The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation

Peng, Jingjing; Yu, Yunyue; Yu, Peng; Liang, Shunlin

doi:10.3390/rs10111826

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The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation

2018
By Peng, Jingjing ; Yu, Yunyue ; Yu, Peng ; ...
Source: Remote Sens. 10(11), 1-23

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Journal Title:

Remote Sensing
Personal Author:

Peng, Jingjing ; Yu, Yunyue ; Yu, Peng ; Liang, Shunlin

Peng, Jingjing ; Yu, Yunyue ; Yu, Peng ; Liang, Shunlin Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CICSM (Cooperative Institute for Climate and Satellites Maryland) ; CICS (Cooperative Institute for Climate and Satellites)

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CICSM (Cooperative Institute for Climate and Satellites Maryland) ; CICS (Cooperative Institute for Climate and Satellites) Less -
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 the limited sample size. The VSIA data will serve for weather prediction applications and climate model calibrations. Combined with the historical observations from MODIS, current S-NPP VIIRS, and NOAA-20 VIIRS observations, VSIA will dramatically contribute to providing high-accuracy routine sea-ice albedo products and irreplaceable records for monitoring the long-term sea-ice albedo for climate research.
Keywords:

[+]

Albedo Algorithms Climatology Remote Sensing Sea Ice
Source:

Remote Sens. 10(11), 1-23
DOI:

https://doi.org/10.3390/rs10111826
Document Type:

Journal Article
Funding:

Grant no. NA14NES4320003
Rights Information:

CC BY
Compliance:

PMC
Main Document Checksum:

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urn:sha256:63a50365f8567d98638074f42bd22b61b7a82dfed4712d4c911299e97da58cf7
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https://repository.library.noaa.gov/view/noaa/25255/noaa_25255_DS1.pdf
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