A new methodology for cloud detection and classification with ASTER data

Abstract

High spatial resolution sensors such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) have the potential to produce gridded, large area datasets of surface parameters such as elevation and emissivity. These datasets are typically derived by combining all clear-sky pixels over a given location for a specified time period necessitating the use of an automated cloud detection and classification algorithm. The current ASTER L1A cloud mask lacks several key features needed to use it for this purpose. We have developed a new cloud detection algorithm which addresses these limitations by using a 2-pass approach similar to Landsat-7 and including further spectral tests for cirrus and cloud shadows from MODIS. The new cloud detection methodology is described together with several case studies that highlight key aspects of the algorithm and comparisons with the MODIS and the current ASTER L1A cloud mask. Copyright 2008 by the American Geophysical Union.