A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data

Abstract

Abstract. Melt ponds are key elements in the energy balance of Arctic sea ice. Observing their temporal evolution is crucial for understanding melt processes and predicting sea ice evolution. Remote sensing is the only technique that enables large-scale observations of Arctic sea ice. However, monitoring melt pond deepening in this way is challenging because most of the optical signal reflected by a pond is defined by the scattering characteristics of the underlying ice. Without knowing the influence of meltwater on the reflected signal, the water depth cannot be determined. To solve the problem, we simulated the way meltwater changes the reflected spectra of bare ice. We developed a model based on the slope of the log-scaled remote sensing reflectance at 710 nm as a function of depth that is widely independent from the bottom albedo and accounts for the influence of varying solar zenith angles. We validated the model using 49 in situ melt pond spectra and corresponding depths from shallow ponds on dark and bright ice. Retrieved pond depths are accurate (root mean square error, RMSE=2.81 cm; nRMSE=16 %) and highly correlated with in situ measurements (r=0.89; p=4.34×10-17). The model further explains a large portion of the variation in pond depth (R2=0.74). Our results indicate that our model enables the accurate retrieval of pond depth on Arctic sea ice from optical data under clear sky conditions without having to consider pond bottom albedo. This technique is potentially transferrable to hyperspectral remote sensors on unmanned aerial vehicles, aircraft and satellites.

Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice

“…However, the other parameters in WASI were the same as KO20. Figure 1a is the spectral look up table (LUT), which shows the linear mixtures of the two measured bottom albedos in 25% steps and is quite similar to the results in Figure 6 of König and Oppelt [20]. Every bottom-type mixture contains 101 spectral, namely the pond depth ranging from 0 to 100 cm.…”

“…The retrieved H p and measured in situ of the solar zenith angle between 58.9 • and 61 • exhibited a powerful correlation (R = 0.89 and ε = 0.028 m) under the clear sky. In Figure 11 of König and Oppelt [20], validations data of pond depth were quite shallow within the range of 6-25 cm and solar zenith angles around 60 • .…”

“…The dependence of pond reflectance on its depth is apparent only in the red range and only for light ponds with high albedos of the underlying ice [7,31]. König and Oppelt [20] (KO20 hereafter) used this effect and developed a linear model to derive melt pond depth. The Water Color Simulator (WASI) can simulate the way pond water changes the reflected spectra of bare ice.…”

“…Optical data have enough spatial resolution to distinguish shallow melt ponds, and different algorithms addressing this issue have also been proposed [17][18][19][20][21]. The reflectance or the albedo of melt ponds is a common input parameter for these algorithms.…”

Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network

“…In addition, the temporal and spatial distribution of melt pond is not considered in this study. At present, the researches on melt pond mainly focus on the identification of melt pond [49,50], the depth of melt pond [51] and the factors affecting the coverage area [52]. Only in small areas such as the Canadian Arctic Archipelago has the literature studied the evolution of the melt pond [53].…”