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Cbers4asat

Description

Python library to consume data from INPE'S CBERS4A and AMAZONIA1 catalog and perform operations.

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Installation using pip

pip install cbers4asat

Summary

Documentation

  • query: Do a search on INPE's catalog
    • Parameters:
      • location -> List[float] | Tuple(int, int) : Bounding box or Path Row of area of interest
      • initial_date: -> date : Range start date
      • end_date -> date : Range end date
      • cloud -> int : Cloud percentage
      • limit -> int : Limit of product returned by query
      • collections (Optional) -> List[str] : Collection dataset
  • download: Download bands from query result
    • Parameters:
      • products -> Dict | GeoDataFrame : Returned scenes from query
      • bands -> List[str] : Available bands from scenes
      • threads (Optional) -> int : Thread limit for parallel download
      • outdir (Optional) -> str : Output directory
      • with_folder (Optional) -> bool : Option to enable download grouping
  • to_geodataframe: Convert GeoJSON-like dictionary to GeoDataFrame
    • Parameters:
      • products -> Dict : Returned scenes from query
      • crs (Optional)-> str : Coordinate reference (e.g.: EPSG:4326)

Examples

Search by bounding box:

# Import cbers4asat and datetime lib
from src.cbers4asat import Cbers4aAPI
from datetime import date

# (Only required for downloading) Same Login used in http://www2.dgi.inpe.br/catalogo/explore
# You can add later using a setter: api.user('my@mail.com')
api = Cbers4aAPI('my@mail.com')

# Bounding box of area of interest
bbox = [-63.92944335937501,
        -8.819260401678381,
        -63.79211425781251,
        -8.722218306198739]

# Date interval
initial_date = date(2021, 8, 25)
end_date = date(2021, 9, 25)

# Do a search on inpe's catalog
products = api.query(location=bbox,
                     initial_date=initial_date,
                     end_date=end_date,
                     cloud=100,
                     limit=100,
                     collections=['AMAZONIA1_WFI_L2_DN', 'CBERS4A_WPM_L4_DN'])  # Optional

print(products)
# {'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'id': 'AMAZONIA1_WFI03901620210911CB11', ...

Search by path and row:

from src.cbers4asat import Cbers4aAPI
from datetime import date

api = Cbers4aAPI('my@mail.com')

# Path and row respectively
path_row = (229, 124)

initial_date = date(2021, 8, 25)
end_date = date(2021, 9, 25)

# Fazer uma busca no catálogo e exibir resultados
products = api.query(location=path_row,
                     initial_date=initial_date,
                     end_date=end_date,
                     cloud=100,
                     limit=100,
                     collections=['AMAZONIA1_WFI_L2_DN', 'CBERS4A_WPM_L4_DN'])  # Optional

print(products)
# {'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'id': 'CBERS4A_WPM22912420210830', ...

Downloading products:

from src.cbers4asat import Cbers4aAPI
from datetime import date

api = Cbers4aAPI('my@mail.com')

path_row = (229, 124)

initial_date = date(2021, 8, 25)
end_date = date(2021, 9, 25)

products = api.query(location=path_row,
                     initial_date=initial_date,
                     end_date=end_date,
                     cloud=100,
                     limit=1,
                     collections=['CBERS4A_WPM_L4_DN'])

# Chosen bands: red, green and blue
# Output is optional, if you not fill, the current directory is used
api.download(products=products,
             bands=['red', 'green', 'blue'],
             threads=3,  # Threads for simultaneous download
             outdir='./downloads',
             with_folder=True)  # Group downloaded bands into subfolder(s) in the ./downloads directory

# ./downloads directory will be like this if with_folder=true :
# downloads/
# +- CBERS4A_WPM22912420210830/
# ++- CBERS_4A_WPM_20210830_229_124_L4_BAND3.tif
# ++- CBERS_4A_WPM_20210830_229_124_L4_BAND2.tif
# ++- CBERS_4A_WPM_20210830_229_124_L4_BAND1.tif

Convert products collection to GeoDataFrame:

from src.cbers4asat import Cbers4aAPI
from datetime import date
import geopandas as gpd

api = Cbers4aAPI('my@mail.com')

path_row = (229, 124)

initial_date = date(2021, 8, 25)
end_date = date(2021, 9, 25)

products = api.query(location=path_row,
                     initial_date=initial_date,
                     end_date=end_date,
                     cloud=100,
                     limit=3,
                     collections=['CBERS4A_WPM_L4_DN'])

# Convert products collection to GeoDataFrame with SIRGAS 2000 Coordinate reference
gdf = api.to_geodataframe(products, 'EPSG:4674')

print(gdf.to_string())

Downloading products in GeoDataFrame:

from src.cbers4asat import Cbers4aAPI
from datetime import date
import geopandas as gpd

api = Cbers4aAPI('my@mail.com')

bbox = [-63.92944335937501,
        -8.819260401678381,
        -63.79211425781251,
        -8.722218306198739]

initial_date = date(2021, 8, 25)
end_date = date(2021, 9, 25)

products = api.query(location=bbox,
                     initial_date=initial_date,
                     end_date=end_date,
                     cloud=100,
                     limit=3,
                     collections=['CBERS4A_WPM_L4_DN'])

# You can filter using Geo-Pandas methods and then downloading...
gdf = api.to_geodataframe(products)

# Same logic for downloading GeoDataFrame and GeoJSON-like dictionary
api.download(products=gdf, bands=['red'], outdir='./downloads', with_folder=False)

Contributing

We invite anyone to participate by contributing code, reporting bugs, fixing bugs, writing documentation and tutorials and discussing the future of this project. Please check CONTRIBUTING.md

License

Copyright (c) 2022 Gabriel Russo

Copyright (c) 2020 Sandro Klippel

Use is provided under the MIT License. See under LICENSE for more details