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Organization: PEcAn Project

Student: Ayush Prasad

Mentors: Istem Fer

Background

PEcAn is an ecological informatics toolbox that makes process-based ecosystem models easier to run. The aim of this project was to add ICOS as an input data source for PEcAn's meteorological data processing pipeline. This would allow models to be driven using the data collected by the wide network of ICOS ground stations across Europe. The other objective of this project was to add a GEDI data source to the remote data module.

Work Done

Phase 1

The first phase was focused on building the ICOS pipeline. We had initially planned to use the ICOS's Python library to query and download data from the ICOS Carbon data portal but while downloading the data for an initial site, I found that the dataset lacked a few variables that were otherwise present when downloaded directly from the portal. As these variables were necessary to drive the models we decided on an alternative approach utilizing the ICOS SPARQL endpoint to query and find out product URL and then directly download it using a GET request.

The main R function which downloads the ICOS data is download.ICOS.R . met2CF.ICOS.R was developed to convert the data into CF. Tests were written for the developed workflow to ensure its reliability.

Pull request:

Phase 2

During the first two weeks of phase 2, I worked on integrating the ICOS download function with the main met.process.R function. After completing the ICOS link, I started working on developing a python script for downloading and subsetting GEDI data from LP DAAC's Data Pool. The function datapool2pecan.py finds out the GEDI granules for the area of interest using the NASA CMR API and then uses data pool to download the file. I also developed another script to retrieve the Global Forest Canopy Height, 2019 dataset on GEE which is based on GEDI data. Building upon my GSoC 2020 work, I then integrated these functions with the rest of the remote data module.

Pull request:

Acknowledgements

I am very thankful to my mentor for all her help and guidance throughout the project. I thank PEcAn Project and Google Summer of Code for providing me the opportunity to work on an area that I am deeply interested in.

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