Snowpit near-infrared (NIR) images collected during the MOSAiC expedition

DOI

A NIR camera allows a measurement of the reflectance of the snow profile wall to identify layers of snow grains with different specific surface area (SSA) with a spatial resolution of about 1 mm. The processing of the images of RGB-cameras must consider the sensitivity of the different color pixels. The setup of the reference targets, the flat surface and the diffuse illumination is important to get high-quality images. A geometrically corrected NIR-photo gives an objective measure of the snow stratigraphy and is observer independent. This efficient measurement has been adapted for use in polar night and day by blocking out external sunlight and packaging the camera and the illuminating infrared lights into a wooden box. The width and height of the inside of the box was 500 x 675 mm. A blanket was used to prevent any light seeping through. Inside the box, lambertian reflectance targets were mounted to account for any irregular light conditions. For MOSAiC, we used lights with two different wavelengths, 850 nm and 940 nm. We used the NIR camera in excavated snowpit walls to record the vertical and horizontal spatial variability of the SSA and snow stratigraphy. If the snowpit was higher than the NIR box, then measurement was repeated for different heights, using a ruler as a reference. NIR was also used to take images of the surface to account for small-scale spatial variability of the SSA of the snow/SSL surface. For each set of images, a dark image (without any lamps, recorded in this dataset as 0 nm) was taken, followed by images with each of the two lamps (using external light switches), to account for potential light leaks. The MAPIR camera (Survey3N Camera - Near Infrared (NIR) - MAPIR CAMERA) was built into the box and triggered by an external button. Please direct inquiries to; David Wagner (PS122/1), Martin Schneebeli (PS122/2), Amy Macfarlane (PS122/3 and PS122/4), Ruzica Dadic (PS122/5).

Identifier
DOI https://doi.org/10.1594/PANGAEA.940129
Related Identifier https://doi.org/10.1594/PANGAEA.935934
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.940129
Provenance
Creator Macfarlane, Amy R ORCID logo; Schneebeli, Martin ORCID logo; Dadic, Ruzica ORCID logo; Wagner, David N ORCID logo; Arndt, Stefanie ORCID logo; Clemens-Sewall, David; Hämmerle, Stefan; Hannula, Henna-Reetta ORCID logo; Jaggi, Matthias; Kolabutin, Nikolai; Krampe, Daniela ORCID logo; Lehning, Michael; Matero, Ilkka ORCID logo; Nicolaus, Marcel ORCID logo; Oggier, Marc ORCID logo; Pirazzini, Roberta; Polashenski, Chris; Raphael, Ian ORCID logo; Regnery, Julia ORCID logo; Shimanchuck, Egor; Smith, Madison M; Tavri, Aikaterini ORCID logo
Publisher PANGAEA
Publication Year 2022
Funding Reference Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven https://doi.org/10.13039/501100003207 Crossref Funder ID AFMOSAiC-1_00 Multidisciplinary drifting Observatory for the Study of Arctic Climate; Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven https://doi.org/10.13039/501100003207 Crossref Funder ID AWI_PS122_00 Multidisciplinary drifting Observatory for the Study of Arctic Climate / MOSAiC; Horizon 2020 https://doi.org/10.13039/501100007601 Crossref Funder ID 730965 doi:10.3030/730965 Arctic Research Icebreaker Consortium: A strategy for meeting the needs for marine-based research in the Arctic (ARICE); Swiss Federal Institute for Forest, Snow and Landscape Research https://doi.org/10.13039/501100015742 Crossref Funder ID WSL_201812N1678 ; Swiss National Science Foundation https://doi.org/10.13039/501100001711 Crossref Funder ID 179130 https://data.snf.ch/grants/grant/179130 From Cloud to Ground: Snow Accumulation in Extreme Environments; Swiss Polar Institute https://doi.org/10.13039/501100015594 Crossref Funder ID DIRCR-2018-003
Rights Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/
OpenAccess true
Representation
Resource Type Dataset
Format text/tab-separated-values
Size 17044 data points
Discipline Earth System Research
Spatial Coverage (-2.646W, 79.115S, 128.147E, 89.153N); Arctic Ocean
Temporal Coverage Begin 2019-10-25T01:15:00Z
Temporal Coverage End 2020-09-30T09:00:00Z