Reflectances measured in the visible frequency range at three channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observation Satellite (EOS) TERRA were used to derive the melt pond fraction on Arctic sea ice using an artificial neural network. This analysis was done on reflectances gridded onto a polar-stereographic grid tangent to the Earths' surface at 70 deg N with 500 m grid resolution. The reflectances used originate from the 8-day composite reflectances provided via https://wist.echo.nasa.gov/api/ as product: "MODIS surface Reflectance 8-Day L3 Global 500m SIN Grid V005". After gridding and flagging for clouds and other disturbances the artificial neural network was applied, providing fractions of three surface classes: 1) melt ponds, 2) sea ice and snow, and 3) open water at 500 m grid resolution. This data has been interpolated onto a similar polar-stereographic grid but with 12.5 km grid resolution. The data set offered here comprises several data layers: the melt pond fraction, its standard deviation, the open water fraction, and the number of individual valid grid cells with 500 m grid resolution included in each 12.5 km grid cell. In addition, in three separate data layers melt pond fraction, its standard deviation, and the open water fraction are given only for those grid cells (with 12.5 km grid resolution) where more than 90 % of the native 500 m grid resolution data indicate clear sky conditions. Grid cells with an open water fraction larger than 85 % have been generally flagged as invalid. The data set is updated annually.
Rösel, Anja; Kaleschke, Lars; Kern, Stefan (2013). Gridded Melt Pond Cover Fraction on Arctic Sea Ice derived from TERRA-MODIS 8-day composite Reflectance Data. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.1594/WDCC/MODIS__Arctic__MPF
observational: Level3b - Gridded Geophysical Variable - Basic Quality Control Accuracy and reliability of the offered data have been tested against a number of independent data comprising ship-based observations, air-borne observations, and satellite observations; results of these activities are published as Roesel, A., L. Kaleschke, and G. Birnbaum, 2012. Melt ponds on Arctic sea ice determined from MODIS satellite data using an artifical neural network. The Cryosphere, 6, 431-446, doi: 10.5194/tc-6-431-2012.
Completeness report
There is no specific report about the horizontal accuracy of this data set available....
Description
There is no specific report about the horizontal accuracy of this data set available. Temporal completeness: The data set covers the entire EOS-TERRA MODIS time series from 2000-2011. The data is however limited to the melting period which is defined, for our data set as beginning at May 9 (May 8 in leap years) and ending at September 13 (September 12 in leap years). Data are missing for 8-day composites starting at June 18 and June 26 2001. Spatial completeness: Several factors, as are detailed below, limit the spatial coverage: i) Limited day light and gaps in acquisition of the original MODIS data causes a data gap centered at the pole which has a varying latitudinal extent. ii) Varying cloud cover and sea ice extent causes data gaps to occur allover the region. iii) In order to focus on the Arctic Ocean regions south of 60°N are excluded.
Consistency report
Accuracy and reliability of the offered data have been tested against a number of independent data comprising ship-based observations, air-borne obser...
Description
Accuracy and reliability of the offered data have been tested against a number of independent data comprising ship-based observations, air-borne observations, and satellite observations; results of these activities are published as Roesel, A., L. Kaleschke, and G. Birnbaum, 2012. Melt ponds on Arctic sea ice determined from MODIS satellite data using an artifical neural network. The Cryosphere, 6, 431-446, doi: 10.5194/tc-6-431-2012.
FAIR
F-UJI result: total 62 %
Description
Summary: Findable: 6 of 7 level; Accessible: 2 of 3 level; Interoperable: 3 of 4 level; Reusable: 4 of 10 level
There is no specific report about the horizontal accuracy of this data set available. ...
Description
There is no specific report about the horizontal accuracy of this data set available. The data set is offered at a certain spatial resolution which should be regarded as a compromise between a reasonable spatial coverage with valid data and an as high as possible degree of resolving small-scale variations. The temporal resolution to 8-day composites poses limits to the spatial resolution and thus horizontal accuracy anyways. Users should be aware of the fact that usage of an 8-day composite of clear-sky radiances could mean that the information retrieved for adjacent grid cells may originate, in extreme cases, from data of two different days which are 7 days apart. Users interested in products at native spatial resolution (500 m) or finer temporal resolution (daily) should contact the author and/or the investigator (see above).
Scientific Quality Assurance (SQA)
SQA - Scientific Quality Assurance 'approved by author'
Result Date
2013-05-15
Technical Quality Assurance (TQA)
TQA - Technical Quality Assurance 'approved by WDCC'
Description
1. Number of data sets is correct and > 0: passed; 2. Size of every data set is > 0: passed; 3. The data sets and corresponding metadata are accessible: passed; 4. The data sizes are controlled and correct: passed; 5. The temporal coverage description (metadata) is consistent to the data: passed; 6. The format is correct: passed; 7. Variable description and data are consistent: passed
Method
WDCC-TQA checklist
Method Description
Checks performed by WDCC. The list of TQA metrics are documented in the 'WDCC User Guide for Data Publication' Chapter 8.1.1
[1] DOIRösel, A.; Kaleschke, L.; Birnbaum, G. (2012). Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network. doi:10.5194/tc-6-431-2012