Can satellite remote sensing detect early stage crop stress at high resolution across whole-farm scale?

Hydrosat produces a proprietary "fused" thermal infrared land surface temperature (LST) imagery product using sharpening and interpolation algorithms to produce near daily sub-30 m resolution imagery from a combination of MODIS, Sentinel and Landsat imaging platforms. This project aims to explore the input data and intermediate outputs of the algorithms used to produce the LST product in order to detect errors potentially introduced by data quality issues or the application of the algorithms while also exploring applications within agricultural contexts.

See the blog post here.

• Erik Anderson
• Tyler Cruickshank
• Joe McGlinchy We thank Joe McGlinchy of Hydrosat for providing project guidance and data access.

To reproduce this workflow, use the provided environment.yml file to create a conda environment (you should already have Python and conda installed on your system).

After cloning this directory to your system, use the following bash commands to create and activate the environment:

conda env create -f environment.yml
conda activate ea-lst

For access to Hydrosat's proprietary products, you must receive credentials using the instructions described at the Hydrosat Fusion Hub. Add your credentials to the secrets/ folder in a file called creds.json with the format:

{
    "username":"",
    "password":""
}

See the Hydrosat Fusion Hub Documentation for additional guidance.

Meteorological data are provided by Ameriflux and included in the project's data/Ameriflux directory.

Follow the instructions below to reproduce the workflows in the notebook blog.ipynb and convert the Jupyter Notebook to an HTML report.

To reproduce the analysis in blog.ipynb, first follow the instructions to set up the environment and access data, described above.

This project uses a configuration file to facilitate repeat workflows on new Ameriflux meteorological towers. To repeat the analysis on a new met tower, add the necessary information to the file config.yml by copy/pasting an existing configuration section and updating with the necessary parameters. You must include

• the longitude, latitude for the meteorological tower center point,
• the the longitude, latitude for the ag field center point,
• either a bounding box (list of four coordinates) or area of interest (list of four longitude, latitude pairs), and
• the parameters for reading the Ameriflux data.

In the notebook, change the met_tower variable to match. Note that Hydrosat does not currently include coverage for the entire CONUS area.

To convert the file blog.ipynb to an HTML report, you must have the library nbconvert installed (note that this library is not included in this project's environment; read the documentation for installation instructions).

Use the following bash command to convert the notebook:

jupyter nbconvert --to html_embed --no-input notebook-name.ipynb