Ordering Planet Data - Command Line to API

Derived from: https://planetlabs.github.io/planet-client-python/cli/examples.html

To install:

• pip install planet

• planet init

  • log in with usename/password credentials

Ensure you have your API KEY set up (may not be necessary if logged in as above).

• Set this as a system variable

  • In python:

    • os.environ['PL_API_KEY']='12345'

  • In BASH:

    • PL_API_KEY = '12345'

Make list of scenes to order

• planet data search --item-type PSScene --geom /Users/dan.sousa/Downloads/map.geojson --date acquired gt 2015-01-01 --date acquired lt 2017-01-01 --range cloud_cover lt .1

  • --item-type specifies the dataset type (PSScene or orthotile; 3 or 4 or 8 band)

  • --geom specifies the path to the geojson defining the study area

  • --date acquired gt is the beginning of the order window

  • --date acquired lt is the end of the order window

  • --range cloud_cover lt is cloud cover (less than % of scene)

Save the order

• planet data create-search --item-type PSScene --geom /Users/dan.sousa/Downloads/map.geojson --date acquired gt 2015-01-01 --date acquired lt 2017-01-01 --range cloud_cover lt .1 --asset-type analytic_sr

  • --item-type specifies the dataset type (PSScene or orthotile; 3 or 4 or 8 band)

  • --geom specifies the path to the geojson defining the study area

  • --date acquired gt is the beginning of the order window

  • --date acquired lt is the end of the order window

  • --range cloud_cover lt is cloud cover (less than % of scene)

  • --asset-type specifies the processing type (analytic_sr is surface reflectance; analytic is TOA)

Execute the saved search order

• planet data saved-search <4782d4118fee4275860665129a1e23c1>

  • Replace <4782d4118fee4275860665129a1e23c1> with search # from previous command, like
    

Download the order

• planet data download --item-type PSScene asset-type analytic_sr --dest /Users/dan.sousa/Downloads/NewPlanet

  • --item-type specifies the dataset type (PSScene or orthotile; 3 or 4 or 8 band)

  • --asset-type specifies the processing type (analytic_sr is surface reflectance; analytic is TOA)

  • --dest specifies the download location

Convert to common (WGS84) projection and ENVI format

• for i in *math.tif; do gdalwarp -t_srs EPSG:4326 -of ENVI ${i} ${i}_e; done

  • Assumes files of interest end in "math.tif"

  • WGS-84 (lat/lon) is set using the EPSG code. Pick another if you want a different projection.

  • Assumes ENVI-formatted output

Pull out 1 of the bands

• for i in *_e; do gdal_translate -b 1 -of ENVI ${i} ${i}EVI2; done

  • Assumes you want band 1. Change the number after -b to change the band

Stack all the single-band images into a single multiband timeseries

• gdal_merge.py -o 2020_2021_PlanetSedg_EVI2stack -separate -of ENVI *_eEVI2