Google Earth Engine (GEE) – From Exploration to Export

Step 1: Explore & Visualize Data in GEE Code Editor

Access the Code Editor: Earth Engine Code Editor

Understanding the Code Editor Features

The GEE Code Editor is a web-based IDE for the Earth Engine JavaScript API, designed for geospatial analysis. It includes:

Step 2: Load data from project assets on Code Editor GEE

// Load temperature image from project assets
var temperature = ee.Image("projects/ee-udomporn/assets/AvgSurfT");

// Calculate statistics
var stats = temperature.reduceRegion({
  reducer: ee.Reducer.minMax(),
  geometry: temperature.geometry(),
  scale: 1000,
  bestEffort: true
});

// Print statistics
print("Min/Max:", stats);
print("Bands:", temperature.bandNames());
print("Resolution:", temperature.projection().nominalScale());
print("Projection:", temperature.projection());
print("Image Geometry:", temperature.geometry());

// Load Thailand boundary (Example: FAO GAUL dataset)
var thailand = ee.FeatureCollection('FAO/GAUL/2015/level0')
                .filter(ee.Filter.eq('ADM0_NAME', 'Thailand'));
});

Step 3: Select the band data and set for Visualization

// Select the first month of temperature data
var month1 = temperature.select('b1');
  palette: ['blue', 'cyan', 'yellow', 'orange', 'red']

// Visualization settings
var visParams = {
  min: 290,
  max: 315,
  palette: ['blue', 'cyan', 'yellow', 'orange', 'red']

Step 4: Display the map

// Display results on map
Map.centerObject(thailand, 6);
Map.addLayer(month1, visParams, 'Temperature');
Map.addLayer(thailand.style({color: 'black', width: 1}), {}, 'Boundary');
Map

Step 5: Select and clip for specific area

// Load Thailand Boundary Data (Level1 - Province)
var provinces = ee.FeatureCollection('FAO/GAUL/2015/level1')\
            .filter(ee.Filter.eq('ADM0_NAME', 'Thailand'))

// Filter Bangkok for example
var bangkok = provinces.filter(ee.Filter.eq('ADM1_NAME', 'Bangkok'))

// Clip the data by Bangkok Boundary
var bangkok_clipped = month1.clip(bangkok)

// Visualise the map
Map = geemap.Map(center=[13.7563, 100.5018], zoom=10)  

// Add more details on the map
Map.centerObject(bangkok, 6);
Map.addLayer(bangkok_clipped, visParams, 'Temperature in Bangkok')

Step 6: Export Data to Google Drive

To save the clipped temperature data, we use Export.image.toDrive():

Export.image.toDrive({
  image: month1,
  description: 'Thailand_SurfaceTemp',
  folder: 'GEE',  // Creates a folder named "GEE" in Google Drive
  fileNamePrefix: 'AvgSurfT_Thailand',
  region: thailand.geometry(),
  scale: 1000,  // Resolution in metre
  crs: 'EPSG:4326',
  maxPixels: 1e13
    

Step 7: Running the Export Process

1. Click "Tasks" tab.
2. Click "Run" next to your export task.
3. Wait for the process to finish—your image will be available in Google Drive.

Additional Notes

var month1 = temperature.select('b1').clip(thailand);
var month2 = temperature.select('b2').clip(thailand);
    

Google Colab – Using GEE Asset & Project

Step 1: Install Required Packages

To work with Google Earth Engine in Colab: Google Colab

# Install required packages
!pip install geemap rasterio localtileserver

Step 2: Import Libraries

import ee
import geemap
import os
from google.colab import drive
import matplotlib.pyplot as plt
import rasterio
from rasterio.plot import show
import numpy as np

Step 3: Connect Google Drive to Your Colab Session:

Google Colab does not have direct access to files, so we need to mount Google Drive:

# Mount Google Drive
drive.mount('/content/drive')

Grant Permissions

!ls /content/drive/MyDrive/

Step 4: Authenticate Earth Engine

Earth Engine requires authentication and initialisation before using its functions.

# Initialize Earth Engine
ee.Authenticate()

Step 5: Initialise Earth Engine with Your Project

# Initialize Earth Engine
ee.Initialize(project='ee-udomporn') # Your GEE Project Name

Step 6: Load Data from GEE Asset

# Load Data from GEE Asset
asset_id = "projects/ee-udomporn/assets/AvgSurfT"
temperature_image = ee.Image(asset_id)

Step 7: Check Min/Max for Each Band

To analyze the range of values for each band (month), loop through the bands and check their min/max values.

for i in range(1, 6):
    band_name = f'b{i}'
    stats = temperature_image.select(band_name).reduceRegion(
        reducer=ee.Reducer.minMax(),
        geometry=temperature_image.geometry(),
        scale=1000,  # Resolution in meters
        maxPixels=1e9
    ).getInfo()
    print(f'{band_name}: {stats}')

Step 8: Load Thailand Boundary

# Load Thailand Boundary from FAO GAUL
fao_admin = ee.FeatureCollection("FAO/GAUL_SIMPLIFIED_500m/2015/level1")

# Filter for Thailand
thailand = fao_admin.filter(ee.Filter.eq('ADM0_NAME', 'Thailand'))

Step 9: Select & Clip Data

# Extract Band 1 (Month 1) 
month1 = temperature_image.select('b1')

Step 10: Visualize Data in geemap

Create an interactive map centered over Thailand.

Map = geemap.Map(center=[13, 101], zoom=6)

# Define visualization parameters
vis_params = {
    'min': 290,
    'max': 315,
    'palette': ['blue', 'cyan', 'yellow', 'orange', 'red']
}

# Create a style dictionary for a thin boundary line
thailand_outline = thailand.style(
    color='black',   # Line color
    width=0.5,       # Line width
    fillColor='00000000'  # Transparent fill color
)

# Add layers to the map
Map.addLayer(month1, vis_params, 'Surface Temp')
Map.addLayer(thailand_outline, {}, 'Thailand Boundary (Thin)')
Map.add_colorbar(vis_params, label='Temperature (K)')
Map.addLayerControl()

Map

Step 9: Clip Data to Specific Area

# Select Bangkok Boundary
bangkok = thailand.filter(ee.Filter.eq('ADM1_NAME', 'Bangkok'))

# Clip data to Bangkok Boundary
bangkok_clipped = month1.clip(bangkok)
# Visualise the result
Map.addLayer(bangkok_clipped, vis_params, 'Surface Temperature for Bangkok')  # มีศูนย์กลางที่กรุงเทพฯ

Map

Step 10: Export Data to Google Drive

# Export to Google Drive
task = ee.batch.Export.image.toDrive(
image=clipped_image,
description='Thailand_SurfaceTemp',
folder='GEE_Export', # Folder in your Google Drive
fileNamePrefix='AvgSurfT_Thailand',
region=thailand.geometry(),
scale=1000,
crs='EPSG:4326',
maxPixels=1e13
)
task.start()

print('Export Task Started. Check Earth Engine Tasks tab.')

AFTER export completed in GEE Task tab

# Download from Drive to Colab
!cp /content/drive/MyDrive/GEE_Export/AvgSurfT_Thailand.tif /content/

# Download to Local Computer
files.download('/content/AvgSurfT_Thailand.tif')

Final Workflow Summary

Important Notes

🔍 Link to Example code : Google Colab

Working with Google Earth Engine for Geospatial Analysis

This tutorial shows how to analyse and visualise geospatial temperature data using Google Earth Engine (GEE) and Python. We’ll cover authentication, loading Earth Engine assets, processing data, and creating interactive maps.

Step-By-Step Earth Engine Workflow in Python

Step 1. install geemap and library

!pip install geemap earthengine-api

Step 2. Authenticate Earth Engine

import ee

# Authenticate
ee.Authenticate()

Step 3. Initialize Earth Engine with Your Project

# Initialize Earth Engine with your specific project
ee.Initialize(project='ee-udomporn')  # Replace with your GEE Project Name

Step 4. Load Data from GEE Asset

# Load temperature data from your Earth Engine asset
asset_id = "projects/ee-udomporn/assets/AvgSurfT"
temperature_image = ee.Image(asset_id)

# Print basic information about the image
print(temperature_image.bandNames().getInfo())

Step 5. Check Min/Max for Each Band

# Function to get min/max values for a band
def get_band_stats(image, band_name):
    stats = image.select(band_name).reduceRegion(
        reducer=ee.Reducer.minMax(),
        geometry=image.geometry(),
        scale=1000,
        maxPixels=1e9
    )
    return stats.getInfo()

# Get band names
band_names = temperature_image.bandNames().getInfo()

# Print stats for each band
for band in band_names:
    stats = get_band_stats(temperature_image, band)
    print(f"แบนด์ {band}: ต่ำสุด = {stats[band+'_min']}, สูงสุด = {stats[band+'_max']}")

Step 6. Load Thailand Boundary from FAO GAUL

# Load Thailand boundary from FAO's Global Administrative Unit Layers
thailand = ee.FeatureCollection('FAO/GAUL/2015/level0')\
           .filter(ee.Filter.eq('ADM0_NAME', 'Thailand'))

# Print information about the boundary
print(f"Thailand boundary: {thailand.first().getInfo()['properties']['ADM0_NAME']}")

Step 7. Visualise Data in GeeMap

import geemap

# Create a map centered on Thailand
Map = geemap.Map(center=[13, 101], zoom=6)

# Define visualization parameters
vis_params = {
    'min': 290,
    'max': 315,
    'palette': ['blue', 'cyan', 'yellow', 'orange', 'red']
}

# Create a style dictionary for a thin boundary line
thailand_outline = thailand.style(
    color='black',   # Line color
    width=0.5,       # Line width
    fillColor='00000000'  # Transparent fill color
)

# Add layers to the map
Map.addLayer(clipped_image, vis_params, 'Thailand Surface Temperature')
Map.addLayer(thailand_outline, {}, 'Thailand Boundary (Thin)')
Map.add_colorbar(vis_params, label='Temperature (K)')
Map.addLayerControl()

# Display the map
Map

Step 8. Selecting & Clipping Data to Bangkok Boundary

# Load Thailand admin level 1 (province) boundaries
provinces = ee.FeatureCollection('FAO/GAUL/2015/level1')\
            .filter(ee.Filter.eq('ADM0_NAME', 'Thailand'))

# Filter to get only Bangkok
bangkok = provinces.filter(ee.Filter.eq('ADM1_NAME', 'Bangkok'))

# Print the name to verify
print(f"Selected area: {bangkok.first().getInfo()['properties']['ADM1_NAME']}")

# Select Band 1 (Month 1)
month1 = temperature_image.select('b1')

# Clip image to Bangkok boundary
bangkok_clipped = month1.clip(bangkok)

Step 9. Visualising Bangkok Temperature Data

# Create a map centered on Bangkok
Map = geemap.Map(center=[13.7563, 100.5018], zoom=10)  # Centered on Bangkok

# Define visualization parameters
vis_params = {
    'min': 290,
    'max': 315,
    'palette': ['blue', 'cyan', 'yellow', 'orange', 'red']
}

# Create a style dictionary for a thin boundary line
bangkok_outline = bangkok.style(
    color='black',   # Line color
    width=1,         # Line width
    fillColor='00000000'  # Transparent fill color
)

# Add layers to the map
Map.addLayer(bangkok_clipped, vis_params, 'Bangkok Surface Temperature')
Map.addLayer(bangkok_outline, {}, 'Bangkok Boundary')
Map.add_colorbar(vis_params, label='Temperature (K)')
Map.addLayerControl()

# Display the map
Map

Step 10. Exporting Earth Engine Data to Your Local Drive

# Export PNG
Map.to_image('bangkok_temperature_map.png')
print("ส่งออกแผนที่เป็น PNG สำเร็จแล้ว")

# Export JPG
Map.to_jpg('bangkok_temperature_map.jpg')
print("Exported JPG file successfully")

# Export PDF
Map.to_pdf('bangkok_temperature_map.pdf')
print("Exported PDF file successfully")

import os

# set directory to save the file
output_dir = './exports'
if not os.path.exists(output_dir):
    os.makedirs(output_dir)

# set the path for file
output_file = os.path.join(output_dir, 'bangkok_temp.tif')

# export GeoTIFF 
geemap.ee_export_image(
    bangkok_clipped,  # image
    filename=output_file,  # file name
    scale=100,  # resolution
    region=bangkok.geometry(),  # boundary
    file_per_band=False
)
print(f"Exported to {output_file} successfully")

By combining Earth Engine’s cloud processing capabilities with local file access, you can efficiently analyse geospatial data while maintaining the flexibility of your local development environment.

🔍 Link to Download Jupyter Notebook: Jupyter Notebook