Published November 21, 2022
| Version
v1
Dataset
Open
LTSERZoneAtelierAlpes - Phenological Annual Summary Statistics - 2019/2020
Description
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/rs12010083Technical 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 yearsFiles
LTSERZoneAtelierAlpePhenologicalAnnualSummaryStatistics20192020.zip
Files
(3.0 GB)
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|---|---|---|
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Checksum: md5:88d12b48ddb989f4cb7dd38c471fa304
PID: http://hdl.handle.net/11304/2659d315-42b7-4797-9dd7-284f5365b8b4 |
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Additional details
Identifiers
- b2rec
- 227bede4ca97433bb86405ad30c8b0f4
LTER metadata
- Metadata URL
- https://deims.org/79d6c1df-570f-455f-a929-6cfe5c4ca1e9