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We propose here an approach where we modify the mod-eled sea ice thickness using passive microwave data as well as C-band SAR satellite data to generate an ice thickness chart with reduced uncertainty and better spatial resolution compared to the model data. We will present the algorithm. We also show some validation results. The study area covers...
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... spa- tial resolution of ITC is 1 km, and it shows a thin ice class (< 30 cm), ice thickness in 31-200 cm range, and three WMO SIC classes for SIC less than 70%, see Fig. 8. ITC is delivered as 8-bit GeoTIFF-file. An example of the different stages of ITC and the resulting ITC is shown in Figures 3-8. ...
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... The AMSR2 and MWRI daily thin ice charts are intended to complement SAR data in various sea ice classification tasks. Previously, we have used AMSR2 thin ice chart in conjunction with SAR and sea ice modeling data for ship navigation purposes [33]. In addition, the updated charts can indicate the presence of thin ice in the SIC and snow depth estimation for either pixel flagging or corrective actions. ...
Thin ice with a thickness of less than half a meter produces strong salt and heat fluxes which affect deep water circulation and weather in the polar oceans. The identification of thin ice areas is essential for ship navigation. We have developed thin ice detection algorithms for the AMSR2 and FY-3C MWRI radiometer data over the Arctic Ocean. Thin ice (<20 cm) is detected based on the classification of the H-polarization 89–36-GHz gradient ratio (GR8936H) and the 36-GHz polarization ratio (PR36) signatures with a linear discriminant analysis (LDA) and thick ice restoration with GR3610H. The brightness temperature (TB) data are corrected for the atmospheric effects following an EUMETSAT OSI SAF correction method in sea ice concentration retrieval algorithms. The thin ice detection algorithms were trained and validated using MODIS ice thickness charts covering the Barents and Kara Seas. Thin ice detection is applied to swath TB datasets and the swath charts are compiled into a daily thin ice chart using 10 km pixel size for AMSR2 and 20 km for MWRI. On average, the likelihood of misclassifying thick ice as thin in the ATIDA2 daily charts is 7.0% and 42% for reverse misclassification. For the MWRI chart, these accuracy figures are 4% and 53%. A comparison of the MWRI chart to the AMSR2 chart showed a very high match (98%) for the thick ice class with SIC > 90% but only a 53% match for the thin ice class. These accuracy disagreements are due to the much coarser resolution of MWRI, which gives larger spatial averaging of TB signatures, and thus, less detection of thin ice. The comparison of the AMSR2 and MWRI charts with the SMOS sea ice thickness chart showed a rough match in the thin ice versus thick ice classification. The AMSR2 and MWRI daily thin ice charts aim to complement SAR data for various sea ice classification tasks.
... We target to use later ATIDA2 thin ice charts together with SAR imagery and other data for various sea ice classification products for ship navigation and offshore operations. In [49], we also used sea ice model data in addition to the thin ice charts and SAR data. The charts can be used also as input data to manual ice charting by ice services. ...
We have developed a thin ice detection algorithm for the AMSR2 radiometer data. The algorithm, denoted as ATIDA2 (AMSR2 thin ice detection algorithm – version 2), is targeted for the Arctic Ocean. The detection of thin ice with maximum thickness of 20 cm is based on the classification of 36 GHz polarization ratio (PR36) and H-polarization 89-36 GHz gradient ratio (GR8936H) signatures with a linear discrimination analysis (LDA), and thick ice restoration with GR3610H. The thick ice restoration removes erroneous thin ice detections due to thin and thick ice PR36 and GR8936H signature mixing. ATIDA2 is applied only when sea ice concentration (SIC) is ≥ 70% and air temperature is ≤-5 °C to decrease misclassification of thick ice as thin ice. For the AMSR2 L1R brightness temperature data an atmospheric correction is applied following an EUMETSAT OSI SAF correction scheme in SIC retrieval algorithms. ATIDA2 is applied to L1R swath datasets, and then the results are combined to a daily thin ice chart. ATIDA2 was developed and validated using MODIS ice thickness charts over the Barents and Kara Seas. The average probability for misclassification of thick ice as thin ice in the daily chart is 8.7%, and 37.0% for vice versa. Comparison of the ATIDA2 chart and SMOS ice thickness chart over the Arctic showed rough correspondence in the thin vs. thick ice classification. The ATIDA2 chart is targeted to be used together with SAR imagery for various sea ice classifications..
... We study here thin ice charting in the Barents and Kara Seas in winter conditions using Advanced Microwave Scanning Radiometer 2 (AMSR2) high-frequency (36.5 and 89 GHz) brightness temperature (T B ) data. Our purpose is to use later the resulting AMSR2 thin ice chart as one input data source for an ice thickness chart for the first-year ice (FYI) [1]. The thickness chart is also based on synthetic aperture radar (SAR) and sea ice model data. ...
... Therefore, GR8936H is more sensitive to changes in snow and sea ice top layer properties than PR36. We adopted in [24] that LDA as our statistical tool [33] and determined the following LDA score (LDA s )-based classifier: (1) where LDA st is the LDA s threshold for detecting thin ice with h it of 0.30 m. In the World Meteorological Organization (WMO) sea ice nomenclature, this thickness range includes nilas (<10 cm) and young ice (10-30 cm) types [34]. ...
... The LDA classifier in (1) cannot detect both the thin ice and thick ice areas simultaneously with high accuracies. For our application of the thin ice chart, combining it with SAR and sea ice model data for an ice thickness chart for ship navigation [1], we require type I error to be small. We found that the optimal LDA parameters in the classifier and h it depended on the daily mean air temperature (T am ), but we could not parameterize the classifier optimally according to T am due to lack of the MODIS h iM data for thick ice (>0.30 ...
We have developed an algorithm for thin ice detection under winter conditions using the Advanced Microwave Scanning Radiometer 2 (AMSR2) radiometer high-frequency brightness temperature (36 and 89 GHz) L1R swath data, and a method to combine thin ice swath charts to a more reliable daily thin ice chart. Moderate Resolution Imaging Spectroradiometer (MODIS) ice thickness swath charts were used as reference data for the algorithm development. The algorithm is based on the classification of 36-GHz polarization ratio (PR36) and H-polarization 89-36-GHz gradient ratio (GR3689H) signatures with linear discriminant analysis. We applied an atmospheric correction to the AMSR2 L1R data following established sea ice concentration (SIC) retrieval algorithms. The PR36 and GR8936H signatures were adjusted to a constant air temperature (Tₐ) before the thin ice detection using an empirical relationship between them and Tₐ. The maximum thickness of detected thin ice was estimated to be 20 cm. The thin ice detection with the L1R data is conducted only when SIC ≥ 70% and Tₐłe-5 °C to limit conditions where thick ice may be erroneously detected as thin ice. The thin ice detection algorithm was developed for the Barents and Kara Seas, but it should also be applicable for other Arctic marginal ice zones (MIZs). The daily thin ice chart was validated using an independent set of MODIS daily ice thickness charts. The average probability for misclassification of thick ice as thin ice was 10% and 32% for vice versa. We demonstrate the use of the daily thin ice chart for monitoring the thin ice fraction in the Barents and Kara Seas.
... The aim is to include the LFI estimation in the FMI operational Arctic sea ice product portfolio. Technical details of the FMI operational Arctic sea ice thickness and concentration test products can be found in Simila et al. (2016) and Makynen and Karvonen (2018). One purpose is to utilize the LFI algorithm result first to locate the static ice fields and then apply the FMI HIGH-resolution Thermodynamic Snow/Ice model (HIGHTSI) (Launiainen and Cheng, 1998;Cheng et al., 2003) to estimate the ice growth or melt during the static ice periods to improve the ice thickness estimates over the static ice areas. ...
... According to this study the proposed algorithm (FMI-A) is considered suitable for operational LFI detection to be included in the daily automated FMI sea ice products (Simila et al., 2016;Makynen and Karvonen, 2018), which have thus far already been run in an operational test mode over the studied area during a few winters. There also exist plans at FMI to replace the TOPAZ-4 ice model (Sakov et al., 2012) data used as background information for ice thickness estimation from Earth observation (EO) data with the coarse-scale ice thickness from radar altimeter data and with the FMI thermodynamic ice model HIGHTSI over the static ice areas during the static ice time periods detected by the proposed LFI algorithm. ...
Here a method for estimating the land-fast ice (LFI) extent from
dual-polarized Sentinel-1 SAR mosaics of an Arctic study area over the Kara
and Barents seas is presented. The method is based on temporal
cross-correlation between adjacent daily SAR mosaics. The results are
compared to the LFI of the Russian Arctic and Antarctic Research Institute
(AARI) ice charts. Two versions of the method were studied: in the first
version (FMI-A) the overall performance was optimized, and in the second
version (FMI-B) the target was a low LFI misdetection rate. FMI-A detected
over 73 % of the AARI ice chart LFI, and FMI-B a little over 50 % of
the AARI ice chart LFI. During the winter months the detection rates were
higher than during the melt-down season for both the studied algorithm
versions. An LFI time series covering the time period from October 2015 to
the end of August 2017 computed using the proposed methodology is provided on
the FMI ftp server. The time series will be extended twice annually.
... We therefore suggest that the most promising approach for an operational SIT estimation over the Bohai Sea is based on dual-polarization (HH and HV) imagery available from RS-2 (used here) or SENTINEL-1 SAR and a SIT background field derived from an ice chart (as has been conducted for the Baltic Sea [53,54]), or from a sea ice model, as for the Barents and Kara Seas [55,56]. SAR backscatter statistics are used to modify this background SIT field into a finer spatial resolution. ...
... This approach can be complemented by microwave radiometer data to improve the detection of thin-ice areas. Such approaches have been proposed, for example, in [53][54][55][56]. The method developed here is a variant of this approach, combining a background SIT field from an ice model and microwave radiometer data and SAR backscattering and texture features for a fine-resolution SIT estimation. ...
In this paper we estimate two essential sea ice parameters, namely sea ice concentration and sea ice thickness, for the Bohai Sea using a combination of a thermodynamic sea ice model and earth observation (EO) data from synthetic aperture radar (SAR) and a passive microwave radiometer, which can be applied also in cloudy conditions and without daylight. We compare the estimation results with in-situ measurements conducted in the study area and estimates based on independent EO data from near-infrared/optical instruments. These comparisons suggest that the SAR-based discrimination between sea ice and open water works well, and areas of thinner and thicker ice can be distinguished. A larger comprehensive training dataset is needed to set up an operational algorithm for sea ice concentration and thickness.
Keywords: Synthetic aperture radar, passive microwave remote sensing, ice concentration, ice thickness, thermodynamic ice model
We demonstrate the use of eye tracking methodology as a non-invasive way to identify elements behind uncertainties typically introduced during the process of sea ice charting using satellite synthetic aperture radar (SAR) imagery. To our knowledge, this is the first time eye tracking is used to study the interpretation of satellite SAR images over sea ice. We describe differences and similarities between expert and novice analysts while visually interpreting a set of SAR sea ice images. In ice charting, SAR imagery serves as the base layer for mapping the sea ice conditions. Linking the backscatter signatures in the SAR imagery and the actual sea ice parameters is a complex task which requires highly trained experts. Mapping of sea ice types and parameters in the SAR imagery is therefore subject to an analyst's performance which may lead to inconsistencies between the ice charts. By measuring the fixation duration over different sea ice types we can identify the features in a SAR image that require more cognitive effort in classification, and thus are more prone to miss-classification. Ambiguities in classification were found especially for regions less restrictive for navigation, consisting of mixed sea ice properties and uneven thicknesses. We also show that the experts are able to correctly map large sea ice covered areas only by looking at the SAR images. Based on the eye movement data, ice categories with most of the surface covered by ice, i.e. in ice charts fast ice and very close ice, were easier to classify than areas with mixed ice thicknesses such as open ice or very open ice.
We present a method to combine CryoSat-2 (CS-2) radar altimeter and Sentinel-1 synthetic aperture radar (SAR) data to obtain sea ice thickness (SIT) estimates for the Barents and Kara Seas. Our approach yields larger spatial coverage and better accuracy compared to estimates based on either CS-2 or SAR alone. The SIT estimation method developed here is based on interpolation and extrapolation of CS-2 sea ice thickness (SIT) utilizing SAR segmentation and segmentwise SAR texture features. The SIT results are compared to SIT data derived from the AARI ice charts, to ORAS5. PIOMAS and TOPAZ4 ocean-sea ice data assimilation system reanalyses, and to daily MODIS based ice thickness charts. Our results are directly applicable to the future CRISTAL mission and Copenicus programme SAR missions.
