K. Andrea Scott

Sea Ice Concentration Estimation During Melt From Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study

High-resolution ice concentration maps are of great interest for ship navigation and ice hazard forecasting. In this case study, a convolutional neural network (CNN) has been used to estimate ice concentration using synthetic aperture radar (SAR) scenes captured during the melt season. These dual-pol RADARSAT-2 satellite images are used as input, and the ice concentration is the direct output from the CNN. With no feature extraction or segmentation postprocessing, the absolute mean errors of the generated ice concentration maps are less than 10% on average when compared with manual interpretation of the ice state by ice experts. The CNN is demonstrated to produce ice concentration maps with more detail than produced operationally. Reasonable ice concentration estimations are made in melt regions and in regions of low ice concentration.

An Assessment of Sea-Ice Thickness Along the Labrador Coast From AMSR-E and MODIS Data for Operational Data Assimilation

In this paper, sea-ice thickness values are calculated along the Labrador coast using data from two sensors representative of those available for operational data assimilation. Data from the first sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), are used to calculate the ice thickness using a heat balance equation. Relationships between the MODIS ice thickness and polarization ratio from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) are used to calculate the thickness of thin ice (less than 0.2 m) from the AMSR-E data. This is done for each frequency on the AMSR-E sensor in the range of 6.9-36.5 GHz. Through comparison with data from ice charts, it is found that the errors are lowest for thickness values calculated from low-frequency AMSR-E data. The accuracies of the ice thickness from MODIS, AMSR-E, operational ice charts, and two moored upward looking sonars are further assessed using the triple collocation method. It is found that the error associated with ice thickness from AMSR-E is the lowest and the error associated with ice thickness from MODIS is the highest. While the MODIS data represent the small-scale variability of the sea-ice thickness better than the AMSR-E data, the MODIS data can produce spurious values of ice thickness due to unmasked clouds. To use ice thickness from MODIS in an automated algorithm, quality control would need to be applied to the MODIS data to remove unmasked clouds which lead to spurious values of thick ice. The errors calculated for the ice thickness from AMSR-E, which are calculated based on a relationship calibrated with MODIS ice thickness from a clear-sky day, indicate that these data would be useful for operational data assimilation.

Direct Assimilation of AMSR-E Brightness Temperatures for Estimating Sea Ice Concentration

AbstractIn this paper a method to directly assimilate brightness temperatures from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) to produce ice concentration analyses within a three-dimensional variational data assimilation system is investigated. To assimilate the brightness temperatures a simple radiative transfer model is used as the forward model that maps the state vector to the observation space. This allows brightness temperatures to be modeled for all channels as a function of the total ice concentration, surface wind speed, sea surface temperature, ice temperature, vertically integrated water vapor, and vertically integrated cloud liquid water. The brightness temperatures estimated by the radiative transfer model are sensitive to the specified values for the sea ice emissivity. In this paper, two methods of specifying the sea ice emissivity are compared. The first uses a constant value for each polarization and frequency, while the second uses a simple emissivity ...

Improved Sea Ice Concentration Estimation Through Fusing Classified SAR Imagery and AMSR-E Data

Abstract. A method to automatically combine binary ice/water information from synthetic aperture radar (SAR) sea ice images with the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) daily ice concentration product is proposed for the purpose of generating sea ice concentration estimates with improved detail and accuracy. First, each pixel in the SAR image is labeled as ice or water using the MAp-Guided Ice Classification (MAGIC) SAR image classification system. Second, the labeled pixels are modeled as a Bernoulli process and combined with the AMSR-E ice concentration data in a Bayesian framework to generate improved ice concentration estimation. Visually interpreted ice/water extent and sea ice image analyses from the Canadian Ice Service (CIS) are used as comparison data. The combination of SAR ice/water labeled pixels with the AMSR-E ice concentration is shown to improve the ice concentration estimates, especially at the ice edge where substantial improvements are observed. Although the present study uses ice/water information from SAR, the method is general and could be used with other sources of ice/water remote sensed data. Résumé. Une méthode pour combiner automatiquement les informations binaires de glace/eau des images de radar à ouverture synthétique «synthetic aperture radar» (SAR) de la glace de mer avec le produit de la concentration quotidienne des glaces de «Advanced Microwave Scanning Radiometer-EOS» (AMSR-E), est proposée dans le but de produire des estimations de concentration de glace de mer en améliorant les détails et la précision. Tout d’abord, chaque pixel de l’image SAR est étiqueté comme de la glace ou de l’eau en utilisant le système de classification de l’image SAR «MAp-Guided Ice Classification» (MAGIC). Deuxièmement, les pixels étiquetés sont modélisés comme un processus de Bernoulli et combinés avec les données de concentration de glace AMSR-E dans un cadre Bayésien pour générer une meilleure estimation de la concentration de glace. L’étendue de la glace/eau interprétée visuellement et les analyses de l’image de la glace de mer du Service canadien des glaces «Canadian Ice Service» (CIS) sont utilisées comme données de comparaison. La combinaison des pixels SAR étiquetés glace/eau avec la concentration de glace AMSR-E améliore les estimations de concentration de glace, en particulier à la lisière de glace où des améliorations substantielles sont observées. Bien que la présente étude utilise les informations de glace/eau de SAR, la méthode est générale et peut être utilisée avec d’autres sources de données de télédétection de glace/eau.

Sea Ice Surface Temperature Estimation Using MODIS and AMSR-E Data Within a Guided Variational Model Along the Labrador Coast

In this study, a new method, entitled as the multi-modality guided variational (MGV) method, is proposed, in which the data from a passive microwave sensor is used jointly with the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the sea ice surface temperature (IST). The method augments existing sea IST values from the MODIS IST map, while filling in areas in the MODIS image that may be sparsely sampled due to the cloud cover, or due to increased spacing between the pixels at the swath edges. The former issue is particularly problematic in the marginal ice zone, where the atmospheric conditions often lead to persistent cloud cover. The sea IST is of interest because it can be used to estimate the sea ice thickness, an important parameter for shipping, climate change, and weather forecasting applications. The impact of the MGV method is checked through a comparison between the sea ice thickness calculated using the swath surface temperature and that calculated using the surface temperature from MGV. Using the operational ice charts as a guideline, it is found that the sea ice thickness values calculated using the MGV surface temperature are realistic, and there is a 16% increase in the number of sea ice thickness data points available when the MGV method is used as compared to when the swath data are used.

Assimilation of SAR data in the marginal ice zone

The assimilation of Synthetic Aperture Radar (SAR) data in an operational data assimilation system for the purpose of estimating sea ice properties is a relatively unexplored area. One of the areas where SAR data can provide key information regarding the details of the ice cover is in the marginal ice zone. However, automatic interpretation of the images in this area is challenging due to sensitivity of the backscattered signal to changing surface conditions. In this study different texture features are assimilated into a background sea ice state in the marginal ice zone. The contribution of the various texture features to the analysis is evaluated.

Sea ice and open water classification of sar imagery using cnn-based transfer learning

Mapping sea ice and open water in the oceans is significant for many applications. Accurate and robust classification methods of sea ice and open water are in demand by ice services. Convolutional neural networks (CNNs) are becoming increasingly popular in many research communities due to availability of large image datasets and high-performance computing systems. As Convolutional networks (ConvNets) have achieved great success on many image classification tasks, we pursue this method for the classification of image patches from synthetic aperture radar (SAR) imagery into ice and water. In this study we use image patches with three dimensions (HH polarization, HV polarization, and incidence angle) in a transfer learning method with CNNs: extracting features of the patches from AlexNet and applying a softmax classifier. Our method achieves an overall classification accuracy of 92.36% based on the held-out test data.

Collecting Empirically Derived SAR Characteristic Values over One Year of Sea Ice Environments for Use in Data Assimilation

AbstractA new tool has been developed to calculate dynamic, state-specific tie points, to aid in the assimilation of various types of satellite data into Environment and Climate Change Canada’s Regional Ice Ocean Prediction System. These tie points are referred to as characteristic values (CVs). In this study, CVs are calculated for RadarSat-2 ScanSAR-Wide-A HH-HV backscatter data from October 2010 to September 2011. In this collection, the mean, standard deviation, and percentile distribution of backscatter at locations and times identified as being either ice or open water are represented over different relevant categories affecting the signal. The resulting water CVs are compared with modeled backscatter values, and are in close agreement at midrange wind speeds (5–14 m s−1), where wind slicks are not present. When compared against previously reported values, the ice CVs correspond best for ice conditions with fairly uniform backscatter distributions, such as the Arctic during the spring. When the ice ...

A hierarchical selective ensemble randomized neural network hybridized with heuristic feature selection for estimation of sea-ice thickness

In this paper, a hybrid intelligent system is developed to estimate sea-ice thickness along the Labrador coast of Canada. The developed intelligent system consists of two main parts. The first part is a heuristic feature selection algorithm used for processing a database to select the most effective features. The second part is a hierarchical selective ensemble randomized neural network (HSE-RNN) that is used to create a nonlinear map between the selected features and sea-ice thickness. The required data for processing have been collected from two sensors, i.e. moderate resolution imaging spectro-radiometer (MODIS), and advanced microwave scanning radiometer-earth (AMSR-E) observing system. To evaluate the computational advantages of the proposed intelligent framework, it is given brightness temperatures data captured at two different frequencies (low frequency, 6.9GHz, and high frequency, 36.5GHz) in addition to both atmospheric and oceanic variables from forecasting models. The obtained results demonstrate the computational power of the developed intelligent algorithm for the estimation of sea-ice thickness along the Labrador coast.

Curvelet based feature extraction of dynamic ice from SAR imagery

Synthetic Aperture Radar (SAR) images of sea ice have proven to be very useful toward classification of ice cover into ice types. However, using SAR images to separate the marginal ice zone (MIZ) from consolidated ice and open water has not been explicitly considered before. One typical feature of MIZ is that it is more dynamic than consolidated ice, and includes floes, fast and thin ice or ice eddies. The current paper utilizes the dynamic feature of MIZ to investigate a curvelet-based feature extraction method in order to classify a SAR image into open water, dynamic ice and consolidated ice, as a first step toward using SAR imagery to identify the MIZ. An experiment of 10-fold cross validation is conducted to demonstrate that the proposed feature extraction method is effective. Finally, an SVM classifier is used on a SAR image to test the performance of the curvelet-based feature. The result shows that curvelet-based feature can classify the dynamic ice accurately.