Example data: Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data

Published: 5 December 2016| Version 1 | DOI: 10.17632/8nj4fs9yx4.1
Francesco Vuolo


The data-set represents an example of the results obtained with the smoothing and gap-filling algorithm to be published in the International Journal of Applied Earth Observation and Geoinformation. 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.



Remote Sensing, Smoothing Algorithm, Time Series