A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data
<p>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, monito...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-08-01
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| Series: | The Cryosphere |
| Online Access: | https://tc.copernicus.org/articles/14/2567/2020/tc-14-2567-2020.pdf |
| _version_ | 1819019531923750912 |
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| author | M. König N. Oppelt |
| author_facet | M. König N. Oppelt |
| author_sort | M. König |
| collection | DOAJ |
| description | <p>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 <span class="inline-formula">nm</span> 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, <span class="inline-formula">RMSE=2.81</span> <span class="inline-formula">cm</span>; <span class="inline-formula"><i>n</i>RMSE=16</span> %) and
highly correlated with in situ measurements (<span class="inline-formula"><i>r</i>=0.89</span>; <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>p</mi><mo>=</mo><mn mathvariant="normal">4.34</mn><mo>×</mo><msup><mn mathvariant="normal">10</mn><mrow><mo>-</mo><mn mathvariant="normal">17</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="81pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="e889bc451f3575818ff1fb9c7014edd0"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-14-2567-2020-ie00001.svg" width="81pt" height="15pt" src="tc-14-2567-2020-ie00001.png"/></svg:svg></span></span>). The model further explains a large portion of the
variation in pond depth (<span class="inline-formula"><i>R</i><sup>2</sup>=0.74</span>). 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.</p> |
| first_indexed | 2024-12-21T03:36:48Z |
| format | Article |
| id | doaj.art-49d417fc272b4f61b4268a6bba87e6a0 |
| institution | Directory Open Access Journal |
| issn | 1994-0416 1994-0424 |
| language | English |
| last_indexed | 2024-12-21T03:36:48Z |
| publishDate | 2020-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The Cryosphere |
| spelling | doaj.art-49d417fc272b4f61b4268a6bba87e6a02022-12-21T19:17:19ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242020-08-01142567257910.5194/tc-14-2567-2020A linear model to derive melt pond depth on Arctic sea ice from hyperspectral dataM. KönigN. Oppelt<p>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 <span class="inline-formula">nm</span> 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, <span class="inline-formula">RMSE=2.81</span> <span class="inline-formula">cm</span>; <span class="inline-formula"><i>n</i>RMSE=16</span> %) and highly correlated with in situ measurements (<span class="inline-formula"><i>r</i>=0.89</span>; <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>p</mi><mo>=</mo><mn mathvariant="normal">4.34</mn><mo>×</mo><msup><mn mathvariant="normal">10</mn><mrow><mo>-</mo><mn mathvariant="normal">17</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="81pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="e889bc451f3575818ff1fb9c7014edd0"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-14-2567-2020-ie00001.svg" width="81pt" height="15pt" src="tc-14-2567-2020-ie00001.png"/></svg:svg></span></span>). The model further explains a large portion of the variation in pond depth (<span class="inline-formula"><i>R</i><sup>2</sup>=0.74</span>). 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.</p>https://tc.copernicus.org/articles/14/2567/2020/tc-14-2567-2020.pdf |
| spellingShingle | M. König N. Oppelt A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data The Cryosphere |
| title | A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data |
| title_full | A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data |
| title_fullStr | A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data |
| title_full_unstemmed | A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data |
| title_short | A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data |
| title_sort | linear model to derive melt pond depth on arctic sea ice from hyperspectral data |
| url | https://tc.copernicus.org/articles/14/2567/2020/tc-14-2567-2020.pdf |
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