Inter-comparison and evaluation of sea ice type concentration algorithms

Abstract

Abstract. Sea ice has been monitored in terms of concentration and types with microwave satellite observations since the late 1970s. However, it remains an open question as to which sea ice type concentration (SITC) method is most appropriate for ice type distribution and hence climate monitoring. This paper presents key results of inter-comparison and evaluation for eight SITC methods. The SITC methods were inter-compared with two sea ice age (SIA) and three sea ice type (SIT) products using microwave radiometer and scatterometer data from 2000 to 2015. Their performances were evaluated quantitatively with samples that are used for generating tie points, and qualitatively with the RADARSAT imagery. The methods that combined scatterometer and radiometer data have overall better performances on ice type discrimination. The best methods are ECICE-QSCAT-2 for the years 2000–2009 and ECICE-ASCAT for 2009–2015, both using scatterometer data along with radiometer data. Although the SIA and SIT products are fairly good datasets for delineating ice type distributions, the SITC methods are better on preserving details like varied concentration of different ice types and work better under specific sea ice conditions, for instance, homogeneous sea ice regions with little artifact for SIA algorithms to track.

Inter-comparison and evaluation of Arctic sea ice type products

“…In all the five cases, NSIDC-SIA can generally capture the SITY distribution pattern but exhibits a slight over-or underestimation of MYI, which can be explained by the ice age assignment of the oldest ice and different temporal resolution of NSIDC-SIA compared to SAR. These results agree with previous studies (Korosov et al, 2018;Ye et al, 2019) and once again confirm the use of the NSIDC-SIA product as a cross-validation dataset.…”

“…However, this parameter can be impacted by surface features (e.g. snow properties) during the winter (Rostosky et al, 2018;Ye et al, 2019;Comiso, 1983). In the beginning and ending stages of winter, the variability in GR 37v19v can be significant when air temperature exhibits warm-cold cycles, which trigger wet-dry cycles or melt-refreeze cycles of snow (Voss et al, 2003;Ye et al, 2016a, b), or when wet or high snow precipitation appears (Voss et al, 2003;Rostosky et al, 2018).…”

“…Synthetic aperture radar (SAR) is an active microwave sensor like scatterometers but with a spatial resolution that is several orders of magnitude finer. SAR images are also used to evaluate ice type classification accuracy (Ye et al, 2019;Zhang et al, 2019). The inconsistencies between products are attributed to the usage of different thresholds and satellite observation inputs (Ezraty and Cavanié, 1999;Belmonte Rivas et al, 2012).…”

First results of Antarctic sea ice type retrieval from active and passive microwave remote sensing data

“…This method (i.e. ECICE and the correction schemes) has been successfully applied and tested in the Arctic (Ye et al, 2016a, b), and results from it have recently also been compared to other sea ice type retrieval results (Ye et al, 2019). In this study, we show that this method can be adapted to Antarctic conditions.…”

Antarctic sea ice types from active and passive microwave remote sensing

“…The modification uses surface temperature from meteorological reanalysis and ice drift from satellite data in order to correct misclassifications caused by melt-refreeze cycles and by snow metamorphosis. This has been successfully applied and tested in the Arctic (Ye et al, 2016a, b) and has recently also been compared to other sea ice type retrieval results (Ye et al, 2019). In this study, we have adapted this method to the Antarctic conditions with the aim of eventually filling the data gap in the Antarctic -the lack of ice type, in particular, multiyear ice, data.…”