The melt pond fraction and spectral sea ice albedo retrieval from MERIS data: validation and trends of sea ice albedo and melt pond fraction in the Arctic for years 2002–2011

Istomina, Larysa; Heygster, Georg; Huntemann, Marcus; Schwarz, Pascal; Birnbaum, Gerit; Scharien, R.; Polashenski, Chris; Perovich, Donald K.; Zege, Eleonora P.; Malinka, Aleksey; Prikhach, Alexander S.; Katsev, I. L.

doi:10.5194/tcd-8-5227-2014

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…The areal fraction data of sea ice surface types presented in this paper can be used to estimate the albedos of the investigated regions assuming arbitrary values for each surface type. These datasets can also validate and assess recently developed algorithms for retrieving melt pond fraction from air- and space-borne optical sensors (Rösel and others, 2012; Istomina and others, 2014).…”

Section: Discussionmentioning

confidence: 75%

Melt pond distribution and geometry in high Arctic sea ice derived from aerial investigations

Huang¹,

Lü

Lei

et al. 2016

Ann. Glaciol.

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ABSTRACT. Aerial photography was conducted in the high Arctic Ocean during a Chinese research expedition in summer 2010. By partitioning the images into three distinct surface categories (sea ice/ snow, water and melt ponds), the areal fraction of each category, ice concentration and the size and geometry of individual melt ponds, are determined with high-spatial resolution. The ice concentration and melt pond coverage have large spatial deviations between flights and even between images from the marginal ice zone to the pack ice zone in the central Arctic. Ice concentration and pond coverage over high Arctic (from 84°N to north) was ∼75% and ∼6.8%, respectively, providing 'ground truth' for the unusual transpolar reduction strip of ice indicated concurrently by AMSR-E data and for the regions (north of 88°N) where no passive microwave sensors can cover. Melt pond size and shape distributions are examined in terms of pond area (S), perimeter (P), mean caliper dimension (MCD) (L), roundness (R), convex degree (C), the ratio of P/S and fractal dimension (D). Power-law relationships are developed between pond size and number. Some general trends in geometric metrics are identified as a function of pond area including R, C, P/S and D. The scale separation of pond complexity is demonstrated by analyzing area-perimeter data. The results will potentially help the modelling of melt pond evolution and the determination of heterogeneity of under-ice transmitted light fields.

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Section: Discussionmentioning

confidence: 75%

Melt pond distribution and geometry in high Arctic sea ice derived from aerial investigations

Huang¹,

Lü

Lei

et al. 2016

Ann. Glaciol.

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“…The experimental spectra of ice and melt ponds used are taken from the databases of the Cold Regions Research and Engineering Laboratory (CRREL, USA) (Polashenski et al, 2012) and from the Polarstern expedition 2012 measurements (University of Bremen) (Istomina, Nicolaus, et al, 2013). The algorithm verification with the in situ measurements and aerial data is published separately (Istomina et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

“…The comparison is most informative for the cases of landfast or multiyear sea ice (high ice concentration, no thrashed overturned ice floes) for the situation where ponds are surrounded with white ice. The full MPD validation description can be found in (Istomina et al, 2014). This Section presents a typical example of the validation efforts.…”

Section: Validation With the Field Datamentioning

confidence: 99%

Algorithm to retrieve the melt pond fraction and the spectral albedo of Arctic summer ice from satellite optical data

Zege

Malinka

Katsev

et al. 2015

Remote Sensing of Environment

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A new algorithm to retrieve characteristics (albedo and melt pond fraction) of summer ice in the Arctic from optical satellite data is described. In contrast to other algorithms this algorithm does not use a priori values of the spectral albedo of the sea-ice constituents (such as melt ponds, white ice etc.). Instead, it is based on an analytical solution for the reflection from sea ice surface. The algorithm includes the correction of the sought-for ice and ponds characteristics with the iterative procedure based on the Newton-Raphson method. Also, it accounts for the bi-directional reflection from the ice/snow surface, which is particularly important for Arctic regions where the sun is low. The algorithm includes an original procedure for the atmospheric correction, as well. This algorithm is implemented as computer code called Melt Pond Detector (MPD). The input to the current version of the MPD algorithm is the MERIS Level 1B data, including the radiance coefficients at ten wavelengths and the solar and observation angles (zenith and azimuth). Also, specific parameters describing surface and atmospheric state can be set in a configuration input file. The software output is the map of the melt ponds area fraction and the spectral albedo of sea-ice in HDF5 format. The numerical verification shows that the MPD algorithm provides more accurate results for the light ponds than for the dark ones. The spectral albedo is retrieved with high accuracy for any type of ice and ponds.

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“…Additionally, ice spectra reflect most in the blue, whereas water spectra tend to reflect most in the green portion of the spectrum. Cluster centroids match well with reflectance spectra of various ice and water types that have been directly measured in the central Arctic Ocean (Istomina et al, 2016) and in the mouth of the Mackenzie River (Klein et al, 2021). C1 was most similar to fine white ice, while C3 resembled ridged ice.…”

Section: Clustering Oli Imagery During the Sagavanirktok Spring Freshetsupporting

confidence: 68%

“…In situ reflectance (dotted lines) shown in cluster 1,3, and 4 are taken from Istomina et al (2016) in the PANGEA database and are representative of 'fine white ice (260812purice1e00000)', 'ice ridge (110812ROVtransect17e00000)', and 'dark pond with transparent layer of white ice (150812ROV67pbwe00000)', respectively. The in situ reflectance shown in cluster 2 is representative of open water and was measured 50 m offshore near Herschel Island Qikiqtaruk at the mouth of the Mackenzie River in Summer 2019 (Klein et al, 2021)…”

Section: List Of Figuresmentioning

confidence: 99%

Interactions between rivers and coastal margins in the Alaskan Arctic

Catipovic

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Arctic coastal margins serve as the interface between Arctic terrestrial and oceanic carbon reservoirs. Characterized by extreme seasonality and annual variability, Alaskan Arctic coastal margins only receive riverine input of terrestrial organic material from late spring through late summer. This work examines the delivery, transformation, and distribution of organic material within Stefansson Sound, an Alaskan river-fed estuary, at the beginning and end of this period. We found that during the spring freshet, the landfast icescape over Stefansson Sound is composed of five ice and water features that are optically unique in terms of their surface reflectance spectra. One of these features is representative of freshwater flooding on ice which was found to persist in Stefansson Sound until late July. This floodwater is characterized by decreased reflectance in the blue portion of the visible light spectrum which indicates strong absorption by organic material contained within the freshet waters. Alaskan Arctic rivers drain watersheds composed of mixed proportions of tundra and mountains. During the freshet, organic material leached from the thawed soil surface horizons in the tundra is more available for microbial consumption over short periods than organic material originating in the mountains. This labile material is estimated to be higher molecular weight and more aromatic according to absorbance and fluorescence optical proxies. Fluorescence analysis revealed that tundra water contained more humic material and less amino acids when compared to water originating in the Brooks Range. This result challenges traditional assumptions that high molecular weight material in Arctic fluvial networks is refractory. In late summer, distributions of organic material in the surface waters of Stefansson Sound are driven by meteorological conditions such as precipitation over the watersheds and wind direction and speed in the estuary. Increased riverine discharge results in higher concentrations of organic material in the surface waters while faster wind speeds create deeper, well-mixed riverine plumes. Strong linear relationships between salinity and organic material indicate conservative mixing of freshwater in Stefansson sound, while new relationships between optical properties of water and organic material provide avenues forward for monitoring future climate driven changes regarding the biogeochemistry of this delicate ecosystem.

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The melt pond fraction and spectral sea ice albedo retrieval from MERIS data: validation and trends of sea ice albedo and melt pond fraction in the Arctic for years 2002–2011

Cited by 7 publications

References 21 publications

Melt pond distribution and geometry in high Arctic sea ice derived from aerial investigations

Melt pond distribution and geometry in high Arctic sea ice derived from aerial investigations

Algorithm to retrieve the melt pond fraction and the spectral albedo of Arctic summer ice from satellite optical data

Interactions between rivers and coastal margins in the Alaskan Arctic

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