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Remote Sensing Monitoring for Arctic Region

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 21176

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Special Issue Editors

Dr. Tonghua Wu

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Guest Editor

Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: remote sensing of permafrost; dynamics of permafrost; arctic terrestrial changes; cryosphere service

Dr. Florent Garnier

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Guest Editor

LEGOS/CNRS, University of Toulouse, Toulouse, France
Interests: sea ice thickness; altimetry

Dr. Lin Liu

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Guest Editor

Earth System Science Programme, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
Interests: permafrost; glaciers; ice sheets; remote sensing; deep learning; geodesy and geophysics

Prof. Dr. Yubao Qiu

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Guest Editor

1. International Research Center of Big Data for Sustainable Development, Beijing 100094, China
2. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing of snow and ice; mocrowave remote sensing; global change
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Special Issue Information

Dear Colleagues,

Recent research has revealed that the Arctic is warming four times faster than the rest of the world. The terrestrial and marine environment in the Arctic has been undergoing drastic changes in the cryosphere, ecosystem, and ocean. As most of the Arctic regions are located in places that are generally remote from human habitation and infrastructure, with sparse and limited ground observations, remote sensing techniques offer useful tools for detecting and monitoring the changes and processes of the Arctic terrestrial and marine environment. This Special Issue is dedicated to advancing our knowledge in the applications of remote sensing techniques for the quantitative analysis of the Arctic terrestrial and marine environment. We call for papers to be submitted in the context of the broad array of remote sensing platforms (e.g., handheld, drone, airborne, and satellite) and sensors (e.g., optical, microwave, radar, LiDAR), across various spatial, temporal, and spectral resolutions and extents, to examine the changes and processes of the Arctic terrestrial and marine environment. Contributions using new sensors, platforms, or algorithms that consider the integration of datasets or compare spatial heterogeneity in the Arctic are especially welcome.

Dr. Tonghua Wu
Dr. Florent Garnier
Dr. Lin Liu
Prof. Dr. Yubao Qiu
Guest Editors

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Keywords

cryosphere
sea ice
ice sheet
glacier
snow
permafrost
vegetation
ecology
arctic ocean

Published Papers (12 papers)

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14 pages, 6131 KiB

Open AccessArticle

Improvement of Ice Surface Temperature Retrieval by Integrating Landsat 8/TIRS and Operation IceBridge Observations

by Lijuan Song, Yifan Wu, Jiaxing Gong, Pei Fan, Xiaopo Zheng and Xi Zhao

Remote Sens. 2023, 15(18), 4577; https://doi.org/10.3390/rs15184577 - 17 Sep 2023

Viewed by 1161

Abstract

Accurate retrieval of ice surface temperature (IST) over the Arctic ice-water mixture zone (IWMZ) is significantly essential for monitoring the change of the polar sea ice environment. Previous researchers have focused on evaluating the accuracy of IST retrieval in pack ice regions, possibly on account of the availability of in situ measurement data. Few of them have assessed the accuracy of IST retrieval on IWMZ. This study utilized Landsat 8/TIRS and Operation IceBridge observations (OIB) to evaluate the accuracy of the current IST retrieval method in IWMZ and proposed an adjustment method for improving the overall accuracy. An initial comparison shows that Landsat 8 IST and OIB IST have minor differences in the pack ice region with RMSE of 0.475 K, MAE of 0.370 K and cold bias of −0.256 K. In the thin ice region, however, the differences are more significant, with RMSE of 0.952 K, MAE of 0.776 K and warm bias of 0.703 K. We suggest that this phenomenon is because the current ice-water classification method misclassified thin ice as water. To address this issue, an adjusted method is proposed to refine the classification of features within the IWMZ and thus improve the accuracy of IST retrieval using Landsat 8 imagery. The results demonstrate that the accuracy of the retrieved IST in the two cases was improved in the thin ice region, with RMSE decreasing by about 0.146 K, Bias decreasing by about 0.311 K, and MAE decreasing by about 0.129 K. After the adjustment, high accuracy was achieved for both pack ice and thin ice in IWMZ. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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20 pages, 12624 KiB

Open AccessArticle

Effects of Thermokarst Lake Drainage on Localized Vegetation Greening in the Yamal–Gydan Tundra Ecoregion

by Aobo Liu, Yating Chen and Xiao Cheng

Remote Sens. 2023, 15(18), 4561; https://doi.org/10.3390/rs15184561 - 16 Sep 2023

Viewed by 865

Abstract

As the climate warms, the Arctic permafrost region has undergone widespread vegetation changes, exhibiting overall greening trends but with spatial heterogeneity. This study investigates an underexamined mechanism driving heterogeneous greening patterns, thermokarst lake drainage, which creates drained lake basins (DLBs) that represent localized greening hotspots. Focusing on the Yamal–Gydan region in Siberia, we detect 2712 lakes that have drained during the period of 2000–2020, using Landsat time-series imagery and an automated change detection algorithm. Vegetation changes in the DLBs and the entire study area were quantified through NDVI trend analysis. Additionally, a machine learning model was employed to correlate NDVI trajectories in the DLBs with environmental drivers. We find that DLBs provide ideal conditions for plant colonization, with greenness levels reaching or exceeding those of the surrounding vegetation within about five years. The greening trend in DLBs is 8.4 times the regional average, thus contributing disproportionately despite their small area share. Number of years since lake drainage, annual soil temperature, latitude, air temperature trends, and summer precipitation emerged as key factors influencing DLB greening. Our study highlights lake drainage and subsequent vegetation growth as an important fine-scale process augmenting regional greening signals. Quantifying these dynamics is critical for assessing climate impacts on regional vegetation change. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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18 pages, 5208 KiB

Open AccessArticle

Dual-Parameter Simultaneous Full Waveform Inversion of Ground-Penetrating Radar for Arctic Sea Ice

by Ying Liu, Mengyuan Liu, Junhui Xing and Yixin Ye

Remote Sens. 2023, 15(14), 3614; https://doi.org/10.3390/rs15143614 - 20 Jul 2023

Viewed by 979

Abstract

With global warming, Arctic sea ice, as one of the important factors regulating climate, has put forward new requirements for research. At present, the ground penetrating radar (GPR) is a powerful tool to obtain the structure of Arctic sea ice. Traditional offset imaging techniques no longer meet research requirements, and the two-parameter full waveform inversion (FWI) method has received widespread attention. To solve the high nonlinearity and ill-posed problem of FWI, the L-BFGS optimization algorithm and Wolfe criterion of inexact line search were used to update the model. The parameter scale factor, multiscale inversion strategy, and total variation (TV) regularization were introduced to optimize the inversion results. Finally, the inversion of anomalous bodies with different scales and different physical parameters is carried out, which verifies the reliability of the proposed method for dual-parameter imaging of Arctic sea ice and provides a powerful tool for the study of Arctic sea ice. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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31 pages, 4126 KiB

Open AccessArticle

Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications

by Émilie Desjardins, Sandra Lai, Laurent Houle, Alain Caron, Véronique Thériault, Andrew Tam, François Vézina and Dominique Berteaux

Remote Sens. 2023, 15(12), 3090; https://doi.org/10.3390/rs15123090 - 13 Jun 2023

Cited by 1 | Viewed by 1241

Abstract

The use of remote sensing for developing land cover maps in the Arctic has grown considerably in the last two decades, especially for monitoring the effects of climate change. The main challenge is to link information extracted from satellite imagery to ground covers due to the fine-scale spatial heterogeneity of Arctic ecosystems. There is currently no commonly accepted methodological scheme for high-latitude land cover mapping, but the use of remote sensing in Arctic ecosystem mapping would benefit from a coordinated sharing of lessons learned and best practices. Here, we aimed to produce a highly accurate land cover map of the surroundings of the Canadian Forces Station Alert, a polar desert on the northeastern tip of Ellesmere Island (Nunavut, Canada) by testing different predictors and classifiers. To account for the effect of the bare soil background and water limitations that are omnipresent at these latitudes, we included as predictors soil-adjusted vegetation indices and several hydrological predictors related to waterbodies and snowbanks. We compared the results obtained from an ensemble classifier based on a majority voting algorithm to eight commonly used classifiers. The distance to the nearest snowbank and soil-adjusted indices were the top predictors allowing the discrimination of land cover classes in our study area. The overall accuracy of the classifiers ranged between 75 and 88%, with the ensemble classifier also yielding a high accuracy (85%) and producing less bias than the individual classifiers. Some challenges remained, such as shadows created by boulders and snow covered by soil material. We provide recommendations for further improving classification methodology in the High Arctic, which is important for the monitoring of Arctic ecosystems exposed to ongoing polar amplification. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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13 pages, 8464 KiB

Open AccessCommunication

Early Freeze-Up over the Bering Sea Controlled by the Aleutian Low

by Weibo Wang, Chunsheng Jing and Xiaogang Guo

Remote Sens. 2023, 15(9), 2232; https://doi.org/10.3390/rs15092232 - 23 Apr 2023

Cited by 2 | Viewed by 992

Abstract

Early freeze-up affects the local marine environment and ecosystem throughout the entire Bering Sea. However, the process governing early freeze-up, which is responsible for the most significant interannual variation in the December sea ice area (SIA), is not well understood. Here, we show that the SIA in December is modulated by the Aleutian low in November by altering poleward heat transport (PHT). The stronger the November PHT is, the lower the December SIA. The rise in heat transport across the Bering Strait in November is consistent with the decrease in SIA in December, with a correlation of −0.71, further validating the regulatory role of PHT. The Aleutian low anomaly controls the local wind field, further altering the sea surface temperature and PHT. The center of the anomalous low-pressure in the east (west) generates the northerly (southeasterly) anomaly over the northern Bering Sea, leading to acceleration (suppression) of seawater cooling and weakening (enhancement) of the PHT. It is also found that a strong northerly surface current has a greater influence on the later SIA than warm water temperature. Hence, atmospheric forcing causing changes in ocean forcing is imperative to understand early freeze-up. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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25 pages, 15365 KiB

Open AccessArticle

Classification of Arctic Sea Ice Type in CFOSAT Scatterometer Measurements Using a Random Forest Classifier

by Xiaochun Zhai, Rui Xu, Zhixiong Wang, Zhaojun Zheng, Yixuan Shou, Shengrong Tian, Lin Tian, Xiuqing Hu, Lin Chen and Na Xu

Remote Sens. 2023, 15(5), 1310; https://doi.org/10.3390/rs15051310 - 27 Feb 2023

Cited by 4 | Viewed by 1587

Abstract

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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29 pages, 10239 KiB

Open AccessArticle

Changes in Sea Surface Temperature and Sea Ice Concentration in the Arctic Ocean over the Past Two Decades

by Meng Yang, Yubao Qiu, Lin Huang, Maoce Cheng, Jianguo Chen, Bin Cheng and Zhengxin Jiang

Remote Sens. 2023, 15(4), 1095; https://doi.org/10.3390/rs15041095 - 17 Feb 2023

Cited by 3 | Viewed by 3541

Abstract

With global warming, the decrease in sea ice creates favorable conditions for Arctic activities. Sea surface temperature (SST) is not only an important driven factor of sea ice concentration (SIC) changes but also an important medium of the ocean–atmosphere interaction. However, the response of sea surface temperature to Arctic sea ice varies in different sea areas. Using the optimal interpolated SST data from the National Centers for Environmental Information (NCEI) and SIC data from the University of Bremen, the temporal and spatial characteristics of SST and SIC in the Arctic above 60°N and their relationship are studied, and the melting and freezing time of sea ice are calculated, which is particularly important for the prediction of Arctic shipping and sea ice. The results show that (1) the highest and lowest monthly mean Arctic SST occur in August and March, respectively, while those of SIC are in March and September. The maximum trends of SST and SIC changes are in autumn, which are +0.01 °C/year and −0.45%/year, respectively. (2) There is a significant negative correlation between the Arctic SST and SIC with a correlation coefficient of −0.82. (3) The sea ice break-up occurs on Day of the Year (DoY) 143 and freeze-up occurs on DoY 296 in the Arctic. The melting and freezing processes lasted for 27 days and 14 days, respectively. (4) The Kara Sea showed the strongest trend of sea ice melting at −1.22 d/year, followed by the Laptev Sea at −1.17 d/year. The delay trend of sea ice freezing was the most significant in the Kara Sea +1.75 d/year, followed by the Laptev Sea +1.70 d/year. In the Arctic, the trend toward earlier melting of sea ice is smaller than the trend toward later freezing. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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17 pages, 4665 KiB

Open AccessArticle

Radiative Effects and Costing Assessment of Arctic Sea Ice Albedo Changes

by Hairui Hao, Bo Su, Shiwei Liu and Wenqin Zhuo

Remote Sens. 2023, 15(4), 970; https://doi.org/10.3390/rs15040970 - 10 Feb 2023

Viewed by 2428

Abstract

The rapid loss of Arctic Sea ice cover and thickness diminishes the surface albedo, which increases the ocean’s absorption of solar heat and exacerbates the Arctic amplification effect. According to the most recent research from the Intergovernmental Panel on Climate Change, the Sixth Assessment Report (IPCC, AR6), the extent of summer sea ice is anticipated to decrease below 1 million km² by the 2050s as a result of the extreme climate. Nevertheless, past and future changes in sea ice albedo radiative forcing and the resulting economic cost remain to be explored in systematic and multi-disciplinary manners. In this study, we first analyze the evolution of Arctic sea ice radiative forcing (SIRF) from 1982 to 2100 using a radiative kernel method based on albedo data from the Polar Pathfinder-Extent (APP-x) and Coupled Model Intercomparison Project 5 (CMIP5). Then, the SIRF is converted to CO₂ equivalent emissions via the Dynamic Integrated Model of Climate and Economy (DICE) model. Finally, the associated costs are calculated using the substitute cost method, based on the social cost of carbon to achieve the Paris Agreement targets. The results show that the average Arctic SIRF was −0.75 ± 0.1

W \cdot m^{- 2}

between 1982 and 2020, and increased by 0.12

W \cdot m^{- 2}

during this period. The SIRF in April–June accounts for nearly 77% of the average annual value, with a maximum absolute value of –3.2

W \cdot m^{- 2}

in May. Through model transformation, it is shown that the Arctic SIRF rising leads to global warming comparable to the effect of an increase of 34.5 Gt of CO₂ in the atmosphere relative to pre-industrialization, and results in a loss of 24.4–48.8 trillion USD for climate regulation service (CRS). From 2020 to 2100, in the representative concentration pathway (RCP) 8.5, the Arctic SIRF is projected to increase by 0.31

W \cdot m^{- 2}

. Combined with the discount rate, the estimated average annual cost over the period ranges from 6.7–13.3 trillion USD. These findings provide a systematic understanding of the radiative effect of Arctic sea ice change on the global climate and the corresponding economic cost. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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13 pages, 10771 KiB

Open AccessArticle

An Assessment of the Lancaster Sound Polynya Using Satellite Data 1979 to 2022

by R.F. Vincent

Remote Sens. 2023, 15(4), 954; https://doi.org/10.3390/rs15040954 - 09 Feb 2023

Viewed by 1299

Abstract

Situated between Devon Island and Baffin Island, Lancaster Sound is part of Tallurutiup Imanga, which is in the process of becoming the largest marine conservation area in Canada. The cultural and ecological significance of the region is due, in part, to a recurring polynya in Lancaster Sound. The polynya is demarcated by an ice arch that generally forms in mid-winter and collapses in late spring or early summer. Advanced Very High Resolution imagery from 1979 to 2022 was analyzed to determine the position, formation and collapse of the Lancaster Sound ice arch. The location of the ice arch demonstrates high interannual variability, with 512 km between the eastern and western extremes, resulting in a polynya area that can fluctuate between 6000 km² and 40,000 km². The timing of the seasonal ice arch formation and collapse has implications with respect to ice transport through Lancaster Sound and the navigability of the Northwest Passage. The date of both the formation and collapse of the ice arch is variable from season to season, with the formation observed between November and April and collapse usually occurring in June or July. A linear trend from 1979 to 2022 indicates that seasonal ice arch duration has declined from 150 to 102 days. The reduction in ice arch duration is a result of earlier collapse dates over the study period and later formation dates, particularly from 1979 to 2000. Lancaster Sound normally freezes west to east each season until the ice arch is established, but there is no statistical relationship between the ice arch location and duration. Satellite surface temperature mapping of the region indicates that the polynya is characterized by sub-resolution leads during winter. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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13 pages, 6437 KiB

Open AccessArticle

Changes in Onset of Vegetation Growth on Svalbard, 2000–2020

by Stein Rune Karlsen, Arve Elvebakk, Hans Tømmervik, Santiago Belda and Laura Stendardi

Remote Sens. 2022, 14(24), 6346; https://doi.org/10.3390/rs14246346 - 15 Dec 2022

Cited by 1 | Viewed by 1674

Abstract

The global temperature is increasing, and this is affecting the vegetation phenology in many parts of the world. The most prominent changes occur at northern latitudes such as our study area, which is Svalbard, located between 76°30′N and 80°50′N. A cloud-free time series of MODIS-NDVI data was processed. The dataset was interpolated to daily data during the 2000–2020 period with a 231.65 m pixel resolution. The onset of vegetation growth was mapped with a NDVI threshold method which corresponds well with a recent Sentinel-2 NDVI-based mapping of the onset of vegetation growth, which was in turn validated by a network of in-situ phenological data from time lapse cameras. The results show that the years 2000 and 2008 were extreme in terms of the late onset of vegetation growth. The year 2020 had the earliest onset of vegetation growth on Svalbard during the 21-year study. Each year since 2013 had an earlier or equally early timing in terms of the onset of the growth season compared with the 2000–2020 average. A linear trend of 0.57 days per year resulted in an earlier onset of growth of 12 days on average for the entire archipelago of Svalbard in 2020 compared to 2000. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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17 pages, 18236 KiB

Open AccessArticle

How Many Pan-Arctic Lakes Are Observed by ICESat-2 in Space and Time?

by Tan Chen, Chunqiao Song, Pengfei Zhan and Jinsong Ma

Remote Sens. 2022, 14(23), 5971; https://doi.org/10.3390/rs14235971 - 25 Nov 2022

Cited by 4 | Viewed by 1220

Abstract

High-latitude lakes are sensitive indicators of climate change. Monitoring lake dynamics in high-latitude regions (e.g., pan-Arctic regions) is essential to improving our understanding of the impacts of climate change; however, the lack of in situ water level measurements limits comprehensive quantification of the lake hydrologic dynamics in high-latitude regions. Fortunately, the newly launched ICESat-2 laser altimeter can provide finer footprint measurements and denser ground tracks, thus enabling us to measure the water level changes for more lakes than with conventional radar altimeters. This study aims to comprehensively assess the number and frequency of pan-Arctic lakes (>1 km², north of 60°N) observable by the ICESat-2 in space and time over the past three years. Further, we analyze the spatial and temporal characteristics of the ICESat-2-based water level observations of these pan-Arctic lakes based on our customized classification of seasonal coverage patterns (wet/dry season, monthly, and ten-day). We find that the ICESat-2 observed 80,688 pan-Arctic lakes (97% of the total). Among the observed lakes, the ICESat-2 retrieved the seasonal coverage patterns for 40,192 lakes (~50% of observed lakes), accounting for nearly 84% of the area and 95% of the volumetric capacity. Most lakes (99%) have seasonal water-level fluctuation amplitudes within a range of 0–1 m. The latitudinal zonality analysis demonstrates that the seasonal change in pan-Arctic lake levels gently fluctuates around 0.5 m between 60°N and 74°N and becomes intense (range of level change from 1 m to 2 m) beyond 74°N. Our results are expected to offer an overall reference for the spatio-temporal coverage of the ICESat-2’s observations of pan-Arctic lakes, which is crucial for comprehending the hydrologic response of high-latitude lakes to ongoing climate change. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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13 pages, 5750 KiB

Open AccessTechnical Note

Incidence Angle Normalization of Dual-Polarized Sentinel-1 Backscatter Data on Greenland Ice Sheet

by Xiao Chen, Gang Li, Zhuoqi Chen, Qi Ju and Xiao Cheng

Remote Sens. 2022, 14(21), 5534; https://doi.org/10.3390/rs14215534 - 02 Nov 2022

Cited by 3 | Viewed by 1471

Abstract

The backscatter coefficients of Synthetic Aperture Radar (SAR) images that observe the Greenland Ice Sheet (GrIS) are incidence angle dependent, which impedes subsequent applications, such as monitoring its surface melting. Therefore, backscatter intensities with varying incidence angles should be normalized. This study proposes an incidence angle normalization method for dual-polarized Sentinel-1 images for GrIS. A multiple linear regression model is trained using the ratio between the backscatter coefficient differences and the incidence angle differences of quasi-simultaneously observed ascending and descending image pairs. Regression factors include the geographical position and elevation. The precision evaluation to the ascending and descending images suggests better normalization results than the widely used cosine-square correction method for horizontal transmit and horizontal receive (HH) images and a slight improvement for horizontal transmit and vertical receive (HV) images. Another dataset of GrIS Sentinel-1 mosaics in four 6-day repeating periods in 2020 is also tested to evaluate the proposed method and yields similar results. For HH images, the proposed method performs better than the cosine-square method, reducing 0.34 dB RMSE on average. The overall accuracy of our proposed method is 0.77 and 0.75 dB for HH and HV images, respectively. The proposed incidence angle normalization method can benefit the application of wide-swath SAR images to the study of large-scale and long-period observation on GrIS. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Arctic Region)

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