SICEv2.3.2 Northern Arctic Canada snow and ice broadband albedo and surface optical properties from Sentinel-3’s OLCI at 1000 m resolution, 2017-2023
Bibliographic record
Abstract
SICE v2.3.2. Multi-regional 1 km Arctic land ice SSA, broadband albedo and spectral reflectance for 2017 to 2023 Timespan 1 April, to 31 September for each year 2017 to 2023 and is being updated starting ~April each year Description For multiple Arctic glaciated regions (Table 1), 1km daily data (Table 2) from the SICE v1.6 algorithm, see Wehrlé et al (2021) and Kokhanovsky et al (2019) for details. Broadband albedo "albedo_bb_planar_sw" is after Kokhanovsky et al (2019). "BBA_combination" is albedo_bb_planar_sw for albedo_bb_planar_sw values above 0.565 and is combined with an ampirical albedo for albedo_bb_planar_sw below or equal to 0.565, see Wehrlé et al (2021). Data format is GeoTiff in the EPSG:3413 - WGS 84 / NSIDC Sea Ice Polar Stereographic North. We suggest using rasterio to read the data files. The data are also available from https://thredds.geus.dk/ Data Format Data format is GeoTiff in the EPSG:3413 - WGS 84 / NSIDC Sea Ice Polar Stereographic North projection. We suggest using rasterio to read the data files. Table 1, SICE regions, sorted by decreasing area region, area, sq km, percent of multi-regional area Greenland, 1,744,666, 82.7 Arctic Canada North, 100,691, 4.8 Alaska and Yukon, 96,909, 4.6 Arctic Canada South, 40,970, 1.9 Norway, 34,018, 1.6 Svalbard, 32,506, 1.5 Novaya Zemlya, 21,506, 1.0 Severnaya Zemlya, 15,842, 0.8 Frans Josef Land, 12,131, 0.6 Iceland, 11,489, 0.5 Table 2, SICE v1 data name, description BBA_combination, broadband albedo based on albedo_bb_planar_sw for albedo_bb_planar_sw above 0.565 and based on empirical algorithm for albedo_bb_planar_sw less than 0.565 SCDA_final, cloud mask albedo_bb_planar_sw, diagnostic_retrieval, per pixel diagnostic info num_scenes, number of scenes r_TOA_01, TOA reflectance, band 1 r_TOA_06, TOA reflectance, band 6 r_TOA_17, TOA reflectance, band 17 r_TOA_21, TOA reflectance, band 21 snow_specific_surface_area, SSA Reference Publications Kokhanovsky A., Lamare M., Danne O., Brockmann C., Dumont M., Picard G., Arnaud L., Favier V., Jourdain B., Le Meur E., Di Mauro B., Aoki T., Niwano M., Rozanov V., Korkin S., Kipfstuhl S., Freitag J., Hoerhold M., Zuhr A., Vladimirova D., Faber A-K., Steen-Larsen HC., Wahl S., Andersen JK., Vandecrux B., van As D., Mankoff KD., Kern M., Zege E., Box JE. 2019. Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument. Remote Sensing 11:2280. https://doi.org/10.3390/rs11192280 Kokhanovsky, A., Box, J., and Vandecrux, B.: Pre-operational Sentinel-3 snow and ice (SICE) products: Algorithm theoretical basis document, https://doi.org/10.20944/preprints202009.0529.v1, 23 September 2020. Wehrlé A., Box JE., Niwano M., Anesio AM., Fausto RS. 2021. Greenland bare-ice albedo from PROMICE automatic weather station measurements and Sentinel-3 satellite observations. GEUS Bulletin 47. https://doi.org/10.34194/geusb.v47.5284 Code Wehrlé, A., Mankoff, K., Vandecrux, B., and Box, J. E.: Sentinel-3 snow and ice optical properties retrieval (SICE) version 2.3.2, https://doi.org/10.5281/zenodo.10036416, 2023. How to gather and read the data see https://github.com/GEUS-SICE/SICE_gather and raise any issues there. Related Publications Kokhanovsky A., Lamare M., Di Mauro B., Picard G., Arnaud L., Dumont M., Tuzet F., Brockmann C., Box JE. 2018. On the reflectance spectroscopy of snow. The Cryosphere 12:2371–2382. https://doi.org/10.5194/tc-12-2371-2018 Acknowledgements SICE has been supported by the following contracts to the European Space Agency (ESA): Dec. 2016 – Jan. 2019 SEOM S34Sci Land Study 1: Snow, ESRIN Contract 4000118926/16/I-NB Dec. 2018 – Jul. 2020 EO Science For Society, ESA/Contract 4000125043/18/I-NB – ESA/AO/1-9101/17/I-NB EO SCIENCE FOR SOCIETY, Pre-operational Sentinel-3 Snow and Ice Products (SICE) Jan. 2019 – Dec. 2020 ESA PRODEX, An operational service of new Sentinel-3 algorithms for climate monitoring of the Greenland Cryosphere within the CryoClim network May 2021 – June 2023 ESA PRODEX, Seamless Integration of Sentinel-3 Albedos in a Weather-modelling System (SISAWS) Feb 2022 – Oct. 2023 ESA EO Science For Society, Snow and ICE optical and physical properties from Sentinel-3 (SICE), ESA CCN contract 4000125043/18/I-NB and the ESA Network of Resources, Related Information description of variables, inputs and outputs https://github.com/GEUS-SICE/pySICE/tree/pySICEv2.1 https://snow.geus.dk/ Questions? contact Jason Box, jeb@geus.dk
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".