The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation

Abstract

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. TOA: top-of-atmosphere; BSAs: black-sky albedos; WSA: white-sky albedo; 6S: Second Simulation of 205 the Satellite Signal in the Solar Spectrum.206 2.3. The operational VSIA algorithm 207The NDE albedo process consists of two components, as shown in Figure 2. The granule albedo, 208 i.e., the published VIIRS albedo product, is computed online from a combination of the directly 209 estimated albedo and a historical temporally filtered gap-free albedo; the historical albedo is 210 computed offline from the granule albedo of previous days. 211The instant retrieval of sea-ice albedo is calculated online from ...

A sensor-agnostic albedo retrieval method for realistic sea ice surfaces – Model and validation

“…-When compared to the LUT-based method for albedo retrieval (e.g. Qu et al (2016); Peng et al (2018)), this method is advantageous in terms of obtaining the underlying logic of the training data, availed by the RTM/SciML model.…”

“…Although the OLCI-albedo product is the successor of MERIS (Istomina, 2020), which was also generated based on the melt-pond detector (MPD) algorithm (Zege et al, 2015), a comparison of the OLCI-albedo with aircraft observations (see Section 4.3) reveals an overestimation of the OLCI-albedo over the snow-covered sea-ice surface. The two direct-estimation products (hereafter referred as Qu's and Peng's, Qu et al (2016); Peng et al (2018))…”

An Improved Retrieval of Snow and Ice Properties Using Spaceborne OLCI/S-3 Spectral Reflectance Measurements: Updated Atmospheric Correction and Snow Impurity Load Estimation

“…Interestingly, PROMICE data provide almost the same BBA (0.8) for the EGP and KAN-U sites. Peng et al (2018) have found that PROMICE data is biased low as compared to VIIRS satellite albedo retrievals. We have arrived to the same conclusion as far as OLCI retrievals are of concern for retrievals over EGP site.…”

Steffen K, Abdalati W and Stroeve J (1993) Climate sensitivity studies of the Greenland ice sheet using satellite AVHRR, SMMR SSM/I and in situ data. Meteorology and Atmospheric Physics 51(3–4): 239–258. DOI:10.1007/bf01030497

“…Stroeve’s work set the stage for numerous subsequent evaluations of Greenland snow and ice albedo changes (e.g. Tedesco et al, 2011; Box et al, 2012; Peng et al, 2018) with the advantages of advanced sensors, better image geolocation, higher spatial and spectral resolution, and the ease of much greater and less costly data storage.…”