The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation
Ice albedo feedback amplifies climate change signals and thus affects the global climate. Global long-term records on sea-ice albedo are important to characterize the regional or global energy budget. As the successor of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared...
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ftunivmaryland:oai:drum.lib.umd.edu:1903/31450 2023-12-24T10:07:43+01:00 The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation Peng, Jingjing Yu, Yunyue Yu, Peng Liang, Shunlin 2018-11-17 application/pdf http://hdl.handle.net/1903/31450 https://doi.org/10.13016/dspace/ld2r-7zal en_US eng MDPI College of Computer, Mathematical & Natural Sciences Geology Digital Repository at the University of Maryland University of Maryland (College Park, MD) https://doi.org/10.13016/dspace/ld2r-7zal Peng, J.; Yu, Y.; Yu, P.; Liang, S. The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation. Remote Sens. 2018, 10, 1826. http://hdl.handle.net/1903/31450 albedo sea ice VIIRS Arctic PROMICE GC-NET validation Article 2018 ftunivmaryland https://doi.org/10.13016/dspace/ld2r-7zal 2023-11-26T17:55:33Z Ice albedo feedback amplifies climate change signals and thus affects the global climate. Global long-term records on sea-ice albedo are important to characterize the regional or global energy budget. As the successor of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite) started its observation from October 2011 on S-NPP (Suomi National Polar-orbiting Partnership). It has improved upon the capabilities of the operational Advanced Very High Resolution Radiometer (AVHRR) and provides observation continuity with MODIS. We used a direct estimation algorithm to produce a VIIRS sea-ice albedo (VSIA) product, which will be operational in the National Oceanic and Atmospheric Administration’s (NOAA) S-NPP Data Exploration (NDE) version of the VIIRS albedo product. The algorithm is developed from the angular bin regression method to simulate the sea-ice surface bidirectional reflectance distribution function (BRDF) from physical models, which can represent different sea-ice types and vary mixing fractions among snow, ice, and seawater. We compared the VSIA with six years of ground measurements at 30 automatic weather stations from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-NET) as a proxy for sea-ice albedo. The results show that the VSIA product highly agreed with the station measurements with low bias (about 0.03) and low root mean square error (RMSE) (about 0.07) considering the Joint Polar Satellite System (JPSS) requirement is 0.05 and 0.08 at 4 km scale, respectively. We also evaluated the VSIA using two datasets of field measured sea-ice albedo from previous field campaigns. The comparisons suggest that VSIA generally matches the magnitude of the ground measurements, with a bias of 0.09 between the instantaneous albedos in the central Arctic and a bias of 0.077 between the daily mean albedos near Alaska. The discrepancy is mainly due to the scale difference at both spatial and temporal dimensions and ... Article in Journal/Newspaper albedo Arctic Climate change Greenland Ice Sheet Sea ice Alaska University of Maryland: Digital Repository (DRUM) Arctic Greenland |
institution |
Open Polar |
collection |
University of Maryland: Digital Repository (DRUM) |
op_collection_id |
ftunivmaryland |
language |
English |
topic |
albedo sea ice VIIRS Arctic PROMICE GC-NET validation |
spellingShingle |
albedo sea ice VIIRS Arctic PROMICE GC-NET validation Peng, Jingjing Yu, Yunyue Yu, Peng Liang, Shunlin The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation |
topic_facet |
albedo sea ice VIIRS Arctic PROMICE GC-NET validation |
description |
Ice albedo feedback amplifies climate change signals and thus affects the global climate. Global long-term records on sea-ice albedo are important to characterize the regional or global energy budget. As the successor of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite) started its observation from October 2011 on S-NPP (Suomi National Polar-orbiting Partnership). It has improved upon the capabilities of the operational Advanced Very High Resolution Radiometer (AVHRR) and provides observation continuity with MODIS. We used a direct estimation algorithm to produce a VIIRS sea-ice albedo (VSIA) product, which will be operational in the National Oceanic and Atmospheric Administration’s (NOAA) S-NPP Data Exploration (NDE) version of the VIIRS albedo product. The algorithm is developed from the angular bin regression method to simulate the sea-ice surface bidirectional reflectance distribution function (BRDF) from physical models, which can represent different sea-ice types and vary mixing fractions among snow, ice, and seawater. We compared the VSIA with six years of ground measurements at 30 automatic weather stations from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-NET) as a proxy for sea-ice albedo. The results show that the VSIA product highly agreed with the station measurements with low bias (about 0.03) and low root mean square error (RMSE) (about 0.07) considering the Joint Polar Satellite System (JPSS) requirement is 0.05 and 0.08 at 4 km scale, respectively. We also evaluated the VSIA using two datasets of field measured sea-ice albedo from previous field campaigns. The comparisons suggest that VSIA generally matches the magnitude of the ground measurements, with a bias of 0.09 between the instantaneous albedos in the central Arctic and a bias of 0.077 between the daily mean albedos near Alaska. The discrepancy is mainly due to the scale difference at both spatial and temporal dimensions and ... |
format |
Article in Journal/Newspaper |
author |
Peng, Jingjing Yu, Yunyue Yu, Peng Liang, Shunlin |
author_facet |
Peng, Jingjing Yu, Yunyue Yu, Peng Liang, Shunlin |
author_sort |
Peng, Jingjing |
title |
The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation |
title_short |
The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation |
title_full |
The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation |
title_fullStr |
The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation |
title_full_unstemmed |
The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation |
title_sort |
viirs sea-ice albedo product generation and preliminary validation |
publisher |
MDPI |
publishDate |
2018 |
url |
http://hdl.handle.net/1903/31450 https://doi.org/10.13016/dspace/ld2r-7zal |
geographic |
Arctic Greenland |
geographic_facet |
Arctic Greenland |
genre |
albedo Arctic Climate change Greenland Ice Sheet Sea ice Alaska |
genre_facet |
albedo Arctic Climate change Greenland Ice Sheet Sea ice Alaska |
op_relation |
College of Computer, Mathematical & Natural Sciences Geology Digital Repository at the University of Maryland University of Maryland (College Park, MD) https://doi.org/10.13016/dspace/ld2r-7zal Peng, J.; Yu, Y.; Yu, P.; Liang, S. The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation. Remote Sens. 2018, 10, 1826. http://hdl.handle.net/1903/31450 |
op_doi |
https://doi.org/10.13016/dspace/ld2r-7zal |
_version_ |
1786215084776751104 |