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