Spring melt pond fraction in the Canadian Arctic Archipelago predicted from RADARSAT-2

Howell, Stephen E. L.; Scharien, Randall K.; Landy, Jack; Brady, Michael S.

doi:10.5194/tc-14-4675-2020

Cited by 6 publications

(12 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This decrease in the surface albedo can lead to more absorption of solar radiation, which can further amplify the climate change in the Arctic via the sea-ice feedback mechanism [18]. It has also been reported that the MPF is considered to be a good predictor for the Arctic sea ice extent in September and can be used to improve forecasts of seasonal sea ice changes and climate models [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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

Peng

Ding

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

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 = −0.002). 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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Peng

Ding

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Polashenski et al, 2012). More recently it has been used to improve forecasts of seasonal ice changes and has been reported to be a good predictor for the Arctic sea ice extent in September (Flocco et al, 2012;Schröder et al, 2014;Howell et al, 2020;Ding et al, 2020a;Feng et al, 2021), which underscores the importance of monitoring the evolution of MPF over the Arctic sea ice. Melt pond fraction is defined as the ponded area relative to the sea ice, or in other words, fraction of sea ice covered by melt ponds (Webster et al, 2015;Perovich et al, 2002), and can be estimated following equation:…”

Section: Melt Pond Fraction (Mpf) Satellite-based Retrievalsmentioning

confidence: 99%

“…Although MPF optical-based retrieval outnumbers radar-based MPF retrievals, few MPF retrievals have been developed as well for radar sensors, such as SAR (e.g. Fors et al, 2017;Li et al, 2017;Scharien et al, 2017;Howell et al, 2020) followed 615 by passive microwave data (e.g. Tanaka et al, 2016;Tanaka and Scharien, 2022) (Table 1).…”

Section: Radar Data Traditional Approachesmentioning

confidence: 99%

“…Yackel and Barber (2000) investigated the evolution of melt pond coverage in Canadian islands using RADARSAT-1 SAR images, and determined that backscattering coefficient was significantly correlated with MPF. Howell et al (2020) used RADARSAT-2 to map the MPF on Canadian islands from 2009 to 2018 based on this relationship. In fact, this was one of the few studies to have used radar based for direct MPF retrieval (Mäkynen et al, 2014;Scharien et al, 2017;Howell et al, 2020).…”

Section: Radar Data Traditional Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Review Article: Earth observations of Melt Ponds on Sea Ice

Aparício¹

2023

Preprint

View full text Add to dashboard Cite

Abstract. Melt ponds are pools of open water that form during summer on sea ice surface, playing a key role in the Arctic climate and sea ice energy budget. Due to their lower albedo, melt ponds absorb more radiation than un-ponded ice, spurring further ice melt and enhancing the positive ice-albedo feedback. This feedback is expected to increase as the Arctic ice cover is moving towards a regime where multiyear ice (MYI) is being replaced by first-year ice (FYI), which presents wider melt pond coverage as well as more energy absorption with profound consequences from an energy balance perspective. Nevertheless, the lack of knowledge or inclusion of melt pond fraction (MPF) on global climate and sea ice models, is pointed as their main source of uncertainty and disparity of predictions results. This, along with the recent conclusions on the potential of MPF for enhancing the forecasting ability of summer sea ice extent, underscores the importance of accurately obtaining large and spatiotemporal scale of MPF, across the Arctic. However, observations of melt ponds are far from adequate for the Arctic ocean and for both MYI and FYI, and on a large scale this is only possible through satellite-based Earth observations (EO). This paper provides an overview of efforts in EO remote sensing studies of melt ponds, for both optical sensors and radar-based approaches. The main algorithms used for melt pond identification and the different methods for MPF retrievals are outlined, ranging from the early traditional techniques to the increasingly prevalent use of Artificial intelligence (AI), namely machine and deep learning. The current large-scale optical-based pan-Arctic MPF datasets are intercompared along with the main advantages and disadvantages of various optical and radar data-based methods for MPF retrievals. The potential of radar, namely Synthetic Aperture Radar (SAR) technical abilities to the enhancement of reliability is analysed, since optical approaches, despite being more used, are hampered by cloud cover, spectral representativeness and resolution. Finally, current gaps in melt pond knowledge and MPF retrievals are discussed and summarised leading to the outline of further directions of research development.

show abstract

“…The available SAR MPF retrievals (Scharien et al, 2017;Han et al, 2016;Fors et al, 2017;Li et al, 2017;Ramjan et al, 2018;Howell et al, 2020), in addition to limitations on the spatial coverage, are also affected by the inability to distinguish melt ponds and open water due to their equally low backscatter signal. In general case, an unknown sea ice surface roughness has to be resolved from an unknown water/melt pond surface roughness, and the angular backscatter dependency delivers additional challenges in terms of signal-to-noise ratio.…”

Section: Introductionmentioning

confidence: 99%

Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data

Istomina,

Niehaus,

Spreen

2023

Preprint

View full text Add to dashboard Cite

Abstract. Melt ponds on the Arctic sea ice affect the radiative balance of the region as they introduce darkening of the sea ice during the Arctic summer. Temporal and spatial extent of the ponding as well as its amplitude reflect the state of the Arctic sea ice and are important for our understanding of the Arctic sea ice change. Remote sensing retrievals of melt pond fraction (MPF) provide information both on the present state of the melt pond development as well as its change throughout the years, which is a valuable information in the context of climate change and Arctic amplification. In this work, we transfer the earlier published Melt Pond Detector remote sensing retrieval (MPD) to the Ocean and Land Colour Instrument (OLCI) data onboard the Sentinel-3 satellite and so complement the existing Medium Resolution Imaging Spectrometer (MERIS) MPF dataset (2002–2011) from Environmental Satellite (ENVISAT) with the recent data (2017–present). To evaluate the bias of the MPF product, comparisons to Sentinel-2 MultiSpectral Instrument (MSI) high resolution satellite imagery are presented, in addition to earlier published validation studies. Both MERIS and OLCI MPD tend to overestimate the small MPFs, which can be attributed to the presence of water saturated snow and sea ice before melt onset. Good agreement for middle range MPF is observed, and the areas of exceptionally high MPF = 100 % are recognized as well. The earlier published MERIS MPFs (2002–2011) were reprocessed using an improved cloud clearing routine and together with the recent Sentinel-3 data provide an internally consistent dataset, which allows to analyse the MPF development in the past 20 years. Although the total summer hemispheric MPF trend is moderate with +0.75 % per decade, the regional weekly MPF trends display pronounced dynamic and range from −10 % to as high as +20 % per decade, depending on the region. We conclude on the following effects: the global Arctic melt onset shifted towards spring by at least 2 weeks, with the melt onset happening in late May in the recent years as compared to early-mid June in the beginning of the dataset. there is a change of the melt onset regime in the recent years, with East Siberian and Laptev Sea dominating the melt onset and not the Beaufort Gyre region as before. the Central Arctic, North Greenland and CAA show signs of increasing first year ice (FYI) fraction in the recent years. The daily gridded MPF averages are available at the webpage of the Institute of Environmental Physics, University of Bremen, as a historic dataset for the ENVISAT data, and as ongoing operational processing for the Sentinel-3 data.

show abstract

Spring melt pond fraction in the Canadian Arctic Archipelago predicted from RADARSAT-2

Cited by 6 publications

References 51 publications

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

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

Review Article: Earth observations of Melt Ponds on Sea Ice

Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data

Contact Info

Product

Resources

About