 was jointly influenced by the Turkey double earthquakes. Through stress analysis of all the active faults within 300 km of the M_W 7.8 Pazarcik earthquake’s epicenter, the Ecemis segment, Camliyayla fault, Aadag fault, Ayvali fault, and Pula segment were all found to be under stress loading. Particularly, the Ayvali fault and Pula segment exhibited conspicuous stress loading, signaling a higher risk of future seismic activity. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)

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20 pages, 10613 KiB

Open AccessArticle

Characterizing the Water Storage Variation of Kusai Lake by Constructing Time Series from Multisource Remote Sensing Data

by Zhengkai Huang, Xin Wu, Haihong Wang, Zehui Zhao, Liting Du, Xiaoxing He and Hangyu Zhou

Remote Sens. 2024, 16(1), 128; https://doi.org/10.3390/rs16010128 - 28 Dec 2023

Viewed by 616

Abstract

In September 2011, Zhuonai Lake (ZL) in the northeast of Hoh Xil (HX) on the Qinghai–Tibet Plateau (QTP) broke out. The outburst event seriously changed the environmental hydraulics in this region. Due to the insufficient temporal resolution of observations, it is challenging to assess the impact of this event on short-period variations of water volumes in three lakes downstream of ZL. Combining multisource remote sensing data, we constructed long and high-temporal-resolution time series for the lake level, area, and lake water storage (LWS) of Kusai Lake (KL) to characterize the variabilities before and after the outburst. The water level, area, and LWS time series contain 1051 samples from 1990 to 2022, with uncertainties of 0.16 m, 2.5 km², and 0.016 km³, respectively. The accuracies verified using the Database for Hydrological Time Series of Inland Waters (DAHITI) are 0.26 m, 2.64 km², and 0.08 km³ for water level, area, and LWS, respectively. We characterized the LWS variations during the past 30 years based on the high temporal resolution LWS time series. Before the outburst, the 1-year and 3.5-year variations dominated the LWS time series, and there was no obvious semi-annual signal. After the outburst, the 3.5-year variation disappeared, and a strong semi-annual oscillation was observed. From 2012 to 2015, the periodic LWS variations in KL were disturbed by the ZL outburst and the subsequent outflow of KL led by the outburst. Regular cyclic signals have been restored since 2016, with an amplified annual fluctuation. By analysis, precipitation, evaporation, and glacier area change are excluded as driving factors of the pattern change in LWS variations of KL. It can be concluded that the altered recharge pattern of KL triggered by the outburst directly resulted in the observed changes in TWS behavior. For the first time, we identified the periodic patterns of LWS variations of KL during the past 30 years and revealed that the ZL outburst event significantly influenced these patterns. This finding contributes to the comprehensive understanding of the effects of the ZL outburst on downstream lake dynamics. Furthermore, the presented procedure for constructing long and high-resolution time series of LWS allows for monitoring and characterizing the short-period variabilities of Tibetan lakes that lack hydrological data. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)

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20 pages, 14353 KiB

Open AccessArticle

A Method for Detecting Ionospheric TEC Anomalies before Earthquake: The Case Study of Ms 7.8 Earthquake, February 06, 2023, Türkiye

by Jiandi Feng, Yuan Xiao, Jianghe Chen, Shuyi Sun and Fuyang Ke

Remote Sens. 2023, 15(21), 5175; https://doi.org/10.3390/rs15215175 - 30 Oct 2023

Cited by 1 | Viewed by 1099

Abstract

The ionospheric anomalies before an earthquake may be related to earthquake preparation. The study of the ionospheric anomalies before an earthquake provides potential value for earthquake prediction. This paper proposes a method for detecting ionospheric total electron content (TEC) anomalies before an earthquake, taking the M_S 7.8 earthquake in Türkiye on 6 February 2023 as an example. First, the data of four ground-based GNSS stations close to the epicenter were processed by using the sliding interquartile range method and the long short-term memory (LSTM) network. The anomaly dates detected by the two methods were identified as potential pre-earthquake TEC anomaly dates after eliminating solar and geomagnetic interference. Then, by using the sliding interquartile range method to process and analyze the CODE GIM (Center for Orbit Determination in Europe, Global Ionospheric Map) data from a global perspective, we further verified the existence of TEC anomalies before the earthquake on the above TEC anomaly days. Finally, the influence of the equatorial ionospheric anomaly (EIA) on the TEC anomaly disturbance was excluded. The results show that the ionospheric TEC anomalies on January 20, January 27, February 4, and February 5 before the Türkiye earthquake may be correlated with the earthquake. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)

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19 pages, 5918 KiB

Open AccessArticle

The Impact of Different Filters on the Gravity Field Recovery Based on the GOCE Gradient Data

by Qinglu Mu, Changqing Wang, Min Zhong, Yihao Yan and Lei Liang

Remote Sens. 2023, 15(20), 5034; https://doi.org/10.3390/rs15205034 - 20 Oct 2023

Viewed by 754

Abstract

The electrostatic gravity gradiometer carried by the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite is affected by accelerometer noise and other factors; hence, the observation data present complex error characteristics in the low-frequency domain. The accuracy of the recovered gravity field will be directly affected by the design of the filters based on the error characteristics of the gradient data. In this study, the applicability of various filters to different errors in observation is evaluated, such as the

1 / f

error and the orbital frequency errors. The experimental results show that the cascade filter (DARMA), which is formed of a differential filter and an autoregressive moving average filter (ARMA) filter, has the best accuracy for the characteristic of the

1 / f

low-frequency error. The strategy of introducing empirical parameters can reduce the orbital frequency errors, whereas the application of a notch filter will worsen the final solution. Frequent orbit changes and other changes in the observed environment have little impact on the new version gradient data (the data product is coded 0202), while the influence cannot be ignored on the results of the old version data (the data product is coded 0103). The influence can be effectively minimized by shortening the length of the arc. By analyzing the above experimental findings, it can be concluded that the inversion accuracy can be effectively improved by choosing the appropriate filter combination and filter estimation frequency when solving the gravity field model based on the gradient data of the GOCE satellite. This is of reference significance for the updating of the existing models. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)

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23 pages, 20261 KiB

Open AccessArticle

Spatial Development of Strong Storm-Induced Ionospheric Perturbations during 25–27 August 2018

by Wang Li, Dongsheng Zhao, Jiandi Feng, Xuequn Wu and Zhen Zhang

Remote Sens. 2023, 15(10), 2549; https://doi.org/10.3390/rs15102549 - 12 May 2023

Viewed by 942

Abstract

The 25–27 August 2018 geomagnetic storm was the third largest storm in the 24th solar cycle. It was a surprising space event that originated from low-level solar activity. This study provides an overview of the temporal–spatial behaviors of plasma irregularities as functions of geographic longitude, latitude, and altitude using ground-based (GNSS receivers and ionosonde) instruments and space-borne Swarm satellites. The results not only reveal enhanced equatorial ionization anomaly (EIA) and hemispheric asymmetry over the Asian–Australian and American sectors at a particular time but also hemispheric asymmetric features of global ROT in the main and recovery phases. Additionally, this storm triggered positive plasma irregularities in altitudes of 100 to 150 km near the Auroral zone, and the changed ratio of bottom-side plasma irregularities exceeded 250%. This finding has been cross-validated by other instruments and models. Furthermore, the storm significantly affected the thermospheric O/N₂ density ratio, equatorial electrojet, and vertical E×B drifts. The equatorial and mid-latitude plasma irregularities may be a combined action of thermospheric composition change, equatorial electrojet, and vertical E×B drifts. Finally, the storm induced positive Joule heating irregularities in the Auroral ionosphere in altitudes of 100 to 400 km with a maximum changed ratio of over 200%, as well as enhanced cross-Polar voltage to ~90 kv. The Polar ionospheric irregularities may be associated with additional energy input through particle precipitation, Joule heating, and ionospheric current intensification. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)

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13 pages, 4825 KiB

Open AccessCommunication

A Double-Adaptive Adjustment Algorithm for Ionospheric Tomography

by Debao Wen, Yinghao Tang, Xi Chen and Yucong Zou

Remote Sens. 2023, 15(9), 2307; https://doi.org/10.3390/rs15092307 - 27 Apr 2023

Cited by 1 | Viewed by 831

Abstract

A double-adaptive adjustment algorithm (DAAA) is proposed to reconstruct three-dimensional ionospheric electron density (IED) distribution. In the DAAA method, the relaxation factor of the multiplicative algebraic reconstruction technique (MART) is first adaptively adjusted by introducing adaptive MART (AMART). To avoid the voxels without any rays traversing them becoming dependent on the initial IED values, smoothing constraints are generally imposed on the adaptive multiplicative algebraic reconstruction technique (AMART). In general, the elements of the smoothing matrices are invariant in the iterative process. They affect the accuracy and efficiency of the IED inversion. To overcome the above limitation, the adaptive adjustments of the constrained matrix elements are subsequently carried out. Both numerical simulation and actual global navigation satellite system (GNSS) experimental results validate that the accuracy and efficiency of ionospheric tomography have been improved by the DAAA method. Finally, the new algorithm is applied to reconstruct the three-dimensional structure of the ionosphere during different geomagnetic activities. The comparisons show that the vertical profiles of the DAAA method are in agreement with those recorded from the ionosonde, and the inverted vertical total electron content (VTEC) of the DAAA method also agrees with the ionospheric products of center for orbit determination in Europe (CODE) during geomagnetic quiet and geomagnetic storms. The comparisons confirm the reliability and superiority of the DAAA method. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)

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19 pages, 3922 KiB

Open AccessArticle

Improving the Detection Accuracy of Underwater Obstacles Based on a Novel Combined Method of Support Vector Regression and Gravity Gradient

by Tengda Fu, Wei Zheng, Zhaowei Li, Yifan Shen, Huizhong Zhu and Aigong Xu

Remote Sens. 2023, 15(8), 2188; https://doi.org/10.3390/rs15082188 - 20 Apr 2023

Viewed by 1441

Abstract

Underwater gravity gradient detection techniques are conducive to ensuring the safety of submersible sailing. In order to improve the accuracy of underwater obstacle detection based on gravity gradient detection technology, this paper studies the gravity gradient underwater obstacle detection method based on the combined support vector regression (SVR) algorithm. First, the gravity gradient difference ratio (GGDR) equation, which is only related to the obstacle’s position, is obtained based on the gravity gradient equation by using the difference and ratio methods. Aiming at solving the shortcomings of the GGDR equation based on Newton–Raphson method (NRM), combined with SVR algorithm, a novel SVR–gravity gradient joint method (SGJM) is proposed. Second, the differential ratio dataset is constructed by simulating the gravity gradient data generated by obstacles, and the obstacle location model is trained using SVR. Four measuring lines were selected to verify the SVR-based positioning model. The verification results show that the mean absolute error of the new method in the x, y, and z directions is less than 5.39 m, the root-mean-square error is less than 7.58 m, and the relative error is less than 4% at a distance of less than 500 m. These evaluation metrics validate the reliability of the novel SGJM-based detection of underwater obstacles. Third, comparative experiments based on the novel SGJM and traditional NRM were carried out. The experimental results show that the positioning accuracy of x and z directions in the obstacle’s position calculation based on the novel SGJM is improved by 88% and 85%, respectively. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)

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27 pages, 8550 KiB

Open AccessArticle

Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data

by Jianbo Wang, Jinyang Wang, Shunde Chen, Jianbo Luo, Mingzhi Sun, Jialong Sun, Jiajia Yuan and Jinyun Guo

Remote Sens. 2023, 15(7), 1746; https://doi.org/10.3390/rs15071746 - 24 Mar 2023

Cited by 2 | Viewed by 1151

Abstract

Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10⁹ m³ from July 2002 to December 2022, with a growth rate of 5.3766 × 10⁸ m³/a. The growth rate from January 2005 to January 2015 is 4.4850 × 10⁸ m³/a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10⁸ m³/a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)

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