scholarly journals Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

2019 ◽  
Vol 11 (3) ◽  
pp. 777 ◽  
Author(s):  
Yu Yan ◽  
Kaiyue Huang ◽  
Dongdong Shao ◽  
Yingjun Xu ◽  
Wei Gu
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Bohai Sea ◽  
Ocean Color ◽  
High Temporal Resolution ◽  
The Bohai Sea ◽  
Ice Conditions ◽  
Ice Disaster

Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.

Large-scale Arctic sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission

2021 ◽  
Author(s):  
Stephen Howell ◽  
Mike Brady ◽  
Alexander Komarov
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
High Temporal Resolution ◽  
Sar Images ◽  
Sea Ice Extent ◽  
Arctic Sea

<p>As the Arctic’s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.  Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent a collection of 5 spaceborne SAR sensors that together can routinely cover Arctic sea ice with a high spatial resolution (20-90 m) but also with a high temporal resolution (1-7 days) typically associated with passive microwave sensors. Here, we used ~28,000 SAR image pairs from Sentinel-1AB together with ~15,000 SAR images pairs from RCM to generate high spatiotemporal large-scale sea ice motion products across the pan-Arctic domain for 2020. The combined Sentinel-1AB and RCM sea ice motion product provides almost complete 7-day coverage over the entire pan-Arctic domain that also includes the pole-hole. Compared to the National Snow and Ice Data Center (NSIDC) Polar Pathfinder and Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) sea ice motion products, ice speed was found to be faster with the Senintel-1AB and RCM product which is attributed to the higher spatial resolution of SAR imagery. More sea ice motion vectors were detected from the Sentinel-1AB and RCM product in during the summer months and within the narrow channels and inlets compared to the NSIDC Polar Pathfinder and OSI-SAF sea ice motion products. Overall, our results demonstrate that sea ice geophysical variables across the pan-Arctic domain can now be retrieved from multi-sensor SAR images at both high spatial and temporal resolution.</p>

scholarly journals Quantifying the spatial ripple effect of the Bohai Sea ice disaster in the winter of 2009/2010 in 31 provinces of China

2018 ◽  
Vol 9 (1) ◽  
pp. 986-1005 ◽  
Author(s):  
Cailin Wang ◽  
Jidong Wu ◽  
Xin He ◽  
Mengqi Ye ◽  
Yu Liu
Keyword(s):  
Sea Ice ◽  
Bohai Sea ◽  
Ripple Effect ◽  
The Bohai Sea ◽  
Ice Disaster

scholarly journals Sea Ice Extent Detection in the Bohai Sea Using Sentinel-3 OLCI Data

2019 ◽  
Vol 11 (20) ◽  
pp. 2436 ◽  
Author(s):  
Hua Su ◽  
Bowen Ji ◽  
Yunpeng Wang
Keyword(s):  
Sea Ice ◽  
Bohai Sea ◽  
Regional Climate ◽  
Regional Climate Change ◽  
The Novel ◽  
Sea Ice Extent ◽  
Important Indicator ◽  
The Bohai Sea ◽  
Information Index ◽  
Ice Conditions

Sea ice distribution is an important indicator of ice conditions and regional climate change in the Bohai Sea (China). In this study, we monitored the spatiotemporal distribution of the Bohai Sea ice in the winter of 2017–2018 by developing sea ice information indexes using 300 m resolution Sentinel-3 Ocean and Land Color Instrument (OLCI) images. We assessed and validated the index performance using Sentinel-2 MultiSpectral Instrument (MSI) images with higher spatial resolution. The results indicate that the proposed Normalized Difference Sea Ice Information Index (NDSIIIOLCI), which is based on OLCI Bands 20 and 21, can be used to rapidly and effectively detect sea ice but is somewhat affected by the turbidity of the seawater in the southern Bohai Sea. The novel Enhanced Normalized Difference Sea Ice Information Index (ENDSIIIOLCI), which builds on NDSIIIOLCI by also considering OLCI Bands 12 and 16, can monitor sea ice more accurately and effectively than NDSIIIOLCI and suffers less from interference from turbidity. The spatiotemporal evolution of the Bohai Sea ice in the winter of 2017–2018 was successfully monitored by ENDSIIIOLCI. The results show that this sea ice information index based on OLCI data can effectively extract sea ice extent for sediment-laden water and is well suited for monitoring the evolution of Bohai Sea ice in winter.

Sea ice drift tracking in the Bohai Sea using geostationary ocean color imagery

2014 ◽  
Vol 8 (1) ◽  
pp. 083650 ◽  
Author(s):  
Wenhui Lang ◽  
Qing Wu ◽  
Xi Zhang ◽  
Junmin Meng ◽  
Ning Wang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Bohai Sea ◽  
Ocean Color ◽  
The Bohai Sea ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Ocean Color Imagery ◽  
Color Imagery

High spatial and temporal resolution land surface temperature for the Antarctic Dry Valleys

2020 ◽  
Author(s):  
Maite Lezama Valdes ◽  
Marwan Katurji ◽  
Hanna Meyer
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Temporal Resolution ◽  
Dry Valleys ◽  
Model Complex ◽  
The Antarctic

<p>Anthropogenic Climate Change is expected to take a toll on the Antarctic environment and its biodiversity, which is concentrated on the continent’s few ice-free areas, such as the McMurdo Dry Valleys (MDV). To model the current terrestrial habitat distribution and predict possible climate induced changes, high spatial and temporal resolution abiotic variables, especially land surface temperature (LST) and soil moisture are needed, but are currently unavailable.</p><p>The aim of this project is to fill this gap and create a high resolution LST dataset of the Antarctic Dry Valleys. This variable is acquired in a high temporal resolution (sub-daily) by the MODIS sensor aboard Terra and Aqua satellites. However, as LST varies greatly in space, the spatial resolution provided by this data source (1000 m) is too low to give a meaningful impression of LST and to study biodiversity patterns. Therefore, we use data from Landsat and ASTER sensors as a reference to downscale MODIS LST to a spatial resolution of 30 m. 7 year’s worth of satellite data as well as terrain-derived auxiliary variables went into the development of the model, which predicts 30 m LST for the Antarctic Dry Valleys. </p><p>To model complex relations between terrain, radiation, land cover and LST, machine learning models are used. Multiple algorithms (Random Forest, NN, SVM, Gradient Boosting) are compared to find the best approach for predicting high resolution LST based on MODIS data. Using the best performing model, a daily dataset is created that provides LST for the Antarctic Dry Valleys from 2002 on.</p>

scholarly journals Analysis of Influence Factors of GF-4 Registration Accuracy on Sea Ice Drift in the Bohai Sea

2019 ◽  
Vol 131 ◽  
pp. 01073
Author(s):  
Ruifu Wang ◽  
Pan Wei ◽  
Yingjie Zhao
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Bohai Sea ◽  
Time Interval ◽  
Liaodong Bay ◽  
Registration Accuracy ◽  
The Bohai Sea ◽  
Migration Direction ◽  
Ice Drift ◽  
Sea Ice Drift

The “GF-4” satellite is China’s first high resolution geostationary optical remote sensing satellite. It has the unique advantages of short imaging time interval (20s) and high resolution (50m). In order to analyze the effect of GF-4 satellite image registration accuracy on sea ice drift in Bohai Sea, firstly, the orthorectification of the 28 image data available from August 2016 to March 2018 in the Bohai Sea area was carried out. Then we selected the sea-land edge points as control points, and registration of two images which have the same time interval. Next, we recorded the marked same name points which searched from the bottom of Liaodong bay, east of Liaodong bay and west of Liaodong bay respectlly. We statisticed the direction and frequency of land point offset sub-regionally, then we created the rose plots and maked histogram of the offset of land point. The results show that, when the time interval is 4 hours and 24 hours, the dominant migration direction in the three regions in Liaodong bay is east; when the time interval is 1 minute, the dominant migration direction in Liaodong Bay bottom and Liaodong Bay west coast land is south, Followed by east and southeast respectively; the dominant migration in Liaodong Bay East Coast is north, followed by east; When the time interval is 3 hours, the dominant migration direction in west of Liaodong Bay, bottom of Liaodong Bay and east of Liaodong bay are east, west and south respectively, followed by southeast, east, southeast respectively. The land offset in three regions is major centralized distribution in a range which is from 60m to 80m. That is to say, the offset of land is basically equal to 1.2 times of pixels, and the maximum land offset is less than 2 times of pixels. Through statistical analysis, it can be seen that with the increase of time interval, the land offset will not change much. This study also paves the way for the study of the drift of sea ice.

scholarly journals Long-Term Ice Conditions in Yingkou, a Coastal Region Northeast of the Bohai Sea, between 1951/1952 and 2017/2018: Modeling and Observations

2022 ◽  
Vol 14 (1) ◽  
pp. 182
Author(s):  
Yuxian Ma ◽  
Bin Cheng ◽  
Ning Xu ◽  
Shuai Yuan ◽  
Honghua Shi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Large Scale ◽  
Bohai Sea ◽  
Coastal Region ◽  
Ice Thickness ◽  
Climate Indices ◽  
Clear Trend ◽  
The Bohai Sea ◽  
Ice Conditions

Bohai Sea ice creates obstacles for maritime navigation and offshore activities. A better understanding of ice conditions is valuable for sea-ice management. The evolution of 67 years of seasonal ice thickness in a coastal region (Yingkou) in the Northeast Bohai Sea was simulated by using a snow/ice thermodynamic model, using local weather-station data. The model was first validated by using seasonal ice observations from field campaigns and a coastal radar (the season of 2017/2018). The model simulated seasonal ice evolution well, particularly ice growth. We found that the winter seasonal mean air temperature in Yingkou increased by 0.33 °C/decade slightly higher than air temperature increase (0.27 °C/decade) around Bohai Sea. The decreasing wind-speed trend (0.05 m/s perdecade) was a lot weaker than that averaged (0.3 m/s per decade) between the early 1970s and 2010s around the entire Bohai Sea. The multi-decadal ice-mass balance revealed decreasing trends of the maximum and average ice thickness of 2.6 and 0.8 cm/decade, respectively. The length of the ice season was shortened by 3.7 days/decade, and ice breakup dates were advanced by 2.3 days/decade. All trends were statistically significant. The modeled seasonal maximum ice thickness is highly correlated (0.83, p < 0.001) with the Bohai Sea Ice Index (BoSI) used to quantify the severity of the Bohai Sea ice condition. The freezing-up date, however, showed a large interannual variation without a clear trend. The simulations indicated that Bohai ice thickness has grown continuously thinner since 1951/1952. The time to reach 0.15 m level ice was delayed from 3 January to 21 January, and the ending time advanced from 6 March to 19 February. There was a significant weakening of ice conditions in the 1990s, followed by some recovery in 2000s. The relationship between large-scale climate indices and ice condition suggested that the AO and NAO are strongly correlated with interannual changes in sea-ice thickness in the Yingkou region.

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

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