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Advances in Spaceborne SAR – Technology and Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 30709

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Special Issue Editors

Prof. Dr. Joong-Sun Won

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Guest Editor

Earth System Sciences, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul 03722, Korea
Interests: optic; MWIR/TIR and SAR remote sensing; tidal flats; geohazard; radar interferometry; and velocity retrieval from SAR.

Dr. Sanggyu Lee

E-Mail Website
Guest Editor

Satellite Research Directorate, KARI, 169-84 Gwahak-ro Yuseong, Daejeon, 34133, Korea
Interests: R&D of satellite payloads: compact SAR; MW radiometer and EO camera

Dr. Euiho Hwang

E-Mail Website
Guest Editor

Water Resources Satellite Research Center, K-water Institute, K-water, 1689 beon-gil, Yuseong-daero, Yuseong-gu, Daejeon 34045, Korea
Interests: water hazard information platform using Satellite-Radar-AWS; hydroinformatics and SAR

Dr. Hyongki Lee

E-Mail Website
Guest Editor

Department of Civil and Environmental Engineering, University of Houston, 5000 Gulf Freeway Building 4, Room 216, Houston, TX 77204-5059, USA
Interests: monitoring and forecasting of terrestrial water dynamics using altimetry; SAR/InSAR and gravimetry with hydrologic modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, several synthetic aperture radar (SAR) systems have been proposed and developed. In addition to conventional high-performance spaceborne SAR systems, low-cost SAR systems, of small size and light-weight, mounted on aircrafts at low altitude, including UAVs and drones as well as small to medium satellites at high altitude, provide all-weather and high-resolution imaging capability. Those new small and low-cost SAR systems can dramatically reduce an observation period by deploying several sensors and consequently offers a new era of SAR applications. A high temporal resolution with a competitive performance encourages remote sensing community to develop many innovative applications for Earth monitoring and management as well as for surveillance.

This Special Issue focuses on current and upcoming developments in the international SAR constellation that promote synergies among the different SAR missions for interdisciplinary scientific and societal applications. Recent progress in new SAR technology and missions would bring perfect opportunities for introducing their potentials to the remote sensing community. With the ongoing progress of high-performance SAR in UAVs, aircrafts, and spaceborne missions, SAR constellation has become an effective tool for imaging and quantifying surface properties and dynamics. Along with SAR technological innovation, applications of SAR are rapidly diversified with increasing data availability and short temporal resolution, leading the development of a wide variety of new and novel application methods and products useful for the public interest, as well as scientific research. This Special Issue encourages submissions of studies about innovative SAR applications to land, ocean, and polar regions, as well as the synergistic use of multiple sensors at multiple scales. We invite all scientists, engineers, and government decision-makers devoted to various activities around SAR technology and applications. The following summary of topics draws guidelines on the paper invitation, but all relevant topics and papers are welcome to this Special Issue.

- Current and upcoming SAR missions: introducing state of the art and performance of current, and planned and being developed SAR systems and missions.

- Micro-SAR sensor and applications: concept and technology of micro-SAR antenna and systems mounted on UAV platforms, airplanes, and small satellites for cost-effective applications such as surveillance, monitoring natural and anthropogenic disasters, surface water management, etc.

- Multi-sensor and multi-scale data analysis and processing: data processing and application methods to fully exploit the enhanced capability of various current and upcoming SAR systems. Synergistic use of multiple sensors to enhance the spatial, temporal, and polarimetric scales.

- Methodological progress in SAR application methods: innovative methods and applications utilizing machine learning, SAR interferometry and polarimetry, quantified applications and physical model inversion, hybrid methods and data merging, etc.

- Applications of SAR to land, oceans, cryosphere, and other fields: qualitative and quantitative applications and results obtained by SAR and/or synergetic use of optical and microwave remote sensing, which make a significant contribution to both scientific understanding, forecasting, and consequently to societal benefits in various respects.

Prof. Dr. Joong-Sun Won
Dr. Sanggyu Lee
Dr. Euiho Hwang
Dr. Hyongki Lee
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

New innovative SAR system
Missions and sensors
Micro-SAR sensor and applications
Data analysis and methods
Machine learning and/or new applications
Land applications
Ocean and cryosphere applications
Other applications

Published Papers (11 papers)

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24 pages, 9761 KiB

Open AccessArticle

Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

by Fabiano G. da Silva, Lucas P. Ramos, Bruna G. Palm and Renato Machado

Remote Sens. 2022, 14(13), 2966; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14132966 - 21 Jun 2022

Cited by 2 | Viewed by 1949

Abstract

This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR – Technology and Applications)

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28 pages, 9950 KiB

Open AccessArticle

Analysis of Environmental and Atmospheric Influences in the Use of SAR and Optical Imagery from Sentinel-1, Landsat-8, and Sentinel-2 in the Operational Monitoring of Reservoir Water Level

by Wendson de Oliveira Souza, Luis Gustavo de Moura Reis, Antonio Miguel Ruiz-Armenteros, Doris Veleda, Alfredo Ribeiro Neto, Carlos Ruberto Fragoso Jr., Jaime Joaquim da Silva Pereira Cabral and Suzana Maria Gico Lima Montenegro

Remote Sens. 2022, 14(9), 2218; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092218 - 05 May 2022

Cited by 10 | Viewed by 2004

Abstract

In this work, we aim to evaluate the feasibility and operational limitations of using Sentinel-1 synthetic aperture radar (SAR) data to monitor water levels in the Poço da Cruz reservoir from September 2016–September 2020, in the semi-arid region of northeast Brazil. To segment water/non-water features, SAR backscattering thresholding was carried out via the graphical interpretation of backscatter coefficient histograms. In addition, surrounding environmental effects on SAR polarization thresholds were investigated by applying wavelet analysis, and the Landsat-8 and Sentinel-2 normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used to compare and discuss the SAR results. The assessment of the observed and estimated water levels showed that (i) SAR accuracy was equivalent to that of NDWI/Landsat-8; (ii) optical image accuracy outperformed SAR image accuracy in inlet branches, where the complexity of water features is higher; and (iii) VV polarization outperformed VH polarization. The results confirm that SAR images can be suitable for operational reservoir monitoring, offering a similar accuracy to that of multispectral indices. SAR threshold variations were strongly correlated to the normalized difference vegetation index (NDVI), the soil moisture variations in the reservoir depletion zone, and the prior precipitation quantities, which can be used as a proxy to predict cross-polarization (VH) and co-polarization (VV) thresholds. Our findings may improve the accuracy of the algorithms designed to automate the extraction of water levels using SAR data, either in isolation or combined with multispectral images. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR – Technology and Applications)

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22 pages, 6729 KiB

Open AccessArticle

Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components

by Jeehun Chung, Yonggwan Lee, Jinuk Kim, Chunggil Jung and Seongjoon Kim

Remote Sens. 2022, 14(3), 465; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030465 - 19 Jan 2022

Cited by 14 | Viewed by 3506

Abstract

This study estimates soil moisture content (SMC) using Sentinel-1A/B C-band synthetic aperture radar (SAR) images and an artificial neural network (ANN) over a 40 × 50-km² area located in the Geum River basin in South Korea. The hydrological components characterized by the antecedent precipitation index (API) and dry days were used as input data as well as SAR (cross-polarization (VH) and copolarization (VV) backscattering coefficients and local incidence angle), topographic (elevation and slope), and soil (percentage of clay and sand)-related data in the ANN simulations. A simple logarithmic transformation was useful in establishing the linear relationship between the observed SMC and the API. In the dry period without rainfall, API did not decrease below 0, thus the Dry days were applied to express the decreasing SMC. The optimal ANN architecture was constructed in terms of the number of hidden layers, hidden neurons, and activation function. The comparison of the estimated SMC with the observed SMC showed that the Pearson’s correlation coefficient (R) and the root mean square error (RMSE) were 0.85 and 4.59%, respectively. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR – Technology and Applications)

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17 pages, 4874 KiB

Open AccessArticle

Efficient SAR Azimuth Ambiguity Reduction in Coastal Waters Using a Simple Rotation Matrix: The Case Study of the Northern Coast of Jeju Island

by Joon Hyuk Choi and Joong-Sun Won

Remote Sens. 2021, 13(23), 4865; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234865 - 30 Nov 2021

Cited by 3 | Viewed by 2053

Abstract

Azimuth ambiguities, or ghosts on SAR images, represent one of the main obstacles for SAR applications involving coastal monitoring activities such as ship detection. While most previous methods based on azimuth antenna pattern and direct filtering are effective for azimuth ambiguity suppression, they may not be effective for fast cruising small ships. This paper proposes a unique approach for the reduction of azimuth ambiguities or ghosts in SAR single-look complex (SLC) images using a simple rotation matrix. It exploits the fact that the signal powers of azimuth ambiguities are concentrated on narrow bands, while those of vessels or other true ground targets are dispersed over broad bands. Through sub-aperture processing and simple axis rotation, it is possible to concentrate the dispersed energy of vessels onto a single axis while the ghost signal powers are dispersed onto three different axes. Then, the azimuth ambiguities can be easily suppressed by a simple calculation of weighted sum and difference, while preserving vessels. Applied results achieved by processing TerrSAR-X SLC images are provided and discussed. An optimum weight of 0.5 was determined by Receiver Operating Characteristic (ROC) analysis. Capabilities of ship detection from the test image were significantly improved by removing 93% of false alarms. Application results demonstrate its high performance of ghost suppression. This method can be employed as a pre-processing tool of SAR images for ship detection in coastal waters. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR – Technology and Applications)

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19 pages, 3646 KiB

Open AccessArticle

Onboard Digital Beamformer with Multi-Frequency and Multi-Group Time Delays for High-Resolution Wide-Swath SAR

by Wei Xu, Qi Yu, Chonghua Fang, Pingping Huang, Weixian Tan and Yaolong Qi

Remote Sens. 2021, 13(21), 4354; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214354 - 29 Oct 2021

Cited by 3 | Viewed by 1738

Abstract

Scan-on-receive (SCORE) digital beamforming (DBF) in elevation can significantly improve the signal-to-noise ratio (SNR) and suppress range ambiguities in spaceborne synthetic aperture radar (SAR). It has been identified as one of the important methods to obtain high-resolution wide-swath (HRWS) SAR images. However, with the improvement of geometric resolution and swath width, the residual pulse extension loss (PEL) due to the long pulse duration in the conventional spaceborne onboard DBF processor must be considered and reduced. In this paper, according to the imaging geometry of the spaceborne DBF SAR system, the reason for the large attenuation of the receiving gain at the edge of the wide swath is analyzed, and two improved onboard DBF methods to mitigate the receive gain loss are given and analyzed. Taking account of both the advantages and drawbacks of the two improved DBF methods presented, a novel onboard DBF processor with multi-frequency and multi-group time delays in HRWS SAR is proposed. Compared with the DBF processor only with multi-group time delays, the downlink data rate was clearly reduced, while focusing performance degradation due to phase and amplitude errors between different frequency bands could be mitigated compared with the DBF processor only with multi-frequency time delays. The simulation results of both point and distributed targets validate the proposed DBF processor. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR – Technology and Applications)

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10 pages, 3368 KiB

Open AccessCommunication

An Accurate Doppler Parameters Calculation Method of Geosynchronous SAR Considering Real-Time Zero-Doppler Centroid Control

by Faguang Chang, Chunrui Yu, Dexin Li, Yifei Ji and Zhen Dong

Remote Sens. 2021, 13(20), 4061; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204061 - 11 Oct 2021

Cited by 1 | Viewed by 1535

Abstract

The zero-Doppler centroid control in geosynchronous synthetic aperture radar (GEO SAR) is beneficial to reduce the imaging complexity (reduces range-azimuth coupling in received data), which can be realized by adjusting the radar line of sight (RLS). In order to maintain the zero-Doppler centroid throughout the whole orbit of the GEO SAR satellite, the RLS needs to be adjusted in real-time. Due to the ultra-long synthetic aperture time of GEO SAR, the RLS variation during the synthetic aperture time cannot be neglected. However, in the previous related papers, the real-time variation of RLS during the synthetic aperture time was not taken into account in the calculation of Doppler parameters, which are closely related to the RLS, resulting in inaccurate calculation of Doppler parameters. Considering this issue, an accurate Doppler model (the model of relative motion between satellite and ground target) of GEO SAR is proposed in this paper for the accurate calculation of Doppler parameters (Doppler centroid and Doppler bandwidth and other parameters). Finally, simulation experiments are designed to confirm the effectiveness and necessity of the proposed model. The results indicate that the RLS variation during the synthetic aperture time has a considerable effect on Doppler parameters performance of the GEO SAR, and refers to a more stable azimuth resolution performance (the resolution is kept near a relatively stable value at most positions of the elliptical orbit) compared with the case that does not consider the real-time zero-Doppler centroid control. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR – Technology and Applications)

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22 pages, 58243 KiB

Open AccessArticle

Shallow Landslides Physically Based Susceptibility Assessment Improvement Using InSAR. Case Study: Carpathian and Subcarpathian Prahova Valley, Romania

by Iuliana Armaș, Mihaela Gheorghe and George Cătălin Silvaș

Remote Sens. 2021, 13(12), 2385; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122385 - 18 Jun 2021

Cited by 6 | Viewed by 2227

Abstract

A multi-temporal satellite radar interferometry technique is used for deriving the actual surface displacement patterns in a slope environment in Romania, in order to validate and improve a landslide susceptibility map. The probability the occurrence of future events is established using a deterministic approach based on a classical one-dimension infinite slope stability model. The most important geotechnical parameters for slope failure in the proposed study area are cohesion, unit weight and friction angle, and the triggering factor is a rapid rise in groundwater table under wetting conditions. Employing a susceptibility analysis using the physically based model under completely saturated conditions proved to be the most suitable scenario for identifying unstable areas. The kinematic characteristics are assessed by the Small BAseline Subsets (SBAS) interferometry technique applied to C-band synthetic aperture radar (SAR) Sentinel-1 imagery. The analysis was carried out mainly for inhabited areas which present a better backscatter return. The validation revealed that more than 22% of the active landslides identified by InSAR were predicted as unstable areas by the infinite slope model. We propose a refinement of the susceptibility map using the InSAR results for unravelling the danger of the worst-case scenario. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR – Technology and Applications)

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16 pages, 61546 KiB

Open AccessArticle

Development of a Shoreline Detection Method Using an Artificial Neural Network Based on Satellite SAR Imagery

by Yoshimitsu Tajima, Lianhui Wu and Kunihiro Watanabe

Remote Sens. 2021, 13(12), 2254; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122254 - 09 Jun 2021

Cited by 13 | Viewed by 2591

Abstract

Monitoring shoreline change is one of the essential tasks for sustainable coastal zone management. Due to its wide coverage and relatively high spatiotemporal monitoring resolutions, satellite imagery based on synthetic aperture radar (SAR) is considered a promising data source for shoreline monitoring. In this study, we developed a robust shoreline detection method based on satellite SAR imagery using an artificial neural network (NN). The method uses the feedforward NN to classify the pixels of SAR imagery into two categories, land and sea. The shoreline location is then determined as a boundary of these two groups of classified pixels. To enhance the performance of the present NN for land–sea classification, we introduced two different approaches in the settings of the input layer that account not only for the local characteristics of pixels but also for the spatial pixel patterns with a certain distance from the target pixel. Two different approaches were tested against SAR images, which were not used for model training, and the results showed classification accuracies higher than 95% in most SAR images. The extracted shorelines were compared with those obtained from eye detection. We found that the root mean square errors of the shoreline position were generally less than around 15 m. The developed method was further applied to two long coasts. The relatively high accuracy and low computational cost support the advantages of the present method for shoreline detection and monitoring. It should also be highlighted that the present method is calibration-free, and has robust applicability to the shoreline with arbitrary angles and profiles. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR – Technology and Applications)

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22 pages, 7027 KiB

Open AccessArticle

Monitoring Wet Snow Over an Alpine Region Using Sentinel-1 Observations

by Fatima Karbou, Gaëlle Veyssière, Cécile Coleou, Anne Dufour, Isabelle Gouttevin, Philippe Durand, Simon Gascoin and Manuel Grizonnet

Remote Sens. 2021, 13(3), 381; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030381 - 22 Jan 2021

Cited by 24 | Viewed by 3673

Abstract

The main objective of this study was to monitor wet snow conditions from Sentinel-1 over a season, to examine its variation over time by cross-checking wet snow with independent snow and weather estimates, and to study its distribution taking into account terrain characteristics such as elevation, orientation, and slope. One of our motivations was to derive useful representations of daily or seasonal snow changes that would help to easily identify wet snow elevations and determine melt-out days in an area of interest. In this work, a well-known approach in the literature is used to estimate the extent of wet snow cover continuously over a season and an analysis of the influence of complex mountain topography on snow distribution is proposed taking into account altitude, slope, and aspect of the terrain. The Sentinel-1 wet snow extent product was compared with Sentinel-2 snow products for cloud free scenes. We show that while there are good agreements between the two satellite products, differences exist, especially in areas of forests and glaciers where snow is underestimated. This underestimation must be considered alongside the areas of geometric distortion that were excluded from our study. We analysed retrievals at the scale of our study area by examining wet snow Altitude–Orientation diagrams for different classes of slopes and also wet snow Altitude–Time diagrams for different classes of orientations. We have shown that this type of representation is very useful to get an overview of the snow distribution as it allows to identify very easily wet snow lines for different orientations. For an orientation of interest, the Altitude–Time diagrams can be used to track the evolution of snow to locate altitudes and dates of snow loss. We also show that ascending/descending Sentinel-1 image time series are complementary to monitor wet snow over the French alpine areas to highlight wet snow altitude ranges and identify melt-out days. Links have also been made between Sentinel-1 responses (wet snow) and snow/meteorological events carefully listed over the entire 2017–2018 season. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR – Technology and Applications)

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11 pages, 2946 KiB

Open AccessTechnical Note

Statistical Analysis for Tidal Flat Classification and Topography Using Multitemporal SAR Backscattering Coefficients

by Keunyong Kim, Hahn Chul Jung, Jong-Kuk Choi and Joo-Hyung Ryu

Remote Sens. 2021, 13(24), 5169; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245169 - 20 Dec 2021

Cited by 4 | Viewed by 3003

Abstract

Coastal zones are very dynamic natural systems that experience short-term and long-term morphological changes. Their highly dynamic behavior requires frequent monitoring. Tidal flat topography for a large spatial coverage has been generated mainly by the waterline extraction method from multitemporal remote sensing observations. Despite the efficiency and robustness of the waterline extraction method, the waterline-based digital elevation model (DEM) is limited to representing small scale topographic features, such as localized tidal tributaries. Tidal flats show a rapid increase in SAR backscattering coefficients when the tide height is lower than the tidal flat topography compared to when the tidal flat is covered by water. This leads to a tidal flat with a distinct statistical behavior on the temporal variability of our multitemporal SAR backscattering coefficients. Therefore, this study aims to suggest a new method that can overcome the constraints of the waterline-based method by using a pixel-based DEM generation algorithm. Jenks Natural Break (JNB) optimization was applied to distinguish the tidal flat from land and ocean using multitemporal Senitnel-1 SAR data for the years 2014–2020. We also implemented a logistic model to characterize the temporal evolution of the SAR backscattering coefficients along with the tide heights and estimated intertidal topography. The Sentinel-1 DEM from the JNB classification and logistic function was evaluated by an airborne Lidar DEM. Our pixel-based DEM outperformed the waterline-based Landsat DEM. This study demonstrates that our statistical approach to intertidal classification and topography serves to monitor the near real-time spatiotemporal distribution changes of tidal flats through continuous and stable SAR data collection on local and regional scales. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR – Technology and Applications)

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14 pages, 4811 KiB

Open AccessTechnical Note

Monitoring of Water Level Change in a Dam from High-Resolution SAR Data

by Yoon-Kyung Lee, Sang-Hoon Hong and Sang-Wan Kim

Remote Sens. 2021, 13(18), 3641; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183641 - 12 Sep 2021

Cited by 5 | Viewed by 4214

Abstract

Accurate measurement of water levels and variations in lakes and reservoirs is crucial for water management. The retrieval of the accurate variations in water levels in lakes and reservoirs with small widths from high-resolution synthetic aperture radar (SAR) images such as the TerraSAR add-on for Digital Elevation Measurements (TanDEM-X) and COnstellation of small Satellites for the Mediterranean basin Observation (COSMO-SkyMed) are presented here. A detailed digital surface model (DSM) for the upstream face of the dam was constructed using SAR interferometry with TanDEM-X data to estimate the water level. The elevation of the waterline below that of the interferometric SAR (InSAR) DSM was estimated based on upstream face modeling. The waterline boundary detected using the SAR Edge Detection Hough Transform algorithm was applied to the restored DSM. The SAR-derived water level variations showed a high correlation coefficient of 0.99 and a gradient of 1.08 with the gauged data. The difference between the gauged data and SAR-derived data was within ±1 m, and the standard deviation of the residual was 0.60 m. These results suggest that water level estimation can be used as an operational supplement for traditional gauged data at remote sites. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR – Technology and Applications)

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