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White zones in the right figure stand for a cloud mask.
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These include (i) a cloud mask, (ii) a snow/sea ice discriminator, (iii) algorithms for retrieval of snow properties (snow grain size and impurities) and (iv) surface temperature of snow/ice and open ocean water.
A cloud mask and confidence quality assessment data were used to create cloud-free image collections, which were used to derive per-pixel time-series spectral metrics for each epoch.
Up to now, most high resolution images have been distributed without an associated cloud mask.
Taravat et al. [13] introduced and evaluated a multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI images.
The cloud mask and snow/ice discriminator are used for cloud clearing as well as surface classification.
First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask.
The cloud mask correlation function is chosen because the clear/cloud boundaries are thought to be important for 3D radiative transfer effects in cumulus.
The Atmosphere data types are aerosol, water vapor, cloud, temperature and moisture profiles, and cloud mask.
Cloud cover is determined from the standard MODIS cloud mask of Ackerman et al. [ 1998].
For other land regions where the cloud scaling threshold is lower than 0.8 (Fig. 3), the dense cloud masks aerosol DFE and leaves very limited ΔGPP (Fig. 4c).
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