000K utf8 1100 $c2020 1500 eng 2050 urn:nbn:de:gbv:8:3-2022-00339-6 2051 10.5194/tc-14-2567-2020 3000 König, Marcel 3010 Oppelt, Natascha 4000 A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data [König, Marcel] 4209 Melt ponds are key elements in the energy balance of Arctic sea ice. Observing their temporal evolution is crucial for understanding melt processes and predicting sea ice evolution. Remote sensing is the only technique that enables large-scale observations of Arctic sea ice. However, monitoring melt pond deepening in this way is challenging because most of the optical signal reflected by a pond is defined by the scattering characteristics of the underlying ice. Without knowing the influence of meltwater on the reflected signal, the water depth cannot be determined. To solve the problem, we simulated the way meltwater changes the reflected spectra of bare ice. We developed a model based on the slope of the log-scaled remote sensing reflectance at 710 nm as a function of depth that is widely independent from the bottom albedo and accounts for the influence of varying solar zenith angles. We validated the model using 49 in situ melt pond spectra and corresponding depths from shallow ponds on dark and bright ice. Retrieved pond depths are accurate (root mean square error, RMSE=2.81 cm; nRMSE=16 %) and highly correlated with in situ measurements (r=0.89; p=4.34×10−17) 4950 https://doi.org/10.5194/tc-14-2567-2020$xR$3Volltext$534 4950 https://nbn-resolving.org/urn:nbn:de:gbv:8:3-2022-00339-6$xR$3Volltext$534 4961 https://macau.uni-kiel.de/receive/macau_mods_00002757 5051 550 5550 arctic sea 5550 hyperspectral remote sensors 5550 ice 5550 meltwater 5550 pond depth