Assessment of the AMSR-E Sea Ice-Concentration Product at the Ice Edge Using RADARSAT-1 and MODIS Imagery

Heinrichs, J.; Cavalieri, Donald J.; Markus, T.

doi:10.1109/tgrs.2006.880622

Cited by 52 publications

(37 citation statements)

References 33 publications

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“…Extensive field work has been conducted in the Barrow area (e.g., George et al [13], Eicken et al [64], Druckenmiller et al [14]), including collection of snow depth data, laser profilometer data and other aerial observations as part of the NASA AMSR-E validation campaigns for sea ice products in March 2003 and 2006, AMSRIce03 and AMSRIce06 (see Herzfeld et al [31], Maslanik et al [63], Cavalieri et al [65], Sturmet al [66], Markus et al [67], Heinrichs et al [68], Rivas et al [69], Stroeve et al [70]). …”

Section: In-situ Observations and Related Data Analysis Resultant Fromentioning

confidence: 99%

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

et al. 2016

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Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line.

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Section: In-situ Observations and Related Data Analysis Resultant Fromentioning

confidence: 99%

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

et al. 2016

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“…These distance measures are sensitive to the difference in shape and extent of objects and assess the distance/ displacement between forecast and observation features. Several studies have exploited these metrics for spatial verification of precipitation forecasts (Schwedler and Baldwin 2011;Gilleland 2011Gilleland , 2017Gilleland et al 2008;Venugopal et al 2005;Zhu et al 2011) and sea ice prediction (Hebert et al 2015;Heinrichs et al 2006;Dukhovskoy et al 2015).…”

Section: The Setup Of the Mesovict Projectmentioning

confidence: 99%

The Setup of the MesoVICT Project

Dorninger

Gilleland

Casati

et al. 2018

Bulletin of the American Meteorological Society

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MesoVICT focuses on the application, capability, and enhancement of spatial verification methods as applied to deterministic and ensemble forecasts of precipitation, wind, and temperature over complex terrain and includes observation uncertainty assessment. THE SETUP OF THE MesoVICT PROJECTManfreD Dorninger, eriC gillelanD, BarBara Casati, Marion P. MitterMaier, elizaBeth e. eBert, BarBara g. Brown, anD laurenCe J. wilson A s numerical weather prediction (NWP) models began to increase considerably in resolution, it became clear that traditional gridpoint-bygridpoint verification methods did not provide adequate or sufficient diagnostic information about forecast performance for some users (e.g., Mass et al. 2002). Double penalties arising from spatial displacement (or perhaps timing) errors and more rapid growth of small-scale errors often result in poorer performance scores for higher-resolution forecasts than their coarser counterparts, even when subjective evaluation would support the higher-resolution models as being better. Subsequently, a host of new verification methods were developed very rapidly (Ebert and McBride 2000;Harris et al. 2001;Casati et al. 2004;Nachamkin 2004;Davis et al. 2006;Keil and Craig 2007;Roberts and Lean 2008;Marzban et al. 2009;Gilleland et al. 2010b), which we will refer to as spatial methods. There still are gaps in our understanding when it comes to interpreting what all the new spatial methods tell us; gaining an in-depth understanding of forecast performance depends on grasping the full meaning of the verification results. Furthermore, the investment required to implement a new spatial method is relatively high compared to traditional verification methods, making it important to have some criteria on which to decide which methods would best suit a particular user's need(s). Therefore, a spatial methods meta-verification, or intercomparison, project was created to try to answer some of these questions.The first spatial verification methods intercomparison project (ICP;Gilleland et al. 2009Gilleland et al. , 2010a was initiated in 2007 with the aim of better understanding the rapidly increasing literature concerning new spatial verification methods, and several questions were addressed:• How does each method inform about forecast performance overall?• Which aspects of forecast error does each method best identify (e.g., location error, scale dependence of the skill, etc.)? • Which methods yield identical information to each other and which methods provide complementary information?The aim of the ICP was to analyze the behavior and provide a structured and systematic cataloging of the existing spatial verification methods, with the final goal of providing guidance to the users on which methods are best for which purpose. The project focused on prescribed errors for idealized cases and quantitative precipitation forecasts, where the ability of the verification methods to diagnose the known error was assessed (Ahijevych et al. 2009). For this first intercomparison, complex terrain ...

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“…The visible sensors usually achieve a high resolution that reduces land contamination and enables finer estimation of ice presence in narrow passages and archipelago regions. Although highly affected by cloud cover and daylight conditions [18], visible and TIR data are often used to assess the quality and accuracy of SAR and passive microwave maps [19][20][21][22]. Different sea ice mapping approaches are used with optical sensors such as MODIS [9,[23][24][25] and VIIRS [26].…”

Section: Introductionmentioning

confidence: 99%

IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data

et al. 2017

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Abstract:The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3-7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a majority filter, makes the sea ice presence map even more accurate by filtering out the effects of isolated classification errors. The synthesis maps show spatial consistency through time when compared to passive microwave and national ice services maps.

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Assessment of the AMSR-E Sea Ice-Concentration Product at the Ice Edge Using RADARSAT-1 and MODIS Imagery

Cited by 52 publications

References 33 publications

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

The Setup of the MesoVICT Project

IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data

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