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

1
Institute of Atmospheric Pollution Research–Italian National Research Council C/O Department of Physics, University of Bari, via Orabona 4, 70125 Bari, Italy
2
Department of Botany and Inter-University Institute for Earth System Research (IISTA), University of Granada, 18071 Granada, Spain
3
Andalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería, 04120 Almería, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 83; https://doi.org/10.3390/rs12010083
Received: 6 November 2019 / Revised: 6 December 2019 / Accepted: 17 December 2019 / Published: 24 December 2019
(This article belongs to the Special Issue Remote Sensing in Ecosystem Modelling)
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. View Full-Text
Keywords: Time-Series; MSAVI2; cloud cover; Ecosystem Functional Attributes (EFA) Time-Series; MSAVI2; cloud cover; Ecosystem Functional Attributes (EFA)
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MDPI and ACS Style

Vicario, S.; Adamo, M.; Alcaraz-Segura, D.; Tarantino, C. Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sens. 2020, 12, 83. https://doi.org/10.3390/rs12010083

AMA Style

Vicario S, Adamo M, Alcaraz-Segura D, Tarantino C. Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sensing. 2020; 12(1):83. https://doi.org/10.3390/rs12010083

Chicago/Turabian Style

Vicario, Saverio; Adamo, Maria; Alcaraz-Segura, Domingo; Tarantino, Cristina. 2020. "Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires" Remote Sens. 12, no. 1: 83. https://doi.org/10.3390/rs12010083

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