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

TL;DR

This paper introduces a neural network model for accurately retrieving Arctic melt pond fractions from satellite imagery, improving understanding of sea-ice albedo and forecasting.

Contribution

The study presents a novel multi-layer neural network model that effectively utilizes multi-spectral satellite data and prior melt pond information for Arctic MPF retrieval.

Findings

Achieved RMSE of 3.91% in MPF retrieval from MODIS data

Correlation coefficient of 0.73 indicates strong model performance

Seasonal MPF distribution aligns with previous research

Abstract

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.