Estimation of Melt Pond Fractions on First Year Sea Ice Using Compact Polarization SAR

Abstract

Melt ponds are a common feature on Arctic sea ice. They are linked to the sea ice surface albedo and transmittance of energy to the ocean from the atmosphere and thus constitute an important process to parameterize in Arctic climate models and simulations. This paper presents a first attempt to retrieve the melt pond fraction from hybrid‐polarized compact polarization (CP) SAR imagery, which has wider swath and shorter revisit time than the quad‐polarization systems, e.g., from RADARSAT‐2 (RS‐2). The co‐polarization (co‐pol) ratio has been verified to provide estimates of melt pond fractions. However, it is a challenge to link CP parameters and the co‐pol ratio. The theoretical possibility is presented, for making this linkage with the CP parameter C22/C11 (the ratio between the elements of the coherence matrix of CP SAR) for melt pond detection and monitoring with the tilted‐Bragg scattering model for the ocean surface. The empirical transformed formulation, denoted as the “compact polarization and quad‐pol” (“CPQP”) model, is proposed, based on 2062 RS‐2 quad‐pol SAR images, collocated with in situ measurements. We compared the retrieved melt pond fraction with CP parameters simulated from quad‐pol SAR data with results retrieved from the co‐pol ratio from quad‐pol SAR observations acquired during the Arctic‐Ice (Arctic‐Ice Covered Ecosystem in a Rapidly Changing Environment) field project. The results are shown to be comparable for observed melt pond measurements in spatial and temporal distributions. Thus, the utility of CP mode SAR for melt pond fraction estimation on first year level ice is presented.

Retrieval of Oil–Water Mixture Ratio at Ocean Surface Using Compact Polarimetry Synthetic Aperture Radar

“…• VV (also denoted HH/VV) leads to a minimization of uncertainty caused by surface roughness [37,41], whereas differences in HH/VV for oil-cover and clean ocean water are mainly due to the complex dielectric constants [10,[26][27][28]. When crude oil appears on the ocean surface, the medium becomes a mixture of sea water and oil.…”

“…Based on the property of reciprocity and reflection symmetry [42] of the ocean background and following Li et al [37], the covariance matrix of the HP CP Equation (13) can be approximated as:…”

“…However, the co-pol ratio method cannot be extend to HP SAR observations directly because we cannot link the elements in HP covariance matrixes with the co-pol ratios directly [16,37]. Collins et al [38] used the HH/VV ratio to estimate the oil-water mixture ratio from emulated HP data, by building a pseudo QP covariance matrix.…”

“…Collins et al [38] used the HH/VV ratio to estimate the oil-water mixture ratio from emulated HP data, by building a pseudo QP covariance matrix. Nevertheless, building of the pseudo QP matrix is still open to question, and for remotely sensed data, assumptions used in the reconstruction process may not be valid [37,39]. For these reasons, we do not discuss further the reconstructed method of HP data for oil spill detection and monitoring.…”

“…However, the relationships among the elements of covariance matrix for CP SAR and those for QP observations are complex, and the emulated covariance matrix and the related elements for CP SAR cannot be directly used for mixture ratio retrieval. Therefore, we establish the relationship between the diagonal element ratio of the HP mode CP covariance matrix and that of the QP Sinclair matrix [37]. These variables appear in the ratio of the diagonal elements of the HP covariance matrix, and they are quite similar to those appearing in the co-pol ratio in the QP Sinclair matrix; moreover, they are also independent of ocean wave spectra and only depend on the dielectric constant, incidence angles and slopes of long ocean waves.…”