Google Earth Engine Resampling – Resampling Image to Higher Resolution Using Python API

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I try to use resample and reproject function in GEE to downscale the MODIS 250 products into 240m in order to correspond the 8*8 Landsat 30m pixels for furture purpose.

I used a Landsat image for example. Change image resolution in GEE is implemented by ee.image.reproject() function. Followed by the resolution given by Kersten and Tyler Erickson, I re-wrote the JavaScript code into Python as follows:

import ee
ee.Initialize()

Reference a Landsat scene.

image_30m = ee.Image('LANDSAT/LC08/C01/T1/LC08_044034_20170614')

get the image projection by querying the projection of band2

band2 = image_30m.select('B2')
proj = band2.projection().getInfo()
crs = proj['crs']

resample and reproject the image to 10m

image_10m = image_30m.resample('bilinear').reproject({
'crs': crs,
'scale': 10.0})

image_10m_band2 = image_10m.select('B2')

get the projected image's projection and print

newproj = image_10m_band2.projection().getInfo()

print('New projection', newproj)

#I also tried another version

transform_new = [
10.0,
proj['transform'][1],
proj['transform'][2],
proj['transform'][3],
-10.0,
proj['transform'][5]
]

projection_new = {
'crs': crs,
'transform': transform_new,
}

image_10m = image_30m.resample('bilinear').reproject(projection_new)

When I check the reprojected image's projection, I expected to obtain the projection in 10m, but only I got is a exception as follows:

ee.ee_exception.EEException Projection: Argument 'crs': Invalid type.
Expected: String. Actual: Type>.

Best Answer

That's because the process of argument assignment in python is different than that of JS.

This part ({'crs': crs,'scale': 10.0}) needs to be replaced with its python equivalent.

Your code should look like:

image_10m = image_30m.resample('bilinear').reproject(crs=crs, scale=10)

Please note that arguments are never to be put in quotes in python.

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You currently have an image which has an NDVI value for each pixel in the original image. In order to get a single number, you need to reduce the image — to take the mean, median, maximum, minimum, or other such operation on pixels in the image, within a region of interest. Here is the documentation on reduceRegion, which is the way to do this.

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This example I wrote is in JavaScript since I wrote it in the Earth Engine Code Editor, but the differences with Python are fairly small so I hope you can follow along.

var meanNDVICollection = imgs.map(function (img) {
  // Your code.
  var nir = img.select('B5');
  var red = img.select('B4');
  var ndviImage = nir.subtract(red).divide(nir.add(red)).rename('NDVI');

  // Compute the mean of NDVI over the 'region'
  var ndviValue = ndviImage.reduceRegion({
    geometry: region,
    reducer: ee.Reducer.mean(),
  }).get('NDVI');  // result of reduceRegion is always a dictionary, so get the element we want

  // Add the value as a property to each image in the collection.
  // return img.set('NDVI', ndviValue);

  // Or, create a new feature with only the properties we want.
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    // Adding property we computed.
    'NDVI': ndviValue
  }).copyProperties(img, [
    // Picking properties from the original image.
    'system:time_start',
    'SUN_ELEVATION'
  ])
});

// Export, or you can use any other method for getting a table out of Earth Engine.
Export.table.toDrive({
  collection: meanNDVICollection
});

https://code.earthengine.google.com/580f2f06c9978caf43eac42b5dd0ff44

(Disclaimer: I do not have training in the scientific and statistical aspects of remote sensing. The choices of mean and scale: 20 in this script are for demonstration of Earth Engine only and should not be assumed to produce scientifically valid results.)

Best Answer

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