Abstract
The sea ice cover is changing rapidly in polar regions, and sea ice products with high temporal and spatial resolution are of great importance in studying global climate change and navigation. In this paper, an ice map generation model based on Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance bands is constructed to obtain sea ice data with a high temporal and spatial resolution. By constructing a training sample library and using a multi-feature fusion machine learning algorithm for model classification, the high-accuracy recognition of ice and cloud regions is achieved. The first product provided by this algorithm is a near real-time single-scene sea ice presence map. Compared with the photo-interpreted ground truth, the verification shows that the algorithm can obtain a higher recognition accuracy for ice, clouds, and water, and the accuracy exceeds 98%. The second product is a daily and weekly clear sky map, which provides synthetic ice presence maps for one day or seven consecutive days. A filtering method based on cloud motion is used to make the product more accurate. The third product is a weekly fusion of clear sky optical images. In a comparison with the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration products performed in August 2019 and September 2020, these composite images showed spatial consistency over time, suggesting that they can be used in many scientific and practical applications in the future.
Highlights
With the onset of global warming in recent years, the melting rate of polar sea ice has accelerated since the late 1970s [1]
The two parameters ntree and mtry were determined with a test based on the modified out-of-bag (M-OOB) accuracy in this study, and we found that the number of 500 for ntree and four for mtry worked well for the classification task
In order to illustrate the effectiveness of the training sample library established in this article and the accuracy of the results, the classification results of images from different locations acquired by the two satellites at different times using the Multi-Feature Level Fusion Random Forest (MFLFRF) algorithm are compared in Figures 3 and 4
Summary
With the onset of global warming in recent years, the melting rate of polar sea ice has accelerated since the late 1970s [1]. Sea ice plays an important role in the study of global climate change [3,4,5] and biodiversity [6]. Sea ice monitoring will help us to better understand the impact of climate change on species’ habitats. With the increasing frequency of maritime economic activities and traffic in the Arctic, the demand for sea ice information monitoring is increasing rapidly [7,8,9,10,11]. The increasing rate of sea ice melting has put the navigation of the Arctic waterway on the agenda, and sea ice information with a high spatial and temporal resolution will help us to accurately judge sea routes
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