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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 12604

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Dr. Robert Tenzer

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Guest Editor

Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong
Interests: Tibet; satellite gravity missions; Moho; terrain model; seismic data
Special Issues, Collections and Topics in MDPI journals

Dr. Hok Sum Fok

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Guest Editor

School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Interests: topography; satellite; remote sensing; satellite geodesy; sea ice; geophysics; spatial analysis; geomatics; radar
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Prof. Dr. Mohammad Bagherbandi

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Guest Editor

1. Division of Geodesy and Satellite Positioning, Royal Institute of Technology (KTH), SE-10044 Stockholm, Sweden
2. Faculty of Engineering and Sustainable Development, University of Gävle, SE-80176 Gävle, Sweden
Interests: geoid and gravity field; gravity field variation; height systems; mass transportation; quasi-geoid; reference system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Global Navigation Satellite Systems (GNSS), gravity-dedicated satellite missions, Satellite Laser Ranging (SLR), Interferometric Synthetic Aperture Radar (InSAR), Very Long Baseline Interferometry (VLBI), Satellite Altimetry, and other space-geodetic techniques together with airborne, seaborne, ground, and near subsurface geodetic systems have become indispensable to observe and understand the Earth System as well as interactions between geosphere, biosphere, cryosphere, hydrosphere, and atmosphere. In recent years, particular emphasis has been given to monitoring of phenomena related to natural and anthropogenic geo-hazards, such as glacial melting, sea level change, volcanism, earthquake deformations, or subsidence due to groundwater depletion. We have also witnessed a rapid development of geodetic techniques and their applications (along with geophysical methods) in monitoring of geo-tectonics and understanding of the Earth’s inner structure.

This special issue aims at studies covering numerous applications of geodetic techniques to observe dynamic processes in the Earth System along with more traditional geodetic methods of detecting the Earth’s shape, rotation, orientation, gravity field and their temporal changes. Possible topics might cover well-established geodetic methods of monitoring various phenomena at different temporal and spatial scales as well as examples of newly developed techniques and their applications. Studies addressing methods of collecting, processing, and interpreting geodetic data from multiple sources are particularly welcome.

Dr. Robert Tenzer
Dr. Hok Sum Fok
Prof. Dr. Mohammad Bagherbandi
Guest Editors

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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

geodetic observations of the impact of climate change
geodetic monitoring of natural and anthropogenic geo-hazard
earth's inner structure and geodynamics
earth's rotation and orientation
earth's mass transport
space weather, monitoring, and modeling of the atmosphere and ionosphere
topographic and bathymetric models
geodetic multisource data analysis and interpretation
geodetic monitoring of the biosphere
gravity field modeling
definition and realization of geodetic reference systems and frames

Published Papers (10 papers)

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28 pages, 25580 KiB

Open AccessArticle

Spatiotemporal Variations and Sustainability Characteristics of Groundwater Storage in North China from 2002 to 2022 Revealed by GRACE/GRACE Follow-On and Multiple Hydrologic Data

by Wei Qu, Pufang Zhang, Peinan Chen, Jiuyuan Li and Yuan Gao

Remote Sens. 2024, 16(7), 1176; https://doi.org/10.3390/rs16071176 - 27 Mar 2024

Viewed by 586

Abstract

North China (NC) is experiencing significant groundwater depletion. We used GRACE and GRACE-FO RL06 Level-2 data with Mascon data from April 2002 to July 2022. We fused these two types of data through the generalized three-cornered hat method and further combined them with hydrological models, precipitation, in situ groundwater-level, and groundwater extraction (GWE) data to determine and verify temporal and spatial variations in groundwater storage (GWS) in NC. We quantitatively assessed groundwater sustainability by constructing a groundwater index in NC. We further explored the dynamic cyclic process of groundwater change and quantified the impact of the South-to-North Water Transfer Project (SNWTP) on GWS change in NC. The overall GWS shows a decreasing trend. The GRACE/GRACE-FO-derived GWS change results are consistent with those shown by the in situ groundwater-level data from the monitoring well. Groundwater in NC is in various states of unsustainability throughout the period 2002 to 2022. The SNWTP affected the water use structure to some extent in NC. This study elucidates the latest spatial–temporal variations in GWS, especially in the groundwater sustainability assessment and quantitative description of the effects of the SNWTP on changes in GWS in NC. The results may provide a reference for groundwater resource management. Full article

(This article belongs to the Special Issue Geodesy of Earth Monitoring System)

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18 pages, 11598 KiB

Open AccessArticle

Glacier Mass Balance and Its Impact on Land Water Storage in the Southeastern Tibetan Plateau Revealed by ICESat-2 and GRACE-FO

by Jinwei Tong, Zhen Shi, Jiashuang Jiao, Bing Yang and Zhen Tian

Remote Sens. 2024, 16(6), 1048; https://doi.org/10.3390/rs16061048 - 15 Mar 2024

Viewed by 625

Abstract

The southeastern Tibetan Plateau (SETP), which hosts the most extensive marine glaciers on the Tibetan Plateau (TP), exhibits enhanced sensitivity to climatic fluctuations. Under global warming, persistent glacier mass depletion within the SETP poses a risk to water resource security and sustainability in adjacent nations and regions. This study deployed a high-precision ICESat-2 satellite altimetry technique to evaluate SETP glacier thickness changes from 2018 to 2022. Our results show that the average change rate in glacier thickness in the SETP is −0.91 ± 0.18 m/yr, and the corresponding glacier mass change is −7.61 ± 1.52 Gt/yr. In the SETP, the glacier mass loss obtained via ICESat-2 data is larger than the mass change in total land water storage observed by the Gravity Recovery and Climate Experiment follow-on satellite (GRACE-FO), −5.13 ± 2.55 Gt/yr, which underscores the changes occurring in other land water components, including snow (−0.44 ± 0.09 Gt/yr), lakes (−0.06 ± 0.02 Gt/yr), soil moisture (1.88 ± 1.83 Gt/yr), and groundwater (1.45 ± 0.70 Gt/yr), with a closure error of −0.35 Gt/yr. This demonstrates that this dramatic glacier mass loss is the main reason for the decrease in total land water storage in the SETP. Generally, there are decreasing trends in solid water storage (glacier and snow) against stable or increasing trends in liquid water storage (lakes, soil moisture, and groundwater) in the SETP. This persistent decrease in solid water is linked to the enhanced melting induced by rising temperatures. Given the decreasing trend in summer precipitation, the surge in liquid water in the SETP should be principally ascribed to the increased melting of solid water. Full article

(This article belongs to the Special Issue Geodesy of Earth Monitoring System)

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20 pages, 10347 KiB

Open AccessArticle

Water Storage Variations Recovered from Global Navigation Satellite System Network Using Spatial Constraints: A Case Study of the Contiguous United States

by Peng Yin, Dapeng Mu and Tianhe Xu

Remote Sens. 2023, 15(24), 5753; https://doi.org/10.3390/rs15245753 - 16 Dec 2023

Viewed by 753

Abstract

Global Navigation Satellite System (GNSS) vertical displacements are widely used to infer terrestrial water storage (TWS) variations. The traditional Laplacian inversion requires dedicated efforts to determine the optimal parameters, which has an important effect on the spatial patterns. In this study, we develop a new GNSS inversion method with flexible spatial constraints. One major merit is that the new method only requires loose boundary conditions rather than optimal parameters. A closed-loop simulation shows that the inversion using spatial constraints is improved by 7–21% compared with the Laplacian constraints. We apply this method to 18 watersheds across the Contiguous United States (CONUS) to infer daily TWS variations from January 2018 to August 2022. The results show that the amplitudes of monthly TWS time series from the spatial and Laplacian constraints are comparable to the Gravity Recovery and Climate Experiment (GRACE) Follow-On (GFO) in 16 watersheds. Furthermore, the standard deviation between the spatial constraints and GFO is at the same level as that between the Laplacian constraints and GFO. We also extract the daily TWS variations caused by heavy precipitation events in California. Our results demonstrate that spatial constraint inversion supplements the existing constraint strategies of GNSS inversion in hydrogeodesy; therefore, spatial constraint inversion can be an alternative tool for GNSS inversion. Full article

(This article belongs to the Special Issue Geodesy of Earth Monitoring System)

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21 pages, 12290 KiB

Open AccessArticle

Integration of Residual Terrain Modelling and the Equivalent Source Layer Method in Gravity Field Synthesis for Airborne Gravity Gradiometer Test Site Determination

by Meng Yang, Wei-Kai Li, Wei Feng, Roland Pail, Yan-Gang Wu and Min Zhong

Remote Sens. 2023, 15(21), 5190; https://doi.org/10.3390/rs15215190 - 31 Oct 2023

Cited by 2 | Viewed by 844

Abstract

To calibrate airborne gravity gradiometers currently in development in China, it is urgent to build an airborne gravity gradiometer test site. The site’s selection depends on the preknowledge of high-resolution gravity and gradient structures. The residual terrain modelling (RTM) technique is generally applied to recover the short-scale gravity field signals. However, due to limitations in the quality and resolution of density models, RTM terrain generally assumes a constant density. This assumption can introduce significant errors in areas with substantial density anomalies and of reggued terrain, such as volcano areas. In this study, we promote a method to determine a high-resolution gravity field by integrating long-wavelength signals generated by EGM2008 with short-wavelength signals from terrain relief and shallow density anomalies. These short wavelength signals are recovered using the RTM technique with both constant density and density anomalies obtained through the equivalent source layer (ESL) method, utilizing sparse terrestrial gravity measurements. Compared to the recovery rate of

54.62 %

using the classical RTM method, the recovery rate increases to

86.22 %

after involving density anomalies. With this method, we investigate the gravity field signals over the Wudalianchi Volcano Field (WVF) both on the Earth’s surface and at a flight height of 100 m above the terrain. The contribution of each part and their attenuation characters are studied. In particular, the 5 km × 5 km area surrounding Bijiashan (BJS) and Wohushan (WHS) volcanos shows a strong gravity signature, making it a good candidate for the test site location. This study gives the location of the airborne gravity gradiometer test site which is an essential step in the instruments’ development. Furthermore, the method presented in this study offers a foundational framework for future data processing within the test site. Full article

(This article belongs to the Special Issue Geodesy of Earth Monitoring System)

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22 pages, 12520 KiB

Open AccessArticle

Groundwater Potential Assessment in Gannan Region, China, Using the Soil and Water Assessment Tool Model and GIS-Based Analytical Hierarchical Process

by Zeyi Zhang, Shuangxi Zhang, Mengkui Li, Yu Zhang, Meng Chen, Qing Zhang, Zhouqing Dai and Jing Liu

Remote Sens. 2023, 15(15), 3873; https://doi.org/10.3390/rs15153873 - 04 Aug 2023

Viewed by 1387

Abstract

The Gannan region is situated in Ganzhou City, Jiangxi Province, China, and has a complicated geological background. Seasonal droughts significantly jeopardize the water security of the local population. Groundwater is essential to alleviate the region’s water needs. In this research, the groundwater potential (GWP) of the Gannan region was assessed using the Soil and Water Assessment Tool (SWAT) and the Analytical Hierarchical Process (AHP). The groundwater recharge and rainfall estimated by the SWAT model exhibited notable inconsistencies regarding their spatial distribution. Eight groundwater potential assessment factors (lithology, fault density, land use, slope, convergence index, drainage density, rainfall, and groundwater recharge) were constructed by integrating remote sensing, geological, and SWAT output data. Two GWP maps were constructed by an overlay analysis based on the obtained weights using the AHP, with the rainfall and groundwater recharge assigned the same weight to calculate the GWP with the other six factors separately. Each map was split into five classes: excellent, good, moderate, poor, and very poor. Data from 23 wells and 42 springs were collected to validate the two maps by correlation analysis between the GWP and flow rates of wells and springs. The correlation analysis result indicates that the GWP calculated by the recharge (R² = 0.8 and 0.74, respectively) is more accurate than the GWP calculated by the rainfall (R² = 0.21 and 0.48, respectively) and can provide a theoretical basis for groundwater management and exploration in the area. Full article

(This article belongs to the Special Issue Geodesy of Earth Monitoring System)

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20 pages, 13407 KiB

Open AccessArticle

Multi-Scale Encoding (MSE) Method with Spectral Shape Information (SSI) for Detecting Marine Oil-Gas Leakages

by Dong Zhao and Bin Tan

Remote Sens. 2023, 15(8), 2184; https://doi.org/10.3390/rs15082184 - 20 Apr 2023

Viewed by 1377

Abstract

Remote sensing technologies are suitable for detecting marine oil-gas leakages on a large scale. It is important to structure an accurate method for detecting marine oil-gas leakages in varied remote sensing images. However, traditional spectral indexes have limited applicability. Machine learning methods need plenty of training and testing samples to establish the optimized models, which is too rigorous for satellite images. Thus, we proposed a multi-scale encoding (MSE) method with spectral shape information (SSI) to detect the oil-gas leakages in multi-source remote sensing data. First, the spectral amplitude information (SAI) and SSI of the original spectra were encoded into a series of code words according to the scales. Then, the differential code words of the marine oil-gas leakage objects were extracted from the SAI and SSI code words. Finally, the pixels of the encoded hyperspectral image (HSI) and multispectral image (MSI) would be determined by the differential code words. Seven images captured by different platforms/sensors (Landsat 7, Landsat 8, MODIS, Sentinel 2, Zhuhai-1, and AVIRIS) were used to validate the performance of the proposed method. The experimental results indicated that the MSE method with SSI was convergent and could detect the oil-gas leakages accurately in different images using a small set of samples. Full article

(This article belongs to the Special Issue Geodesy of Earth Monitoring System)

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24 pages, 16639 KiB

Open AccessArticle

A High-Resolution Global Moho Model from Combining Gravimetric and Seismic Data by Using Spectral Combination Methods

by Arash Dashtbazi, Behzad Voosoghi, Mohammad Bagherbandi and Robert Tenzer

Remote Sens. 2023, 15(6), 1562; https://doi.org/10.3390/rs15061562 - 13 Mar 2023

Cited by 1 | Viewed by 1625

Abstract

The high-resolution Moho depth model is required in various geophysical studies. However, the available models’ resolutions could be improved for this purpose. Large parts of the world still need to be sufficiently covered by seismic data, but existing global Moho models do not fit the present-day requirements for accuracy and resolution. The isostatic models can relatively reproduce a Moho geometry in regions where the crustal structure is in an isostatic equilibrium, but large segments of the tectonic plates are not isostatically compensated, especially along active convergent and divergent tectonic margins. Isostatic models require a relatively good knowledge of the crustal density to correct observed gravity data. To overcome the lack of seismic data and non-uniqueness of gravity inversion, seismic and gravity data should be combined to estimate Moho geometry more accurately. In this study, we investigate the performance of two techniques for combining long- and short-wavelength Moho geometry from seismic and gravity data. Our results demonstrate that both Butterworth and spectral combination techniques can be used to model the Moho geometry. The results show the RMS of Moho depth differences between our model and the reference models are between 1.7 and 4.7 km for the Butterworth filter and between 0.4 and 4.1 km for the spectral combination. Full article

(This article belongs to the Special Issue Geodesy of Earth Monitoring System)

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19 pages, 7621 KiB

Open AccessArticle

Parallel Processing Method for Microseismic Signal Based on Deep Neural Network

by Chunchi Ma, Wenjin Yan, Weihao Xu, Tianbin Li, Xuefeng Ran, Jiangjun Wan, Ke Tong and Yu Lin

Remote Sens. 2023, 15(5), 1215; https://doi.org/10.3390/rs15051215 - 22 Feb 2023

Cited by 2 | Viewed by 1326

Abstract

The microseismic signals released by rock mass fracture can be captured via microseismic monitoring to evaluate the development of geological disasters. This is crucial for underground engineering construction, underground mining, and earthquake and geological disaster evaluation. However, extracting information effectively is difficult due to the low signal-to-noise ratio of microseismic signals caused by complex environmental factors. Therefore, denoising and detection (onset time picking) are essential to processing microseismic signals and extracting source information. To improve the efficiency and accuracy of microseismic signal processing, we propose a parallel dual-tasking network, which is an advanced deep learning model that can simultaneously perform microseismic denoising and detection tasks. The network, comprising one encoder and two parallel decoders, is customised to extract input data features, and two outputs can be simultaneously generated to denoise and detect microseismic signals. The model exhibits excellent denoising and detection performance for microseismic signals containing various types of noise. Compared with traditional methods, the signal-to-noise ratio of the denoised signal is greatly improved, and the waveform distortion of the denoised signal is small. Even when the signal-to-noise ratio is low, the proposed model can maintain good onset time pickup performance. This method obviates the need for different denoising methods for different types of noise and precludes setting thresholds artificially to improve the denoising effect and detection accuracy. Moreover, the dual processing characteristics of the model facilitate simultaneous denoising and detection, which improves the processing efficiency of microseismic data and meets the demand for automatically processing massive microseismic data. Therefore, this method has excellent data processing potential in exploration seismology, and earthquake and disaster assessment. Full article

(This article belongs to the Special Issue Geodesy of Earth Monitoring System)

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20 pages, 5679 KiB

Open AccessArticle

Fine Classification Method for Massive Microseismic Signals Based on Short-Time Fourier Transform and Deep Learning

by Chunchi Ma, Xuefeng Ran, Weihao Xu, Wenjin Yan, Tianbin Li, Kunkun Dai, Jiangjun Wan, Yu Lin and Ke Tong

Remote Sens. 2023, 15(2), 502; https://doi.org/10.3390/rs15020502 - 14 Jan 2023

Cited by 4 | Viewed by 1651

Abstract

Numerous microseismic signals are produced by rock mass fracture during earthquakes, geological disasters, or underground excavations. Moreover, a large amount of noise signals are captured during microseismic signal monitoring. Specifically, some noise signals closely resemble microseismic signals, which severely impedes the rapid and accurate detection of the latter and the assessment of geological hazards. Therefore, we propose a precise model for identifying and classifying microseismic signals based on deep learning technology and short-time Fourier transform (STFT) technology. First, the STFT time–frequency analysis reveals the unique characteristics of noise, microseismic, and blasting signals, thereby allowing noise signals that are very similar to microseismic signals in the time domain to be finely distinguished. Second, the introduced attention mechanism focuses the classification on essential signal features. Finally, because tens of thousands of actual monitoring data points are considered, the deep neural network for microseismic classification is trained and tested under complex geological engineering conditions. The results demonstrate that the neural network model has good time–frequency feature extraction ability, and the well-trained model can satisfactorily complete daily classifications. Moreover, the model performs well when classifying similar noise and low-SNR microseismic signals. We believe that this type of signal-processing method, which considers multiple perspectives, can be extended to data processing in many other data-driven fields. Full article

(This article belongs to the Special Issue Geodesy of Earth Monitoring System)

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18 pages, 5192 KiB

Open AccessTechnical Note

Evaluation and Analysis of Remotely Sensed Water Vapor from the NASA VIIRS/SNPP Product in Mainland China Using GPS Data

by Linghao Zhou, Lei Fan and Chuang Shi

Remote Sens. 2023, 15(6), 1528; https://doi.org/10.3390/rs15061528 - 10 Mar 2023

Cited by 1 | Viewed by 1172

Abstract

Precipitable water vapor (PWV) is a vitally important factor in atmospheric circulation. PWV is significant for forecasting extreme weather and understanding the dynamics of climate change. Comprehensively evaluating the performance of newly proposed remotely sensed water vapor products is crucial for guaranteeing their suitability for futural PWV applications. In this study, the accuracy of the recently established remotely sensed water vapor product from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensor on the Suomi National Polar-orbiting Partnership (SNPP) (VIIRS-PWV) platform within various regions of mainland China was evaluated via the PWV from Global Positioning System (GPS) observations. The GPS-derived PWV from 231 stations of the Crustal Movement Observation Network of China (CMONOC) from 2012 to 2018 was obtained through precise point positioning (PPP) techniques. The results showed that the mean value of the correlation coefficient (CC), the mean bias (MB), and the root-mean-square error (RMSE) between the VIIRS-PWV and the GPS-PWV were 0.92, −1.6 mm, and 4.7 mm, respectively. These values were comparable with the results of the PWV data derived from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. This indicates that the VIIRS product could provide PWV data with satisfactory accuracy for large-area scientific applications. Moreover, the MB and RMSE of the differences between the GPS-PWV and VIIRS-PWV showed obvious seasonal variations. The VIIRS-PWV generally performed better in winter (with the MB and RMSE values of 0.1 mm and 2.3 mm) than in summer (with the MB and RMSE values of −4.4 and 7.0 mm). Analysis among different regions revealed that the Central South (CS) region of China attained the largest mean RMSE value of 6.3 mm, and the North West (NW) region attained the smallest mean RMSE value of 3.8 mm. In addition, the southern region of China obtained a mean RMSE value of 5.6 mm, while that for the northern region of China was 3.9 mm. This indicates that the VIIRS-PWV has better accuracy within the northern region of China than within the southern region. Full article

(This article belongs to the Special Issue Geodesy of Earth Monitoring System)

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