Measuring Deformed Sea Ice in Seasonal Ice Zones Using L-Band SAR Images

Nose, Takehiko; Ishiyama, Junno; Kimura, Noriaki

doi:10.1109/tgrs.2020.3043335

Cited by 10 publications

(12 citation statements)

References 47 publications

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“…In Figure 2 the seasonal evolution of the backscatter values in C-and L-band are shown for the three different sea ice types. Similar to recent work by [6,7] we observe larger differences between YI and DI backscatter values in the L-band data (9-10 dB) compared to the C-band data (5-6 dB). Moreover, the separation between the SI and the DI is larger in L-band compared to C-band regardless of season.…”

Section: Resultssupporting

confidence: 91%

“…The standard deviation is significantly smaller for the SI compared to the DI in the L-band data, and moreover were the variability within the Lband data smaller than the C-band data. Toyota et al [7] show that thin level ice can be better separated from deformed thin (pancake) and thicker FYI in L-band data compared to C-band data, here we argue that L-band could also be more suitable to reliable separate DI from SI.…”

Section: Resultssupporting

confidence: 50%

“…During the campaign were a large dataset of spatially and temporally overlapping C-(RADARSAT-2) and L-band (ALOS-2 PALSAR-2) SAR images acquired. Previous studies e.g., [2,3,4,5,6,7] have shown that L-band SAR can provide better separability between different sea ice types in specific scenarios and two frequencies can provide complementary information. Here we will compare the backscatter evolution for three different sea ice types; young ice (YI), smooth Ice (SI) and Rough/Deformed Ice (RDI).…”

Section: Introductionmentioning

confidence: 99%

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Year-Around C- and L- Band Observation Around the Mosaic Ice Floe with High Spatial and Temporal Resolution

Singha

Johansson

Spreen

et al. 2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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In September 2019, the German research icebreaker Polarstern started the largest multidisciplinary Arctic expedition, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) drift experiment. Being moored to ice floes at high Arctic for a whole year, thus including the winter season, the main goal of the expedition is to better understand and quantify relevant processes within the atmosphere-ice-ocean system that impact the sea ice, ultimately leading to improved climate models. Satellite remote sensing, specially multi-frequency synthetic aperture radar (SAR) plays a major role to achieve this goal. Two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large coverage, and on the other hand to obtain a radar response that carries as much information as possible. A comprehensive set of C-and L-band SAR images were acquired during the course of MOSAiC. In this initial study we evaluate the effects of seasonal changes on C-and L-band backscatter in respect to three different sea ice types, i.e., Young Ice, Smooth Ice and Rough/Deformed Ice along with the performance of sea ice type retrieval of a established algorithm. Areas of deformed, smooth and young sea ice were observed in the vicinity of R/V Polarstern and these areas are included whenever possible in the yearlong time series. For both frequencies a change in all backscatter

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Section: Resultssupporting

confidence: 91%

Section: Resultssupporting

confidence: 50%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Year-Around C- and L- Band Observation Around the Mosaic Ice Floe with High Spatial and Temporal Resolution

Singha

Johansson

Spreen

et al. 2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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“…Studies have isolated deformed ice using the classification of airborne and fully polarimetric, high-resolution satellite SAR data (e.g. Casey et al, 2014;Herzfeld et al, 2015), and linked sea ice roughness to wide-swath SAR backscatter intensities through correlation analyses, thus mapping sea ice deformation (Cafarella et al, 2019;Segal et al, 2020;Toyota et al, 2020). Gegiuc et al, (2018) estimated the degree of ice ridging from the classification of texture features from segmented RS2 ScanSAR Wide A (SCWA) data.…”

Section: Introductionmentioning

confidence: 99%

“…The decrease of SAR backscatter intensity with IA is traditionally treated as an image property, and remedied by a global correction based on the approximate linear decrease rate in the log-domain (Zakhvatkina et al, 2019;Toyota et al, 2020). However, per-class IA correction is found to be necessary as the decrease rates (slopes of backscatter intensities versus IA, i.e.…”

Section: Introductionmentioning

confidence: 99%

Cross-platform application of a sea ice classification method considering incident angle dependency of backscatter intensity and its use in separating level and deformed ice

Guo

Itkin

Lohse

et al. 2021

Preprint

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Abstract. Wide-swath C-band synthetic aperture radar (SAR) has been used for sea ice classification and estimates of sea ice drift and deformation since it first became widely available in the 1990s. Here, we examine the potential to distinguish surface features created by sea ice deformation using ice type classification of SAR data. To perform this task with extended spatial and temporal coverage, we investigate the cross-platform transferability between training sets derived from Sentinel-1 Extra Wide (S1 EW) and RADARSAT-2 (RS2) ScanSAR Wide A (SCWA) and Fine Quad-polarimetric (FQ) data, as the same radiometrically calibrated backscatter coefficients are expected from these two C-band SAR platforms. For this, we use a novel sea ice classification method developed based on Arctic-wide S1 EW training, which considers the ice-type-dependent change of SAR backscatter intensity with incident angle (IA). This study focuses on the region near Fram Strait north of Svalbard to utilize expert knowledge of ice conditions from co-authors who participated in the Norwegian young sea ICE (N-ICE2015) expedition in the region. Separate training sets for S1 EW, RS2 SCWA and RS2 FQ data are derived using manually drawn polygons of different ice types, and are used to re-train the classifier. Results show that although the best classification accuracy is achieved for each dataset using its own training, different training sets yield similar results and IA slopes, with the exception of leads with calm open water, nilas or newly formed ice (the “leads”' class). This is found to be caused by different noise floor configurations of S1 and RS2 data, which lead to different IA slopes of this class. This indicates that dataset-specific re-training is needed for leads in the cross-platform application of the classifier. Based on the classifier thus re-trained for each dataset, the classification scheme is altered to target the separation of level and deformed ice, which enables direct comparison with independently derived sea ice deformation maps. The comparisons show that the classification of C-band SAR can be used to distinguish areas of ice divergence occupied by leads, young ice and level first-year ice (LFYI). However, it has limited capacity in delineating areas of ice deformation due to ambiguities in ice types represented by classes with higher backscatter intensities. This study provides reference to future studies seeking cross-platform application of training sets so they are fully utilized, and we expect further development of the classifier and the inclusion of other SAR datasets to enable image classification-based ice deformation detection using only satellite SAR data.

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