Spatial and Temporal Subsetting of Gridded SIF Data

Presented by the ORNL DAAC https://daac.ornl.gov

January 28, 2020

Keywords: Python, NCSS, netCDF, THREDDS

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1. Overview

This tutorial demonstrates two simple scenarios of how to use Python to subset gridded data from the Solar-Induced Chlorophyll Fluorescence-Earth System Data Record (SIF-ESDR) project through the ORNL DAAC's Thematic Real-time Environmental Distributed Data Services (THREDDS) Data Server (TDS).

2. Dataset

Two datasets are used in the tutorial to demonstrate the “interoperability” of ORNL DAAC data products: it is easy to use different SIF data products in the same analysis workflow while making minimal changes.

2.1 High Resolution Global Contiguous SIF Estimates from OCO-2 SIF and MODIS, Version 2

Yu, L., J. Wen, C.Y. Chang, C. Frankenberg, and Y. Sun. 2021. High Resolution Global Contiguous SIF Estimates from OCO-2 SIF and MODIS, Version 2. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1863

2.2 Global High-Resolution Estimates of SIF from Fused SCIAMACHY and GOME-2, 2002-2018

Wen, J., P. Koehler, G. Duveiller, N.C. Parazoo, T. Magney, G. Hooker, L. Yu, C.Y. Chang, and Y. Sun. 2021. Global High-Resolution Estimates of SIF from Fused SCIAMACHY and GOME-2, 2002-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1864

3. Prerequisites

Participants should have an understanding of Python, how to install Python modules, and how to execute Python code in a Jupyter Notebook.

3.1 Python

1. Download Jupyter
2. Download Anaconda Recommended
3. Review Package Installation Recommended

4. Procedure

4.1 Tutorial

1. Notebook

5. Credits

• Python - 3.9.7
• Jupyter Lab - 3.2.1
• Anaconda - 3.0