Downloading Images and Collections

In this tutorial, we’ll look at how we can use wxee and the wx accesor to quickly and easily download images and image collections from Earth Engine to a local drive.

wxee isn’t designed to replace Earth Engine’s native downloading functionality–if you need high resolution images or large areas, downloading to a Drive using ee is probably the best option. What wxee’s downloading system is great for is quickly pulling small regions or mesoscale images for further processing in Python without the hassle of intermediate steps.

Setup

!pip install wxee

import ee
import wxee

ee.Authenticate()
wxee.Initialize()

Downloading an Image

Load an image from MODIS. For convenience, we’ll select just the red, green, and blue bands.

img = ee.Image("MODIS/006/MOD09GA/2021_06_01").select(["sur_refl_b01", "sur_refl_b04" ,"sur_refl_b03"])

Set the download parameters.

# The file name to save
description = "modis_img"
# The coordinate reference system to use (NAD83 Albers CONUS)
crs = "EPSG:5070"
# Spatial resolution in CRS units (meters)
scale = 500
# The region to download the image within.
region = ee.Geometry.Polygon(
    [[[-120.0576549852371, 46.185091976777684],
      [-120.0576549852371, 45.577074504710005],
      [-118.91782344226834, 45.577074504710005],
      [-118.91782344226834, 46.185091976777684]]]
)

Warning: If an image download fails due to size limits, try using a smaller region or a coarser scale.

Download the image to GeoTIFF. The ee.Image.wx.to_tif method returns a path to the downloaded image(s). wxee always saves files with the name format {id}.{dimension}.{coordinate}. If no description is passed, the system:id property of the image will determine the file ID.

file = img.wx.to_tif(out_dir='data', description=description, region=region, scale=scale, crs=crs)
file

['data/modis_img.time.20210601T000000.tif']

Above, we downloaded 1 three-band image, but we could also download it as 3 one-band images by setting file_per_band to True.

file = img.wx.to_tif(
    out_dir='data',
    description=description,
    region=region,
    scale=scale,
    crs=crs,
    file_per_band=True
)

file

['data/modis_img.time.20210601T000000.sur_refl_b01.tif',
 'data/modis_img.time.20210601T000000.sur_refl_b04.tif',
 'data/modis_img.time.20210601T000000.sur_refl_b03.tif']

Downloading an Image Collection

Load the MODIS collection, choose a date range with 4 images, and select the red, green, and blue bands.

collection = (ee.ImageCollection("MODIS/006/MOD09GA")
    .filterDate("2021-06-01", "2021-06-05")
    .select(["sur_refl_b01", "sur_refl_b04" ,"sur_refl_b03"])
)

Download each image in the collection to GeoTIFF. We’ll use the same parameters as above except for description. ee.ImageCollection.wx.to_tif doesn’t take a description parameter, but it does take a prefix that can be added to the beginning of each file name. File names are based on the system:id property of each image.

files = collection.wx.to_tif(
    out_dir="data",
    prefix="wx_",
    region=region,
    scale=scale,
    crs=crs
)

files

['data/wx_MODIS_006_MOD09GA_2021_06_02.time.20210602T000000.tif',
 'data/wx_MODIS_006_MOD09GA_2021_06_01.time.20210601T000000.tif',
 'data/wx_MODIS_006_MOD09GA_2021_06_04.time.20210604T000000.tif',
 'data/wx_MODIS_006_MOD09GA_2021_06_03.time.20210603T000000.tif']

Note

This page was auto-generated from a Jupyter notebook. For full functionality, download the notebook from Github and run it in a local Python environment.