Evaluating and reducing errors in seasonal profiles of AVHRR vegetation indices over a Canadian northern national park using a cloudiness index

High-temporal coarse resolution remote-sensing data have been widely used for monitoring plant phenology and productivity. Residual errors in pre-processed composite data from these sensors can still be substantial due to cloud contamination and aerosol variations, especially over high cloud-cover a...

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Bibliographic Details
Published in:International journal of remote sensing 2013-06, Vol.34 (12), p.4320-4343
Main Authors: Chen, W., Foy, N., Olthof, I., Latifovic, R., Zhang, Y., Li, J., Fraser, R., Chen, Z., McLennan, D., Poitevin, J., Zorn, P., Quirouette, J., Stewart, H.M.
Format: Article
Language:English
Subjects:
Animal, plant and microbial ecology
Applied geophysics
Bias
Biological and medical sciences
Clouds
Contamination
Distortion
Earth sciences
Earth, ocean, space
Exact sciences and technology
Filtering
Filtration
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Internal geophysics
Pixels
Teledetection and vegetation maps
Vegetation
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Summary:High-temporal coarse resolution remote-sensing data have been widely used for monitoring plant phenology and productivity. Residual errors in pre-processed composite data from these sensors can still be substantial due to cloud contamination and aerosol variations, especially over high cloud-cover areas such as the Arctic. Commonly used smoothing and filtering methods try to reform the often heavily distorted seasonal profiles of vegetation indices one way or another, instead of explicitly dealing with the errors that cause the distortion. As the distortion varies from year to year for a pixel or from pixel to pixel, so does the performance of various smoothing and filtering methods. Consequently, change detection results are likely method dependent. In this study, we investigate alternative methods in order to eliminate bias caused by cloud contamination and reduce random errors due to aerosol variations in the 10 day Advanced Very High Resolution Radiometer (AVHRR) composite data, so that accurate seasonal profiles of vegetation indices can be constructed without the need to apply a smoothing and filtering method. The best alternative method corrects cloud contaminations by spatially pairing averages of simple ratio over cloud-contaminated and clear-sky pixels in a class (SPAC). The SPAC method eliminates bias caused by cloud contamination and reduces the relative random errors to
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2013.775536