我已经能够使用matplotlib绘制和显示我的光栅图像。这部分是成功的。我坚持的部分是能够以某种方式保存绘图。对于rasterio我找到了两个有用的教程：
https://rasterio.readthedocs.io/en/latest/topics/windowed-rw.html
以及
https://www.earthdatascience.org/courses/earth-analytics-python/multispectral-remote-sensing-in-python/export-numpy-array-to-geotiff-in-python/
我计算了一个叫做NDVI的函数，通过matplotlib，我可以用下面的代码显示它，但是当我把文件保存为GeoTIFF时，桌面上的图像全黑了。我也打算重新投影数据，我把代码注解掉了。
下面是我的代码：

import rasterio
import matplotlib.pyplot as plt
import numpy as np

nirband = r"LC08_L1TP_015033_20170822_20170912_01_T1_B5.TIF"

redband =r"LC08_L1TP_015033_20170822_20170912_01_T1_B4.TIF"

#rasterio.windows.Window(col_off, row_off, width, height)
window = rasterio.windows.Window(2000,2000,800,600)

with rasterio.open(nirband) as src:
    subset = src.read(1, window=window)

fig, ax = plt.subplots(figsize=(12,6))
plt.imshow(subset)
plt.title(f'Band 5 Subset')



with rasterio.open(nirband) as src:
    nir = src.read(1, window=window)

with rasterio.open(redband) as src:
    red = src.read(1, window=window)

red = red.astype(float)
nir = nir.astype(float)
np.seterr(divide='ignore', invalid='ignore')

ndvi = np.empty(nir.shape, dtype=rasterio.float32)
check = np.logical_or ( red > 0, nir > 0 )
naip_ndvi = np.where ( check,  (1.0*(nir - red )) / (1.0*( nir + red )),-2 )

fig, ax = plt.subplots(figsize=(12,6))
ndvi = ax.imshow(naip_ndvi)
ax.set(title="NDVI")


with rasterio.open("LC08_L1TP_015033_20170822_20170912_01_T1_B5.TIF") as src:
    naip_data_ras = src.read()
    naip_meta = src.profile

with rasterio.open('MyExample.tif', 'w',**naip_meta) as dst:
    dst.write(naip_ndvi, window=window)

# =============================================================================
# with rasterio.open('example.tif') as dataset:
# 
#     # Read the dataset's valid data mask as a ndarray.
#     mask = dataset.dataset_mask()
# 
#     # Extract feature shapes and values from the array.
#     for geom, val in rasterio.features.shapes(
#             mask, transform=dataset.transform):
# 
#         # Transform shapes from the dataset's own coordinate
#         # reference system to CRS84 (EPSG:4326).
#         geom = rasterio.warp.transform_geom(
#             dataset.crs, 'EPSG:4326', geom, precision=6)
# 
#         # Print GeoJSON shapes to stdout.
#         print(geom)
# =============================================================================

以下是使用matplotlib时NDVI的外观（我想将其作为GeoTIFF文件保存到桌面）：

谢谢你的帮助!