Monthly gridded sea ice surface roughness derived from MISR (Multi-angle Imaging SpectroRadiometer) 2000-2020
GB/NERC/BAS/PDC/02178
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Summary
Abstract:
This dataset provides a 21 year (2000 - 2020) time series of Arctic sea ice surface roughness at 1km aggregated at a monthly timescale. Roughness is defined as the standard deviation of within-pixel lidar elevations. The data is generated using angular reflectance signatures from MISR processed via a support vector regression model that is trained on airborne near-coincident lidar data from Operation IceBridge. The dataset covers April of each year, providing a pan-Arctic snapshot of springtime sea ice deformation, and is validated against independent pre-IceBridge laser campaigns and CryoSat-2/SMOS thickness data.
Funding was provided by: European Space Agency by project "Polarice" (grant no. ESA/AO/1-9132/17/NL/MP), project "CryoSat + Antarctica" (grant no. ESA AO/1-9156/17/I-BG), project "Polar + Snow" (grant no. ESA AO/1-10061/19/I-EF), and project "ALBATROSS" (ESA-Contract No. 4000134597/21/I-NB) as well as from UK Natural Environment Research Council (NERC) Projects "PRE-MELT" under Grant NE/T000546/1 and "Empowering Our Communities To Map Rough Ice And Slush For Safer Sea-ice Travel In Inuit Nunangat"under Grant NE/X004643/1.
Keywords:
Arctic ocean, MISR, remote sensing, sea ice, surface roughness
Citation
Johnson, T., Tsamados, M., Muller, J., & Stroeve, J. (2026). Monthly gridded sea ice surface roughness derived from MISR (Multi-angle Imaging SpectroRadiometer) 2000-2020 (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/f0396be5-a7cd-48c4-b524-7640b9d1b96f
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Constraints
| Access Constraints: | No restrictions apply |
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| Use Constraints: | This data is governed by the NERC data policy (http://www.nerc.ac.uk/research/sites/data/policy/) and supplied under Open Government Licence v.3 (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/). |
Basic Information
| Creation Date: | 2026-04-14 |
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| Dataset Progress: | Complete |
| Dataset Language: | English |
| ISO Topic Categories: |
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| Parameters: |
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| Personnel: | |
| Name | UK PDC |
| Role(s) | Metadata Author |
| Organisation | British Antarctic Survey |
| Name | Thomas Johnson |
| Role(s) | Investigator, Technical Contact |
| Organisation | University College London |
| Name | Michel Tsamados |
| Role(s) | Investigator |
| Organisation | University College London |
| Name | Jan-Peter Muller |
| Role(s) | Investigator |
| Organisation | University College London |
| Name | Julienne Stroeve |
| Role(s) | Investigator |
| Organisation | University College London |
| Parent Dataset: | N/A |
Additional Information
| Reference: | Johnson, T.; Tsamados, M.; Muller, J.-P.; Stroeve, J. Mapping Arctic Sea- Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer. Remote Sens. 2022, 14, 6249. https:// doi.org/10.3390/rs14246249 | |
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| Quality: | Blocked k-fold cross-validation is implemented to tune hyperparameters using grid optimisation and to assess model performance, with an R^2 (coefficient of determination) of 0.43 and MAE (mean absolute error) of 0.041 m. Product performance is assessed through independent validation by comparison with unseen similarly generated surface-roughness characterisations from pre-IceBridge missions (Pearson's r averaged over six scenes, r = 0.58, p < 0.005), and with AWI CS2-SMOS sea-ice thickness (Spearman's rank, r_s = 0.66, p < 0.001), a known roughness proxy. | |
| Lineage/Methodology: | Surface roughness was modelled using Support Vector Regression (SVR) with a radial basis function (RBF) kernel. The model was trained on a dataset of 19,367 instances where MISR swaths intersected Operation IceBridge (OIB) ATM L1B laser altimetry within 36 hours, and coarse displacement is estimated at less than half pixel resolution. The SVR utilizes 10 optimal features selected by a forward floating sequential feature selection scheme: top of atmosphere bidirectional reflectance factors from the red band (Aa, An, Ca) and NIR band (An, Af, Ca); solar zenith angle; relative azimuth angles for the Ca and Cf cameras; and MODIS-derived ice-surface temperature. Cloud masking was performed using a combination of MISR's Stereoscopically Derived Cloud Mask (SDCM), Angular Signature Cloud Mask (ASCM), and MODIS cloud/shadow products. | |
Locality
| Temporal Coverage: | |
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| Start Date | 2000-04-01 |
| End Date | 2020-04-30 |
| Spatial Coverage: | |
| Latitude | |
| Southernmost | 62 |
| Northernmost | 84 |
| Longitude | |
| Westernmost | -180 |
| Easternmost | 180 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Data Resolution: | |
| Latitude Resolution | N/A |
| Longitude Resolution | N/A |
| Horizontal Resolution Range | 500 meters - < 1 km |
| Vertical Resolution | N/A |
| Vertical Resolution Range | 100 meters - < 1 km |
| Temporal Resolution | N/A |
| Temporal Resolution Range | N/A |
| Location: | |
| Location | Arctic |
| Detailed Location | Arctic Ocean |
Instrumentation
| Sensor(s): |
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| Source(s): |
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| Data Collection: | Primary sensor: Multi-angle Imaging SpectroRadiometer (MISR) on the Terra satellite; MI1B2E v3 as input. Additional sensors: MODIS (Terra) and Operation IceBridge ATM Python (3.6)-based processing pipeline using LIBSVM (3.23.0.4), and Sci-kit Learn (0.18.2). |
Storage
| Data Storage: | 21x HDF5 files |
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