ee.Image.arrayMask

AI-generated Key Takeaways

• Image.arrayMask creates a new array image where each pixel is masked by another array image, keeping elements only where the mask is non-zero.

• If the mask image has a single band, it is applied to all bands of the input image; otherwise, the mask and input images must have the same number of bands.

Creates an array image where each array-valued pixel is masked with another array-valued pixel, retaining only the elements where the mask is non-zero. If the mask image has one band it will be applied to all the bands of 'input', otherwise they must have the same number of bands.

Usage	Returns
Image.arrayMask(mask)	Image

Argument	Type	Details
this: input	Image	Array image to mask.
mask	Image	Array image to mask with.

Examples

Code Editor (JavaScript)

// A function to print arrays for a selected pixel in the following examples.
function sampArrImg(arrImg) {
  var point = ee.Geometry.Point([-121, 42]);
  return arrImg.sample(point, 500).first().get('array');
}

// Create a 1D array image with length 6.
var arrayImg1D = ee.Image([0, 1, 2, 4, 0, 5]).toArray();
print('1D array image (pixel)', sampArrImg(arrayImg1D));
// [0, 1, 2, 4, 0, 5]

// Create a mask using a relational operator to mask values greater than 2.
var mask1D = arrayImg1D.lte(2);
print('1D mask for greater than value 2 (pixel)', sampArrImg(mask1D));
// [1, 1, 1, 0, 1, 0]

var arrayImg1DMask = arrayImg1D.arrayMask(mask1D);
print('1D array image mask (pixel)', sampArrImg(arrayImg1DMask));
// [0, 1, 2, 0]

// Self mask the 1D array image. Value zero will be masked out.
var arrayImg1DselfMask = arrayImg1D.arrayMask(arrayImg1D);
print('1D array image self mask (pixel)', sampArrImg(arrayImg1DselfMask));
// [1, 2, 4, 5]

// Create a 2D array image.
var arrayImg2D = arrayImg1D.arrayReshape(ee.Image([2, 3]).toArray(), 2);
print('2D 2x3 array image (pixel)', sampArrImg(arrayImg2D));
// [[0, 1, 2],
//  [4, 0, 5]]

// Slice out a row to use as a column mask.
var rowAsMaskForCols = arrayImg2D.arraySlice(0, 1, 2);
print('2D mask for cols (pixel)', sampArrImg(rowAsMaskForCols));
// [[4, 0, 5]]

var arrayImg2DMaskCols = arrayImg2D.arrayMask(rowAsMaskForCols);
print('2D array image cols masked (pixel)', sampArrImg(arrayImg2DMaskCols));
// [[0, 2],
//  [4, 5]]

// Slice out a column to use as a row mask.
var colAsMaskForRows = arrayImg2D.arraySlice(1, 1, 2);
print('2D mask for rows (pixel)', sampArrImg(colAsMaskForRows));
// [[1],
//  [0]]

var arrayImg2DMaskRows = arrayImg2D.arrayMask(colAsMaskForRows);
print('2D array image rows masked (pixel)', sampArrImg(arrayImg2DMaskRows));
// [[0, 1, 2]]

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# A function to print arrays for a selected pixel in the following examples.
def samp_arr_img(arr_img):
  point = ee.Geometry.Point([-121, 42])
  return arr_img.sample(point, 500).first().get('array')

# Create a 1D array image with length 6.
array_img_1d = ee.Image([0, 1, 2, 4, 0, 5]).toArray()
print('1D array image (pixel):', samp_arr_img(array_img_1d).getInfo())
# [0, 1, 2, 4, 0, 5]

# Create a mask using a relational operator to mask values greater than 2.
mask_1d = array_img_1d.lte(2)
print(
    '1D mask for greater than value 2 (pixel):',
    samp_arr_img(mask_1d).getInfo()
)
# [1, 1, 1, 0, 1, 0]

array_img1d_mask = array_img_1d.arrayMask(mask_1d)
print('1D array image mask (pixel):', samp_arr_img(array_img1d_mask).getInfo())
# [0, 1, 2, 0]

# Self mask the 1D array image. Value zero will be masked out.
array_img_1d_self_mask = array_img_1d.arrayMask(array_img_1d)
print(
    '1D array image self mask (pixel):',
    samp_arr_img(array_img_1d_self_mask).getInfo()
)
# [1, 2, 4, 5]

# Create a 2D array image.
array_img_2d = array_img_1d.arrayReshape(ee.Image([2, 3]).toArray(), 2)
print('2D 2x3 array image (pixel):', samp_arr_img(array_img_2d).getInfo())
# [[0, 1, 2],
#  [4, 0, 5]]

# Slice out a row to use as a column mask.
row_as_mask_for_cols = array_img_2d.arraySlice(0, 1, 2)
print('2D mask for cols (pixel):', samp_arr_img(row_as_mask_for_cols).getInfo())
# [[4, 0, 5]]

array_img_2d_mask_cols = array_img_2d.arrayMask(row_as_mask_for_cols);
print(
    '2D array image cols masked (pixel):',
    samp_arr_img(array_img_2d_mask_cols).getInfo()
)
# [[0, 2],
#  [4, 5]]

# Slice out a column to use as a row mask.
col_as_mask_for_rows = array_img_2d.arraySlice(1, 1, 2)
print('2D mask for rows (pixel):', samp_arr_img(col_as_mask_for_rows).getInfo())
# [[1],
#  [0]]

array_img_2d_mask_rows = array_img_2d.arrayMask(col_as_mask_for_rows)
print(
    '2D array image rows masked (pixel):',
    samp_arr_img(array_img_2d_mask_rows).getInfo()
)
# [[0, 1, 2]]