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
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
- Download Jupyter
- Download Anaconda Recommended
- Review Package Installation Recommended
4. Procedure
4.1 Tutorial
5. Credits
- Python - 3.9.7
- Jupyter Lab - 3.2.1
- Anaconda - 3.0