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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 42744

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Special Issue Editors

Prof. Qimao Wang

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Guest Editor

National Satellite Ocean Application Service, Beijing 100081, China
Interests: oceanic remote sensing; sea ice remote sensing

Prof. Dr. Dongmei Chen

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Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Interests: remote sensing; GIS; change detection; feature extraction; environmental modeling
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Prof. Dr. Lin Li

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Department of Earth Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
Interests: remote sensing; lunar and planetary geology; environmental sciences; data processing; radiative transfer models
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Dr. Marko Makynen

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Finnish Meteorological Institute, Erik Palmenin aukio 1, FI-00560 Helsinki, Finland
Interests: sea ice; Arctic environments; ship-based observations
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Dr. Lijian Shi

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National Satellite Ocean Application Service, Beijing 100081, China
Interests: oceanic remote sensing and application; satellite-based sea ice monitoring

Dr. Hongyan Xi

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Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany
Interests: phytoplankton and marine primary production; remote sensing application; geoinformation; ocean optics

Prof. Dr. Yuanzhi Zhang

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School of Marine Sciences, Nanjing University of Information Science and Technology, 219 Road Ninglu, Pukou District, Nanjing 210044, China
Interests: remote sensing of water quality; coastal environments and hazards; sea ice and snow
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

The Arctic plays an important role in the global climate system, which is undergoing unprecedented changes, including reduction in the extent of sea ice, ice sheet melting, and sea temperature rise. Recent increases in ship-based navigation of the Northeast Passage during summer will permit systematic observation of Arctic sea ice, water, snow, coastal zone, and oceanic and atmospheric circulation and produce long time series of geophysical data to support future climate projection.

This Special Issue on “Remote Sensing Monitoring of Arctic Environments” will invite original research articles as well as review articles that focus ongoing efforts on understanding the Arctic response to global climate change and its effect on Arctic environments through Earth observations and ship-based measurements. The suggested topics are relevant but not limited to the study of sea ice extent, melt pond fraction, snow water equivalent, Arctic Ocean primary production, coastal change monitoring, oceanic and atmospheric circulation, and long-term series products based on in situ and remote measurements.

Prof. Qimao Wang
Prof. Dongmei Chen
Prof. Lin Li
Dr. Marko Makynen
Dr. Lijian Shi
Dr. Hongyan Xi
Prof. Yuanzhi Zhang
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Arctic ocean
Sea ice and snow
Oceanic and coastal environments
Oceanic and atmospheric circulation
Satellite-based measurements
Ship-track observations

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Published Papers (13 papers)

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24 pages, 52501 KiB

Open AccessEditor’s ChoiceArticle

A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data

by Zhaoqing Dong, Lijian Shi, Mingsen Lin and Tao Zeng

Remote Sens. 2022, 14(4), 1041; https://doi.org/10.3390/rs14041041 - 21 Feb 2022

Cited by 6 | Viewed by 3276

Abstract

Arctic sea ice and snow affect the energy balance of the global climate system through the radiation budget. Accurate determination of the snow cover over Arctic sea ice is significant for the retrieval of the sea ice thickness (SIT). In this study, we developed a new snow depth retrieval method over Arctic sea ice with a long short-term memory (LSTM) deep learning algorithm based on Operation IceBridge (OIB) snow depth data and brightness temperature data of AMSR-2 passive microwave radiometers. We compared climatology products (modified W99 and AWI), altimeter products (Kwok) and microwave radiometer products (Bremen, Neural Network and LSTM). The climatology products and altimeter products are completely independent of the OIB data used for training, while microwave radiometer products are not completely independent of the OIB data. We also compared the SITs retrieved from the above different snow depth products based on Cryosat-2 radar altimeter data. First, the snow depth spatial patterns for all products are in broad agreement, but the temporal evolution patterns are distinct. Snow products of microwave radiometers, such as Bremen, Neural Network and LSTM snow depth products, show thicker snow in early winter with respect to the climatology snow depth products and the altimeter snow depth product, especially in the multiyear ice (MYI) region. In addition, the differences in all snow depth products are relatively large in the early winter and relatively small in spring. Compared with the OIB and IceBird observation data (April 2019), the snow depth retrieved by the LSTM algorithm is better than that retrieved by the other algorithms in terms of accuracy, with a correlation of 0.55 (0.90), a root mean square error (RMSE) of 0.06 m (0.05 m) and a mean absolute error (MAE) of 0.05 m (0.04 m). The spatial pattern and seasonal variation of the SITs retrieved from different snow depths are basically consistent. The total sea ice decreases first and then thickens as the seasons change. Compared with the OIB SIT in April 2019, the SIT retrieved by the LSTM snow depth is superior to that retrieved by the other SIT products in terms of accuracy, with the highest correlation of 0.46, the lowest RMSE of 0.59 m and the lowest MAE of 0.44 m. In general, it is promising to retrieve Arctic snow depth using the LSTM algorithm, but the retrieval of snow depth over MYI still needs to be verified with more measured data, especially in early winter. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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21 pages, 9298 KiB

Open AccessFeature PaperArticle

An Analysis of Arctic Sea Ice Leads Retrieved from AMSR-E/AMSR2

by Ming Li, Jiping Liu, Meng Qu, Zhanhai Zhang and Xi Liang

Remote Sens. 2022, 14(4), 969; https://doi.org/10.3390/rs14040969 - 16 Feb 2022

Cited by 5 | Viewed by 2962

Abstract

In this study, we retrieve an Arctic sea ice lead fraction from AMSR2 passive microwave data in winter from 2012 to 2020 based on an algorithm developed for AMSR-E data. The derived AMSR2 sea ice lead fraction is validated against MODIS images. The results show that the derived AMSR2 sea ice lead detects approximately 50% of the ice leads shown in the MODIS images, which is close to the amount of sea ice lead detected from the AMSR-E data from 2002 to 2011. Utilizing the retrievals from both the AMSR-E and AMSR2, our analysis shows no significant trend, but moderate interannual variation exists for the ice lead fraction in the Arctic basin scale over the past two decades. The maximum width and total length of sea ice lead show a significant decreasing trend for the whole Arctic, but the mean width does not exhibit a significant change over the studied period. In the Beaufort Sea the lead fraction varies from 2.06% to 12.35%, with a mean value of 5.72%. In the Greenland Sea the mean lead fraction over the studied period is 5.77%, and there is a significant increase in the lead fraction, with a rate of 0.13% per year. The maximum width in the Greenland Sea is substantially higher than that of other regions, and the mean width increases significantly. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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28 pages, 32683 KiB

Open AccessArticle

An Improved Sea Ice Classification Algorithm with Gaofen-3 Dual-Polarization SAR Data Based on Deep Convolutional Neural Networks

by Jiande Zhang, Wenyi Zhang, Yuxin Hu, Qingwei Chu and Lei Liu

Remote Sens. 2022, 14(4), 906; https://doi.org/10.3390/rs14040906 - 14 Feb 2022

Cited by 32 | Viewed by 4119

Abstract

The distribution of sea ice is one of the major safety hazards for sea navigation. As human activities in polar regions become more frequent, monitoring and forecasting of sea ice are of great significance. In this paper, we use SAR data from the C-band synthetic aperture radar (SAR) Gaofen-3 satellite in the dual-polarization (VV, VH) fine strip II (FSII) mode of operation to study the Arctic sea ice classification in winter. SAR data we use were taken in the western Arctic Ocean from January to February 2020. We classify the sea ice into four categories, namely new ice (NI), thin first-year ice (tI), thick first-year ice (TI), and old ice (OI), by referring to the ice maps provided by the Canadian Ice Service (CIS). Then, we use the deep learning model MobileNetV3 as the backbone network, input samples of different sizes, and combine the backbone network with multiscale feature fusion methods to build a deep learning model called Multiscale MobileNet (MSMN). Dual-polarization SAR data are used to synthesize pseudocolor images and produce samples of sizes 16 × 16 × 3, 32 × 32 × 3, and 64 × 64 × 3 as input. Ultimately, MSMN can reach over 95% classification accuracy on testing SAR sea ice images. The classification results using only VV polarization or VH polarization data are tested, and it is found that using dual-polarization data could improve the classification accuracy by 10.05% and 9.35%, respectively. When other classification models are trained using the training data from this paper for comparison, the accuracy of MSMN is 4.86% and 1.84% higher on average than that of the model built using convolutional neural networks (CNNs) and ResNet18 model, respectively. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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19 pages, 5431 KiB

Open AccessArticle

The Roles of Sea Ice Export, Atmospheric and Oceanic Factors in the Seasonal and Regional Variability of Arctic Sea Ice during 1979–2020

by Mengmeng Li, Changqing Ke, Bin Cheng, Xiaoyi Shen, Yue He and Dexuan Sha

Remote Sens. 2022, 14(4), 904; https://doi.org/10.3390/rs14040904 - 14 Feb 2022

Cited by 1 | Viewed by 2773

Abstract

The seasonal and regional variability of Arctic sea ice area (SIA) and thickness (SIT) were investigated between 1979 and 2020 for the Atlantic sector (AS), Pacific sector (PS) and Barents–Kara Seas (BKSs). We applied the SIA data from remote sensing observations and SIT data from numerical model calculations. We found the large summer variability of SIA and SIT in AS and PS compared with those in winter. The opposite feature was seen in the BKSs. The annual declining rates of SIA and SIT were the largest in PS (−1.73 × 10⁴ km² yr⁻¹) and AS (−3.36 × 10⁻² m yr⁻¹), respectively. The SIA variability was modest for winter PS and the northern Canadian Arctic Archipelago of AS. The annual and winter SIA flux from PS to AS gradually increased in 1979–2020; the summer SIA flux accounted for 11% of the PS summer SIA decline. The annual and seasonal SIA outflow through the Fram Strait during 1979–2020 steadily increased while for annual and winter SIA export, the increase mainly occurred in 1979–2000; the summer SIA outflow was only 1.45% equivalent to the decrease in the entire Arctic summer SIA. We concluded that sea ice export was not a major impact factor on the seasonal and regional decline of SIA and SIT except for the individual years. The near surface air temperature (SAT) and sea surface temperature (SST) were responsible for the retreat and thinning of the sea ice. The dramatic increase in SAT in winter resulted in a strong decrease in winter sea ice in BKS. The outgoing longwave radiation had significant negative correlations with SIA and SIT and positive correlations with SAT and SST. The Atlantic multi-decadal oscillation, related to the North Atlantic Ocean’s SST anomalies, had significant negative correlations with SIA and SIT. The SIT had higher correlations with the atmospheric and oceanic factors compared with SIA, which indicates that SIT is important for predictions of Arctic sea ice and climate change. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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16 pages, 3825 KiB

Open AccessArticle

Remote Sensing Analysis of Erosion in Arctic Coastal Areas of Alaska and Eastern Siberia

by Juan Wang, Dongling Li, Wenting Cao, Xiulin Lou, Aiqin Shi and Huaguo Zhang

Remote Sens. 2022, 14(3), 589; https://doi.org/10.3390/rs14030589 - 26 Jan 2022

Cited by 10 | Viewed by 3759

Abstract

In this study, remote sensing analysis of coastal erosion is conducted for three typical regions of Alaska and Eastern Siberia based on remote sensing data collected between 1974 and 2017. The comparative studies were made on the difference in coastal erosion at different latitudes and the difference and influencing factors in coastal erosion at similar latitudes. The coastline retreatment is used to indicate coastal erosion. It is found that the most extensive erosion occurred along Alaska’s coast, followed by that of the Eastern Siberian coasts. Based on the analysis of the historical time series of snow and ice as well as climate data, it is found that at similar latitudes, the erosion of the Arctic coasts is closely related to the trend and fluctuations of the sea surface temperature (SST). Specifically, it is found that in Alaska, coastal erosion is closely related to the fluctuation of the SST, while in Eastern Siberia, it is related to the increasing or decreasing trend of the SST. A decreasing trend is associated with low coastal erosion, whereas an increasing trend is associated with accelerated coastal erosion. In the Arctic, the strong fluctuations of the SST, the continuous decline of the sea ice cover, and the consequent increase of the significant wave height are the critical factors that cause changes in coastal permafrost and coastal erosion. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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19 pages, 5050 KiB

Open AccessArticle

Analyzing Variations in the Association of Eurasian Winter–Spring Snow Water Equivalent and Autumn Arctic Sea Ice

by Jiajun Feng, Yuanzhi Zhang, Jin Yeu Tsou and Kapo Wong

Remote Sens. 2022, 14(2), 243; https://doi.org/10.3390/rs14020243 - 6 Jan 2022

Cited by 2 | Viewed by 2353

Abstract

Because Eurasian snow water equivalent (SWE) is a key factor affecting the climate in the Northern Hemisphere, understanding the distribution characteristics of Eurasian SWE is important. Through empirical orthogonal function (EOF) analysis, we found that the first and second modes of Eurasian winter SWE present the distribution characteristics of an east–west dipole and north–south dipole, respectively. Moreover, the distribution of the second mode is caused by autumn Arctic sea ice, with the distribution of the north–south dipole continuing into spring. As the sea ice of the Barents–Kara Sea (BKS) decreases, a negative-phase Arctic oscillation (AO) is triggered over the Northern Hemisphere in winter, with warm and humid water vapor transported via zonal water vapor flux over the North Atlantic to southwest Eurasia, encouraging the accumulation of SWE in the southwest. With decreases in BKS sea ice, zonal water vapor transport in northern Eurasia is weakened, with meridional water vapor flux in northern Eurasia obstructing water vapor transport from the North Atlantic, discouraging the accumulation of SWE in northern Eurasia in winter while helping preserve the cold climate of the north. The distribution characteristics of Eurasian spring SWE are determined primarily by the memory effect of winter SWE. Whether analyzed through linear regression or support vector machine (SVM) methods, BKS sea ice is a good predictor of Eurasian winter SWE. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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31 pages, 17120 KiB

Open AccessArticle

Arctic Sea Ice Classification Based on CFOSAT SWIM Data at Multiple Small Incidence Angles

by Meijie Liu, Ran Yan, Jie Zhang, Ying Xu, Ping Chen, Lijian Shi, Jin Wang, Shilei Zhong and Xi Zhang

Remote Sens. 2022, 14(1), 91; https://doi.org/10.3390/rs14010091 - 25 Dec 2021

Cited by 18 | Viewed by 3305

Abstract

Sea ice type is the key parameter of Arctic sea ice monitoring. Microwave remote sensors with medium incidence and normal incidence modes are the primary detection methods for sea ice types. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode that is different from traditional remote sensors. The method of sea ice detection using SWIM data is also under development. The research reported here concerns ice classification using SWIM data in the Arctic from October 2019 to April 2020. Six waveform features are extracted from the SWIM echo data at small incidence angles, then the distinguishing capabilities of a single feature are analyzed using the Kolmogorov-Smirnov distance. The classifiers of the k-nearest neighbor and support vector machine are established and chosen based on single features. Moreover, sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier, and optimal combinations are developed. Compared with sea ice charts, the overall accuracy is up to 81% using the optimal classifier and a multi-feature combination at 2°. The results reveal that SWIM data can be used to classify sea water and sea ice types. Moreover, the optimal multi-feature combinations with the KNN method are applied to sea ice classification in the local regions. The classification results are analyzed using Sentinel-1 SAR images. In general, it is concluded that these multifeature combinations with the KNN method are effective in sea ice classification using SWIM data. Our work confirms the potential of sea ice classification based on the new SWIM sensor, and highlight the new sea ice monitoring technology and application of remote sensing at small incidence angles. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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19 pages, 5949 KiB

Open AccessArticle

Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic

by Jiechen Zhao, Yining Yu, Jingjing Cheng, Honglin Guo, Chunhua Li and Qi Shu

Remote Sens. 2021, 13(19), 3882; https://doi.org/10.3390/rs13193882 - 28 Sep 2021

Cited by 2 | Viewed by 2467

Abstract

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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23 pages, 12456 KiB

Open AccessArticle

Monitoring Changes to Arctic Vegetation and Glaciers at Ny-Ålesund, Svalbard, Based on Time Series Remote Sensing

by Guangbo Ren, Jianbu Wang, Yunfei Lu, Peiqiang Wu, Xiaoqing Lu, Chen Chen and Yi Ma

Remote Sens. 2021, 13(19), 3845; https://doi.org/10.3390/rs13193845 - 26 Sep 2021

Cited by 2 | Viewed by 2808

Abstract

Climate change has profoundly affected global ecological security. The most vulnerable region on Earth is the high-latitude Arctic. Identifying the changes in vegetation coverage and glaciers in high-latitude Arctic coastal regions is important for understanding the process and impact of global climate change. Ny-Ålesund, the northern-most human settlement, is typical of these coastal regions and was used as a study site. Vegetation and glacier changes over the past 35 years were studied using time series remote sensing data from Landsat 5/7/8 acquired in 1985, 1989, 2000, 2011, 2015 and 2019. Site survey data in 2019, a digital elevation model from 2009 and meteorological data observed from 1985 to 2019 were also used. The vegetation in the Ny-Ålesund coastal zone showed a trend of declining and then increasing, with a breaking point in 2000. However, the area of vegetation with coverage greater than 30% increased over the whole study period, and the wetland moss area also increased, which may be caused by the accelerated melting of glaciers. Human activities were responsible for the decline in vegetation cover around Ny-Ålesund owing to the construction of the town and airport. Even in areas with vegetation coverage of only 13%, there were at least five species of high-latitude plants. The melting rate of five major glaciers in the study area accelerated, and approximately 82% of the reduction in glacier area occurred after 2000. The elevation of the lowest boundary of the five glaciers increased by 50–70 m. The increase in precipitation and the average annual temperature after 2000 explains the changes in both vegetation coverage and glaciers in the study period. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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19 pages, 4830 KiB

Open AccessArticle

Seasonal Trends in Clouds and Radiation over the Arctic Seas from Satellite Observations during 1982 to 2019

by Xi Wang, Jian Liu, Bingyun Yang, Yansong Bao, George P. Petropoulos, Hui Liu and Bo Hu

Remote Sens. 2021, 13(16), 3201; https://doi.org/10.3390/rs13163201 - 12 Aug 2021

Cited by 8 | Viewed by 3126

Abstract

A long-term dataset of 38 years (1982–2019) from the Advanced Very High Resolution Radiometer (AVHRR) satellite observations is applied to investigate the spatio-temporal seasonal trends in cloud fraction, surface downwelling longwave flux, and surface upwelling longwave flux over the Arctic seas (60~90° N) by the non-parametric methods. The results presented here provide a further contribution to understand the cloud cover and longwave surface radiation trends over the Arctic seas, and their correlations to the shrinking sea ice. Our results suggest that the cloud fraction shows a positive trend for all seasons since 2008. Both surface downwelling and upwelling longwave fluxes present significant positive trends since 1982 with higher magnitudes in autumn and winter. The spatial distribution of the trends is nearly consistent between the cloud fraction and the surface longwave radiation, except for spring over the Chukchi and Beaufort Seas. We further obtained a significant negative correlation between cloud fraction (surface downwelling/upwelling longwave fluxes) and sea-ice concentration during autumn, which is largest in magnitude for regions with substantial sea ice retreat. We found that the negative correlation between cloud fraction and sea-ice concentration is not as strong as that for the surface downwelling longwave flux. It indicates the increase in cloudiness may result in positive anomalies in surface downwelling longwave flux which is highly correlated with the sea-ice retreat in autumn. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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21 pages, 8053 KiB

Open AccessArticle

Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

by Lijian Shi, Sen Liu, Yingni Shi, Xue Ao, Bin Zou and Qimao Wang

Remote Sens. 2021, 13(11), 2174; https://doi.org/10.3390/rs13112174 - 2 Jun 2021

Cited by 11 | Viewed by 3481

Abstract

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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20 pages, 3918 KiB

Open AccessArticle

Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region

by Jingjing Hu, Yansong Bao, Jian Liu, Hui Liu, George P. Petropoulos, Petros Katsafados, Liuhua Zhu and Xi Cai

Remote Sens. 2021, 13(10), 1884; https://doi.org/10.3390/rs13101884 - 11 May 2021

Cited by 8 | Viewed by 3181

Abstract

The acquisition of real-time temperature and relative humidity (RH) profiles in the Arctic is of great significance for the study of the Arctic’s climate and Arctic scientific research. However, the operational algorithm of Fengyun-3D only takes into account areas within 60°N, the innovation of this work is that a new technique based on Neural Network (NN) algorithm was proposed, which can retrieve these parameters in real time from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) observations in the Arctic region. Considering the difficulty of obtaining a large amount of actual observation (such as radiosonde) in the Arctic region, collocated ERA5 data from European Centre for Medium-Range Weather Forecasts (ECMWF) and HIRAS observations were used to train the neural networks (NNs). Brightness temperature and training targets were classified using two variables: season (warm season and cold season) and surface type (ocean and land). NNs-based retrievals were compared with ERA5 data and radiosonde observations (RAOBs) independent of the NN training sets. Results showed that (1) the NNs retrievals accuracy is generally higher on warm season and ocean; (2) the root-mean-square error (RMSE) of retrieved profiles is generally slightly higher in the RAOB comparisons than in the ERA5 comparisons, but the variation trend of errors with height is consistent; (3) the retrieved profiles by the NN method are closer to ERA5, comparing with the AIRS products. All the results demonstrated the potential value in time and space of NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from HIRAS observations under clear-sky conditions. As such, the proposed NN algorithm provides a valuable pathway for retrieving reliably temperature and RH profiles from HIRAS observations in the Arctic region, providing information of practical value in a wide spectrum of practical applications and research investigations alike.All in all, our work has important implications in broadening Fengyun-3D’s operational implementation range from within 60°N to the Arctic region. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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12 pages, 6411 KiB

Open AccessTechnical Note

Partial Shape Recognition for Sea Ice Motion Retrieval in the Marginal Ice Zone from Sentinel-1 and Sentinel-2

by Mingfeng Wang, Marcel König and Natascha Oppelt

Remote Sens. 2021, 13(21), 4473; https://doi.org/10.3390/rs13214473 - 8 Nov 2021

Cited by 7 | Viewed by 2686

Abstract

We present an algorithm for computing ice drift in the marginal ice zone (MIZ), based on partial shape recognition. With the high spatial resolution of Sentinel-1 and Sentinel-2 images, and the low sensitivity to atmospheric influences of Sentinel-1, a considerable quantity of ice floes is identified using a mathematical morphology method. Hausdorff distance is used to measure the similarity of segmented ice floes. It is tolerant to perturbations and deficiencies of floe shapes, which enhances the density of retrieved sea ice motion vectors. The PHD algorithm can be applied to sequential images from different sensors, and was tested on two combined image mosaics consisting of Sentinel-1 and Sentinel-2 data acquired over the Fram Strait; the PHD algorithm successfully produced pairs of matched ice floes. The matching result has been verified using shape and surface texture similarity of the ice floes. Moreover, the present method can naturally be extended to the problem of multi-source sea ice image registration. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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