A linear model to derive melt pond depth from hyperspectral data
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 vert...
| Main Authors: | , |
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| Format: | Text |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/tc-2019-261 https://tc.copernicus.org/preprints/tc-2019-261/ |
| Summary: | 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 vertical melt pond evolution 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 melt water on the reflected signal, the water depth cannot be determined. To solve the problem, we simulated the way melt water 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. We validated the model using 49 in situ melt pond spectra and corresponding depths from ponds on dark and bright ice. Retrieved pond depths are precise ( RMSE = 2.81 cm) and highly correlated with in situ measurements ( r = 0.89; p = 4.34e−17). The model further explains a large portion of the variation in pond depth ( R 2 = 0.74). Our results indicate that pond depth is retrievable from optical data under clear sky conditions. This technique is potentially transferrable to hyperspectral remote sensors on UAVs, aircraft and satellites. |
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