geoprepare

geoprepare

A Python package to prepare (download, extract, process input data) for GEOCIF and related models

• Free software: MIT license
• Documentation: https://ritviksahajpal.github.io/geoprepare

Installation

Note: The instructions below have only been tested on a Linux system

Install Anaconda

We recommend that you use the conda package manager to install the geoprepare library and all its dependencies. If you do not have it installed already, you can get it from the Anaconda distribution

Using the CDS API

If you intend to download AgERA5 data, you will need to install the CDS API. You can do this by following the instructions here

Create a new conda environment (optional but highly recommended)

geoprepare requires multiple Python GIS packages including gdal and rasterio. These packages are not always easy to install. To make the process easier, you can optionally create a new environment using the following commands, specify the python version you have on your machine (python >= 3.9 is recommended). we use the pygis library to install multiple Python GIS packages including gdal and rasterio.

conda create --name <name_of_environment> python=3.x
conda activate <name_of_environment>
conda install -c conda-forge mamba
mamba install -c conda-forge gdal
mamba install -c conda-forge rasterio
mamba install -c conda-forge xarray
mamba install -c conda-forge rioxarray
mamba install -c conda-forge pyresample
mamba install -c conda-forge cdsapi
mamba install -c conda-forge pygis
pip install wget
pip install pyl4c

Install the octvi package to download MODIS data

pip install git+https://github.com/ritviksahajpal/octvi.git

Downloading from the NASA distributed archives (DAACs) requires a personal app key. Users must configure the module using a new console script, octviconfig. After installation, run octviconfig in your command prompt to prompt the input of your personal app key. Information on obtaining app keys can be found here

Using PyPi (default)

pip install --upgrade geoprepare

Using Github repository (for development)

pip install --upgrade --no-deps --force-reinstall git+https://github.com/ritviksahajpal/geoprepare.git

Local installation

Navigate to the directory containing setup.py and run the following command:

pip install .

Usage

• Execute the following code to download the data

from geoprepare import geoprepare

# Provide full path to the configuration files
# Download and preprocess data
geoprepare.run([r"PATH_TO_geoprepare.txt"])

• Execute the following code to extract crop masks and EO data

from geoprepare import geoextract

# Extract crop masks and EO variables
geoextract.run([r"PATH_TO_geoprepare.txt", r"PATH_TO_geoextract.txt"])

• Execute the following code to prepare the data for the crop yield ML model and AgMet graphics

from geoprepare import geomerge

# Merge EO files into one, this is needed to create AgMet graphics and to run the crop yield model
geomerge.run([r"PATH_TO_geoprepare.txt", r"PATH_TO_geoextract.txt"])

Before running the code above, we need to specify the two configuration files:

• geoprepare.txt contains configuration settings for downloading and processing the input data.
• geoextract.txt contains configuration settings for extracting crop masks and EO variables.

Configuration files

geoprepare.txt

NOTE: dir_base needs to be changed to your specific directory structure

• datasets: Specify which datasets need to be downloaded and processed
• dir_base: Path where to store the downloaded and processed files
• start_year, end_year: Specify time-period for which data should be downloaded and processed
• logfile: What directory name to use for the log files
• level: Which level to use for logging
• parallel_process: Whether to use multiple CPUs
• fraction_cpus: What fraction of available CPUs to use

[DATASETS]
datasets = ['NDVI', 'AGERA5', 'CHIRPS', 'CPC', 'CHIRPS-GEFS', 'NSIDC']

[PATHS]
dir_base = /gpfs/data1/cmongp1/GEOGLAM
dir_input = ${dir_base}/Input
dir_log = ${dir_base}/log
dir_interim = ${dir_input}/intermed
dir_download = ${dir_input}/download
dir_output = ${dir_base}/Output
dir_global_datasets = ${dir_input}/Global_Datasets
dir_metadata = ${dir_input}/metadata
dir_masks = ${dir_global_datasets}/masks
dir_regions = ${dir_global_datasets}/regions
dir_regions_shp = ${dir_regions}/shps
dir_crop_masks = ${dir_input}/crop_masks
dir_models = ${dir_input}/models

[AGERA5]
variables = ['Precipitation_Flux', 'Temperature_Air_2m_Max_24h', 'Temperature_Air_2m_Min_24h']

[AVHRR]
data_dir = https://www.ncei.noaa.gov/data/avhrr-land-normalized-difference-vegetation-index/access

[CHIRPS]
fill_value = -2147483648
prelim = /pub/org/chc/products/CHIRPS-2.0/prelim/global_daily/tifs/p05/
final = /pub/org/chc/products/CHIRPS-2.0/global_daily/tifs/p05/

[CHIRPS-GEFS]
fill_value = -2147483648
data_dir = /pub/org/chc/products/EWX/data/forecasts/CHIRPS-GEFS_precip_v12/15day/precip_mean/

[CPC]
data_dir = ftp://ftp.cdc.noaa.gov/Datasets

[ESI]
data_dir = https://gis1.servirglobal.net//data//esi//

[FLDAS]

[LST]
num_update_days = 7

[VHI]
data_historic = https://www.star.nesdis.noaa.gov/data/pub0018/VHPdata4users/VHP_4km_GeoTiff/
data_current = https://www.star.nesdis.noaa.gov/pub/corp/scsb/wguo/data/Blended_VH_4km/geo_TIFF/

[NDVI]
product = MOD09CMG
vi = ndvi
scale_glam = False
scale_mark = True
print_missing = False

[VIIRS]
product = VNP09CMG
vi = ndvi
scale_glam = False
scale_mark = True
print_missing = False

[NSIDC]

[SOIL-MOISTURE]
data_dir = https://gimms.gsfc.nasa.gov/SMOS/SMAP/L03/

[FPAR]
data_url = https://agricultural-production-hotspots.ec.europa.eu/data/indicators_fpar/fpar/

[LOGGING]
level = ERROR

[DEFAULT]
logfile = log
parallel_process = False
fraction_cpus = 0.75
start_year = 2001
end_year = 2024

geoextract.txt

NOTE: For each country add a new section to this file, using kenya as an example

• countries: List of countries to process
• forecast_seasons: List of seasons to process
• mask: Name of file to use as a mask for cropland/croptype
• redo: Redo the processing for all days (True) or only days with new data (False)
• threshold: Use a threshold value (True) or a percentile (False) on the cropland/croptype mask
• floor: Value below which to set the mask to 0
• ceil: Value above which to set the mask to 1
• eo_model: List of datasets to extract from

[kenya]
category = EWCM
scales = ['admin_1']  ; can be admin_1 (state level) or admin_2 (county level)
growing_seasons = [1]  ; 1 is primary/long season, 2 is secondary/short season
crops = ['mz', 'sr', 'ml', 'rc', 'ww', 'tf']
use_cropland_mask = True

[rwanda]
category = EWCM
scales = ['admin_1']  ; can be admin_1 (state level) or admin_2 (county level)
growing_seasons = [1]  ; 1 is primary/long season, 2 is secondary/short season
crops = ['mz', 'sr', 'ml', 'rc', 'ww', 'tf']
use_cropland_mask = True

[malawi]
category = EWCM
scales = ['admin_1']  ; can be admin_1 (state level) or admin_2 (county level)
growing_seasons = [1]  ; 1 is primary/long season, 2 is secondary/short season
crops = ['mz', 'sr', 'ml', 'rc', 'ww', 'tf']
use_cropland_mask = True

[zambia]
category = EWCM
scales = ['admin_1']  ; can be admin_1 (state level) or admin_2 (county level)
growing_seasons = [1]  ; 1 is primary/long season, 2 is secondary/short season
crops = ['mz', 'sr', 'ml', 'rc', 'ww', 'tf']
use_cropland_mask = True

[united_republic_of_tanzania]
category = EWCM
scales = ['admin_1']  ; can be admin_1 (state level) or admin_2 (county level)
growing_seasons = [1]  ; 1 is primary/long season, 2 is secondary/short season
crops = ['mz', 'sr', 'ml', 'rc', 'ww', 'tf']
use_cropland_mask = True

[ww]
mask = cropland_v9.tif  ; A tif file specifying name of cropland/crop-type mask

[mz]
mask = cropland_v9.tif

[sb]
mask = cropland_v9.tif

[rc]
mask = cropland_v9.tif

[tf]
mask = cropland_v9.tif

[sr]
mask = cropland_v9.tif

[ml]
mask = cropland_v9.tif

[EWCM]
calendar_file = EWCM_2021-6-17.xlsx

[AMIS]
calendar_file = AMISCM_2021-6-17.xlsx

[DEFAULT]
redo = False
threshold = True
floor = 20
ceil = 90
scales = ['admin_1']
growing_seasons = [1]
countries = ['kenya']
forecast_seasons = [2022]
mask = cropland_v9.tif
shp_boundary = EWCM_Level_1.shp
statistics_file = statistics.csv
zone_file = countries.csv
calendar_file = crop_calendar.csv
eo_model = ['ndvi', 'cpc_tmax', 'cpc_tmin', 'chirps', 'chirps_gefs', 'esi_4wk', 'soil_moisture_as1', 'soil_moisture_as2']

Accessing EO data using the earthaccess library

import geopandas as gpd
from tqdm import tqdm
from pathlib import Path

from geoprepare.eoaccess import eoaccess

dg = gpd.read_file(PATH_TO_SHAPEFILE, engine="pyogrio")

# Convert to CRS 4326 if not already
if dg.crs != "EPSG:4326":
    dg = dg.to_crs("EPSG:4326")

# Iterate over each row of the shapefile
for index, row in tqdm(dg.iterrows(), desc="Iterating over shapefile", total=len(dg)):
    # Get bbox from geometry of the row
    bbox = row.geometry.bounds

    obj = eoaccess.NASAEarthAccess(
        dataset=["HLSL30", "HLSS30"],
        bbox=bbox,
        temporal=(f"{row['year']}-01-01", f"{row['year']}-12-31"),
        output_dir=".",
    )

    obj.search_data()
    if obj.results:
        obj.download_parallel()

obj = eoaccess.EarthAccessProcessor(
    dataset=["HLSL30", "HLSS30"],
    input_dir=".",
    shapefile=Path(PATH_TO_SHAPEFILE),
)
obj.mosaic()

Upload package to pypi

1. Update requirements.txt
2. Update version="A.B.C" in setup.py
3. Navigate to the directory containing setup.py and run the following command:

pip freeze > requirements.txt
python setup.py sdist
twine upload dist/geoprepare-A.B.C.tar.gz

Credits

This package was created with Cookiecutter and the giswqs/pypackage project template.