spectral-trend-database 0.0.1

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SPECTRAL TREND DATABASE

DSE’s Spectral Trends Database monitors uses data from NASA's Landsat satellites to track over 14,000 points in corn and soy fields in the midwestern United States. The database contains daily values for 36 different vegetation indices from the year 2000 to present, along with a number of derivative metrics that are useful for detecting crop planting and harvesting. The data will be useful for myriad agriculture applications, including the study and monitoring of yield, yield-stability, soil health, cover-cropping, and other sustainable agricultural practices.

• Project Description
• API Documentation
• DSE

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DATABASE DESCRIPTION

The Spectral Trend Database lives on Google Big Query and can be accessed directly using big query. However, we've built a number of python tools to make accessing the data eaiser (docs, example). The database tables are listed in the table below. Detailed descriptions of the indvidual tables can be found in our api-docs (here).

Table	Keys	Dates	Daily	Description
SAMPLE_POINTS	sample_id	False	False	location information such as lat, lon and geohashes
ADMINISTRATIVE_BOUNDARIES	sample_id	False	False	administrative information such as state and county
QDANN_YIELD	sample_id, year	True	False	yield estimations for year
LANDSAT_RAW_MASKED	sample_id, year	True	False	masked landsat band values for year
RAW_INDICES_V1	sample_id, year	True	False	spectral indices built from LANDSAT_RAW_MASKED
SMOOTHED_INDICES_V1	sample_id, year	True	True	interpolated and smoothed daily values for indices contained in RAW_INDICES_V1
MACD_INDICES_V1	sample_id, year	True	True	additional indices dervived from SMOOTHED_INDICES_V1 whose values are useful for detecting cover-croping and green-up dates
INDICES_STATS_V1	sample_id, year	True	False	statistical (min, max, mean, median, skew, kurtosis) aggregation of SMOOTHED_INDICES_V1
INDICES_STATS_V1_GROWING_SEASON	sample_id, year	True	False	same as INDICES_STATS_V1 but restricted to the "growing season"
INDICES_STATS_V1_OFF_SEASON	sample_id, year	True	False	same as INDICES_STATS_V1 but restricted to the "off season"

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INSTALL/REQUIREMENTS

1. Clone Repo

git clone https://github.com/SchmidtDSE/spectral_trend_database.git

2. Requirements

Requirements are managed through a Pixi "project" (similar to a conda environment). After pixi is installed use pixi run <cmd> to ensure the correct project is being used. For example,

# lauch jupyter
pixi run jupyter lab .

# run a script
pixi run python scripts/hello_world.py

The first time pixi run is executed the project will be installed (note this means the first run will be a bit slower). Any changes to the project will be updated on the subsequent pixi run. It is unnecessary, but you can run pixi install after changes - this will update your local environment, so that it does not need to be updated on the next pixi run.

Note, the repo's pyproject.toml, and pixi.lock files ensure pixi run will just work. No need to recreate an environment. Additionally, the pyproject.toml file includes fire_risk = { path = ".", editable = true }. This line is equivalent to pip install -e ., so there is no need to pip install this module.

The project was initially created using a package_names.txt and the following steps. Note that this should NOT be re-run as it will create a new project (potentially changing package versions).

#
# IMPORTANT: Do NOT run this unless you explicity want to create a new pixi project
#
# 1. initialize pixi project (in this case the pyproject.toml file had already existed)
pixi init . --format pyproject
# 2. add specified python version
pixi add python=3.11
# 3. add packages (note this will use pixi magic to determine/fix package version ranges)
pixi add $(cat package_names.txt)
pixi add --pypi $(cat pypi_package_names.txt)

Note that pixi is being used to install build/twine are part of the pixi-project so pushing to PYPI requires pixi run

pixi run python -m build
pixi run python -m twine upload --repository testpypi dist/*

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USAGE & DOCUMENTATION

See API Documentation and accompanying notebooks for detailed examples on how access the database and use the spectral_trend_database module.

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STYLE-GUIDE

Following PEP8. See setup.cfg for exceptions. Keeping honest with pycodestyle .