Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires

Vegetation index time series from Landsat and Sentinel-2 have great potential for following
the dynamics of ecosystems and are the key to develop essential variables in the realm of biodiversity.
Unfortunately, the removal of pixels covered mainly by clouds reduces the temporal resolution,
producing irregularity in time series of satellite images. We propose a Bayesian approach based on a
harmonic model, fitted on an annual base. To deal with data sparsity, we introduce hierarchical prior
distribution that integrate information across the years. From the model, the mean and standard
deviation of yearly Ecosystem Functional Attributes (i.e., mean, standard deviation, and peak's
day) plus the inter-year standard deviation are calculated. Accuracy is evaluated with a simulation
that uses real cloud patterns found in the Peneda-Gêres National Park, Portugal. Sensitivity to
the model's abrupt change is evaluated against a record of multiple forest fires in the Bosco Difesa
Grande Regional Park in Italy and in comparison with the BFAST software output. We evaluated the
sensitivity in dealing with mixed patch of land cover by comparing yearly statistics from Landsat
at 30m resolution, with a 2m resolution land cover of Murgia Alta National Park (Italy) using FAO
Land Cover Classification System 2.