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Remote Sens., Volume 11, Issue 18 (September-2 2019)

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Cover Story (view full-size image) Time-variable gravity field models derived from observations of the joint US/German GRACE mission [...] Read more.
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Open AccessArticle
Automatic Methodology to Detect the Coastline from Landsat Images with a New Water Index Assessed on Three Different Spanish Mediterranean Deltas
Remote Sens. 2019, 11(18), 2186; https://doi.org/10.3390/rs11182186 - 19 Sep 2019
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Abstract
Due to the importance of coastline detection in coastal studies, different methods have been developed in recent decades in accordance with the evolution of measuring techniques such as remote sensing. This work proposes an automatic methodology with new water indexes to detect the [...] Read more.
Due to the importance of coastline detection in coastal studies, different methods have been developed in recent decades in accordance with the evolution of measuring techniques such as remote sensing. This work proposes an automatic methodology with new water indexes to detect the coastline from different multispectral Landsat images; the methodology is applied to three Spanish deltas in the Mediterranean Sea. The new water indexes use surface reflectance rather than top-of-atmosphere reflectance from blue and shortwave infrared (SWIR 2) Landsat bands. A total of 621 sets of images were analyzed from three different Landsat sensors with a moderate spatial resolution of 30 m. Our proposal, which was compared to the most commonly used water indexes, showed outstanding performance in automatic detection of the coastline in 96% of the data analyzed, which also reached the minimum value of bias of 0.91 m and a standard deviation ranging from ±4.7 and ±7.29 m in some cases in contrast to the existing values. Bicubic interpolation was evaluated for a simple sub-pixel analysis to assess its capability in improving the accuracy of coastline extraction. Our methodology represents a step forward in automatic coastline detection that can be applied to micro-tidal coastal sites with different land covers using many multi-sensor satellite images. Full article
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Open AccessFeature PaperArticle
The Interannual Calibration and Global Nighttime Light Fluctuation Assessment Based on Pixel-Level Linear Regression Analysis
Remote Sens. 2019, 11(18), 2185; https://doi.org/10.3390/rs11182185 - 19 Sep 2019
Viewed by 310
Abstract
The Operational Linescan System (OLS) carried by the National Defense Meteorological Satellite Program (DMSP) can capture the weak visible radiation emitted from earth at night and produce a series of annual cloudless nighttime light (NTL) images, effectively supporting multi-scale, long-term human activities and [...] Read more.
The Operational Linescan System (OLS) carried by the National Defense Meteorological Satellite Program (DMSP) can capture the weak visible radiation emitted from earth at night and produce a series of annual cloudless nighttime light (NTL) images, effectively supporting multi-scale, long-term human activities and urbanization process research. However, the interannual instability and sensor bias of NTL time series products greatly limit further studies of lighting data in time series with OLS. Several calibration models for OLS have been proposed to implement interannual corrections to improve the continuity and consistency of time series NTL products; however, due to the subjective factors intervention and insufficient automation in the calibration process, the interannual correction study of NTL time series images is still worth being developed further. Therefore, to avoid the involvement of subjective factors and to optimize the Pseudo-Invariant Features (PIF) identification, an interannual calibration model Pixel-based PIF (PBPIF) is proposed, which identifies PIF by pixel fluctuation characteristics. Results show that a PBPIF-based model can reduce subjective interference and improve the degree of automation during the NTL interannual calibration process. The calibration performance evaluation based on Total Sum of Lights (TSOL) and Sum of the Normalized Difference Index (SNDI) shows that compared to the traditional PIF-based (tPIF-based) and Ridgeline Sampling Regression based (RSR-based) models, the PBPIF-based one achieves better performance in reducing NTL interannual turbulence and minimizing the deviation between sensors. In addition, based on the corrected NTL time series products, pixel-level linear regression analysis is implemented to maximize the potential of the NTL resolution to produce global Light Intensity Change Coefficient (LICC). The results of global LICC can be widely applied to the detailed study of the characteristics of economic development and urbanization. Full article
(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)
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Open AccessLetter
Detection of Liquefaction Phenomena from the 2017 Pohang (Korea) Earthquake Using Remote Sensing Data
Remote Sens. 2019, 11(18), 2184; https://doi.org/10.3390/rs11182184 - 19 Sep 2019
Viewed by 360
Abstract
On 15 November 2017, liquefaction phenomena were observed around the epicenter after a 5.4 magnitude earthquake occurred in Pohang in southeast Korea. In this study, we attempted to detect areas of sudden water content increase by using SAR (synthetic aperture radar) and optical [...] Read more.
On 15 November 2017, liquefaction phenomena were observed around the epicenter after a 5.4 magnitude earthquake occurred in Pohang in southeast Korea. In this study, we attempted to detect areas of sudden water content increase by using SAR (synthetic aperture radar) and optical satellite images. We analyzed coherence changes using Sentinel-1 SAR coseismic image pairs and analyzed NDWI (normalized difference water index) changes using Landsat 8 and Sentinel-2 optical satellite images from before and after the earthquake. Coherence analysis showed no liquefaction-induced surface changes. The NDWI time series analysis models using Landsat 8 and Sentinel-2 optical images confirmed liquefaction phenomena close to the epicenter but could not detect liquefaction phenomena far from the epicenter. We proposed and evaluated the TDLI (temporal difference liquefaction index), which uses only one SWIR (short-wave infrared) band at 2200 nm, which is sensitive to soil moisture content. The Sentinel-2 TDLI was most consistent with field observations where sand blow from liquefaction was confirmed. We found that Sentinel-2, with its relatively shorter revisit period compared to that of Landsat 8 (5 days vs. 16 days), was more effective for detecting traces of short-lived liquefaction phenomena on the surface. The Sentinel-2 TDLI could help facilitate rapid investigations and responses to liquefaction damage. Full article
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Open AccessArticle
On-Orbit Radiance Calibration of Nighttime Sensor of LuoJia1-01 Satellite Based on Lunar Observations
Remote Sens. 2019, 11(18), 2183; https://doi.org/10.3390/rs11182183 - 19 Sep 2019
Viewed by 312
Abstract
The high-resolution nighttime light (NTL) data of the LuoJia1-01 NTL remote sensing satellite has enriched the available data of NTL remote sensing applications. The radiance calibration used as a reference to convert the digital number (DN) recorded by the nighttime sensor into the [...] Read more.
The high-resolution nighttime light (NTL) data of the LuoJia1-01 NTL remote sensing satellite has enriched the available data of NTL remote sensing applications. The radiance calibration used as a reference to convert the digital number (DN) recorded by the nighttime sensor into the radiance of the corresponding ground object is the basic premise to the effective application of the NTL data. Owing to the lack of on-board calibration equipment and the absence of an absolute radiometric calibration light source at night, it is difficult for LuoJia1-01 to carry out on-orbit radiance calibration. The moon, as an exoatmospheric stable radiation source, is widely used for the radiometric calibration of remote sensing satellite sensors and to monitor the stability of the visible and near-infrared sensors. This study, based on lunar observation of the LuoJia1-01 NTL sensor, focused on on-orbit radiometric calibration and included monitoring changes in the nighttime sensor radiometric response for nearly a year by using the Robotic Lunar Observatory (ROLO) lunar irradiance model (Version 311 g). The results showed that: (1) the consistency of the radiometric calibration results based on the ROLO model and the laboratory calibration results of LuoJia1-01 exceeded 90%; (2) the nighttime sensor of LuoJia1-01 radiometric response underwent approximately 6% degradation during the observation period of nearly one year (353 days). Full article
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Open AccessArticle
Using Canopy Height Model Obtained with Dense Image Matching of Archival Photogrammetric Datasets in Area Analysis of Secondary Succession
Remote Sens. 2019, 11(18), 2182; https://doi.org/10.3390/rs11182182 - 19 Sep 2019
Viewed by 297
Abstract
One of the threats that has a significant impact on the conservation status and on the preservation of non-forest Natura 2000 habitats, is secondary succession, which is currently analyzed using airborne laser scanning (ALS) data. However, learning about the dynamics of this phenomenon [...] Read more.
One of the threats that has a significant impact on the conservation status and on the preservation of non-forest Natura 2000 habitats, is secondary succession, which is currently analyzed using airborne laser scanning (ALS) data. However, learning about the dynamics of this phenomenon in the past is only possible by using archival aerial photographs, which are often the only source of information about the past state of land cover. Algorithms of dense image matching developed in the last decade have provided a new quality of digital surface modeling. The aim of this study was to determine the extent of trees and shrubs, using dense image matching of aerial images. As part of a comprehensive research study, the testing of two software programs with different settings of image matching was carried out. An important step in this investigation was the quality assessment of digital surface models (DSM), derived from point clouds based on reference data for individual trees growing singly and in groups with high canopy closure. It was found that the detection of single trees provided worse results. The final part of the experiment was testing the impact of the height threshold value in elevation models on the accuracy of determining the extent of the trees and shrubs. It was concluded that the best results were achieved for the threshold value of 1.25–1.75 m (depending on the analyzed archival photos) with 10 to 30% error rate in determining the trees and shrubs cover. Full article
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Open AccessEditorial
Advances in Remote Sensing-Based Disaster Monitoring and Assessment
Remote Sens. 2019, 11(18), 2181; https://doi.org/10.3390/rs11182181 - 19 Sep 2019
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Abstract
Extreme weather/climate events have been increasing partly due to on-going climate change [...] Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
Open AccessArticle
Dip Filter and Random Noise Suppression for GPR B-Scan Data Based on a Hybrid Method in f - x Domain
Remote Sens. 2019, 11(18), 2180; https://doi.org/10.3390/rs11182180 - 19 Sep 2019
Viewed by 239
Abstract
Ground-penetrating radar (GPR) is a close-range remote-sensing tool applied in a great many near-surface projects for engineering or environmental purposes. In GPR B-scans, there may exist a variety of reflections and diffractions that corresponds to different structures and targets in the subsurface media, [...] Read more.
Ground-penetrating radar (GPR) is a close-range remote-sensing tool applied in a great many near-surface projects for engineering or environmental purposes. In GPR B-scans, there may exist a variety of reflections and diffractions that corresponds to different structures and targets in the subsurface media, and the noise is always embedded. To assist in the interpretation, GPR B-scans can be generally divided into two parts according to the dip attribute of the reflections, where the sub-horizontal layers and dipping structures are properly separated. In this work, we extend the f - x empirical mode decomposition (f - x EMD) to form a semi-adaptive dip filter for GPR data. In f - x domain, each frequency slice is decomposed by EMD and reconstructed to form a dipping profile and a horizontal profile respectively, where the reflections at different dips are separated adaptively. Then the noises mixed in the dipping profile are further separated by rank-deduction methods in f - x domain. The above two-step scheme constitutes the hybrid scheme, which can separate the dipping structures, sub-horizontal layers, and most of the random noise in GPR B-scans. We briefly review the basics of the f - x EMD, and then introduce the derived hybrid scheme in f - x domain. The proposed method is tested by the synthetic data, the forward simulation data, and the field data, respectively. Full article
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Open AccessArticle
Deriving Aerosol Absorption Properties from Solar Ultraviolet Radiation Spectral Measurements at Thessaloniki, Greece
Remote Sens. 2019, 11(18), 2179; https://doi.org/10.3390/rs11182179 - 19 Sep 2019
Viewed by 288
Abstract
The gap in knowledge regarding the radiative effects of aerosols in the UV region of the solar spectrum is large, mainly due to the lack of systematic measurements of the aerosol single scattering albedo (SSA) and absorption optical depth (AAOD). In the present [...] Read more.
The gap in knowledge regarding the radiative effects of aerosols in the UV region of the solar spectrum is large, mainly due to the lack of systematic measurements of the aerosol single scattering albedo (SSA) and absorption optical depth (AAOD). In the present study, spectral UV measurements performed in Thessaloniki, Greece by a double monochromator Brewer spectrophotometer in the period 1998–2017 are used for the calculation of the aforementioned optical properties. The main uncertainty factors have been described and there is an effort to quantify the overall uncertainties in SSA and AAOD. Analysis of the results suggests that the absorption by aerosols is much stronger in the UV relative to the visible. SSA follows a clear annual pattern ranging from ~0.7 in winter to ~0.85 in summer at wavelengths 320–360 nm, while AAOD peaks in summer and winter. The average AAOD for 2009–2011 is ~50% above the 2003–2006 average, possibly due to increased emissions of absorbing aerosols related to the economic crisis and the metro-railway construction works in the city center. Full article
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Open AccessEditorial
Editorial for the Special Issue “Remote Sensing of Water Quality”
Remote Sens. 2019, 11(18), 2178; https://doi.org/10.3390/rs11182178 - 19 Sep 2019
Viewed by 310
Abstract
The importance of monitoring, preserving, and, where needed, improving the quality of water resources in the open ocean, coastal regions, estuaries, and inland water bodies cannot be overstated [...] Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
Open AccessEditorial
Editorial for the Special Issue “Assimilation of Remote Sensing Data into Earth System Models”
Remote Sens. 2019, 11(18), 2177; https://doi.org/10.3390/rs11182177 - 19 Sep 2019
Viewed by 360
Abstract
This Special Issue is a collection of papers reporting research on various aspects of coupled data assimilation in Earth system models. It includes contributions presenting recent progress in ocean–atmosphere, land–atmosphere, and soil–vegetation data assimilation. Full article
(This article belongs to the Special Issue Assimilation of Remote Sensing Data into Earth System Models)
Open AccessArticle
Multiple-Oriented and Small Object Detection with Convolutional Neural Networks for Aerial Image
Remote Sens. 2019, 11(18), 2176; https://doi.org/10.3390/rs11182176 - 18 Sep 2019
Viewed by 642
Abstract
Detecting objects in aerial images is a challenging task due to multiple orientations and relatively small size of the objects. Although many traditional detection models have demonstrated an acceptable performance by using the imagery pyramid and multiple templates in a sliding-window manner, such [...] Read more.
Detecting objects in aerial images is a challenging task due to multiple orientations and relatively small size of the objects. Although many traditional detection models have demonstrated an acceptable performance by using the imagery pyramid and multiple templates in a sliding-window manner, such techniques are inefficient and costly. Recently, convolutional neural networks (CNNs) have successfully been used for object detection, and they have demonstrated considerably superior performance than that of traditional detection methods; however, this success has not been expanded to aerial images. To overcome such problems, we propose a detection model based on two CNNs. One of the CNNs is designed to propose many object-like regions that are generated from the feature maps of multi scales and hierarchies with the orientation information. Based on such a design, the positioning of small size objects becomes more accurate, and the generated regions with orientation information are more suitable for the objects arranged with arbitrary orientations. Furthermore, another CNN is designed for object recognition; it first extracts the features of each generated region and subsequently makes the final decisions. The results of the extensive experiments performed on the vehicle detection in aerial imagery (VEDAI) and overhead imagery research data set (OIRDS) datasets indicate that the proposed model performs well in terms of not only the detection accuracy but also the detection speed. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle
Unrest at Domuyo Volcano, Argentina, Detected by Geophysical and Geodetic Data and Morphometric Analysis
Remote Sens. 2019, 11(18), 2175; https://doi.org/10.3390/rs11182175 - 18 Sep 2019
Viewed by 404
Abstract
New volcanic unrest has been detected in the Domuyo Volcanic Center (DVC), to the east of the Andes Southern Volcanic Zone in Argentina. To better understand this activity, we investigated new seismic monitoring data, gravimetric and magnetic campaign data, and interferometric synthetic aperture [...] Read more.
New volcanic unrest has been detected in the Domuyo Volcanic Center (DVC), to the east of the Andes Southern Volcanic Zone in Argentina. To better understand this activity, we investigated new seismic monitoring data, gravimetric and magnetic campaign data, and interferometric synthetic aperture radar (InSAR) deformation maps, and we derived an image of the magma plumbing system and the likely source of the unrest episode. Seismic events recorded during 2017–2018 nucleate beneath the southwestern flank of the DVC. Ground deformation maps derived from InSAR processing of Sentinel-1 data exhibit an inflation area exceeding 300 km2, from 2014 to at least March 2018, which can be explained by an inflating sill model located 7 km deep. The Bouguer anomaly reveals a negative density contrast of ~35 km wavelength, which is spatially coincident with the InSAR pattern. Our 3D density modeling suggests a body approximately 4–6 km deep with a density contrast of –550 kg/m3. Therefore, the geophysical and geodetic data allow identification of the plumbing system that is subject to inflation at these shallow crustal depths. We compared the presence and dimensions of the inferred doming area to the drainage patterns of the area, which support long-established incremental uplift according to morphometric analysis. Future studies will allow us to investigate further whether the new unrest is hydrothermal or magmatic in origin. Full article
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Open AccessArticle
Hybrid Urban Canyon Pedestrian Navigation Scheme Combined PDR, GNSS and Beacon Based on Smartphone
Remote Sens. 2019, 11(18), 2174; https://doi.org/10.3390/rs11182174 - 18 Sep 2019
Viewed by 301
Abstract
This study presents a comprehensive urban canyon pedestrian navigation scheme. This scheme combines smart phone internal MEMS sensors, GNSS and beacon observations together. Heading estimation is generally a key issue of the PDR algorithm. We design an orientation fusion algorithm to improve smart [...] Read more.
This study presents a comprehensive urban canyon pedestrian navigation scheme. This scheme combines smart phone internal MEMS sensors, GNSS and beacon observations together. Heading estimation is generally a key issue of the PDR algorithm. We design an orientation fusion algorithm to improve smart phone heading using MEMS measurements. Static and kinematic tests are performed, superiority of the improved heading algorithm is verified. We also present different heading processing solutions for comparison and analysis. Heading bias increases with time due to error accumulation and model inaccuracy. Thus, we develop a related heading calibration method based on beacons. This method can help correct smart phone headings continuously to decrease cumulative error. In addition to PDR, we also use GNSS and beacon measurements to integrate a fusion location. In the fusion procedure, we design related algorithms to adjust or limit the use of these different type observations to constrain large jumps in our Kalman filter model, thereby making the solution stable. Navigation experiments are performed in the streets of Mong Kok and Wanchai, which are typically the most crowded areas of Hong Kong, with narrow streets and many pedestrians, vehicles and tall buildings. The first experiment uses the strategy PDR + GNSS + beacon, in east–west orientation street, in which 10 m positioning error is improved from 30 % (smart phone internal GNSS) to 80 % and in south–north orientation street, in which 15 m positioning error is improved from 20 % (smart phone internal GNSS) to 80 % . The second experiment performs two long-distance tests without any beacons, in which the fusion scheme also has significant improvement, that is, 10 m positioning error is improved from 38 % to 60 % . Full article
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Open AccessArticle
Ship Detection in Optical Satellite Images via Directional Bounding Boxes Based on Ship Center and Orientation Prediction
Remote Sens. 2019, 11(18), 2173; https://doi.org/10.3390/rs11182173 - 18 Sep 2019
Viewed by 374
Abstract
To accurately detect ships of arbitrary orientation in optical remote sensing images, we propose a two-stage CNN-based ship-detection method based on the ship center and orientation prediction. Center region prediction network and ship orientation classification network are constructed to generate rotated region proposals, [...] Read more.
To accurately detect ships of arbitrary orientation in optical remote sensing images, we propose a two-stage CNN-based ship-detection method based on the ship center and orientation prediction. Center region prediction network and ship orientation classification network are constructed to generate rotated region proposals, and then we can predict rotated bounding boxes from rotated region proposals to locate arbitrary-oriented ships more accurately. The two networks share the same deconvolutional layers to perform semantic segmentation for the prediction of center regions and orientations of ships, respectively. They can provide the potential center points of the ships helping to determine the more confident locations of the region proposals, as well as the ship orientation information, which is beneficial to the more reliable predetermination of rotated region proposals. Classification and regression are then performed for the final ship localization. Compared with other typical object detection methods for natural images and ship-detection methods, our method can more accurately detect multiple ships in the high-resolution remote sensing image, irrespective of the ship orientations and a situation in which the ships are docked very closely. Experiments have demonstrated the promising improvement of ship-detection performance. Full article
(This article belongs to the Special Issue Remote Sensing for Maritime Safety and Security)
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Open AccessArticle
Remote Sensing Based Binary Classification of Maize. Dealing with Residual Autocorrelation in Sparse Sample Situations
Remote Sens. 2019, 11(18), 2172; https://doi.org/10.3390/rs11182172 - 18 Sep 2019
Viewed by 284
Abstract
In order to discuss potential sustainability issues of expanding silage maize cultivation in Rhineland-Palatinate, spatially explicit monitoring is necessary. Publicly available statistical records are often not a sufficient basis for extensive research, especially on soil health, where risk factors like erosion and compaction [...] Read more.
In order to discuss potential sustainability issues of expanding silage maize cultivation in Rhineland-Palatinate, spatially explicit monitoring is necessary. Publicly available statistical records are often not a sufficient basis for extensive research, especially on soil health, where risk factors like erosion and compaction depend on variables that are specific to every site, and hard to generalize for larger administrative aggregates. The focus of this study is to apply established classification algorithms to estimate maize abundance for each independent pixel, while at the same time accounting for their spatial relationship. Therefore, two ways to incorporate spatial autocorrelation of neighboring pixels are combined with three different classification models. The performance of each of these modeling approaches is analyzed and discussed. Finally, one prediction approach is applied to the imagery, and the overall predicted acreage is compared to publicly available data. We were able to show that Support Vector Machine (SVM) classification and Random Forests (RF) were able to distinguish maize pixels reliably, with kappa values well above 0.9 in most cases. The Generalized Linear Model (GLM) performed substantially worse. Furthermore, Regression Kriging (RK) as an approach to integrate spatial autocorrelation into the prediction model is not suitable in use cases with millions of sparsely clustered training pixels. Gaussian Blur is able to improve predictions slightly in these cases, but it is possible that this is only because it smoothes out impurities of the reference data. The overall prediction with RF classification combined with Gaussian Blur performed well, with out of bag error rates of 0.5% in 2009 and 1.3% in 2016. Despite the low error rates, there is a discrepancy between the predicted acreage and the official records, which is 20% in 2009 and 27% in 2016. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessLetter
Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images
Remote Sens. 2019, 11(18), 2171; https://doi.org/10.3390/rs11182171 - 18 Sep 2019
Viewed by 303
Abstract
Compact polarimetric synthetic aperture radar (CP SAR), as a new technique or observation system, has attracted much attention in recent years. Compared with quad-polarization SAR (QP SAR), CP SAR provides an observation with a wider swath, while, compared with linear dual-polarization SAR, retains [...] Read more.
Compact polarimetric synthetic aperture radar (CP SAR), as a new technique or observation system, has attracted much attention in recent years. Compared with quad-polarization SAR (QP SAR), CP SAR provides an observation with a wider swath, while, compared with linear dual-polarization SAR, retains more polarization information in observations. These characteristics make CP SAR a useful tool in marine environmental applications. Previous studies showed the potential of CP SAR images for ship detection. However, false alarms, caused by ocean clutter and the lack of detailed information about ships, largely hinder traditional methods from feature selection for ship discrimination. In this paper, a segmentation method designed specifically for ship detection from CP SAR images is proposed. The pixel-wise detection is based on a fully convolutional network (i.e., U-Net). In particular, three classes (ship, land, and sea) were considered in the classification scheme. To extract features, a series of down-samplings with several convolutions were employed. Then, to generate classifications, deep semantic and shallow high-resolution features were used in up-sampling. Experiments on several CP SAR images simulated from Gaofen-3 QP SAR images demonstrate the effectiveness of the proposed method. Compared with Faster RCNN (region-based convolutional neural network), which is considered a popular and effective deep learning network for object detection, the newly proposed method, with precision and recall greater than 90% and a F1 score of 0.912, performs better at ship detection. Additionally, findings verify the advantages of the CP configuration compared with single polarization and linear dual-polarization. Full article
(This article belongs to the Special Issue Compact Polarimetric SAR)
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Open AccessArticle
Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning
Remote Sens. 2019, 11(18), 2170; https://doi.org/10.3390/rs11182170 - 18 Sep 2019
Viewed by 321
Abstract
Sea ice is one of the causes of marine disasters. The classification of sea ice images is an important part of sea ice detection. The labeled samples in hyperspectral sea ice image classification are difficult to acquire, which causes minor sample problems. In [...] Read more.
Sea ice is one of the causes of marine disasters. The classification of sea ice images is an important part of sea ice detection. The labeled samples in hyperspectral sea ice image classification are difficult to acquire, which causes minor sample problems. In addition, most of the current sea ice classification methods mainly use spectral features for shallow learning, which also limits further improvement of the sea ice classification accuracy. Therefore, this paper proposes a hyperspectral sea ice image classification method based on the spectral-spatial-joint feature with deep learning. The proposed method first extracts sea ice texture information by the gray-level co-occurrence matrix (GLCM). Then, it performs dimensionality reduction and a correlation analysis of the spectral information and spatial information of the unlabeled samples, respectively. It eliminates redundant information by extracting the spectral-spatial information of the neighboring unlabeled samples of the labeled sample and integrating the information with the spectral and texture data of the labeled sample to further enhance the quality of the labeled sample. Lastly, the three-dimensional convolutional neural network (3D-CNN) model is designed to extract the deep spectral-spatial features of sea ice. The proposed method combines relevant textural features and performs spectral-spatial feature extraction based on the 3D-CNN model by using a large amount of unlabeled sample information. In order to verify the effectiveness of the proposed method, sea ice classification experiments are carried out on two hyperspectral data sets: Baffin Bay and Bohai Bay. Compared with the CNN algorithm based on a single feature (spectral or spatial) and other CNN algorithms based on spectral-spatial features, the experimental results show that the proposed method achieves better sea ice classification (98.52% and 97.91%) with small samples. Therefore, it is more suitable for classifying hyperspectral sea ice images. Full article
(This article belongs to the Special Issue AI-based Remote Sensing Oceanography)
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Open AccessArticle
Concept Development and Risk Reduction for MISTiC Winds, A Micro-Satellite Constellation Approach for Vertically Resolved Wind and IR Sounding Observations in the Troposphere
Remote Sens. 2019, 11(18), 2169; https://doi.org/10.3390/rs11182169 - 18 Sep 2019
Viewed by 339
Abstract
MISTiC Winds is an instrument and constellation mission approach to simultaneously observe the global thermodynamic state and the vertically resolved horizontal wind field in the troposphere from LEO SSO. The instrument is a wide-field imaging spectrometer operated in the 4.05–5.75 μm range, with [...] Read more.
MISTiC Winds is an instrument and constellation mission approach to simultaneously observe the global thermodynamic state and the vertically resolved horizontal wind field in the troposphere from LEO SSO. The instrument is a wide-field imaging spectrometer operated in the 4.05–5.75 μm range, with the spectral resolution, sampling, radiometric sensitivity, and stability needed to provide temperature and water vapor soundings of the atmosphere, with 1 km vertical resolution in the troposphere-comparable to those of NASA’s atmospheric infrared sounder (AIRS). These instruments have much higher spatial resolution (<3 km at nadir) and finer spatial sampling than current hyperspectral sounders, allowing a sequence of such observations from several micro-satellites in an orbital plane with short time separation, from which atmospheric motion vector (AMV) winds are derived. AMVs for both cloud-motion and water vapor-motion, derived from hyperspectral imagery, will have improved velocity resolution relative to AMVs obtained from multi-spectral instruments operating in GEO. MISTiC’s extraordinarily small size, low mass (<15 kg), and minimal cooling requirements can be accommodated aboard an ESPA-class microsatellite. Low fabrication and launch costs enable this constellation to provide more frequent atmospheric observations than current-generation sounders provide, at much lower mission cost. Key technology and observation method risks have been reduced through recent laboratory and airborne (NASA ER2) testing funded under NASA’s Instrument Incubator Program and BAE Systems IR&D, and through an observing system simulation experiment performed by NASA GMAO. This approach would provide a valuable new capability for the study of the processes driving high-impact weather events, and critical high-resolution observations needed for future numerical weather prediction. Full article
(This article belongs to the Special Issue Satellite-Derived Wind Observations)
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Open AccessArticle
Validation of AERONET-Estimated Upward Broadband Solar Fluxes at the Top-Of-The-Atmosphere with CERES Measurements
Remote Sens. 2019, 11(18), 2168; https://doi.org/10.3390/rs11182168 - 18 Sep 2019
Viewed by 294
Abstract
The AERONET (Aerosol Robotic Network) global network provides estimations of broadband solar radiative fluxes at the surface and at the TOA (Top-Of-the-Atmosphere). This paper reports on the validation of AERONET flux estimations at the TOA with the CERES (Clouds and the Earth’s Radiant [...] Read more.
The AERONET (Aerosol Robotic Network) global network provides estimations of broadband solar radiative fluxes at the surface and at the TOA (Top-Of-the-Atmosphere). This paper reports on the validation of AERONET flux estimations at the TOA with the CERES (Clouds and the Earth’s Radiant Energy System) instrument. The validation was made at eight AERONET sites worldwide with at least seven years of Level 2.0 and Version 3 data and representatives of mineral dust, biomass burning, background continental, and urban-industrial aerosol regimes. To co-locate in time and space the AERONET and CERES fluxes, several criteria based on time and distance differences and cloud coverage were defined. When the strictest criterion was applied to all sites, the linear relationship between the observed and estimated fluxes (y = 1.04x – 3.67 Wm−2) was very close to the 1:1 ideal line. The correlation coefficient was 0.96 and nearly all points were contained in the ±15% region around the 1:1 line. The average flux difference was –2.52 Wm−2 (–0.84% in relative terms). AERONET overestimations were observed at two sites and were correlated with large aerosol optical depth (AOD) (>0.2) Underestimations were observed at one desert site and were correlated with large surface albedos (>0.2). Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle
Ocean Color Quality Control Masks Contain the High Phytoplankton Fraction of Coastal Ocean Observations
Remote Sens. 2019, 11(18), 2167; https://doi.org/10.3390/rs11182167 - 18 Sep 2019
Viewed by 332
Abstract
Satellite estimation of oceanic chlorophyll-a content has enabled characterization of global phytoplankton stocks, but the quality of retrieval for many ocean color products (including chlorophyll-a) degrades with increasing phytoplankton biomass in eutrophic waters. Quality control of ocean color products is achieved primarily through [...] Read more.
Satellite estimation of oceanic chlorophyll-a content has enabled characterization of global phytoplankton stocks, but the quality of retrieval for many ocean color products (including chlorophyll-a) degrades with increasing phytoplankton biomass in eutrophic waters. Quality control of ocean color products is achieved primarily through the application of masks based on standard thresholds designed to identify suspect or low-quality retrievals. This study compares the masked and unmasked fractions of ocean color datasets from two Eastern Boundary Current upwelling ecosystems (the California and Benguela Current Systems) using satellite proxies for phytoplankton biomass that are applicable to satellite imagery without correction for atmospheric aerosols. Evaluation of the differences between the masked and unmasked fractions indicates that high biomass observations are preferentially masked in National Aeronautics and Space Administration (NASA) ocean color datasets as a result of decreased retrieval quality for waters with high concentrations of phytoplankton. This study tests whether dataset modification persists into the default composite data tier commonly disseminated to science end users. Further, this study suggests that statistics describing a dataset’s masked fraction can be helpful in assessing the quality of a composite dataset and in determining the extent to which retrieval quality is linked to biological processes in a given study region. Full article
(This article belongs to the Special Issue Coastal Waters Monitoring Using Remote Sensing Technology)
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Open AccessEditorial
Science of Landsat Analysis Ready Data
Remote Sens. 2019, 11(18), 2166; https://doi.org/10.3390/rs11182166 - 18 Sep 2019
Viewed by 495
Abstract
The free and open policy of Landsat data in 2008 completely changed the way that Landsat data was analyzed and used, particularly for applications such as time series analysis. Nine years later, the United States Geological Survey (USGS) released the first version of [...] Read more.
The free and open policy of Landsat data in 2008 completely changed the way that Landsat data was analyzed and used, particularly for applications such as time series analysis. Nine years later, the United States Geological Survey (USGS) released the first version of Landsat Analysis Ready Data (ARD) for the United States, which was another milestone in Landsat history. The Landsat time series is so convenient and easy to use and has triggered science that was not possible a few decades ago. In this Editorial, we review the current status of Landsat ARD, introduce scientific studies of Landsat ARD from this special issue, and discuss global Landsat ARD. Full article
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)
Open AccessEditorial
Editorial for Special Issue: “Remote Sensing of Environmental Changes in Cold Regions”
Remote Sens. 2019, 11(18), 2165; https://doi.org/10.3390/rs11182165 - 18 Sep 2019
Viewed by 314
Abstract
Cold regions, characterized by the presence of permafrost and extensive snow and ice cover, are significantly affected by changing climate [...] Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions)
Open AccessArticle
Partial Linear NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hyperspectral Remote Sensing Data
Remote Sens. 2019, 11(18), 2164; https://doi.org/10.3390/rs11182164 - 17 Sep 2019
Viewed by 403
Abstract
High-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted [...] Read more.
High-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted with these data to automatically detect and estimate areas of photovoltaic panels, which currently constitute an important part of renewable energy systems in urban areas of developed countries. In this paper, two hyperspectral-unmixing-based methods are proposed to detect and to estimate surfaces of photovoltaic panels. These approaches, related to linear spectral unmixing (LSU) techniques, are based on new nonnegative matrix factorization (NMF) algorithms that exploit known panel spectra, which makes them partial NMF methods. The first approach, called Grd-Part-NMF, is a gradient-based method, whereas the second one, called Multi-Part-NMF, uses multiplicative update rules. To evaluate the performance of these approaches, experiments are conducted on realistic synthetic and real airborne hyperspectral data acquired over an urban region. For the synthetic data, obtained results show that the proposed methods yield much better overall performance than NMF-unmixing-based methods from the literature. For the real data, the obtained detection and area estimation results are first confirmed by using very high-spatial-resolution ortho-images of the same regions. These results are also compared with those obtained by standard NMF-unmixing-based methods and by a one-class-classification-based approach. This comparison shows that the proposed approaches are superior to those considered from the literature. Full article
(This article belongs to the Special Issue Hyperspectral Imagery for Urban Environment)
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Open AccessArticle
A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign
Remote Sens. 2019, 11(18), 2163; https://doi.org/10.3390/rs11182163 - 17 Sep 2019
Viewed by 451
Abstract
The airborne AirSWOT instrument suite, consisting of an interferometric Ka-band synthetic aperture radar and color-infrared (CIR) camera, was deployed to northern North America in July and August 2017 as part of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE). We present validated, open (i.e., vegetation-free) [...] Read more.
The airborne AirSWOT instrument suite, consisting of an interferometric Ka-band synthetic aperture radar and color-infrared (CIR) camera, was deployed to northern North America in July and August 2017 as part of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE). We present validated, open (i.e., vegetation-free) surface water masks produced from high-resolution (1 m), co-registered AirSWOT CIR imagery using a semi-automated, object-based water classification. The imagery and resulting high-resolution water masks are available as open-access datasets and support interpretation of AirSWOT radar and other coincident ABoVE image products, including LVIS, UAVSAR, AIRMOSS, AVIRIS-NG, and CFIS. These synergies offer promising potential for multi-sensor analysis of Arctic-Boreal surface water bodies. In total, 3167 km2 of open surface water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude and broad environmental gradients. Detected water body sizes range from 0.00004 km2 (40 m2) to 15 km2. Power-law extrapolations are commonly used to estimate the abundance of small lakes from coarser resolution imagery, and our mapped water bodies followed power-law distributions, but only for water bodies greater than 0.34 (±0.13) km2 in area. For water bodies exceeding this size threshold, the coefficients of power-law fits vary for different Arctic-Boreal physiographic terrains (wetland, prairie pothole, lowland river valley, thermokarst, and Canadian Shield). Thus, direct mapping using high-resolution imagery remains the most accurate way to estimate the abundance of small surface water bodies. We conclude that empirical scaling relationships, useful for estimating total trace gas exchange and aquatic habitats on Arctic-Boreal landscapes, are uniquely enabled by high-resolution AirSWOT-like mappings and automated detection methods such as those developed here. Full article
(This article belongs to the Special Issue Lake Remote Sensing)
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Open AccessFeature PaperArticle
Multisensor Characterization of Urban Morphology and Network Structure
Remote Sens. 2019, 11(18), 2162; https://doi.org/10.3390/rs11182162 - 17 Sep 2019
Viewed by 423
Abstract
The combination of decameter resolution Sentinel 2 and hectometer resolution VIIRS offers the potential to quantify urban morphology at scales spanning the range from individual objects to global scale settlement networks. Multi-season spectral characteristics of built environments provide an independent complement to night [...] Read more.
The combination of decameter resolution Sentinel 2 and hectometer resolution VIIRS offers the potential to quantify urban morphology at scales spanning the range from individual objects to global scale settlement networks. Multi-season spectral characteristics of built environments provide an independent complement to night light brightness compared for 12 urban systems. High fractions of spectrally stable impervious surface combined with persistent deep shadow between buildings are compared to road network density and outdoor lighting inferred from night light. These comparisons show better spatial agreement and more detailed representation of a wide range of built environments than possible using Landsat and DMSP-OLS. However, they also show that no single low luminance brightness threshold provides optimal spatial correlation to built extent derived from Sentinel in different urban systems. A 4-threshold comparison of 6 regional night light networks shows consistent spatial scaling, spanning 3 to 5 orders of magnitude in size and number with rank-size slopes consistently near −1. This scaling suggests a dynamic balance among the processes of nucleation, growth and interconnection. Rank-shape distributions based on √Area/Perimeter of network components scale similarly to rank-size distributions at higher brightness thresholds, but show both progressive then abrupt increases in fractal dimension of the largest, most interconnected network components at lower thresholds. Full article
(This article belongs to the Special Issue Remote Sensing for Urban Morphology)
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Open AccessTechnical Note
K-Matrix: A Novel Change-Pattern Mining Method for SAR Image Time Series
Remote Sens. 2019, 11(18), 2161; https://doi.org/10.3390/rs11182161 - 17 Sep 2019
Viewed by 295
Abstract
In this paper, we present a novel method for change-pattern mining in Synthetic Aperture Radar (SAR) image time series based on a distance matrix clustering algorithm, called K-Matrix. As it is different from the state-of-the-art methods, which analyze the SAR image time series [...] Read more.
In this paper, we present a novel method for change-pattern mining in Synthetic Aperture Radar (SAR) image time series based on a distance matrix clustering algorithm, called K-Matrix. As it is different from the state-of-the-art methods, which analyze the SAR image time series based on the change detection matrix (CDM), here, we directly use the distance matrix to determine changed pixels and extract change patterns. The proposed scheme involves two steps: change detection in SAR image time series and change-pattern discovery. First, these distance matrices are constructed for each spatial position over the time series by a dissimilarity measurement. The changed pixels are detected by using a thresholding algorithm on the energy feature map of all distance matrices. Then, according to the change detection results in SAR image time series, the changed areas for pattern mining are determined. Finally, the proposed K-Matrix algorithm which clusters distance matrices by the matrix cross-correlation similarity is used to group all changed pixels into different change patterns. Experimental results on two datasets of TerraSAR-X image time series illustrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Time Series Analysis Based on SAR Images)
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Open AccessArticle
Analysis of Ship Detection Performance with Full-, Compact- and Dual-Polarimetric SAR
Remote Sens. 2019, 11(18), 2160; https://doi.org/10.3390/rs11182160 - 17 Sep 2019
Viewed by 314
Abstract
Polarimetric synthetic aperture radar (SAR) is currently drawing more attention due to its advantage in Earth observations, especially in ship detection. In order to establish a reliable feature selection method for marine vessel monitoring purposes, forty features are extracted via polarimetric decomposition in [...] Read more.
Polarimetric synthetic aperture radar (SAR) is currently drawing more attention due to its advantage in Earth observations, especially in ship detection. In order to establish a reliable feature selection method for marine vessel monitoring purposes, forty features are extracted via polarimetric decomposition in the full-polarimetric (FP), compact-polarimetric (CP), and dual-polarimetric (DP) modes. These features were comprehensively quantified and evaluated using the Euclidean distance and mutual information, and the result indicated that the features in CP SAR are better than those of FP or DP SAR in general. The CP SAR features are thus further studied, and a new feature, named phase factor, in CP SAR mode is presented that can distinguish ships and the sea surface by the constant 0 without complex calculation. Furthermore, the phase factor is independent of the sea surface roughness, and hence it performs stably for ship detection even in high sea states. Experiments demonstrated that the ship detection performance of the phase factor detector is better than that of roundness, delta, HESA and CFAR detectors in low, medium and high sea states. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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Open AccessFeature PaperLetter
On Thermal Infrared Remote Sensing of Plastic Pollution in Natural Waters
Remote Sens. 2019, 11(18), 2159; https://doi.org/10.3390/rs11182159 - 17 Sep 2019
Viewed by 595
Abstract
Plastic pollution in the world’s natural waters is of growing concern and currently receiving significant attention. However, remote sensing of marine plastic litter is still in the developmental stage. Most progress has been made in spectral remote sensing using visible to short-wave infrared [...] Read more.
Plastic pollution in the world’s natural waters is of growing concern and currently receiving significant attention. However, remote sensing of marine plastic litter is still in the developmental stage. Most progress has been made in spectral remote sensing using visible to short-wave infrared wavelengths where optical physics applies. Thermal infrared (TIR) sensing could potentially monitor plastic water pollution but has not been studied in detail. We applied radiative transfer theory to predict TIR sensitivity to changes in the surface fraction of water covered by plastic litter and found that the temperature difference between the water surface and the surroundings controls the TIR signal. Hence, we mapped this difference for various months and times of the day using global SST (sea surface temperature) and t2m (temperature at 2 m height) hourly estimates from the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5. The maps show how SST-t2m difference varied, altering the anticipated effectivity of TIR floating plastic litter remote sensing. We selected several locations of interest to predict the effectivity of TIR sensing of the plastic surface fraction. TIR remote sensing has promising potential and is expected to be more effective in areas with a high air–sea temperature difference. Full article
(This article belongs to the Section Environmental Remote Sensing)
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Open AccessArticle
Iterative Pointing Angle Calibration Method for the Spaceborne Photon-Counting Laser Altimeter Based on Small-Range Terrain Matching
Remote Sens. 2019, 11(18), 2158; https://doi.org/10.3390/rs11182158 - 16 Sep 2019
Viewed by 346
Abstract
The satellite, Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) has been equipped with a new type of spaceborne laser altimeter, which has the benefits of having small footprints and a high repetition rate, and it can produce dense footprints on the ground. Focusing [...] Read more.
The satellite, Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) has been equipped with a new type of spaceborne laser altimeter, which has the benefits of having small footprints and a high repetition rate, and it can produce dense footprints on the ground. Focusing on the pointing angle calibration of this new spaceborne laser altimeter, this paper proposes a fast pointing angle calibration method using only a small range of terrain surveyed by airborne lidar. Based on the matching criterion of least elevation difference, an iterative pointing angle calibration method was proposed. In the experiment, the simulated photon-counting laser altimeter data and the Ice, Cloud and Land Elevation Satellite-2 data were used to verify the algorithm. The results show that when 1 km and 2.5 km lengths of track were used, the pointing angle error after calibration could be reduced to about 0.3 arc-seconds and less than 0.1 arc-seconds, respectively. Meanwhile, compared with the traditional pyramid search method, the proposed iterative pointing angle calibration method does not require well-designed parameters, which are important in the pyramid search method to balance calculation time and calibration result, and the iterative pointing angle calibration method could significantly reduce the calibration time to only about one-fifth of that of the pyramid search method. Full article
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Open AccessArticle
Multi-Sensor Geodetic Observations and Modeling of the 2017 Mw 6.3 Jinghe Earthquake
Remote Sens. 2019, 11(18), 2157; https://doi.org/10.3390/rs11182157 - 16 Sep 2019
Viewed by 299
Abstract
The Mw 6.3 Jinghe earthquake struck Xingjiang Province, China, on 8 August 2017 (05:15:04 UTC); the epicenter was near the Kusongmuxieke Piedmont Fault (KPF) of the northern Tian Shan Mountains. We used multi-source and multi-track satellite Synthetic Aperture Radar (SAR) imagery and Interferometric [...] Read more.
The Mw 6.3 Jinghe earthquake struck Xingjiang Province, China, on 8 August 2017 (05:15:04 UTC); the epicenter was near the Kusongmuxieke Piedmont Fault (KPF) of the northern Tian Shan Mountains. We used multi-source and multi-track satellite Synthetic Aperture Radar (SAR) imagery and Interferometric SAR (InSAR) techniques to reconstruct the coseismic displacement field from different line-of-sight geometries. To reduce the phase artifacts, we employed multi-temporal scenes acquired by Sentinel-1, and reconstructed the coseismic deformation through a temporal averaging strategy. Together with a single interferometric pair obtained using the Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR2) sensor aboard the Advanced Land Observing Satellite 2 (ALOS2), we obtained five displacement maps with slightly different viewing geometries; all of which were used to constrain a geodetic inversion to retrieve the fault geometry parameters and slip distribution. Based on the focal mechanism and regional geology, we constructed multiple fault models that differ in dip direction (south and north dipping), and various striking angles. Both models fit the InSAR displacement maps, but have slip distributions of different depths. The slip depth of the south dipping model, with a dip of ~42°, is the most consistent with the relocated earthquake sequence and regional geological structure. Through the geodetic inversion, the maximum slip (0.25 m) occurred at 14.05 km and the associated rake was 89.56°. The result implies that the seismogenic fault is a blind thrust fault north of KPF (towards the foreland). Considering the relative locations of the suggested blind fault, the KPF, and the continuing north to south (N–S) shortening of the Tian Shan Mountains, this fault could be formed by the northward propagation of the regional fold-thrust belt. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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