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Download Detection of Melt Ponds on Arctic Sea Ice with Optical by Anja Rösel PDF

By Anja Rösel

The Arctic sea ice is characterised by means of profound alterations brought on by floor melting techniques and the formation of soften ponds in summer time. soften ponds give a contribution to the ice-albedo suggestions as they decrease the outside albedo of sea ice, and for this reason speed up the decay of Arctic sea ice. To quantify the melting of the full Arctic sea ice, satellite tv for pc dependent observations are important. as a result of diversified spectral houses of snow, ice, and water, theoretically, multi-spectral optical sensors are important for the research of those detailed floor forms. This research demonstrates the opportunity of optical sensors to become aware of soften ponds on Arctic sea ice. For the 1st time, an Arctic-wide, multi-annual soften pond facts set for the years 2000-2011 has been created and analyzed.

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Extra resources for Detection of Melt Ponds on Arctic Sea Ice with Optical Satellite Data

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Since a calm water surface is assumed to be a specular reflector, this dependence is expected. In contrast thereto, the maximum values—representing high reflecting surfaces—display no dependence on the solar zenith. The same effect can also be observed in Fig. 5. Since we cannot observe any dependence in the high values to the solar irradiance, the BRDF correction can be neglected for sea ice areas represented in MODIS pixels. In a comparison of surface albedo of the Arctic Ocean from AVHRR data with SHEBA data it is described, that with the onset of sea ice melt, the surface becomes very inhomogeneous and is assumed to be a Lambertian reflector [86].

5 km grid cells that contains at least 50 % cloud-free pixels. In the following context, this product is called “pixel-weight-mask based on a 50 % threshold”. Reasons for this masking procedure are described in Sect. 3. 1 The used approximation method cannot handle sharp edges of a rectangular function. 40 5 Melt Pond Determination from MODIS Data Fig. 5 km grid. This example is from the data set from July 23, 2003. 2 Regularization As described in Sect. 1, Eq. 3) is both, overdetermined and not well conditioned.

Hence, the threshold must be defined for each satellite scene. Therefore, it is important to investigate how sensitive the results depend on a variation of G2T D ˙1 % and G2T D ˙5 %, and correspondingly 23T D ˙1 % and 23T D ˙5 % for the method of Markus et al. (2003) [44]. 45 %. The almost linear increasing of the error in both procedures shows, that the distribution of the melt pond fraction is in this case a uniform distribution. 5 percentage points lower than for the method of Markus et al. (2003) [44].

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