Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data |
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| Affiliation: | 1. Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Spain;2. Secretary of Research and Postgraduate, CONACYT-UAN, 63155 Tepic, Nayarit, Mexico;1. Research Institute for the Environment and Livelihoods, Charles Darwin University, Ellengowan Drive, Casuarina, NT 0909, Australia;2. College of Science, Technology and Engineering, James Cook University, P.O. Box 6811, Cairns, QLD 4870, Australia;3. Maitec, PO Box U19, Charles Darwin University, NT 0815, Australia;4. Laboratory of Geo-information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700AA Wageningen, The Netherlands;1. School of Economics, University of Economics Ho Chi Minh City, 1A Hoang Dieu St., Phu Nhuan Dist., Ho Chi Minh City, Vietnam;2. Vietnam-Netherlands Programme for M.A. in Development Economics, University of Economics Ho Chi Minh City, 1A Hoang Dieu St., Phu Nhuan Dist., Ho Chi Minh City, Vietnam;1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE, Enschede, the Netherlands;2. Department of Environmental Science, Macquarie University, NSW, 2106, Australia;3. Haramaya University, Department of Geo-information Science, P.O. box 138, Dire Dawa, Ethiopia;4. Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, 94481, Grafenau, Germany;5. Chair of Wildlife Ecology and Wildlife Management, University of Freiburg, Tennenbacher Straße 4, Germany;6. UN Environment World Conservation Monitoring Centre, 219 Huntingdon Road, Cambridge, CB3 0DL, UK;7. European Space Agency - ESRIN, Via Galileo Galilei, Casella Postale 64, 00044, Frascati, RM, Italy |
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| Abstract: | This paper introduces a novel methodology for generating 15-day, smoothed and gap-filled time series of high spatial resolution data. The approach is based on templates from high quality observations to fill data gaps that are subsequently filtered. We tested our method for one large contiguous area (Bavaria, Germany) and for nine smaller test sites in different ecoregions of Europe using Landsat data. Overall, our results match the validation dataset to a high degree of accuracy with a mean absolute error (MAE) of 0.01 for visible bands, 0.03 for near-infrared and 0.02 for short-wave-infrared. Occasionally, the reconstructed time series are affected by artefacts due to undetected clouds. Less frequently, larger uncertainties occur as a result of extended periods of missing data. Reliable cloud masks are highly warranted for making full use of time series. |
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| Keywords: | Time series Gap-filling Filtering |
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