Development of the Landsat Data Continuity Mission cloud-cover assessment algorithms

Pat Scaramuzza; M.A. Bouchard; John L. Dwyer

doi:10.1109/tgrs.2011.2164087

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Development of the Landsat Data Continuity Mission cloud-cover assessment algorithms

Pat Scaramuzza·M.A. Bouchard·John L. Dwyer

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Abstract

The upcoming launch of the Operational Land Imager (OLI) will start the next era of the Landsat program. However, the Automated Cloud-Cover Assessment (CCA) (ACCA) algorithm used on Landsat 7 requires a thermal band and is thus not suited for OLI. There will be a thermal instrument on the Landsat Data Continuity Mission (LDCM)-the Thermal Infrared Sensor-which may not be available during all OLI collections. This illustrates a need for CCA for LDCM in the absence of thermal data. To research possibilities for full-resolution OLI cloud assessment, a global data set of 207 Landsat 7 scenes with manually generated cloud masks was created. It was used to evaluate the ACCA algorithm, showing that the algorithm correctly classified 79.9% of a standard test subset of 3.95 109 pixels. The data set was also used to develop and validate two successor algorithms for use with OLI data-one derived from an off-the-shelf machine learning package and one based on ACCA but enhanced by a simple neural network. These comprehensive CCA algorithms were shown to correctly classify pixels as cloudy or clear 88.5% and 89.7% of the time, respectively.

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BibTeX RIS

Pat Scaramuzza, M.A. Bouchard, John L. Dwyer. 2012. Development of the Landsat Data Continuity Mission cloud-cover assessment algorithms. https://doi.org/10.1109/tgrs.2011.2164087

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