How to download a large joined images as a CSV file on my local computer?

[RuiGao9]

I used a Landsat tile to extract information (images) from different satellites. Then, I add each image to a band like below.
img_joined = img_initial.addBands(img_2).addBands(img_3).addBands(img_4).addBands(img_5).addBands(img_6).addBands(img_7).addBands(img_8).addBands(img_9).addBands(img_10).addBands(img_11).addBands(img_12).addBands(img_13)
So, How can I download it (img_joined) as a CSV file to my local computer? My goal is to extract the coordinates, and pixel values (R, G, B, etc.) from each pixel and save the results as a table to my local folder.

I have used geemap.ee_export_image(img_joined, filename="Test.tif", scale=210, region=region, file_per_band=True), and this can give me each band as one image on my local computer. If

• the scale is smaller than 210, it will show that the size is larger than ~50MB
• the parameter is file_per_band=False, it also shows me errors

Could you please give me some hints and suggestions to help me download large joined images as a CSV file on my local computer?
I am also curious what if I need to download the joined images as a TIFF format image. What different functions do I need to use?

Be more specific, here is one example:

import ee
import geemap
import os
import numpy as np
import pandas as pd

# Authenticate to Earth Engine
ee.Authenticate()
ee.Initialize()

# Initialize geemap
geemap.ee_initialize()

# Define the date range
start_date = '2018-12-04'
end_date = '2018-12-05'
[row,col] = [34,27]

band_1 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').filterDate(start_date, end_date).select('SR_B4').filterMetadata('WRS_PATH','equals',int(row)).filterMetadata('WRS_ROW','equals',int(col)).first()
band_2 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').filterDate(start_date, end_date).select('SR_B3').filterMetadata('WRS_PATH','equals',int(row)).filterMetadata('WRS_ROW','equals',int(col)).first()
band_3 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').filterDate(start_date, end_date).select('SR_B2').filterMetadata('WRS_PATH','equals',int(row)).filterMetadata('WRS_ROW','equals',int(col)).first()
band_4 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').filterDate(start_date, end_date).select('SR_B5').filterMetadata('WRS_PATH','equals',int(row)).filterMetadata('WRS_ROW','equals',int(col)).first()
band_5 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').filterDate(start_date, end_date).select('SR_B1').filterMetadata('WRS_PATH','equals',int(row)).filterMetadata('WRS_ROW','equals',int(col)).first()
band_6 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').filterDate(start_date, end_date).select('SR_B6').filterMetadata('WRS_PATH','equals',int(row)).filterMetadata('WRS_ROW','equals',int(col)).first()
band_7 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').filterDate(start_date, end_date).select('SR_B7').filterMetadata('WRS_PATH','equals',int(row)).filterMetadata('WRS_ROW','equals',int(col)).first()
band_8 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').filterDate(start_date, end_date).select('ST_B10').filterMetadata('WRS_PATH','equals',int(row)).filterMetadata('WRS_ROW','equals',int(col)).first()

geomS2Img = band_1.geometry()

# DEM 30M
dataset = ee.Image("USGS/SRTMGL1_003")
dem = dataset.select('elevation')
band_9 = dem.clip(geomS2Img)

# NLCD static dataset
dataset = ee.ImageCollection("USGS/NLCD_RELEASES/2019_REL/NLCD")
nlcd2016 = dataset.filter(ee.Filter.eq('system:index', '2016')).first()
landcover = nlcd2016.select('landcover')
band_10 = landcover.clip(geomS2Img)

# Coordinates
proj = band_1.select([0]).projection()
latlon = ee.Image.pixelLonLat()

img_join = band_1.addBands(band_2).addBands(band_3).addBands(band_4).addBands(band_5).addBands(band_6).addBands(band_7).addBands(band_8).addBands(band_9).addBands(band_10).addBands(latlon.select('longitude','latitude'))

# Download the joined images as an image 
geemap.ee_export_image(img_join, filename='Test.tif', scale=30, region=geomS2Img, file_per_band=False)

# How to download as a CSV (table) file?

[RuiGao9]

Well, I think I need to turn my mind to another way like this video by Dr. Wu (https://www.youtube.com/watch?v=UbQ8jyc4VP4&t=637s)
Let me try and see if I could get what I want ~

[giswqs]

You can also use zonal_stats: https://geemap.org/notebooks/12_zonal_statistics/

[RuiGao9]

Thank you so much for your suggestion, Dr. Wu 👍