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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 44222

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Special Issue Editors

Dr. Andreas J. Dietz

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Guest Editor

German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchner Strasse 20, D-82234 Wessling, Germany
Interests: snow; multispectral data; cryosphere; hyperspectral data; impact of climate change on the cryosphere
Special Issues, Collections and Topics in MDPI journals

Dr. Sebastian Roessler

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German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Strasse 20, D-82234 Wessling, Germany
Interests: remote sensing; change detection; cryosphere; climate change; water constituents; permafrost; snow; radiative transfer modelling; water

Dr. Celia Amélie Baumhoer

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Guest Editor

German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchner Strasse 20, D-82234 Wessling, Germany
Interests: remote sensing; SAR; glaciology

Special Issue Information

Dear Colleagues,

The polar regions are among the most vulnerable regions on the planet, and are threatened by anthropogenic pressure as well as global climate change. Over the last decades, the sustained increase in air temperature led to shrinking ice caps and inland ice, sea-level rise, reduced snow cover duration and amount, as well as the thawing of permafrost, which is associated with an acceleration of coastal erosion and the further release of climate-relevant trace gases. Polar regions cover vast areas which are difficult to access and only sparsely inhabited. Therefore, remote sensing techniques are the best option for the study of the changes and processes that are occurring. This Special Issue is dedicated to advancing our knowledge in remote sensing techniques for the analysis of polar regions. We call for papers to be submitted in the context of newly developed algorithms, the exploitation of remotely sensed data originating from satellites or aircrafts/UAVs, validation strategies, or the analysis of long time series within the polar regions. Relevant topics for this Special Issue include:

Newly developed algorithms for the analysis of remote sensing data within the polar regions;
Studies concerning long-term changes of glaciers, ice caps, sea ice, ice sheets, permafrost, and snow cover with a focus on the exploitation of remote sensing data;
The use of spaceborne as well as UAVs/airborne data for studying the polar regions;
Employing methods of artificial intelligence for studying/analyzing processes in the polar regions relying on remote sensing data as input;
Large-scale analysis of the polar regions utilizing big data and fully-automated processing techniques;
New possibilities in the remote sensing of sea ice through the fusion of different sensors.

Dr. Andreas J. Dietz
Dr. Sebastian Roessler
Dr. Celia Amélie Baumhoer
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.

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Keywords

antarctica
greenland
cryosphere
artificial intelligence
snow cover
snow hydrology
sea ice
permafrost
copernicus program
arctic environments

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Published Papers (12 papers)

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Research

28 pages, 8242 KiB

Open AccessArticle

A Circum-Arctic Monitoring Framework for Quantifying Annual Erosion Rates of Permafrost Coasts

by Marius Philipp, Andreas Dietz, Tobias Ullmann and Claudia Kuenzer

Remote Sens. 2023, 15(3), 818; https://doi.org/10.3390/rs15030818 - 31 Jan 2023

Viewed by 2768

Abstract

This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June–September for the years 2017–2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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33 pages, 17158 KiB

Open AccessArticle

Satellite-Derived Photosynthetically Available Radiation at the Coastal Arctic Seafloor

by Rakesh Kumar Singh, Anna Vader, Christopher J. Mundy, Janne E. Søreide, Katrin Iken, Kenneth H. Dunton, Laura Castro de la Guardia, Mikael K. Sejr and Simon Bélanger

Remote Sens. 2022, 14(20), 5180; https://doi.org/10.3390/rs14205180 - 17 Oct 2022

Cited by 7 | Viewed by 3842

Abstract

Climate change has affected the Arctic Ocean (AO) and its marginal seas significantly. The reduction of sea ice in the Arctic region has altered the magnitude of photosynthetically available radiation (PAR) entering the water column, impacting primary productivity. Increasing cloudiness in the atmosphere and rising turbidity in the coastal waters of the Arctic region are considered as the major factors that counteract the effect of reduced sea ice on underwater PAR. Additionally, extreme solar zenith angles and sea-ice cover in the AO increase the complexity of retrieving PAR. In this study, a PAR algorithm based on radiative transfer in the atmosphere and satellite observations is implemented to evaluate the effect of these factors on PAR in the coastal AO. To improve the performance of the algorithm, a flag is defined to identify pixels containing open-water, sea-ice or cloud. The use of flag enabled selective application of algorithms to compute the input parameters for the PAR algorithm. The PAR algorithm is validated using in situ measurements from various coastal sites in the Arctic and sub-Arctic seas. The algorithm estimated daily integrated PAR above the sea surface with an uncertainty of 19% in summer. The uncertainty increased to 24% when the algorithm was applied year-round. The PAR values at the seafloor were estimated with an uncertainty of 76%, with 36% of the samples under sea ice and/or cloud cover. The robust performance of the PAR algorithm in the pan-Arctic region throughout the year will help to effectively study the temporal and spatial variability of PAR in the Arctic coastal waters. The calculated PAR data are used to quantify the changing trend in PAR at the seafloor in the coastal AO with depth < 100 m using MODIS-Aqua data from 2003 to 2020. The general trends calculated using the pixels with average PAR > 0.415 mol m

^{- 2}

day

^{- 1}

at the seafloor during summer indicate that the annual average of PAR entering the water column in the coastal AO between 2003 and 2020 increased by 23%. Concurrently, due to increased turbidity, the attenuation in the water column increased by 22%. The surge in incident PAR in the water column due to retreating sea ice first led to increased PAR observed at the seafloor (∼12% between 2003 and 2014). However, in the last decade, the rapid increase in light attenuation of the water column has restricted the increase in average annual PAR reaching the bottom in the coastal AO. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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21 pages, 9356 KiB

Open AccessArticle

Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter

by Zeli Peng, Yinghui Ding, Ying Qu, Mengsi Wang and Xijia Li

Remote Sens. 2022, 14(18), 4538; https://doi.org/10.3390/rs14184538 - 11 Sep 2022

Cited by 5 | Viewed by 2046

Abstract

The melt pond fraction (MPF) is an important geophysical parameter of climate and the surface energy budget, and many MPF datasets have been generated from satellite observations. However, the reliability of these datasets suffers from short temporal spans and data gaps. To improve the temporal span and spatiotemporal continuity, we generated a long-term spatiotemporally continuous MPF dataset for Arctic sea ice, which is called the Northeast Normal University-melt pond fraction (NENU-MPF), from Moderate Resolution Imaging Spectroradiometer (MODIS) data. First, the non-linear relationship between the MODIS reflectance/geometries and the MPF was constructed using a genetic algorithm optimized back-propagation neural network (GA-BPNN) model. Then, the data gaps were filled and smoothed using a statistical-based temporal filter. The results show that the GA-BPNN model can provide accurate estimations of the MPF (R² = 0.76, root mean square error (RMSE) = 0.05) and that the data gaps can be efficiently filled by the statistical-based temporal filter (RMSE = 0.047; bias = −0.022). The newly generated NENU-MPF dataset is consistent with the validation data and with published MPF datasets. Moreover, it has a longer temporal span and is much more spatiotemporally continuous; thus, it improves our knowledge of the long-term dynamics of the MPF over Arctic sea ice surfaces. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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18 pages, 8931 KiB

Open AccessArticle

A Mid- and Long-Term Arctic Sea Ice Concentration Prediction Model Based on Deep Learning Technology

by Qingyu Zheng, Wei Li, Qi Shao, Guijun Han and Xuan Wang

Remote Sens. 2022, 14(12), 2889; https://doi.org/10.3390/rs14122889 - 16 Jun 2022

Cited by 10 | Viewed by 3160

Abstract

Mid- and long-term predictions of Arctic sea ice concentration (SIC) are important for the safety and security of the Arctic waterways. To date, SIC predictions mainly rely on numerical models, which have the disadvantages of a short prediction time and high computational complexity. Another common forecasting approach is based on a data-driven model, which is generally based on traditional statistical analysis or simple machine learning models, and achieves prediction by learning the relationships between data. Although the prediction performance of such methods has been improved in recent years, it is still difficult to find a balance between unstable model structures and complex spatio-temporal data. In this study, a classical statistical method and a deep learning model are combined to construct a data-driven rolling forecast model of SIC in the Arctic, named the EOF–LSTM–DNN (abbreviated as ELD) model. This model uses the empirical orthogonal function (EOF) method to extract the temporal and spatial features of the Arctic SIC, then the long short-term memory (LSTM) network is served as a feature extraction tool to effectively encode the time series, and, finally, the feature decoding is realized by the deep neural network (DNN). Comparisons of the model with climatology results, persistence predictions, other data-driven model results, and the hybrid coordinate ocean model (HYCOM) forecasts show that the ELD model has good prediction performance for the Arctic SIC on mid- and long-term time scales. When the forecast time is 100 days, the forecast root-mean-square error (RMSE), Pearson correlation coefficient (PCC), and anomaly correlation coefficient (ACC) of the ELD model are 0.2, 0.77, and 0.74, respectively. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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22 pages, 7335 KiB

Open AccessArticle

Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery

by Elias Manos, Chandi Witharana, Mahendra Rajitha Udawalpola, Amit Hasan and Anna K. Liljedahl

Remote Sens. 2022, 14(11), 2719; https://doi.org/10.3390/rs14112719 - 6 Jun 2022

Cited by 7 | Viewed by 3510

Abstract

Rapid global warming is catalyzing widespread permafrost degradation in the Arctic, leading to destructive land-surface subsidence that destabilizes and deforms the ground. Consequently, human-built infrastructure constructed upon permafrost is currently at major risk of structural failure. Risk assessment frameworks that attempt to study this issue assume that precise information on the location and extent of infrastructure is known. However, complete, high-quality, uniform geospatial datasets of built infrastructure that are readily available for such scientific studies are lacking. While imagery-enabled mapping can fill this knowledge gap, the small size of individual structures and vast geographical extent of the Arctic necessitate large volumes of very high spatial resolution remote sensing imagery. Transforming this ‘big’ imagery data into ‘science-ready’ information demands highly automated image analysis pipelines driven by advanced computer vision algorithms. Despite this, previous fine resolution studies have been limited to manual digitization of features on locally confined scales. Therefore, this exploratory study serves as the first investigation into fully automated analysis of sub-meter spatial resolution satellite imagery for automated detection of Arctic built infrastructure. We tasked the U-Net, a deep learning-based semantic segmentation model, with classifying different infrastructure types (residential, commercial, public, and industrial buildings, as well as roads) from commercial satellite imagery of Utqiagvik and Prudhoe Bay, Alaska. We also conducted a systematic experiment to understand how image augmentation can impact model performance when labeled training data is limited. When optimal augmentation methods were applied, the U-Net achieved an average F1 score of 0.83. Overall, our experimental findings show that the U-Net-based workflow is a promising method for automated Arctic built infrastructure detection that, combined with existing optimized workflows, such as MAPLE, could be expanded to map a multitude of infrastructure types spanning the pan-Arctic. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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12 pages, 5559 KiB

Open AccessCommunication

Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems

by Jin Ye, Lei Liu, Yi Wu, Wanying Yang and Hong Ren

Remote Sens. 2022, 14(9), 2126; https://doi.org/10.3390/rs14092126 - 28 Apr 2022

Cited by 1 | Viewed by 2027

Abstract

Blowing snow is a common weather phenomenon in Antarctica and plays an important role in the water vapor cycle and ice sheet mass balance. Although it has a significant impact on the climate of Antarctica, people do not know much about this process. Fog events are difficult to distinguish from blowing snow events using existing detection algorithms by a ceilometer. In this study, based on ceilometer, the meteorological parameters observed by surface meteorology systems are further combined to detect blowing snow and fog using the AdaBoost algorithm. The weather phenomena recorded by human observers are ‘true’. The dataset is collected from 1 January 2016 to 31 December 2016 at the AWARE site. Among them, three-quarters of the data are used as the training set and the rest of the data as the testing set. The classification accuracy of the proposed algorithm for the testing set is about 94%. Compared with the Loeb method, the proposed algorithm can detect 89.12% of blowing snow events and 76.10% of fog events, while the Loeb method can only identify 64.29% of blowing snow events and 31.87% of fog events. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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19 pages, 10264 KiB

Open AccessArticle

Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data

by Saurabh Kaushik, Tejpal Singh, Anshuman Bhardwaj, Pawan K. Joshi and Andreas J. Dietz

Remote Sens. 2022, 14(6), 1352; https://doi.org/10.3390/rs14061352 - 10 Mar 2022

Cited by 14 | Viewed by 4481

Abstract

High-mountain glaciers can be covered with varying degrees of debris. Debris over glaciers (supraglacial debris) significantly alter glacier melt, velocity, ice geometry, and, thus, the overall response of glaciers towards climate change. The accumulated supraglacial debris impedes the automated delineation of glacier extent owing to its similar reflectance properties with surrounding periglacial debris (debris aside the glaciated area). Here, we propose an automated scheme for supraglacial debris mapping using a synergistic approach of deep learning and multisource remote sensing data. A combination of multisource remote sensing data (visible, near-infrared, shortwave infrared, thermal infrared, microwave, elevation, and surface slope) is used as input to a fully connected feed-forward deep neural network (i.e., deep artificial neural network). The presented deep neural network is designed by choosing the optimum number and size of hidden layers using the hit and trial method. The deep neural network is trained over eight sites spread across the Himalayas and tested over three sites in the Karakoram region. Our results show 96.3% accuracy of the model over test data. The robustness of the proposed scheme is tested over 900 km² and 1710 km² of glacierized regions, representing a high degree of landscape heterogeneity. The study provides proof of the concept that deep neural networks can potentially automate the debris-covered glacier mapping using multisource remote sensing data. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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26 pages, 10119 KiB

Open AccessArticle

Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions

by Michael Allan Merchant, Mayah Obadia, Brian Brisco, Ben DeVries and Aaron Berg

Remote Sens. 2022, 14(5), 1123; https://doi.org/10.3390/rs14051123 - 24 Feb 2022

Cited by 14 | Viewed by 6239

Abstract

Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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17 pages, 33579 KiB

Open AccessArticle

Application of Machine Learning for Simulation of Air Temperature at Dome A

by Xiaoping Pang, Chuang Liu, Xi Zhao, Bin He, Pei Fan, Yue Liu, Meng Qu and Minghu Ding

Remote Sens. 2022, 14(4), 1045; https://doi.org/10.3390/rs14041045 - 21 Feb 2022

Cited by 2 | Viewed by 3981

Abstract

Dome A is the summit of the Antarctic plateau, where the Chinese Kunlun inland station is located. Due to its unique location and high altitude, Dome A provides an important observatory site in analyzing global climate change. However, before the arrival of the Chinese Antarctic expedition in 2005, near-surface air temperatures had not been recorded in the region. In this study, we used meteorological parameters, such as ice surface temperature, radiation, wind speed, and cloud type, to build a reliable model for air temperature estimation. Three models (linear regression, random forest, and deep neural network) were developed based on various input datasets: seasonal factors, skin temperature, shortwave radiation, cloud type, longwave radiation from AVHRR-X products, and wind speed from MERRA-2 reanalysis data. In situ air temperatures from 2010 to 2015 were used for training, while 2005–2009 and 2016–2020 measurements were used for model validation. The results showed that random forest and deep neural network outperformed the linear regression model. In both methods, the 2005–2009 estimates (average bias = 0.86 °C and 1 °C) were more accurate than the 2016–2020 values (average bias = 1.04 °C and 1.26 °C). We conclude that the air temperature at Dome A can be accurately estimated (with an average bias less than 1.3 °C and RMSE around 3 °C) from meteorological parameters using random forest or a deep neural network. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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15 pages, 5206 KiB

Open AccessArticle

Revisiting Ice Flux and Mass Balance of the Lambert Glacier–Amery Ice Shelf System Using Multi-Remote-Sensing Datasets, East Antarctica

by Derui Xu, Xueyuan Tang, Shuhu Yang, Yun Zhang, Lijuan Wang, Lin Li and Bo Sun

Remote Sens. 2022, 14(2), 391; https://doi.org/10.3390/rs14020391 - 14 Jan 2022

Cited by 3 | Viewed by 2597

Abstract

Due to rapid global warming, the relationship between the mass loss of the Antarctic ice sheet and rising sea levels are attracting widespread attention. The Lambert–Amery glacial system is the largest drainage system in East Antarctica, and its mass balance has an important influence on the stability of the Antarctic ice sheet. In this paper, the recent ice flux in the Lambert Glacier of the Lambert–Amery system was systematically analyzed based on recently updated remote sensing data. According to Landsat-8 ice velocity data from 2018 to April 2019 and the updated Bedmachine v2 ice thickness dataset in 2021, the contribution of ice flux approximately 140 km downstream from Dome A in the Lambert Glacier area to downstream from the glacier is 8.5 ± 1.9

Gt \cdot a^{- 1}

, and the ice flux in the middle of the convergence region is 18.9 ± 2.9

Gt \cdot a^{- 1}

. The ice mass input into the Amery ice shelf through the grounding line of the whole glacier is 19.9 ± 1.3

Gt \cdot a^{- 1}

. The ice flux output from the mainstream area of the grounding line is 19.3 ± 1.0

Gt \cdot a^{- 1}

. Using the annual SMB data of the regional atmospheric climate model (RACMO v2.3) as the quality input, the mass balance of the upper, middle, and lower reaches of the Lambert Glacier was analyzed. The results show that recent positive accumulation appears in the middle region of the glacier (about 74–78°S, 67–85°E) and the net accumulation of the whole glacier is 2.4 ± 3.5

Gt \cdot a^{- 1}

. Although the mass balance of the Lambert Glacier continues to show a positive accumulation, and the positive value in the region is decreasing compared with values obtained in early 2000. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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19 pages, 10541 KiB

Open AccessArticle

A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data

by Lilian-Maite Lezama Valdes, Marwan Katurji and Hanna Meyer

Remote Sens. 2021, 13(22), 4673; https://doi.org/10.3390/rs13224673 - 19 Nov 2021

Cited by 5 | Viewed by 3473

Abstract

To monitor environmental and biological processes, Land Surface Temperature (LST) is a central variable, which is highly variable in space and time. This particularly applies to the Antarctic Dry Valleys, which host an ecosystem highly adapted to the extreme conditions in this cold desert. To predict possible climate induced changes on the Dry Valley ecosystem, high spatial and temporal resolution environmental variables are needed. Thus we enhanced the spatial resolution of the MODIS satellite LST product that is sensed sub-daily at a 1 km spatial resolution to a 30 m spatial resolution. We employed machine learning models that are trained using Landsat 8 thermal infrared data from 2013 to 2019 as a reference to predict LST at 30 m resolution. For the downscaling procedure, terrain derived variables and information on the soil type as well as the solar insolation were used as potential predictors in addition to MODIS LST. The trained model can be applied to all available MODIS scenes from 1999 onward to develop a 30 m resolution LST product of the Antarctic Dry Valleys. A spatio-temporal validation revealed an R

^{2}

of 0.78 and a RMSE of 3.32

^{\circ}

C. The downscaled LST will provide a valuable surface climate data set for various research applications, such as species distribution modeling, climate model evaluation, and the basis for the development of further relevant environmental information such as the surface moisture distribution. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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24 pages, 8981 KiB

Open AccessArticle

DTN Trustworthiness for Permafrost Telemetry IoT Network

by Adrià Mallorquí, Agustín Zaballos and Alan Briones

Remote Sens. 2021, 13(22), 4493; https://doi.org/10.3390/rs13224493 - 9 Nov 2021

Cited by 6 | Viewed by 2520

Abstract

The SHETLAND-NET research project aims to build an Internet of Things (IoT) telemetry service in Antarctica to automatize the data collection of permafrost research studies on interconnecting remote wireless sensor networks (WSNs) through near vertical incidence skywave (NVIS) long fat networks (LFN). The proposed architecture presents some properties from challenging networks that require the use of delay tolerant networking (DTN) opportunistic techniques that send the collected data during the night as a bulk data transfer whenever a link comes available. This process might result in network congestion and packet loss. This is a complex architecture that demands a thorough assessment of the solution’s viability and an analysis of the transport protocols in order to find the option which best suits the use case to achieve superior trustworthiness in network congestion situations. A heterogeneous layer-based model is used to measure and improve the trustworthiness of the service. The scenario and different transport protocols are modeled to be compared, and the system’s trustworthiness is assessed through simulations. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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