Google Earth Engine Tutorial: Spatio-temporal Analysis of NO2 Concentration

Google Earth Engine (GEE) offers powerful tools for spatio-temporal analysis of environmental data, including nitrogen dioxide (NO2) concentrations. NO2 is a key air pollutant that can be monitored using satellite data, and GEE allows for efficient processing of large datasets over time. This tutorial provides a step-by-step guide to analyze NO2 trends using GEE.

Prerequisites

To follow this tutorial, ensure you have:

• A Google Earth Engine account and access to the GEE Code Editor.
• Familiarity with JavaScript syntax and basic GIS concepts.
• Understanding of satellite remote sensing data and its applications in environmental monitoring.

Data Sources

GEE provides access to various NO2 datasets. The most common include:

Sentinel-5P (TROPOMI): Offers high-resolution NO2 data with daily temporal coverage. This dataset often uses the “NO2” band from the “COPERNICUS/S5P/NRTI/L3_NO2” collection.

OMI (Aura): From NASA’s Aura satellite, suitable for long-term historical analysis.

Other datasets: Consider using MODIS or other instruments depending on your research goals.

Step-by-Step Tutorial

Step 1: Initialize the Earth Engine API

var ee = require('ee'); 
ee.Initialize();

Ensure your GEE account is authenticated and the API is enabled.

Step 2: Load NO2 Data

var no2 = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2');

This command accesses the Sentinel-5P NO2 dataset. Adjust the dataset name based on the source you choose.

Step 3: Filter by Time and Location

var filtered = no2.filterDate('2020-01-01', '2020-12-31') 
                  .filter(ee.Filter.geometry(ee.Geometry.Rectangle([minX, minY, maxX, maxY])));

Replace the date range and coordinates with your study area’s bounds.

Step 4: Aggregate Data (Optional)

var annualAverage = filtered.mean();

Use aggregation functions like mean(), median(), or max() to summarize data across time.

Step 5: Visualize NO2 Concentrations

Map.setCenter(longitude, latitude, zoomLevel); 
Map.addLayer(filtered, {min:0, max:0.0002, palette: 'blue, green, yellow, orange, red'}, 'NO2');

Adjust the visualization parameters to highlight concentration patterns effectively.

Step 6: Export Results

Export.image.toDrive({
  image: annualAverage,
  description: 'NO2_Analysis',
  folder: 'GEE_Exports',
  fileNamePrefix: 'no2_concentration',
  region: geometry,
  scale: 1000,
  maxPixels: 1e10
});

Export processed data as a GeoTIFF or CSV for further analysis in GIS software.

Advanced Techniques

Time Series Analysis: Use the getRegion() or reduceRegion() functions to extract NO2 trends for specific locations over multiple years.

Seasonal Patterns: Filter data by months or seasons to analyze periodic variations in NO2 levels.

Correlation with Emissions Sources: Overlay NO2 data with point datasets (e.g., industrial zones or traffic networks) to study spatial relationships.

FAQ

• What is the resolution of NO2 data in GEE?
  Most datasets have a resolution of 3.5 km x 3.5 km, but higher-resolution data may vary by source.
• How do I handle missing or cloudy data?
  Use cloud masking functions or filter the collection to exclude invalid pixels.
• Can I analyze NO2 for a specific city or region?
  Yes, use spatial filters (e.g., geometry()) to focus on any defined area.
• Is there a cost associated with using GEE for this analysis?
  GEE allows free use for personal and academic projects, but larger computations may require credits.
• How do I visualize spatio-temporal trends over years?
  Create an animated visualization using ui.Thumbnail or export time-series data as CSV files for plotting in external tools.