gee 'sampleRectangle()' 返回 1x1 數組

Question

我在 GEE 中嘗試使用“sampleRectangle()”function 時遇到問題，它返回 1x1 arrays，我似乎找不到解決方法。 請參閱下面的 python 代碼，其中我使用了 Justin Braaten 發布的方法。 我懷疑我傳遞給 function 的幾何圖形 object 有問題，但與此同時，我嘗試了幾種方法來檢查這個參數的行為方式，但沒有發現任何重大問題。

任何人都可以幫助我了解正在發生的事情嗎？

謝謝！

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

我有同樣的問題。 它似乎與.mean() 或圖像 collections 的任何縮小有關。

一種解決方案是在減少后重新投影。 例如，您可以嘗試在末尾添加“reproject”：

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

它應該工作。