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TitleReconstruction of Landsat time series in the presence of irregular and sparse observations: development and assessment in north-eastern Alberta, Canada
AuthorPouliot, D; Latifovic, R
SourceRemote Sensing of Environment vol. 204, 2017 p. 979-996,
Alt SeriesEarth Sciences Sector, Contribution Series 20160438
PublisherElsevier BV
Mediapaper; on-line; digital
File formatpdf; html
NTS73M; 73N; 74C; 74D; 74E; 74F; 83O; 83P; 84A; 84B; 84G; 84H
Lat/Long WENS-116.0000 -108.0000 58.0000 55.0000
Subjectsgeophysics; environmental geology; Nature and Environment; remote sensing; satellite imagery; vegetation; models; modelling; climate; reflectance; Boreal Forest Region; Forests; change detection; Drought; Harvest; Air quality
Illustrationsflow diagrams; location maps; time series; satellite images; histograms; plots; bar graphs
ProgramRemote Sensing Science
Released2017 10 06
AbstractTime series analysis of Landsat is limited by sparse and irregular sampling of clear-sky observations due to acquisition limitations, clouds, shadows, atmosphere, and sensor artifacts. Many remote sensing applications utilizing coarse spatial resolution time series methods are not suitable for Landsat due to observation sparsity. In this research we develop an imputation based approach to constrain the harmonic modeling method of Zhu et al. (2012, 2015) and Zhu and Woodcock (2014b) to reconstruct Landsat time series at a regular temporal interval. The approach was assessed for a boreal forest region in central Canada for different sparsity conditions. The imputed Landsat estimates for a specific pixel were predicted from climate or AVHRR data. These estimates were given a small weight relative to available Landsat observations in fitting the final harmonic model essentially constraining it to a more plausible range. In addition we implemented the model in a piecewise manner to handle non-linear temporal drift related to factors such as climate change, drought, or the allometric nature of vegetation regrowth. Results show that the inclusion of imputed estimates improved model predictions in the presence of observation sparsity. Where there were less than 3 observations within ±20 days the imputation approach performed better, with a reduction in average reflectance error of 0.001 to 2.5. Error assessment with hold out observations, comparison to MODIS time series, and example predicted images are presented.
Summary(Plain Language Summary, not published)
The research presents the development of a methodology to handle sparse observations in historical earth observation (EO) data from the Landsat series of satellites. The lack of sufficient observations is a major limitation for using these data to understand historical land surface change in Canada. The method developed allows for reconstruction of the seasonal ice/snow and vegetation phenology cycles as observed by the sensor. These data can be used for or contribute to a large number of applications regarding understanding historical and predicting future forest development, carbon cycling, ground water, water quality, air quality, climate change, weather, land cover, land use, or surficial geology to highlight a few.

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