Research

16 pages, 4688 KiB

Open AccessArticle

A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR

by Peng Yu, Wenxiang Xu, Xiaojing Zhong, Johnny A. Johannessen, Xiao-Hai Yan, Xupu Geng, Yuanrong He and Wenfang Lu

Remote Sens. 2022, 14(9), 2269; https://doi.org/10.3390/rs14092269 - 08 May 2022

Cited by 5 | Viewed by 2431

Abstract

Based on the Ocean Projection and Extension neural Network (OPEN) method, a novel approach is proposed to retrieve sea surface wind speed for C-band synthetic aperture radar (SAR). In order to prove the methodology with a robust dataset, five-year normalized radar cross section [...] Read more.

Based on the Ocean Projection and Extension neural Network (OPEN) method, a novel approach is proposed to retrieve sea surface wind speed for C-band synthetic aperture radar (SAR). In order to prove the methodology with a robust dataset, five-year normalized radar cross section (NRCS) measurements from the advanced scatterometer (ASCAT), a well-known side-looking radar sensor, are used to train the model. In situ wind data from direct buoy observations, instead of reanalysis wind data or model results, are used as the ground truth in the OPEN model. The model is applied to retrieve sea surface winds from two independent data sets, ASCAT and Sentinel-1 SAR data, and has been well-validated using buoy measurements from the National Oceanic and Atmospheric Administration (NOAA) and China Meteorological Administration (CMA), and the ASCAT coastal wind product. The comparison between the OPEN model and four C-band model (CMOD) versions (CMOD4, CMOD-IFR2, CMOD5.N, and CMOD7) further indicates the good performance of the proposed model for C-band SAR sensors. It is anticipated that the use of high-resolution SAR data together with the new wind speed retrieval method can provide continuous and accurate ocean wind products in the future. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments-II)

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26 pages, 5380 KiB

Open AccessArticle

Wind Speed Variation Mapped Using SAR before and after Commissioning of Offshore Wind Farms

by Abdalmenem Owda and Merete Badger

Remote Sens. 2022, 14(6), 1464; https://doi.org/10.3390/rs14061464 - 18 Mar 2022

Cited by 6 | Viewed by 3016

Abstract

When installing offshore wind farms (OWFs) adjacent to the coast, one needs to consider the combined effects of the wind wakes caused by the OWFs and natural horizontal coastal wind speed gradients (HCWSGs). This study exploits the full Sentinel 1A/B and Envisat archive [...] Read more.

When installing offshore wind farms (OWFs) adjacent to the coast, one needs to consider the combined effects of the wind wakes caused by the OWFs and natural horizontal coastal wind speed gradients (HCWSGs). This study exploits the full Sentinel 1A/B and Envisat archive of synthetic aperture radar (SAR) imagery covering the northern European seas. More than 8700 SAR scenes fit well with our selection criteria and are processed as wind maps for the height 10 m above the sea surface. For eight selected wind farm sites, we systematically compare the wind flow variation before and after wind farm commissioning. Before the commissioning, we observe wind speed gradients up to ±4% for onshore and offshore winds. After the commissioning, we detect a 2–10% reduction in the mean wind speed downstream of the turbines after taking into account the background wind speed gradients. These velocity deficits are proportional to the OWF capacity. Our findings indicate that wind speed maps retrieved from SAR can be used to quantify the complex interactions between natural HCWSGs and turbine-induced effects on the mean wind climate. Ultimately, this can be used in connection with farm planning in coastal waters. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments-II)

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22 pages, 6508 KiB

Open AccessArticle

Shape-Constrained Method of Remote Sensing Monitoring of Marine Raft Aquaculture Areas on Multitemporal Synthetic Sentinel-1 Imagery

by Yi Zhang, Chengyi Wang, Jingbo Chen and Futao Wang

Remote Sens. 2022, 14(5), 1249; https://doi.org/10.3390/rs14051249 - 03 Mar 2022

Cited by 11 | Viewed by 1794

Abstract

Large-scale and periodic remote sensing monitoring of marine raft aquaculture areas is significant for scientific planning of their layout and for promoting sustainable development of marine ecology. Synthetic aperture radar (SAR) is an important tool for stable monitoring of marine raft aquaculture areas [...] Read more.

Large-scale and periodic remote sensing monitoring of marine raft aquaculture areas is significant for scientific planning of their layout and for promoting sustainable development of marine ecology. Synthetic aperture radar (SAR) is an important tool for stable monitoring of marine raft aquaculture areas since it is all-weather, all-day, and cloud-penetrating. However, the scattering signal of marine raft aquaculture areas is affected by speckle noise and sea state, so their features in SAR images are complex. Thus, it is challenging to extract marine raft aquaculture areas from SAR images. In this paper, we propose a method to extract marine raft aquaculture areas from Sentinel-1 images based on the analysis of the features for marine raft aquaculture areas. First, the data are preprocessed using multitemporal phase synthesis to weaken the noise interference, enhance the signal of marine raft aquaculture areas, and improve the significance of the characteristics of raft aquaculture areas. Second, the geometric features of the marine raft aquaculture area are combined to design the model structure and introduce the shape constraint module, which adds a priori knowledge to guide the model convergence direction during the training process. Experiments verify that the method outperforms the popular semantic segmentation model with an F1 of 84.52%. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments-II)

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17 pages, 4527 KiB

Open AccessArticle

Marine Oil Pollution in an Area of High Economic Use: Statistical Analyses of SAR Data from the Western Java Sea

by Veronika Mohr and Martin Gade

Remote Sens. 2022, 14(4), 880; https://doi.org/10.3390/rs14040880 - 12 Feb 2022

Cited by 5 | Viewed by 2459

Abstract

In this paper, we analyze more than 2000 Synthetic Aperture Radar (SAR) images of the Western Java Sea acquired by Sentinel-1 SAR-C and ENVISAT ASAR, with the aim to generate oil pollution statistics for a sea region of high economic use. The spatial [...] Read more.

In this paper, we analyze more than 2000 Synthetic Aperture Radar (SAR) images of the Western Java Sea acquired by Sentinel-1 SAR-C and ENVISAT ASAR, with the aim to generate oil pollution statistics for a sea region of high economic use. The spatial distributions show that most oil pollution occurs along the major shipping routes and at oil production sites in that area. The majority of the spills have sizes of less than 1 km² and an axial ratio smaller than 10. For two sets of SAR images, we compared the results obtained by different operators, who analyzed the same images. While more than 50% of the spills were not found by both operators, the overall spatial patterns derived from their results are the same. Our results indicate that the observed differences are mainly due to lookalikes, which can easily be confused with oil spills, but also due to small oil spills that were overseen by one of the operators. These assumptions are supported by the fact that the percentage of spills jointly found by both operators increased when only oil spills were considered that were found on SAR images acquired at higher mean wind speeds. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments-II)

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20 pages, 8689 KiB

Open AccessArticle

Small-Sized Ship Detection Nearshore Based on Lightweight Active Learning Model with a Small Number of Labeled Data for SAR Imagery

by Xiaomeng Geng, Lingli Zhao, Lei Shi, Jie Yang, Pingxiang Li and Weidong Sun

Remote Sens. 2021, 13(17), 3400; https://doi.org/10.3390/rs13173400 - 27 Aug 2021

Cited by 1 | Viewed by 1808

Abstract

Marine ship detection by synthetic aperture radar (SAR) is an important remote sensing technology. The rapid development of big data and artificial intelligence technology has facilitated the wide use of deep learning methods in SAR imagery for ship detection. Although deep learning can [...] Read more.

Marine ship detection by synthetic aperture radar (SAR) is an important remote sensing technology. The rapid development of big data and artificial intelligence technology has facilitated the wide use of deep learning methods in SAR imagery for ship detection. Although deep learning can achieve a much better detection performance than traditional methods, it is difficult to achieve satisfying performance for small-sized ships nearshore due to the weak scattering caused by their material and simple structure. Another difficulty is that a huge amount of data needs to be manually labeled to obtain a reliable CNN model. Manual labeling each datum not only takes too much time but also requires a high degree of professional knowledge. In addition, the land and island with high backscattering often cause high false alarms for ship detection in the nearshore area. In this study, a novel method based on candidate target detection, boundary box optimization, and convolutional neural network (CNN) embedded with active learning strategy is proposed to improve the accuracy and efficiency of ship detection in nearshore areas. The candidate target detection results are obtained by global threshold segmentation. Then, the strategy of boundary box optimization is defined and applied to reduce the noise and false alarms caused by island and land targets as well as by sidelobe interference. Finally, a lightweight CNN embedded with active learning scheme is used to classify the ships using only a small labeled training set. Experimental results show that the performance of the proposed method for small-sized ship detection can achieve 97.78% accuracy and 0.96 F1-score with Sentinel-1 images in complex nearshore areas. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments-II)

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19 pages, 13553 KiB

Open AccessArticle

Automated Rain Detection by Dual-Polarization Sentinel-1 Data

by Yuan Zhao, Nicolas Longépé, Alexis Mouche and Romain Husson

Remote Sens. 2021, 13(16), 3155; https://doi.org/10.3390/rs13163155 - 10 Aug 2021

Cited by 10 | Viewed by 2826

Abstract

Rain Signatures on C-band Synthetic Aperture Radar (SAR) images acquired over ocean are common and can dominate the backscattered signal from the ocean surface. In many cases, the inability to decipher between ocean and rain signatures can disturb the analysis of SAR scenes [...] Read more.

Rain Signatures on C-band Synthetic Aperture Radar (SAR) images acquired over ocean are common and can dominate the backscattered signal from the ocean surface. In many cases, the inability to decipher between ocean and rain signatures can disturb the analysis of SAR scenes for maritime applications. This study relies on Sentinel-1 SAR acquisitions in the Interferometric Wide swath mode and high-resolution measurements from ground-based weather radar to document the rain impact on the radar backscattered signal in both co- and cross-polarization channels. The dark and bright rain signatures are found in connection with the timeliness of the rain cells. In particular, the bright patches are demonstrated by the hydrometeors (graupels, hails) in the melting layer. In general, the radar backscatter under rain increases with rain rate for a given sea state and decreases when the sea state strengthens. The rain also has a stronger impact on the radar signal in both polarizations when the incidence angle increases. The complementary sensitivity of the SAR signal of rain in both channels is then used to derive a filter to locate the areas in SAR scenes where the signal is not dominated by rain. The filter optimized to match the rain observed by the ground-based weather radar is more efficient when both polarization channels are considered. Case studies are presented to discuss the advantages and limitations of such a filtering approach. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments-II)

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16 pages, 3186 KiB

Open AccessArticle

Preliminary Significant Wave Height Retrieval from Interferometric Imaging Radar Altimeter Aboard the Chinese Tiangong-2 Space Laboratory

by Lin Ren, Jingsong Yang, Xiao Dong, Yongjun Jia and Yunhua Zhang

Remote Sens. 2021, 13(12), 2413; https://doi.org/10.3390/rs13122413 - 20 Jun 2021

Cited by 7 | Viewed by 2067

Abstract

The interferometric imaging radar altimeter (InIRA) aboard the Chinese Tiangong-2 space laboratory is the first spaceborne imaging radar working at low incidence angles. This study focuses on the retrieval of significant wave heights (SWHs) from InIRA data. The retrieved SWHs can be used [...] Read more.

The interferometric imaging radar altimeter (InIRA) aboard the Chinese Tiangong-2 space laboratory is the first spaceborne imaging radar working at low incidence angles. This study focuses on the retrieval of significant wave heights (SWHs) from InIRA data. The retrieved SWHs can be used for correcting the sea state bias of InIRA-derived sea surface heights and can supplement SWH products from other spaceborne sensors. First, we analyzed tilt, range bunching and velocity bunching wave modulations at low incidence angles, and we found clear dependencies between the SWH and two defined factors, range and azimuth integration, for ocean waves in the range and azimuth directions, respectively. These dependencies were further confirmed using InIRA measurements and collocated WaveWatch III (WW3) data. Then, an empirical orthogonal SWH model using the range and azimuth integration factors as model inputs was proposed. The model was segmented by the incidence angle, and the model coefficients were estimated by fitting the collocation at each incidence angle bin. Finally, the SWHs were retrieved from InIRA data using the proposed model. The retrievals were validated using both WW3 and altimeter (JASON2, JASON3, SARAL, and HY2A) SWHs. The validation with WW3 data shows a root mean square error (RMSE) of 0.43 m, while the average RMSE with all traditional altimeter data is 0.48 m. This indicates that the InIRA can be used to measure SWHs. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments-II)

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15 pages, 6111 KiB

Open AccessArticle

Detection of Biogenic Oil Films near Aquaculture Sites Using Sentinel-1 and Sentinel-2 Satellite Images

by Andromachi Chatziantoniou, Alexandros Karagaitanakis, Vasileios Bakopoulos, Nikos Papandroulakis and Konstantinos Topouzelis

Remote Sens. 2021, 13(9), 1737; https://doi.org/10.3390/rs13091737 - 30 Apr 2021

Cited by 6 | Viewed by 2330

Abstract

Biogenic films are very thin surface oils, frequently observed near aquaculture farms, that affect the roughness and the optical properties of the sea surface, making them visible in SAR and multispectral images. The purpose of this study is to investigate the potential of [...] Read more.

Biogenic films are very thin surface oils, frequently observed near aquaculture farms, that affect the roughness and the optical properties of the sea surface, making them visible in SAR and multispectral images. The purpose of this study is to investigate the potential of satellite SAR and multispectral sensors in the detection of biogenic oil films near aquaculture farms. Sentinel-1 SAR and Sentinel-2 multispectral data were exploited to detect the films around three aquaculture sites. The study is divided in three stages: (a) preprocessing, (b) main process and (c) accuracy assessment. The preprocessing stage includes subset, filtering, land masking and image corrections. The main process was similar for both datasets, using an adaptive thresholding method to identify dark formations, extract and classify them. Finally, the performance of the algorithm was evaluated based on the estimation of standard classification error statistics. The evaluation of the results was based on empirical photointerpretation and in situ photos. The results are successful and promising, with overall accuracy over 70%, while both sensors are proved to be effective in the detection, with Sentinel-1 SAR presenting slightly better accuracy (81%) than Sentinel-2 MSI (70%). There is no evidence of these films causing stress to the aquaculture farms or the surrounding environment; however, our knowledge on their presence, amount and dissolution is limited and further knowledge could contribute to efficient feeding management and fish welfare. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments-II)

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