Joint mapping of melt pond bathymetry and water volume on sea ice using optical remote sensing images and physical reflectance models

Abstract

Melt ponds are a common phenomenon on the surface of Arctic sea ice during the summer, and their low albedo strongly influences the energy balance of the Arctic sea ice. Estimating Melt Pond Fraction (MPF) and Melt Pond Depth (MPD) using optical remote sensing is crucial for a better understanding of rapid climate change in the Arctic region. However, current retrieval algorithms for monitoring Arctic melt ponds using optical imagery often fail to estimate MPD. In this study, a radiative transfer model for melt ponds is establish to describe the relationship between melt pond reflectance and its physical properties. Using Sentinel-2 observation data, we propose a novel algorithm for the simultaneous retrieval of MPF and MPD, thereby enabling the estimation of Melt Pond Volume (MPV). This method does not depend on prior assumptions regarding the spectral reflectance of sea ice and melt ponds, and it accounts for the spatiotemporal variability in their reflectance. Compared with other high-resolution MPF and MPD products, the results of this study demonstrate comparable spatial distributions. The root mean square error (RMSE) of the retrieved MPF is less than 10 %, and the RMSE for MPD is approximately 24.51 cm. The analysis of melt pond evolution along the MOSAiC track shows the rapid expansion of melt ponds and their significant spatial variability. Ultimately, using Google Earth Engine (GEE) and machine learning, a dataset of MPF, MPD, and MPV for the Arctic from 2013 to 2023 is generated from 57,842 Landsat-8 images. Correlation analysis shows that MPF, MPD, and MPV all have a positive correlation with downward surface radiation. The approach outlined in this study is entirely based on remote sensing imagery, demonstrating significant potential for large scale application. This offers new opportunities for estimating the volume of water stored in Arctic summer melt ponds.