The spatial distribution, evolution, and extent of melt ponds on sea ice are functions of snow thickness and snow redistribution processes through the preceding winter and spring, ice surface roughness, and ice type [1
]. For spatially varying snow covers on sea ice, thicker snow takes longer to melt than thinner snow under consistent atmospheric forcing [1
]. Prior studies advocate that a thinner snow cover leads to dominant surface flooding (i.e., larger areal melt pond fraction,
), whereas a thick winter snow cover leads to a greater fraction of snow patches and thus less surface flooding (i.e., smaller
]. Thicker snow covers generally accumulate on comparatively rough first-year sea ice (FYI), as the uneven surface topography effectively captures wind-blown snow [9
]. The relatively smooth topography of FYI causes thinner snow covers, with consistent drifting of snow following the wind direction during depositional storm events [10
]. Therefore, smooth FYI, with an associated thin snow cover, leads to the horizontal spreading of ponds over a larger area, with
as high as 80% [3
]. Since the formation of FYI melt ponds is influenced by winter snow thickness,
could provide a proxy for winter ice surface roughness and snow thickness.
After melt onset, melt ponds gradually develop over four successive stages. These stages have been previously identified as: (1) topographic control, (2) hydrostatic balance, (3) ice freeboard control and (4) fall freeze up or ice break up [6
]. During stage 1, melt ponds start to form and eventually fill topographic depressions with minimal drainage through the ice. At this stage, the structure of the melt ponds is very unstable and is actually controlled by the pre-melt ice topography [1
]. Stage 2 begins when melt ponds have the same production and drainage rates, and lateral melt water flows towards macroscopic fractures and seal holes promote the formation of melt pond networks by interconnecting melt ponds. Consequently, the geometric structure of melt pond covers becomes prominent during this stage [1
]. Stage 3 is characterized by increased vertical drainage and decreased horizontal discharge to macroscopic fractures. Surface topography of melting and decaying ice cover controls the melt pond coverage in this stage [6
]. Finally, during stage 4, complete decay or disintegration of FYI, or fall freeze up of multiyear sea ice begins.
Estimating melt pond fraction over large areas requires satellite-based methods. Optical approaches [13
] are hampered by cloud cover and are thus limited by the accuracy of cloud identification and screening techniques. They must also contend with regional differences in spectral properties [18
], and issues related to sub-pixel variation. The root-mean-squared error (RMSE) for optical approaches is 0.08 to 0.16. Approaches using passive microwave data [19
] are limited by the sensor resolution, especially in narrow channels where land contamination is likely. They are also only applicable to very high sea ice concentrations; even small areas of open water would dominate the signal.
Approaches using active microwave data have provided varying results. Barber and Yackel [20
] report a relationship between ERS-1 backscatter and
, but note a limitation related to wind roughening of melt ponds. Mäkynen et al. [21
] report poor skill in connecting changes in
with backscatter magnitude and texture using ENVISAT WSM images of sea ice during the ponding stages. Scharien et al. [22
] report on the usefulness of co-polarization and cross-polarization ratios in estimating
during ponding using RADARSAT-2 quad-pol data; their RMSE values range from 0.05 to 0.43. Using TerraSAR-X, Fors et al. [23
] also find the co-polarization ratio useful during the ponding stage, at intermediate wind speeds, with an RMSE is 0.4; they also evaluate four polarimetric parameters and find reasonable correlations. To evaluate prediction of
from winter images, Scharien et al. [24
] use ENVISAT-ASAR and Sentinel-1 co- and cross-polarized backscatter and texture measures thereof. They report overall RMSE values of 0.08 and 0.09, but acknowledge model underestimation of
for smooth FYI related to sensor noise floors.
Estimating melt pond fraction over smaller areas and for validation of the techniques above rely on in-situ observations [11
], or low-level aerial photographs [26
]. A recent study using low-level aerial photographs showed that, due to their fine spatial and temporal resolutions, they are an ideal data source for estimating
since image classification accuracies are as high as 97% [22
Using winter imagery to predict
implies an association of sea ice roughness and/or its snow cover variability with subsequent
. Comprehensive studies on the degree of winter snow thickness spatial variability, as well as relationships with
, are essential to better understand radiative transfer processes leading to snow and sea ice ablation rates during the spring melt transition. However, studying snow thickness variability on sea ice is complicated. The accumulation and redistribution of snow on FYI exhibits high spatio-temporal variability [9
], and logistical challenges, resulting in a shortage of in-situ data [10
]. Snow thickness distributions have been obtained in-situ [9
], using laboratory-based procedures [31
], and using remote sensing methods based on passive microwave data [33
], frequency-modulated continuous-wave airborne radar returns [36
] and Synthetic Aperture Radar (SAR) data [7
], and using a combination of active microwave scatterometer and optical data [40
The use of SAR backscatter must also contend with variability in backscatter with incidence angle (
). Kim et al. [41
] identified the
effect on backscatter intensity, as it strongly influences surface and volume scattering processes. Microwaves are dominated by surface scattering at lower
(<30°); whereas at higher
(>30°), volume scattering dominates over surface scattering [42
The advent of polarimetric SAR data provide additional parameters for characterizing sea ice surfaces. Polarimetric parameters such as the Co-polarized phase difference, Co-polarized correlation coefficient, Entropy, Anisotropy, and Alpha angle can be calculated from second-order derivatives of the scattering matrix (i.e., covariance and coherency) to obtain enhanced information about both surface and volume scattering mechanisms of snow and sea ice [39
In addition to linear and polarimetric parameters, SAR image texture also provides valuable structural information, leading to higher winter sea ice classification accuracy when compared to backscatter intensity alone [48
]. Second-order texture measures, derived from the gray-level co-occurrence matrix (GLCM) introduced by Haralick et al. [52
], were evaluated for sea ice classification [49
], showing that the combination of
(tone/grey-level) and texture measures (spatial dependence in tone) gives better results than using texture measures alone. GLCM texture is the most commonly used texture analysis technique; as it takes into account the spatial organization of grey tones within a moving window and offers a second-order statistical technique for extracting texture features [49
]. Image texture of snow covered sea ice is a function of its near surface characteristics e.g., snow properties, snow thickness variability, and ice roughness [53
]. Several studies successfully demonstrated the potential of GLCM texture measures, calculated from SAR images, to improve classification/segmentation of snow covered sea ice [49
Given that the most consistent skill in estimating
is reported by winter prediction of
], we build on the winter prediction technique in this paper. We maintain the use of texture parameters as in Mäkynen et al. [21
] and Scharien et al. [24
] and expand on the analysis by evaluating the contributions of polarimetric parameters as per Fors et al. [23
], and we assess texture measures of the polarimetric parameters. Furthermore, given the relationship of winter snow thickness and subsequent
, we attempt an initial inversion of
to estimate snow thickness.
The primary objective of this study is to predict early summer from winter C-band SAR polarimetric backscatter. A secondary objective is to relate the predicted to the spatial distribution of snow thickness. Meeting these objectives will aid in understanding the relationship between snow thickness variability on FYI during winter and its evolution into a melt pond icescape in early summer. Since the SAR backscatter coefficient is largely a function of , this study will also examine the dependency on backscatter and its relationship with . The following research questions towards these objectives are stated:
What are the relationships between winter C-band RADARSAT-2 (RS-2) SAR backscatter (linear and polarimetric parameters and texture parameters) and ?
How does the relationship between RS-2 SAR backscatter and change with ?
Can we predict based on linear, polarimetric and texture parameters, and can predicted be used to infer the late winter snow thickness variability?
This paper presents an approach utilizing late winter C-band RADARSAT-2 SAR linear, polarimetric and texture parameters of snow covered first-year sea ice to predict early summer melt pond fraction. Predictions were then used to examine relationships between early melt pond fraction and late winter snow thickness. We estimated melt pond fractions from the aerial photographs collected over first-year sea ice. The correlation of melt pond fraction and late winter backscatter parameters provided us with to the ability to hindcast snow thickness. Nine linear and polarimetric parameters and 72 texture parameters were evaluated for their relationship with pond fraction.
Multivariate models comprised of linear, polarimetric and/or texture parameters are derived at near-, mid- and far-range incidence angles. The best pond fraction prediction capability is exhibited by the model at far-range incidence angles (RMSE = 0.09).
By relating late winter snow thickness and , these predictions help us to understand the winter snow thickness variability. The models are able to distinguish higher snow thickness along sea ice ridges, coastlines and relatively thinner snow cover on smooth surfaces of first-year sea ice, which is consistent with previous findings.
Moreover, the models show that the combination of SAR linear and polarimetric backscatter and texture parameters enhance the strength of the models compared to utilizing them separately for the prediction of pond fraction. The estimation of pond fraction over an entire SAR scene based on the models show logical distributions of melt ponds and snow thickness.
The results of this study add to the suite of seasonal sea ice forecasting tools, and thus can aid ship navigability since melt ponds are associated with advanced sea ice melt and significant weakening of sea ice mechanical strength. At the same time, it provides insight into the late winter snow thickness distribution on first year sea ice. The method is tested on landfast sea ice, is sensitive to the time periods of the collected aerial melt pond distributions and SAR scene acquisitions, and is likely not applicable to drift ice. Future research should test this model on a regional scale, and similar models should also be evaluated for their application over multi-year sea ice and more deformed types of first-year sea ice.