Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data

Karvonen, Juha; Shi, Lijian; Cheng, Bin; Similä, M.; Mäkynen, M.; Vihma, Timo

doi:10.3390/rs9030234

Cited by 26 publications

(23 citation statements)

References 71 publications

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“…We also tested the usefulness of some textural features. In our earlier studies, we observed that spatial autocorrelation ( [32,33]) and entropy ( [8,34,35]) are effective in SAR classification. Here, we computed them in a window size at a scale of 5.5 km in both polarizations.…”

Section: Featuresmentioning

confidence: 96%

Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

Similä

Lensu

2018

Remote Sensing

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The automatic identification system (AIS) was developed to support the safety of marine traffic. In ice-covered seas, the ship speeds extracted from AIS data vary with ice conditions that are simultaneously reflected by features in synthetic aperture radar (SAR) images. In this study, the speed variation was related to the SAR features and the results were applied to generate a chart of expected speeds from the SAR image. The study was done in the Gulf of Bothnia in March 2013 for ships with ice class IA Super that are able to navigate without icebreaker assistance. The speeds were normalized to dimensionless units ranging from 0 to 10 for each ship. As the matching between AIS and SAR was complicated by ice drift during the time gap (from hours to two days), we calculated a set of local statistical SAR features over several scales. Random forest tree regression was used to estimate the speed. The accuracy was quantified by mean squared error and by the fraction of estimates close to the actual speeds. These depended strongly on the route and the day. The error varied from 0.4 to 2.7 units 2 for daily routes. Sixty-five percent of the estimates deviated by less than one speed unit and 82% by less than 1.5 speed units from the AIS speeds. The estimated daily mean speeds were close to the observations. The largest speed decreases were provided by the estimator in a dampened form or not at all. This improved when the ice chart thickness was included as a predictor.

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Section: Featuresmentioning

confidence: 96%

Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

Similä

Lensu

2018

Remote Sensing

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“…Although these satellite data cover the study area with a fairly high spatial resolution (~1 km), they are typically limited in temporal resolution and provide only one image covering the study area per day, which are constrained by the proportion of cloud-free images of the MODIS and the AVHRR data being mostly less than 20% [5,15]. Synthetic aperture radar (SAR) data is also commonly used to monitor sea ice, which has also been applied to detecting ice types and thickness in the Bohai Sea due to their fine spatial resolution and insensitivity to cloud interference [17][18][19]. However, the SAR data are limited by a long repeat cycle (6-44 days) [20].…”

Section: Bandmentioning

confidence: 99%

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Huang

Shao

et al. 2019

Sustainability

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Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.

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“…In the training, MODIS R 1 data set the OW and SI miss-classification percentages were below 0.5%. The R 1 imagery has the best possible MODIS resolution of 250 m. Previously also, another simple algorithm, the reflectance ratio (R r ) between bands 1 and 2 has been used for the OWSI classification [69,70]. We found that this ratio is sometimes contaminated by a detector striping artefact (see [71]) in the R 2 image which leads to false SI detection.…”

Section: Discussionmentioning

confidence: 95%

“…We have previously conducted OWSI classification in the Bohai Sea using the reflectance ratio ( ) between bands 1 and 2 [69]. SI was detected with < 1.8.…”

Section: Open Water-sea Ice Classificationmentioning

confidence: 99%

MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data

Mäkynen

Karvonen

2017

Remote Sensing

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Abstract:We have developed algorithms and procedures for calculating daily sea ice thickness (SIT) and open water-sea ice (OWSI) charts, based on the Moderate Resolution Imaging Spectroradiometer (MODIS), ice surface temperature (IST) (night-time only), and reflectance (R) swath data, respectively. The resolution of the SIT chart is 1 km and that of the OWSI chart is 250 m. The charts are targeted to be used in development and validation of sea ice products from microwave sensor data. We improve the original MODIS cloud masks for the IST and R data, with a focus on identifying larger cloud-free areas in the data. The SIT estimation from the MODIS IST swath data follows previous studies. The daily SIT chart is composed from available swath charts by assigning daily median SIT to a pixel. The OWSI classification is simply conducted by a fixed threshold for the MODIS band 1 R. This was based on manually selected R data for various ice types in late winter, early melt, and advanced melt conditions. The composition procedures for the daily SIT and OWSI charts somewhat compensates for errors due to the undetected clouds. The SIT and OWSI charts were compared against manual ice charts by Arctic and Antarctic Research Institute in Russia and by Norwegian Meteorological Institute, respectively, and on average, a good relationship between the charts was found. Pixel-wise comparison of the SIT and OWSI charts showed very good agreement in open water vs. sea ice classification, which gives further confidence on the reliability of our algorithms. We also demonstrate usage of the MODIS OWSI and SIT charts for validation of sea ice concentration charts based on the SENTINEL-1 SAR and AMSR2 radiometer data and two different algorithms.

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Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data

Cited by 26 publications

References 71 publications

Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data

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