• Phenology on eLTER sites. What do we have?
  • Phenology. Short intro to phenology
  • PhenoApp. Presentation of the current state

Phenology on eLTER Sites

A quick search for phenology shows this only one result:


Phenology on eLTER Sites

eLTER sites & Phenocams

20 sites contains phenocams:

Sites with phenocams

Asa Field-research Infrastructure (LTER) - Sweden
DoÃḟana Long-Term Socio-ecological Research Platform - Spain
IT25 - Val Mazia/Matschertal - Italy
LTSER Northern Negev - Israel
LTSER Platform Tyrolean Alps (TA) - Austria
LTSER Zone Atelier Bassin du RhÃṀne - France
LTSER Zone Atelier du Bassin de la Moselle - France
LTSER Zone Atelier Pyrénées-Garonne - France
LTsER-Montado - Portugal
OZCAR-RI Regional Spatial Observatory in the South West France - France
Podgorski Kras - Slovenia
Rosalia Lehrforst - Austria
Saldur River Catchment - Italy
Siljansfors Experimental Forest - Sweden
Stubai (combination of Neustift meadows and Kaserstattalm) - Austria
Svartberget Field-research Infrastructure (LTER) - Sweden
TERENO Eifel Lower Rhine Valley - Germany
TERENO Harz/Central German Lowland LTER - Germany
Torgnon Larch forest Tronchaney (IT19 Aosta Valley) - Italy
Wytham - United Kingdom

20 sites contains phenocams:

  • All LTSER sites
  • Add new sites is fast for GEE datasets (MODIS), but Sentinel 2 data have to be calculated for them

The aim of PhenoApp is both:

  • To Provide SOVs data from EO products

  • To collect and validate EO products with your in situ data.

The Form accepts data through file upload (according to a provided template) or single year data values for SOS, MOS, EOS.


Phenology is the study of periodic events in biological life cycles and how these are influenced by seasonal and interannual variations in climate, as well as habitat factors (Merriam-Webster. 2020.).





Phenology. Datasets


PhenoPy is a python library developed by Javier Lopatin to generate phenometrics from a vegetation index time series of satelllite data.

It  Generates svereal bands with the DOY and the vegetation index values of the record.

Start Date 2017
Spatial Resolution 10 m
Vegetation Index NDVI (*EVI2)
Growth cycles 1
Source PhenoPy
  • Not GEE tool
  • Process locally
  • Process slow
  • 1 cycle per season
  • Can be applied to any satellite collection
  • Easy to integrate in the python process
  • Lots of bands with phenological info (DOY and Value)

High Resolution Vegetation Phenology and Productivity Parameters. Copernicus Land Monitoring Service (CLMS)

The Vegetation Phenology and Productivity Parameters (VPPs) are yearly metrics that are derived from the Seasonal Trajectories. The VPPs are produced up to 2 seasons.

Start Date 2017
Spatial Resolution 10 m
Vegetation Index PPI
Growth cycles 2
Source Copernicus/Wekeo
  • Not GEE collection
  • Available since 2017 (wekeo)
  • 10 m Spatial Resolution
  • Python script to get the data for eLTER sites

Phenology. Datasets

MODIS MCD12Q2.006. Land Cover Dynamics Yearly Global 500m

The MCD12Q2 V6 Land Cover Dynamics product (informally called the MODIS Global Vegetation Phenology product) provides estimates of the timing of vegetation phenology at global scales. Additionally, it provides information related to the range and summation of the enhanced vegetation index (EVI) computed from MODIS surface reflectance data at each pixel. 

Start Date 2001
Spatial Resolution 500 m
Vegetation Index EVI
Growth cycles 2
  • Lots of NoData!
  • 500 m spatial resolution
  • Available since 2001
  • GEE collection

Phenology. Datasets


  • Google Earth Engine
  • Geemap
  • Ndvi2Gif

Google Earth Engine

  • Cloud computing to visualize and process geographic information
  • Petabytes of data (lots of satellite/sensors)
  • Raster data (Land cover, climatic data, etc...)
  • Vectorial data
  • Use your own datasets (space limitation)
  • Hundreds of algorithms available
  • Huge community very active
  • Design local run global


Python package based in Google Earth Engine python API  and Map application Leaflet


Python packaged built upon Geemap to work with seasonal vegetation indexes by getting pixel statistics for periods of time

Let's see the tool

To Do List

  • Convert local rasters collections to Cloud Optimized Geotiff and uploaded them to Google Cloud Storage
  • Add more tools/buttons to our custom Geemap application (Land Surface Temperature, Flood Detection)
  • Receive and validate your data
  • Download csv data from the Map (picking coords values directly from the form), not only rasters
  • Point Sampling Tool  
  • DataLab!!

Thanks for your attention

I hope to meet you all soon in Mallorca...

...but you never know


By Diego García Díaz


PhenoApp presentation to eLTER WP4

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