NDLI: A Cluster Approach to Cloud Cover Classification over South America and Adjacent Oceans Using a k-means/k-means++ Unsupervised Algorithm on GOES IR Imagery

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A Cluster Approach to Cloud Cover Classification over South America and Adjacent Oceans Using a k-means/k-means++ Unsupervised Algorithm on GOES IR Imagery

Content Provider	MDPI
Author	Yuchechen, Adrián Lakkis, S. Caferri, Agustín Canziani, Pablo Muszkats, Juan
Copyright Year	2020
Description	An unsupervised k-means/k-means++ clustering algorithm was implemented on daily images of standardized anomalies of brightness temperature $(Tb)$ derived from the Geostationary Operational Environmental Satellite (GOES)-13 infrared data for the period 1 December 2010 to 30 November 2016. The goal was to decompose each individual $Tb$ image into four clusters that captures the characteristics of different cloud regimes. The extracted clusters were ordered by their mean value in an ascending fashion so that the lower the cluster order, the higher the clouds they represent. A linear regression between temperature and height with temperature used as the predictor was conducted to estimate cloud top heights (CTHs) from the $Tb$ values. The analysis of the results was performed in two different ways: sample dates and seasonal features. Cluster 1 is the less dominant one, representing clouds with the highest tops and variabilities. Cluster 4 is the most dominant one and represents a cloud regime that spans the lowest 2 km of the troposphere. Clusters 2 and 3 are entangled in the sense that both have their CTHs spanning the middle troposphere. Correlations between the monthly time series of the number of pixels in each cluster and of the entropy with several circulation indices are also introduced. Additionally, a fractal-related analysis was carried out on cluster 1 in order to resolve cirrus and cumulonimbus.
Starting Page	2991
e-ISSN	20724292
DOI	10.3390/rs12182991
Journal	Remote Sensing
Issue Number	18
Volume Number	12
Language	English
Publisher	MDPI
Publisher Date	2020-09-14
Access Restriction	Open
Subject Keyword	Remote Sensing Marine Engineering Brightness Temperature Cloud Cover Cloud Regimes Clustering Goes Ir Imagery Kmeans Kmeans++ South America
Content Type	Text
Resource Type	Article

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