gee 'sampleRectangle()' 返回 1x1 数组

Question

I'm facing an issue when trying to use 'sampleRectangle()' function in GEE, it is returning 1x1 arrays and I can't seem to find a workaround. Please, see below a python code in which I'm using an approach posted by Justin Braaten. I suspect there's something wrong with the geometry object I'm passing to the function, but at the same time I've tried several ways to check how this argument is behaving and couldn't no spot any major issue.

Can anyone give me a hand trying to understand what is happening?

Thanks!

import json
import ee
import numpy as np
import matplotlib.pyplot as plt

ee.Initialize()


point = ee.Geometry.Point([-55.8571, -9.7864])

box_l8sr = ee.Geometry(point.buffer(50).bounds())
box_l8sr2 = ee.Geometry.Polygon(box_l8sr.coordinates())
# print(box_l8sr2)

# Define an image.
# l8sr_y = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_038029_20180810')
oli_sr_coll = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')

## Function to mask out clouds and cloud-shadows present in Landsat images
def maskL8sr(image):
  ## Bits 3 and 5 are cloud shadow and cloud, respectively.
    cloudShadowBitMask = (1 << 3)
    cloudsBitMask = (1 << 5)
  ## Get the pixel QA band.
    qa = image.select('pixel_qa')
  ## Both flags should be set to zero, indicating clear conditions.
    mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
    mask = qa.bitwiseAnd(cloudsBitMask).eq(0)
    return image.updateMask(mask)

l8sr_y = oli_sr_coll.filterDate('2019-01-01', '2019-12-31').map(maskL8sr).mean()

l8sr_bands = l8sr_y.select(['B2', 'B3', 'B4']).sampleRectangle(box_l8sr2)
print(type(l8sr_bands))
# Get individual band arrays.
band_arr_b4 = l8sr_bands.get('B4')
band_arr_b3 = l8sr_bands.get('B3')
band_arr_b2 = l8sr_bands.get('B2')


# Transfer the arrays from server to client and cast as np array.
np_arr_b4 = np.array(band_arr_b4.getInfo())
np_arr_b3 = np.array(band_arr_b3.getInfo())
np_arr_b2 = np.array(band_arr_b2.getInfo())
print(np_arr_b4.shape)
print(np_arr_b3.shape)
print(np_arr_b2.shape)

# Expand the dimensions of the images so they can be concatenated into 3-D.
np_arr_b4 = np.expand_dims(np_arr_b4, 2)
np_arr_b3 = np.expand_dims(np_arr_b3, 2)
np_arr_b2 = np.expand_dims(np_arr_b2, 2)
# # print(np_arr_b4.shape)
# # print(np_arr_b5.shape)
# # print(np_arr_b6.shape)

# # Stack the individual bands to make a 3-D array.
rgb_img = np.concatenate((np_arr_b2, np_arr_b3, np_arr_b4), 2)
# print(rgb_img.shape)

# # Scale the data to [0, 255] to show as an RGB image.
rgb_img_test = (255*((rgb_img - 100)/3500)).astype('uint8')
# plt.imshow(rgb_img)
plt.show()

# # # create L8OLI plot
# fig, ax = plt.subplots()
# ax.set(title = "Satellite Image")
# ax.set_axis_off()
# plt.plot(42, 42, 'ko')
# img = ax.imshow(rgb_img_test, interpolation='nearest')

Answer 1

I have the same issue. It seems to have something to do with.mean(), or any reduction of image collections for that matter.

One solution is to reproject after the reduction. For example, you could try adding "reproject" at the end:

l8sr_y = oli_sr_coll.filterDate('2019-01-01', '2019-12-31').map(maskL8sr).mean().reproject(crs = ee.Projection('EPSG:4326'), scale=30)

It should work.