This script creates a GeoTiff image that can be read by VAPOR, when given a set of lat/lon coordinates.

It performs the the following:
  • Takes user-specified lat/lon coordinates to query NASA’s WorldView WTMS server for satellite imagery

  • WTMS servers and layers can be changed by modifying the “url” and “layer” global variables

  • A NaturalEarth shapfile describing roads in North America are added to the produced GeoTiff

  • Coastlines are added to the map through Cartopy

# sphinx_gallery_thumbnail_path = '_images/map.png'

targetDir = "/Users/pearse/"
fileName = "landSat_test2"
west = -105.5
north = 40.25
east = -104.75
south = 39.6

Size of our output figure. Note: If your specified lat/lon extents have a different aspect ratio than your width and height, the geotiff will have either its dimensions scaled to match the aspect ratio of the specified extents of the west/north/east/south variables.

width = 1920
height = 1080

For the generated tiff to have the correct width and height, the “dpi” variable must be set according to that of your monitor. To find your DPI, see here:

dpi = 96

URL for NASA’s EarthData/WorldView web map tile service

url = ''

Specify the layer from the EarthData WMTS to draw to our geotiff. See Vapor’s Image Renderer documentation for a complete list of available layers. Some options include:

MODIS_Terra_CorrectedReflectance_TrueColor Landsat_WELD_CorrectedReflectance_Bands157_Global_Annual VIIRS_CityLights_2012 GOES-West_ABI_Band2_Red_Visible_1km

To preview these layers, visit

layer = 'Landsat_WELD_CorrectedReflectance_TrueColor_Global_Annual'

Generate our matplotlib figure with a subplot to draw our map upon

import matplotlib.pyplot as plt
import as ccrs
fig = plt.figure(
    figsize=(width/dpi, height/dpi),
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
ax.add_wmts(url, layer)
    [west, east, south, north],

Add coastlines from Cartopy

ax.coastlines(resolution='50m', color='yellow')

Add roads from NaturalEarth

import cartopy.feature as cf
    cf.NaturalEarthFeature('cultural', 'roads_north_america', '10m'),

Generate our initial tiff file

tiffFile = targetDir + fileName + ".tif"
fig.savefig( tiffFile,

Write our tiff file with GeoTiff extent information

from osgeo import gdal
gdal.OpenShared( tiffFile, gdal.GA_Update)
translatedTiff = targetDir + fileName + "Translated.tif"
gdal.Translate( srcDS=tiffFile,
                format = 'GTiff',
                outputBounds = [ west, north, east, south ],
                outputSRS = 'EPSG:4326'

Give our GeoTiff file a projected coordinate system, equivalent to the following proj4 string: Proj4: “+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84”

gdal.Warp(  destNameOrDestDS=tiffFile,
            srcSRS = 'EPSG:4326',

Clean up intermediate translated file

import os

Total running time of the script: ( 0 minutes 0.000 seconds)

Gallery generated by Sphinx-Gallery