SIF-ARSET: Introduction to Solar-Induced Fluorescence (SIF) Data

This repository contains code for the ARSET training session on using solar-induced fluorescence (SIF) data from the OCO-2 and OCO-3 missions to study plant behavior and health at an ecosystem level. This training is comprised of three exercises:

1. 1_exploration.ipynb: Methods for retrieving and spatially gridding SIF data to get a sense of the observational record. In this exercise we reproduce monthly gridded plots like those that are found in Doughty et al., 2022. YouTube recording link
2. 2_oco3_sam.ipynb: A discussion of the Snapshot Area Map (SAM) mode unique to OCO-3, its applications, and a comparison of this data with flux tower data collected from ground-based measurements. The analysis in this notebook is similar to the one found in Pierrat et al., 2022, which looked at tower-based SIF and GPP measurements of boreal forest.
3. 3_gosif.ipynb: One way to look at SIF with consistent spatial coverage, and the limitations of such a technique. In this exercise we retrieve and discuss data from the Drs. Xing Li and Jingfeng Xiao's 2019 GOSIF paper.

Learning Objectives

By the end of this course, you will learn how to:

• Recognize how the Solar Induced Fluorescence (SIF) is measured and used as a complementary measurement to commonly used indices (NDVI) for land and vegetation applications.
• Identify advantages and limitations of using space-based SIF measurements to monitor and evaluate vegetation health and condition.
• Generate a Snapshot Area Map (SAM) plot from OCO-3 data for selected regions of interest to analyze and evaluate vegetation and land change due to fire impacts.
• Use gap-filled data for selected regions of interest to produce visualizations to analyze and interpret episodic land change due to droughts and floods.
• Compare how SIF products aggregated in space and time using open source tools are used to study vegetation change across different regions in a variety of science and applied use cases.

Installation (Video instructions)

• Update (21-10-2025): anaconda has been added and tested against miniconda. Simply run setup-conda.sh/setup-conda.ps1 instead of setup.sh/setup.ps1 in Step 3 of the instructions.
• Windows Users: We strongly recommend using the conda installation method (setup-conda.ps1) as it provides pre-compiled geospatial libraries (GDAL, rasterio) that can be difficult to install via pip on Windows. If you encounter GDAL-related errors with setup.ps1, please use the conda method instead.
• Python version: If not using conda, it is recommended to install python3.13 or use an existing Python 3.11 or newer installation.

1. Clone this Repository to your Computer

Open a new terminal window and navigate to the directory you would like to work in for this training. Once you are in that directory, enter the following command to download the repository:

git clone https://github.com/jackiryan/SIF-ARSET
cd SIF-ARSET

Alternatively, you may wish to use GitHub Desktop which does not require the command line. In the GitHub Desktop app, you would click the "Current Repository" dropdown, then select "Add", then "Clone Repository..." (Shift+Cmd+O on MacOS) and select this repo (you may need to have it starred for it to appear in the list of options).

2. Set up your Earthdata Login

• If you do not already have an account, go to Earthdata Login and create one. Note down the username and password you used for this account.

• Note for some users: Follow the instructions on this page: https://disc.gsfc.nasa.gov/earthdata-login. If you received an Error 401: Unauthorized when attempting to run the code in the notebook, it is likely due to not having added the permissions for the NASA GESDISC Data Archive.

• Create a new file called .env in the same directory as this readme (the repository root) and put your username and password into it the same way you see it in .env.example:

EARTHDATA_USERNAME=user
EARTHDATA_PASSWORD=pass

3. Install the necessary packages

The provided setup script will install the required packages and open the notebook in a new browser tab:

On MacOS/Linux

./setup.sh

or if using conda:

./setup-conda.sh

On Windows (conda recommended):

# Recommended: Using conda (avoids GDAL compilation issues)
.\setup-conda.ps1

# Alternative: Using pip (may require manual GDAL setup)
.\setup.ps1

If you would like to just open the notebook on subsequent uses, assuming your environment is set up, simply run the following command from the SIF-ARSET directory:

jupyter lab notebooks/1_exploration.ipynb

Contact

Please email Jacqueline Ryan at JPL for any questions about the code in this course.

Citations

[1] Basilio, R., Bennett, M., Eldering, A., Lawson, P., and Rosenberg, R. (2019). Orbiting Carbon Observatory-3 (OCO-3), remote sensing from the International Space Station (ISS). Proceedings Volume 11151, Sensors, Systems, and Next-Generation Satellites XXIII, 11151. https://doi.org/10.1117/12.2534996

[2] Doughty, R., Kurosu, T. P., Parazoo, N., Köhler, P., Wang, Y., Sun, Y., and Frankenberg, C. (2022). Global GOSAT, OCO-2, and OCO-3 solar-induced chlorophyll fluorescence datasets. Earth Syst. Sci. Data, 14, 1513–1529. https://doi.org/10.5194/essd-14-1513-2022

[3] OCO Science Team/Michael Gunson, Annmarie Eldering (2024). OCO-3 level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, retrospective processing V11r. Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC). Accessed: 21 Jul 2025. https://doi.org/10.5067/HC776J71KV41

[4] Pierrat, Z., Magney, T., Parazoo, N. C., Grossmann, K., Bowling, D. R., Seibt, U., et al. (2022). Diurnal and seasonal dynamics of solar-induced chlorophyll fluorescence, vegetation indices, and gross primary productivity in the boreal forest. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006588. https://doi.org/10.1029/2021JG006588

[5] Christopher Gough, Gil Bohrer, and Peter Curtis (2023). AmeriFlux FLUXNET-1F US-UMB Univ. of Mich. Biological Station, Ver. 3-5, AmeriFlux AMP. Accessed: 23 Jul 2025. https://doi.org/10.17190/AMF/2204882

[6] Law, B. (2024). AmeriFlux FLUXNET-1F US-Me2 Metolius mature ponderosa pine, Ver. 4-6, AmeriFlux AMP. Accessed: 23 Jul 2025. https://doi.org/10.17190/AMF/1854368

[7] Pastorello, G., Trotta, C., Canfora, E. et al. (2020). The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci Data 7, 225. https://doi.org/10.1038/s41597-020-0534-3

[8] Reichstein, M., Falge, E., Baldocchi, D., Papale, D., et al. (2005). On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11: 1424-1439. https://doi.org/10.1111/j.1365-2486.2005.001002.x

[9] Lasslop, G., Reichstein, M., Papale, D., Richardson, A.D., et al. (2010). Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Global Change Biology, 16: 187-208. https://doi.org/10.1111/j.1365-2486.2009.02041.x

[10] Pierrat, Z. (2023). Evergreen needleleaf forest pigment, MONI-PAM, eddy-covariance, and tower-scale remote sensing data across four different sites [Data set]. In BioScience. Zenodo. https://doi.org/10.5281/zenodo.10048770

[11] Li, X. and Xiao, J. (2019). A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sensing, 11, 517. https://doi.org/10.3390/rs11050517

[12] Yin, Y., Byrne, B., Liu, J., Wennberg, P., Davis, K. J., Magney, T., et al. (2020). Cropland carbon uptake delayed and reduced by 2019 midwest floods. AGU Advances, 1, e2019AV000140. https://doi.org/10.1029/2019AV000140

Data Sources and Attributions

Esri Global Imagery: Base layer tiles used under the Esri Master License Agreement. All map tiles are ©Esri.

AmeriFlux FLUXNET Dataset: Used under CC-BY-4.0 License.

GOSIF Dataset: Used with permission from the author.

MapTiler Base Layer: Used under the Open Database License (ODbL) with contributions from OpenStreetMap contributors.

All Code: Copyright 2025, by the California Institute of Technology. ALL RIGHTS RESERVED. United States Government Sponsorship acknowledged. Any commercial use must be negotiated with the Office of Technology Transfer at the California Institute of Technology.