IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data (original) (raw)
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High-resolution maps of the sea-ice concentration from MODIS satellite data
Geophysical Research Letters, 2004
For realistic simulations of the atmosphere-sea-iceocean interaction in polar regions, maps of the sea-ice concentration of up to one kilometer resolution are needed. In the present paper, an algorithm is described, which is able to derive such maps based on satellite data from MODIS (Moderate Resolution Imaging Spectroradiometer). This algorithm first retrieves sea-ice concentration for each scene from the satellite-sensed surface temperature. Then, data from multiple satellite overpasses within one day are combined to a map of the sea-ice concentration. The remaining gaps are finally filled in by a scheme considering the brightness temperatures in the gap areas. The results are compared to operational sea-ice concentration products based on SSM/I (special sensor microwave imager) and good agreement is found when averaging MODIS-based data to the same resolution.
An Automated Approach for Mapping Persistent Ice and Snow Cover over High Latitude Regions
Remote Sensing, 2015
We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above~65˝N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August-15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user's accuracy of the snow/ice cover class) of 0.92, a mean recall (producer's accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly identify areas where substantial changes in glacier area have occurred since the most recent conventional glacier inventories, highlighting areas where updated inventories are most urgently needed. From a longer term perspective, the automated production of PISC maps represents an important step toward fully automated glacier extent monitoring using Landsat or similar sensors.
Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Mapping different types of sea ice that form, grow, and melt in polar oceans is essential for shipping navigation, climate change modeling, and local community safety. Currently, ice charts are manually generated by analysts at the Canadian Ice Service based on dual-polarized RADARSAT-2/RADARSAT Constellation Mission imagery on a daily basis. Inspired by the demand for a computer-based mapping system, we have developed an automatic sea-ice classification method that integrates spatial contexture (unsupervised segmentation) with textural features (supervised pixellevel labeling). First, the full-scene image is oversegmented, and the segments are merged into homogeneous regions across the entire scene. Second, pixel-based classifiers (support vector machine and random forest) are compared for their ability to label the generated homogeneous regions. Finally, the segmentation and labeling are combined using a proposed energy function. The proposed method was tested on 18 dual-polarization RADARSAT-2 scenes acquired over the Beaufort Sea. This dataset contains water, young ice, first-year ice, and multiyear ice covering melt, summer, and freeze-up seasons. The proposed method obtains an average classification accuracy of 86.33% based on the leave-one-out validation. The experimental results show that the proposed method achieves promising classification results in both the quantity and quality measurements compared with benchmark methods. The robustness against incidence angle variance indicates that the proposed method is well qualified for operational sea-ice mapping.
The Cryosphere Discussions, 2014
The presence of melt ponds on the Arctic sea ice strongly affects the energy balance of the Arctic Ocean in summer. It affects albedo as well as transmittance through the sea ice, which has consequences for the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo from Medium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, shipborne and in situ campaign data. The results show the best correlation for landfast and multiyear ice of high ice concentrations. For broadband albedo, R 2 is equal to 0.85, with the RMS (root mean square) being equal to 0.068; for the melt pond fraction, R 2 is equal to 0.36, with the RMS being equal to 0.065. The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to ice drift and challenging for the retrieval surface conditions. Combining all aerial observations gives a mean albedo RMS of 0.089 and a mean melt pond fraction RMS of 0.22. The in situ melt pond fraction correlation is R 2 = 0.52 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol, which may contribute to the discrepancy between the satellite value and the observed value: mean R 2 = 0.044, mean RMS = 0.16. An additional dynamic spatial cloud filter for MERIS over snow and ice has been developed to assist with the validation on swath data.
Enhanced snow and ice identification with the VIIRS cloud mask algorithm
Remote Sensing Letters, 2013
New procedures have been developed to help identify snow and sea ice with the Suomi-National Polar-orbiting Partnership (S-NPP) Visible Infrared Imager Radiometer Suite (VIIRS) Cloud Mask (VCM) algorithm. The accurate detection of snow and sea ice is necessary in order to apply the correct spectral tests needed to detect clouds and make accurate cloud confidence classifications. During the VCM Calibration Validation activity, it was found that the procedures in place at the time of the satellite launch occasionally produced four types of misclassifications: (1) snow and/or ice surfaces in dry atmospheric regions misclassified as clouds, (2) multi-layered clouds in humid regions misclassified as snow, (3) low-level clouds with glaciated tops misclassified as sea ice, and (4) frozen lakes not classified as ice. The new procedures presented in this article use data collected in the VIIRS mid-wavelength region, i.e. both the 3.7 µm and 4.0 µm bands, as well as the 12.0 µm IR band to eliminate all four types of misclassifications. The results demonstrate that split window, mid-wavelength IR imagery provides valuable information for developers of automated cloud classification algorithms as well as those who generate sea ice analyses in support of ocean navigation during polar wintertime conditions. It is concluded that collecting data in these mid-wavelength IR bands should be considered part of any future satellite sensor designed for environmental monitoring.
Annals of Glaciology, 2007
A new surface classification algorithm for monitoring snow and ice masses based on data from the moderate-resolution imaging spectroradiometer (MODIS) is presented. The algorithm is applied to the Greenland ice sheet for the period 2000-05 and exploits the spectral variability of ice and snow reflectance to determine the surface classes dry snow, wet snow and glacier ice. The result is a monthly glacier surface type (GST) product on a 1 km resolution grid. The GST product is based on a grouped criteria technique with spectral thresholds and normalized indices for the classification on a pixel-by-pixel basis. The GST shows the changing surface classes, revealing the impact of climate variations on the Greenland ice sheet over time. The area of wet snow and glacier ice is combined into the glacier melt area (GMA) product. The GMA is analyzed in relation to the different surface classes in the GST product. The results are validated with data from weather stations and similar types of satellitederived products. The validation shows that the automated algorithm successfully distinguishes between the different surface types, implying that the product is a promising indicator of climate change impact on the Greenland ice sheet.
Annals of Glaciology, 2013
We have studied the accuracy of ice thickness (hi) retrieval based on night-time MODIS (Moderate Resolution Imaging Spectroradiometer) ice surface temperature (Ts) images and HIRLAM (High Resolution Limited Area Model) weather forcing data from the Arctic. The study area is the Kara Sea and eastern part of the Barents Sea, and the study period spans November-April 2008–11 with 199 hi charts. For cloud masking of the MODIS data we had to use manual methods in order to improve detection of thin clouds and ice fog. The accuracy analysis of the retrieved hi was conducted with different methods, taking into account the inaccuracy of the HIRLAM weather forcing data. Maximum reliable hi under different air-temperature and wind-speed ranges was 35–50 cm under typical weather conditions (air temperature <–20cC, wind speed <5ms–1) present in the MODIS data. The accuracy is best for the 15–30 cm thickness range, ∼38%. The largest hi uncertainty comes from air temperature data. Our ice-th...
An automated algorithm for river ice monitoring over the Susquehanna River using the MODIS data
Hydrological Processes, 2014
Reliable and prompt information on river ice condition and extent is needed to make accurate hydrological forecasts to predict ice jams breakups and issue timely flood warnings. This study presents a technique to detect and monitor river ice using observations from the MODIS instrument onboard the Terra satellite. The technique incorporates a threshold-based decision tree image classification algorithm to process MODIS data and to determine the extent of ice. To differentiate between ice-covered and icefree pixels within the riverbed, the algorithm combines observations in the visible and near-infrared spectral bands. The developed technique presents the core of the MODIS-based river ice mapping system, which has been developed to support National Oceanic and Atmospheric Administration NWS's operations. The system has been tested over the Susquehanna River in northeastern USA, where ice jam events leading to spring floods are a frequent occurrence. The automated algorithm generates three products: daily ice maps, weekly composite ice maps and running cloud-free composite ice maps. The performance of the system was evaluated over nine winter seasons. The analysis of the derived products has revealed their good agreement with the aerial photography and with in situ observations-based ice charts. The probability of ice detection determined from the comparison of the product with the high-resolution Landsat imagery was equal to 91%. A consistent inverse relationship was found between the river discharge and the ice extent. The correlation between the discharge and the ice extent as determined from the weekly composite product reached 0.75. The developed CREST River Ice Observation System has been implemented at National Oceanic and Atmospheric Administration-Cooperative Remote Sensing Science and Technology Center as an operational Web tool allowing end users and forecasters to assess ice conditions on the river.
Large-Scale Patterns of Snow Melt on Arctic Sea Ice Mapped from Meteorological Satellite Imagery
Annals of Glaciology
The seasonal progression of snow melt on the Arctic pack ice is mapped from satellite shortwave imagery (0.4–1.1 micrometers) for four spring/summer seasons (1977, 1979, 1984 and 1985). This provides the first detailed information on the temporal change of the ice surface albedo in summer and of its year-to-year variability. The average surface albedo of the Arctic Basin for the years investigated falls from between 0.75 and 0.80 in early May to between 0.35 and 0.45 in late July and early August. In the central Arctic, where ice concentration remains high and ponding on the ice is limited, the July albedo ranges from 0.50 to 0.60. Overall, melt progresses poleward from the Kara and Barents Seas and from the southern Beaufort and Chukchi Seas, with the melt fronts meeting on the American side of the Pole. There are substantial year-to-year differences in the timing, duration and extent of the melt interval. The progression of melt in May and June of the earliest melt year (1977) was...