KerkiniLake-Phenological Annual Summary Statistics-2019/2020

Saverio Vicario; Chiara Richiardi; Maria Adamo; Cristina Tarantino

doi:10.23728/b2share.536114446997472eb390c5c2770271d5

Published November 16, 2022 | Version v1

Dataset Open

KerkiniLake-Phenological Annual Summary Statistics-2019/2020

Phenological Annual Summary Statistics based on Ecosystem Functional Attribute framework. The result are based on S2 interpolated with a bayesian implementation of Harmonic Model.

Methods

Details of the method can be found in Vicario S, Adamo M, Alcaraz-Segura D, Tarantino C (2019) Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sens 12:83 . doi: 10.3390/rs12010083

Technical info

The phenology is not a scalar variable but it is an ensamble of sub-variables all based on MCARI2 vegetation index and for each one two statistics are given: expected value (mean) and a mask for all pixel with standard deviation of uncertianities larger than 10% the mean (CVmask) within the general name rule proposed: locality_variable_timestamp.extension variable formed in: Phenology-SubvariableStatistics The subvariables are: mean: mean value across the year - values range between 0-0.5 stdintra: standard deviation of the value across the year - values range between 0-0.05 maxpos: day of the year of the maximum value - values range between 0-0.5 sdinter: standard deviation across years - values range between 0-0.05 The statistics are: mean: Expected value of the subvariable across 100 simulation CVmask: 0-1 mask with value 1 for pixel with less than 10% of standard deviation compared to the mean The timestamp refer to a year or to two years

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