M5: Open Science for Crop Conditions

How can NASA datasets be used to map crop conditions?

The fifth module of our open climate-science curriculum focuses on how to begin a reproducible computational science project, using crop conditions as a thematic example. At the end of this module, you should be able to:

• Access and utilize satellite-based datasets on plant productivity, condition, and evapotranspiration.
• Compute an index of the water requirements for agricultural crops

Contents

1. Creating a Project Plan
2. Creating a Reproducible Research Environment
3. Setting Up Data Processing Workflows
4. Writing a Transparent Algorithm

Getting Started

See our installation guide here.

You can run the notebooks in this repository using Github Codespaces or as a VSCode Dev Container. Once your container is running, launch Jupyter Notebook by:

# Create your own password when prompted
jupyter server password

# Then, launch Jupyter Notebook; enter your password when prompted
jupyter notebook

The Python libraries required for the exercises can be installed using the pip package manager:

pip install xarray netcdf4 dask

Learning Outcomes

This course covers the following Core Competencies in Computational Data Science:

• Records relationships between code, results, and metadata (CC1.5)
• Uses a package manager to install and manage software dependencies (CC1.10)
• Can scale up a computational workflow (CC2.6)
• Understands software releases and versioning (CC4.4)

In addition, learners will see how to:

• Use Pixi to manage a software environment and dependencies
• Use Snakemake to automate reproducible tasks
• Calculate the Water Requirements Satisfaction Index

Acknowledgements

This curriculum was enabled by a grant from NASA's Transition to Open Science (TOPS) Training program (80NSSC23K0864), part of NASA's TOPS Program