  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.
