Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data

Abstract

Abstract. We report on results of an intercomparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations. For this we use SIC estimated from >350 images acquired in the visible–near-infrared frequency range by the joint National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS) Landsat sensor during the years 2003–2011 and 2013–2015. Conditions covered are late winter/early spring in the Northern Hemisphere and from late winter through fall freeze-up in the Southern Hemisphere. Among the products investigated are the four products of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative (CCI) algorithms SICCI-2 and OSI-450. We stress the importance to consider intercomparison results across the entire SIC range instead of focusing on overall mean differences and to take into account known biases in PMW SIC products, e.g., for thin ice. We find superior linear agreement between PMW SIC and Landsat SIC for the 25 and the 50 km SICCI-2 products in both hemispheres. We discuss quantitatively various uncertainty sources of the evaluation carried out. First, depending on the number of mixed ocean–ice Landsat pixels classified erroneously as ice only, our Landsat SIC is found to be biased high. This applies to some of our Southern Hemisphere data, promotes an overly large fraction of Landsat SIC underestimation by PMW SIC products, and renders PMW SIC products overestimating Landsat SIC particularly problematic. Secondly, our main results are based on SIC data truncated to the range 0 % to 100 %. We demonstrate using non-truncated SIC values, where possible, can considerably improve linear agreement between PMW and Landsat SIC. Thirdly, we investigate the impact of filters often used to clean up the final products from spurious SIC over open water due to weather effects and along coastlines due to land spillover. Benefiting from the possibility to switch on or off certain filters in the SICCI-2 and OSI-450 products, we quantify the impact land spillover filtering can have on evaluation results as shown in this paper.

Comparison of Hemispheric and Regional Sea Ice Extent and Area Trends from NOAA and NASA Passive Microwave-Derived Climate Records

“…Several empirical algorithms have been developed to derive sea ice concentration from the passive microwave brightness temperatures (T B ), e.g., [4]. While substantial differences occur between products from different algorithms [5][6][7][8], trends and variability have been found to be generally consistent [9].…”

“…The purpose of this manuscript is to present the Version 4 CDR product and compare long-term extent and area trends from the CDR with the NT and BT products over both hemispheric and region scales. We do not include a specific validation here because the CDR is based on the NT and BT products that have been thoroughly validated, e.g., [7,8,18]. Here we focus on assessing the consistency between the products over their long-term time series from 1979 through 2020.…”

Resolution enhanced sea ice concentration: a new algorithm applied to AMSR2 microwave radiometry data

“…Overall, the classification is based on surface broadband shortwave albedo derived from channels 3 (533-590 nm), 4 (636-673 nm), and 5 (851-879 nm) of the L8 Operational Land Imager sensor. The surface broadband shortwave albedo images needed for the final classification can be produced with an assumption that the top of atmosphere (TOA) reflectances are equivalent to the TOA albedo (α TOA ), which can be assumed to be related to the surface albedo (α surface ) by using the following equation (Kern and others, 2022):…”

“…The surface broadband shortwave albedo images are then used in the supervised visual classification of open water and ice for the (Kern, 2021) dataset. An in-depth processing description can be found in (Kern and others, 2022). The final L8 SIC at 5 km gridding used for the PMW validation is produced by re-projecting the 30 m L8 data to the same EASE2 polar projection as the PMW data.…”

Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data

“…Satellite remote sensing has been used to produce many of the sea ice ECV datasets to obtain pan-Arctic coverage (Sandven et al, 2023). However, the passive microwave (PM) sea ice concentration, which is used to produce the sea ice area and extent is compromised by summer sea ice surface melt (Ivanova et al, 2015;Kern et al, 2019Kern et al, , 2020Kern et al, , 2022. PM L-band and altimeter sea ice thickness datasets do not provide their products in the presence of melt ponds (Huntemann et al, 2014;Patilea et al 2019;Ricker et al, 2017), and only recently an altimeter-based sea ice thickness retrieval coupled with a machine learning approach has been presented (Landy et al, 2022).…”