by Saverio Vicario; Chiara Richiardi; Maria Adamo; Cristina Tarantino;

Abstract: 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

TechnicalInfo: 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

Disciplines: 3.3.2 → Earth sciences → Environmental science

Keywords: EO data products; e-shape; mySpace; Phenological Annual Summary Statistics; eLTER;

DOI: 10.23728/b2share.34a1f1b96ab6404983a3da0f3e13713f

PID: 11304/00edace6-559c-42ce-8113-78d930509299