Overloaded Operators

Let’s see how to use overloaded operators in eemont!

Before anything, let’s import our modules and authenticate in Google Earth Engine:

import ee, eemont

ee.Authenticate()
ee.Initialize()

Now, we are ready to go!

Overview

The eemont package extends the ee.Image with the binary and unary operators (including rich comparisons).

List of Operators

Binary Operators

The following table shows the list of binary operators that are overloaded:

Binary operators.

Operation	GEE Python method	Overloaded Operator
Addition	Image1.add(Image2)	Image1 + Image2
Subtraction	Image1.subtract(Image2)	Image1 - Image2
Multiplication	Image1.multiply(Image2)	Image1 * Image2
Division	Image1.divide(Image2)	Image1 / Image2
Floor Division	Image1.divide(Image2).floor()	Image1 // Image2
Modulo	Image1.mod(Image2)	Image1 % Image2
Power	Image1.pow(Image2)	Image1 ** Image2
Left Shift	Image1.leftShift(Image2)	Image1 << Image2
Right Shift	Image1.rightShift(Image2)	Image1 >> Image2
And	Image1.And(Image2)	Image1 & Image2
Or	Image1.Or(Image2)	Image1 | Image2

Rich Comparisons

The following table shows the list of rich comparisons that are overloaded:

Rich comparisons.

Operation	GEE Python method	Overloaded Operator
Lower Than	Image1.lt(Image2)	Image1 < Image2
Lower Than or Equal	Image1.lte(Image2)	Image1 <= Image2
Equal	Image1.eq(Image2)	Image1 == Image2
Not Equal	Image1.neq(Image2)	Image1 != Image2
Greater Than	Image1.gt(Image2)	Image1 > Image2
Greater Than or Equal	Image1.gte(Image2)	Image1 >= Image2
Equal	Image1.eq(Image2)	Image1 == Image2

Unary Operators

The following table shows the list of unary operators that are overloaded:

Unary operators.

Operation	GEE Python method	Overloaded Operator
Negation	Image.multiply(-1)	-Image
Invert	Image.Not()	~ Image

Usage

Overloaded operators can be used on any ee.Image object. Let’s see how to compute the EVI using overloaded operators!

Let’s take the Sentinel-2 SR image collection as example (remember to scale your image or image collection!):

point = ee.Geometry.Point([-76.0269,2.92846])
S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
   .filterBounds(point)
   .sort('CLOUDY_PIXEL_PERCENTAGE')
   .first()
   .maskClouds()
   .scale())

Now, let’s take apart the bands that we need (it is not necessary, but it’s easier to use N instead of S2.select('B8')):

N = S2.select('B8')
R = S2.select('B4')
B = S2.select('B2')

And finally, let’s compute the EVI using overloaded operators:

EVI = 2.5 * (N - R) / (N + 6.0 * R - 7.5 * B + 1.0)

Let’s see another example, but using rich comparisons. We are going to compute a snow cover mask!

First, compute the NDSI:

S2 = S2.index('NDSI')

And now, let’s take apart the bands that we need:

NDSI = S2.select('NDSI')
N = S2.select('B8')
G = S2.select('B3')

Finally, compute the snow cover mask (Hall et al., 2001):

snowPixels = (NDSI > 0.4) & (N >= 0.1) & (G > 0.11)

And update the mask (if required):

S2 = S2.updateMask(snowPixels)