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Algorithm for estimating sea surface temperatures based on Aqua/MODIS global ocean data. 2. Automated quality check process for eliminating cloud contamination

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Abstract

This study developed a post-processing quality check (QC) process to eliminate cloud contamination in infrared sea surface temperature (SST) without manual handling. Cloudiness of a pixel was evaluated quantitatively, in which the graduated verifications and a comprehensive decision from a combination of several tests were conducted. Additionally, the quality of SST data at the pixel was measured by acceptable limits from reference SST, which were obtained from historical data. The QC processed data showed good accuracy below 0.8°C, even in the near-cloud area. Before the QC, their accuracies including near-cloud areas were as poor as 2–5°C.

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Acknowledgments

We wish to express our gratitude to the NASA Goddard Space Flight Center (GSFC) Level 1 and Atmospheric Archive and Distribution System (LAADS) for providing MODIS Level 1B data. We also thank Mr. Keiji Imaoka at the Earth Observation Research Center of Japan Aerospace Exploration Agency (JAXA) and Mr. Masanaga Kominami from the Remote Sensing Technology Center of Japan (RESTEC) for their help in obtaining MODIS data. We thank two anonumous reviewers, whose comments greatly helped us to improve the manuscript. For helpful comments and suggestions, our special thanks go to the participants at the Cloud Detection Workshop held at Nara, Japan, August 2011. This research was supported by the “Global Change Observation Mission” project of the JAXA (JX-PSPC-260080).

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  1. Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, 6-3 Aramaki aza Aoba, Aoba, Sendai, 980-8578, Japan

    Kohtaro Hosoda

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  1. Kohtaro Hosoda
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Correspondence to Kohtaro Hosoda.

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Hosoda, K. Algorithm for estimating sea surface temperatures based on Aqua/MODIS global ocean data. 2. Automated quality check process for eliminating cloud contamination. J Oceanogr 67, 791–805 (2011). https://doi.org/10.1007/s10872-011-0077-5

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  • DOI: https://doi.org/10.1007/s10872-011-0077-5

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