Applying High-Resolution Visible Imagery to Satellite Melt Pond Fraction Retrieval: A Neural Network Approach

• Main Authors: Liu, Qi, Zhang, Yawen, Lv, Qin, Shang, Li
• Format: Report
• Language: unknown
• Published: arXiv 2017
• Subjects: Atmospheric and Oceanic Physics physics.ao-ph; Computer Vision and Pattern Recognition cs.CV; FOS Physical sciences; FOS Computer and information sciences; Arctic; albedo; Sea ice
• Online Access: https://dx.doi.org/10.48550/arxiv.1704.04281; https://arxiv.org/abs/1704.04281

During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper proposes a satellite MPF retrieval model based on the multi-layer neural network, named MPF-NN. Our model uses multi-spectral satellite data as model input and MPF information from multi-site and multi-period visible imagery as prior knowledge for modeling. It can effectively model melt ponds evolution of different regions and periods over the Arctic. Evaluation results show that the MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and a correlation coefficient of 0.73. The seasonal distribution of MPF is also consistent with previous results.