Articles | Volume 9, issue 4
https://doi.org/10.5194/tc-9-1551-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-9-1551-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
12 Aug 2015
Research article |
| 12 Aug 2015
Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data
L. Istomina
CORRESPONDING AUTHOR
Institute of Environmental Physics, University of Bremen, Bremen, Germany
G. Heygster
Institute of Environmental Physics, University of Bremen, Bremen, Germany
M. Huntemann
Institute of Environmental Physics, University of Bremen, Bremen, Germany
P. Schwarz
Department of Environmental Meteorology, University of Trier, Trier, Germany
G. Birnbaum
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
R. Scharien
Department of Geography, University of Victoria, Victoria, Canada
C. Polashenski
Cold Regions Research and Engineering Laboratory, Engineer Research and Development Center, Hanover, New Hampshire, USA
D. Perovich
Cold Regions Research and Engineering Laboratory, Engineer Research and Development Center, Hanover, New Hampshire, USA
E. Zege
B. I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus
A. Malinka
B. I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus
A. Prikhach
B. I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus
I. Katsev
B. I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus
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Cited
55 citations as recorded by crossref.
- Broadband albedo of Arctic sea ice from MERIS optical data C. Pohl et al. https://doi.org/10.5194/tc-14-165-2020
- Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data L. Istomina et al. https://doi.org/10.5194/tc-19-83-2025
- Universality in azimuthal asymmetry of anisotropic sea-ice reflectance V. Marinyuk & S. Sheberstov https://doi.org/10.1364/JOSAA.467427
- Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2 E. Buckley et al. https://doi.org/10.5194/tc-17-3695-2023
- Estimating Arctic Sea Ice Thickness with CryoSat-2 Altimetry Data Using the Least Squares Adjustment Method F. Xiao et al. https://doi.org/10.3390/s20247011
- Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data H. Han et al. https://doi.org/10.3390/rs8010057
- An improved radiative forcing scheme for better representation of Arctic under-ice blooms Y. Gao et al. https://doi.org/10.1016/j.ocemod.2022.102075
- Improved cloud detection over sea ice and snow during Arctic summer using MERIS data L. Istomina et al. https://doi.org/10.5194/amt-13-6459-2020
- Impact of a Surface Ice Lid on the Optical Properties of Melt Ponds P. Lu et al. https://doi.org/10.1029/2018JC014161
- Melt pond detection on landfast sea ice using dual co-polarized Ku-band backscatter T. Geldsetzer et al. https://doi.org/10.1016/j.rse.2023.113725
- Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data Y. Tanaka https://doi.org/10.3390/rs12061033
- New insight from CryoSat-2 sea ice thickness for sea ice modelling D. Schröder et al. https://doi.org/10.5194/tc-13-125-2019
- Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data T. Alekseeva et al. https://doi.org/10.3390/rs11212481
- Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic J. Zhao et al. https://doi.org/10.3390/rs13193882
- Reflective properties of melt ponds on sea ice A. Malinka et al. https://doi.org/10.5194/tc-12-1921-2018
- On the importance to consider the cloud dependence in parameterizing the albedo of snow on sea ice L. Foth et al. https://doi.org/10.5194/tc-18-4053-2024
- Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery M. König et al. https://doi.org/10.3390/rs12162623
- Winter Sentinel‐1 Backscatter as a Predictor of Spring Arctic Sea Ice Melt Pond Fraction R. Scharien et al. https://doi.org/10.1002/2017GL075547
- Mosaicking Opportunistically Acquired Very High-Resolution Helicopter-Borne Images over Drifting Sea Ice Using COTS Sensors C. Hyun et al. https://doi.org/10.3390/s19051251
- Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters S. Ramjan et al. https://doi.org/10.3390/rs10101603
- Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010–2018 Y. Xiu et al. https://doi.org/10.1007/s13131-020-1646-5
- Arctic summer sea ice phenology including ponding from 1982 to 2017 X. Chen et al. https://doi.org/10.1007/s13131-022-1993-5
- A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation Y. Zhou et al. https://doi.org/10.5194/tc-17-1053-2023
- Detecting Melt Pond Onset on Landfast Arctic Sea Ice Using a Dual C-Band Satellite Approach S. Maknun et al. https://doi.org/10.3390/rs16122091
- A spectral mixture analysis approach to quantify Arctic first-year sea ice melt pond fraction using QuickBird and MODIS reflectance data J. Yackel et al. https://doi.org/10.1016/j.rse.2017.09.030
- Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery S. Lee et al. https://doi.org/10.1016/j.rse.2020.111919
- Incidence Angle Dependencies for C-Band Backscatter From Sea Ice During Both the Winter and Melt Season T. Geldsetzer & S. Howell https://doi.org/10.1109/TGRS.2023.3315056
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. https://doi.org/10.1016/j.scib.2018.11.018
- The frequency and extent of sub-ice phytoplankton blooms in the Arctic Ocean C. Horvat et al. https://doi.org/10.1126/sciadv.1601191
- Observations and modeling of areal surface albedo and surface types in the Arctic E. Jäkel et al. https://doi.org/10.5194/tc-18-1185-2024
- Regions of open water and melting sea ice drive new particle formation in North East Greenland M. Dall´Osto et al. https://doi.org/10.1038/s41598-018-24426-8
- Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network Y. Ding et al. https://doi.org/10.3390/rs12172746
- 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 Z. Peng et al. https://doi.org/10.3390/rs14184538
- Sea Ice Melt Pond Fraction Derived From Sentinel‐2 Data: Along the MOSAiC Drift and Arctic‐Wide H. Niehaus et al. https://doi.org/10.1029/2022GL102102
- Linking winter and spring thermodynamic sea-ice states at critical scales using an object-based image analysis of Sentinel-1 R. Scharien et al. https://doi.org/10.1017/aog.2017.43
- Estimating Surface Albedo of Arctic Sea Ice Using an Ensemble Back-Propagation Neural Network: Toward a Better Consideration of Reflectance Anisotropy and Melt Ponds Y. Ding et al. https://doi.org/10.1109/TGRS.2022.3202046
- Regional and seasonal evolution of melt ponds on Arctic sea ice H. Niehaus et al. https://doi.org/10.5194/tc-19-3915-2025
- Validation of the sea ice surface albedo scheme of the regional climate model HIRHAM–NAOSIM using aircraft measurements during the ACLOUD/PASCAL campaigns E. Jäkel et al. https://doi.org/10.5194/tc-13-1695-2019
- Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 2: Case studies and trends of sea ice albedo and melt ponds in the Arctic for years 2002–2011 L. Istomina et al. https://doi.org/10.5194/tc-9-1567-2015
- The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation C. Burgard et al. https://doi.org/10.5194/tc-14-2387-2020
- Supplementing Remote Sensing of Ice: Deep Learning-Based Image Segmentation System for Automatic Detection and Localization of Sea-ice Formations From Close-Range Optical Images N. Panchi et al. https://doi.org/10.1109/JSEN.2021.3084556
- 2014 summer Arctic sea ice thickness and concentration from shipborne observations Q. Wang et al. https://doi.org/10.1080/17538947.2017.1421720
- Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing A. Ehrlich et al. https://doi.org/10.5194/amt-10-3215-2017
- A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data M. König & N. Oppelt https://doi.org/10.5194/tc-14-2567-2020
- Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme M. Mäkynen et al. https://doi.org/10.3390/rs12071214
- Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery A. Fors et al. https://doi.org/10.5194/tc-11-755-2017
- The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations S. Kern et al. https://doi.org/10.5194/tc-10-2217-2016
- Step-by-Step Validation of Antarctic ASI AMSR-E Sea-Ice Concentrations by MODIS and an Aerial Image Q. Shi et al. https://doi.org/10.1109/TGRS.2020.2989037
- Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean J. Feng et al. https://doi.org/10.1016/j.jag.2021.102297
- Integrated UAV and Satellite Observations for Antarctic Melt Pond Monitoring: Assessing the Melt-Induced Risk for Transportation on Landfast Ice S. Wei et al. https://doi.org/10.1109/TGRS.2026.3695914
- Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model H. Niehaus et al. https://doi.org/10.5194/tc-18-933-2024
- Statistical characterization of the sea ice extent during different winter scenarios in the Gulf of Riga (Baltic Sea) using optical remote-sensing imagery L. Siitam et al. https://doi.org/10.1080/01431161.2016.1268734
- Spring melt pond fraction in the Canadian Arctic Archipelago predicted from RADARSAT-2 S. Howell et al. https://doi.org/10.5194/tc-14-4675-2020
- ВАРИАЦИИ КАЧЕСТВА ВОДЫ В ЛАДОЖСКОМ ОЗЕРЕ В ВЕСЕННИЙ ПЕРИОД В 2016 И 2017 ГГ.: СПУТНИКОВЫЕ НАБЛЮДЕНИЯ, "Фундаментальная и прикладная гидрофизика" Д. Поздняков & Н. Филатов https://doi.org/10.7868/S2073667321010081
- Reflective properties of white sea ice and snow A. Malinka et al. https://doi.org/10.5194/tc-10-2541-2016
55 citations as recorded by crossref.
- Broadband albedo of Arctic sea ice from MERIS optical data C. Pohl et al. https://doi.org/10.5194/tc-14-165-2020
- Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data L. Istomina et al. https://doi.org/10.5194/tc-19-83-2025
- Universality in azimuthal asymmetry of anisotropic sea-ice reflectance V. Marinyuk & S. Sheberstov https://doi.org/10.1364/JOSAA.467427
- Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2 E. Buckley et al. https://doi.org/10.5194/tc-17-3695-2023
- Estimating Arctic Sea Ice Thickness with CryoSat-2 Altimetry Data Using the Least Squares Adjustment Method F. Xiao et al. https://doi.org/10.3390/s20247011
- Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data H. Han et al. https://doi.org/10.3390/rs8010057
- An improved radiative forcing scheme for better representation of Arctic under-ice blooms Y. Gao et al. https://doi.org/10.1016/j.ocemod.2022.102075
- Improved cloud detection over sea ice and snow during Arctic summer using MERIS data L. Istomina et al. https://doi.org/10.5194/amt-13-6459-2020
- Impact of a Surface Ice Lid on the Optical Properties of Melt Ponds P. Lu et al. https://doi.org/10.1029/2018JC014161
- Melt pond detection on landfast sea ice using dual co-polarized Ku-band backscatter T. Geldsetzer et al. https://doi.org/10.1016/j.rse.2023.113725
- Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data Y. Tanaka https://doi.org/10.3390/rs12061033
- New insight from CryoSat-2 sea ice thickness for sea ice modelling D. Schröder et al. https://doi.org/10.5194/tc-13-125-2019
- Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data T. Alekseeva et al. https://doi.org/10.3390/rs11212481
- Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic J. Zhao et al. https://doi.org/10.3390/rs13193882
- Reflective properties of melt ponds on sea ice A. Malinka et al. https://doi.org/10.5194/tc-12-1921-2018
- On the importance to consider the cloud dependence in parameterizing the albedo of snow on sea ice L. Foth et al. https://doi.org/10.5194/tc-18-4053-2024
- Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery M. König et al. https://doi.org/10.3390/rs12162623
- Winter Sentinel‐1 Backscatter as a Predictor of Spring Arctic Sea Ice Melt Pond Fraction R. Scharien et al. https://doi.org/10.1002/2017GL075547
- Mosaicking Opportunistically Acquired Very High-Resolution Helicopter-Borne Images over Drifting Sea Ice Using COTS Sensors C. Hyun et al. https://doi.org/10.3390/s19051251
- Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters S. Ramjan et al. https://doi.org/10.3390/rs10101603
- Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010–2018 Y. Xiu et al. https://doi.org/10.1007/s13131-020-1646-5
- Arctic summer sea ice phenology including ponding from 1982 to 2017 X. Chen et al. https://doi.org/10.1007/s13131-022-1993-5
- A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation Y. Zhou et al. https://doi.org/10.5194/tc-17-1053-2023
- Detecting Melt Pond Onset on Landfast Arctic Sea Ice Using a Dual C-Band Satellite Approach S. Maknun et al. https://doi.org/10.3390/rs16122091
- A spectral mixture analysis approach to quantify Arctic first-year sea ice melt pond fraction using QuickBird and MODIS reflectance data J. Yackel et al. https://doi.org/10.1016/j.rse.2017.09.030
- Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery S. Lee et al. https://doi.org/10.1016/j.rse.2020.111919
- Incidence Angle Dependencies for C-Band Backscatter From Sea Ice During Both the Winter and Melt Season T. Geldsetzer & S. Howell https://doi.org/10.1109/TGRS.2023.3315056
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. https://doi.org/10.1016/j.scib.2018.11.018
- The frequency and extent of sub-ice phytoplankton blooms in the Arctic Ocean C. Horvat et al. https://doi.org/10.1126/sciadv.1601191
- Observations and modeling of areal surface albedo and surface types in the Arctic E. Jäkel et al. https://doi.org/10.5194/tc-18-1185-2024
- Regions of open water and melting sea ice drive new particle formation in North East Greenland M. Dall´Osto et al. https://doi.org/10.1038/s41598-018-24426-8
- Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network Y. Ding et al. https://doi.org/10.3390/rs12172746
- 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 Z. Peng et al. https://doi.org/10.3390/rs14184538
- Sea Ice Melt Pond Fraction Derived From Sentinel‐2 Data: Along the MOSAiC Drift and Arctic‐Wide H. Niehaus et al. https://doi.org/10.1029/2022GL102102
- Linking winter and spring thermodynamic sea-ice states at critical scales using an object-based image analysis of Sentinel-1 R. Scharien et al. https://doi.org/10.1017/aog.2017.43
- Estimating Surface Albedo of Arctic Sea Ice Using an Ensemble Back-Propagation Neural Network: Toward a Better Consideration of Reflectance Anisotropy and Melt Ponds Y. Ding et al. https://doi.org/10.1109/TGRS.2022.3202046
- Regional and seasonal evolution of melt ponds on Arctic sea ice H. Niehaus et al. https://doi.org/10.5194/tc-19-3915-2025
- Validation of the sea ice surface albedo scheme of the regional climate model HIRHAM–NAOSIM using aircraft measurements during the ACLOUD/PASCAL campaigns E. Jäkel et al. https://doi.org/10.5194/tc-13-1695-2019
- Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 2: Case studies and trends of sea ice albedo and melt ponds in the Arctic for years 2002–2011 L. Istomina et al. https://doi.org/10.5194/tc-9-1567-2015
- The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation C. Burgard et al. https://doi.org/10.5194/tc-14-2387-2020
- Supplementing Remote Sensing of Ice: Deep Learning-Based Image Segmentation System for Automatic Detection and Localization of Sea-ice Formations From Close-Range Optical Images N. Panchi et al. https://doi.org/10.1109/JSEN.2021.3084556
- 2014 summer Arctic sea ice thickness and concentration from shipborne observations Q. Wang et al. https://doi.org/10.1080/17538947.2017.1421720
- Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing A. Ehrlich et al. https://doi.org/10.5194/amt-10-3215-2017
- A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data M. König & N. Oppelt https://doi.org/10.5194/tc-14-2567-2020
- Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme M. Mäkynen et al. https://doi.org/10.3390/rs12071214
- Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery A. Fors et al. https://doi.org/10.5194/tc-11-755-2017
- The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations S. Kern et al. https://doi.org/10.5194/tc-10-2217-2016
- Step-by-Step Validation of Antarctic ASI AMSR-E Sea-Ice Concentrations by MODIS and an Aerial Image Q. Shi et al. https://doi.org/10.1109/TGRS.2020.2989037
- Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean J. Feng et al. https://doi.org/10.1016/j.jag.2021.102297
- Integrated UAV and Satellite Observations for Antarctic Melt Pond Monitoring: Assessing the Melt-Induced Risk for Transportation on Landfast Ice S. Wei et al. https://doi.org/10.1109/TGRS.2026.3695914
- Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model H. Niehaus et al. https://doi.org/10.5194/tc-18-933-2024
- Statistical characterization of the sea ice extent during different winter scenarios in the Gulf of Riga (Baltic Sea) using optical remote-sensing imagery L. Siitam et al. https://doi.org/10.1080/01431161.2016.1268734
- Spring melt pond fraction in the Canadian Arctic Archipelago predicted from RADARSAT-2 S. Howell et al. https://doi.org/10.5194/tc-14-4675-2020
- ВАРИАЦИИ КАЧЕСТВА ВОДЫ В ЛАДОЖСКОМ ОЗЕРЕ В ВЕСЕННИЙ ПЕРИОД В 2016 И 2017 ГГ.: СПУТНИКОВЫЕ НАБЛЮДЕНИЯ, "Фундаментальная и прикладная гидрофизика" Д. Поздняков & Н. Филатов https://doi.org/10.7868/S2073667321010081
- Reflective properties of white sea ice and snow A. Malinka et al. https://doi.org/10.5194/tc-10-2541-2016
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