[NBI] Long timeseries phenology using Landsat data

[ricardobarroslourenco]

What is the notebook about?

Landsat data is probably the longest standardized timeseries of remotely sensed data for land use. Plant phenology is an important proxy for climate change effects. Using long time series of RS data, would allow to see long-term trends, such as drifts on Start-of-season (SOS) and End-of-season (EOS) dates over decades, and how this occurs in the globe.

A secondary goal of the notebook would be how to pull out Landsat data and work on longer time-scales.

• Landsat datasets are available at Google STAC repository : https://gee.stac.cloud/?cp=1&t=catalogs
• This would be loosely inspired on NASA ARSET training on Phenology: https://appliedsciences.nasa.gov/join-mission/training/english/arset-understanding-phenology-remote-sensing
• We would use some concepts of Pangeo as a stack:
  • Using STAC endpoints of repositories;
  • Using intake as an ETL/Exploratory tool;
  • Using Rasterio for Viz.

Data Science Component

• [X] Sensor visualisation
• [ ] Preprocessing
• [ ] Modelling
• [ ] Post-processing
• [ ] Other:

Checklist:

• [X] Input data, pipeline and/or model are public with license/citation
• [X] The proposed notebook reuses existing codebase
• [X] The proposed notebook uses open-source packages
• [X] The proposed notebook is associated to existing publication(s)

Additional information