What You'll Learn
- Navigate the Google Earth Engine Data Catalog
- Evaluate datasets based on spatial and temporal resolution
- Compare spectral bands and coverage across different sensors
- Select appropriate datasets for specific research questions
- Document and justify dataset selection decisions
Building On Previous Learning
In Lab 6, you learned to filter and sort image collections. Now you'll learn to choose the RIGHT collection in the first place - a critical skill for any project.
Why This Matters
Choosing the right dataset is one of the most important decisions in any remote sensing project:
- Wrong resolution → Can't see the features you need
- Wrong time period → Data doesn't cover your study period
- Wrong bands → Can't calculate the indices you need
- Too much data → Processing takes forever
This skill will serve you in every future GEE project!
Before You Start
- Prerequisites: Complete Lab 6 and refine the area of interest you identified in Module 3.
- Estimated time: 75 minutes
- Materials: Earth Engine account, AOI coordinates or shapefile, and links to potential datasets you plan to evaluate.
Key Terms
- Spatial Resolution
- The size of each pixel on the ground (e.g., 10m means each pixel covers 10x10 meters).
- Temporal Resolution
- How frequently the satellite returns to image the same location (e.g., 16-day revisit).
- Temporal Coverage
- The date range for which data is available (e.g., 1984-present for Landsat).
- Spectral Bands
- The wavelength ranges a sensor captures (e.g., visible, NIR, thermal).
- Swath Width
- The width of the area imaged in one pass (wider = faster global coverage).
Introduction
Google Earth Engine provides access to a vast catalog of geospatial datasets. In this challenge lab, you will learn to navigate the GEE Data Catalog, understand dataset characteristics, and select appropriate imagery for your area of interest. This is an essential skill for any remote sensing project.
Challenge Instructions
Your Task
- Choose a location of interest (your hometown, study site, etc.)
- Browse the GEE Data Catalog
- Find at least 3 different datasets suitable for your location
- Load and visualize each dataset in GEE
- Document the characteristics of each dataset
- Compare and contrast the datasets
Dataset Characteristics to Document
For each of the three datasets you choose, document the following:
| Characteristic | Description | Example |
|---|---|---|
| Spatial Resolution | Pixel size (ground coverage) | 10m, 30m, 500m |
| Temporal Coverage | Date range of available data | 1984-present |
| Temporal Resolution | How often new images are collected | Daily, 5-day, 16-day |
| Spectral Bands | Available wavelengths | RGB, NIR, SWIR, thermal |
| Data Provider | Organization responsible | NASA, ESA, USGS |
| Best Use Cases | What is it suited for? | Land cover, vegetation, ocean color |
Suggested Dataset Categories
Consider choosing datasets from different categories to see the variety available:
🛰️ Optical Imagery
- Landsat 8/9: 30m, 16-day, since 2013
- Sentinel-2: 10m, 5-day, since 2015
- MODIS: 250m-1km, daily, since 2000
📡 Radar Imagery
- Sentinel-1: Works through clouds! 10m resolution
- Great for flood mapping, change detection
🌡️ Climate & Environment
- Temperature and precipitation data
- Land surface temperature
- Fire and burn severity
🏔️ Elevation & Terrain
- SRTM: 30m global elevation
- ASTER DEM: Alternative elevation source
Code Template
// Define your area of interest
var aoi = ee.Geometry.Point([longitude, latitude]);
// Center the map on your location
Map.centerObject(aoi, 10);
// Dataset 1: [Name of dataset]
// Characteristics: [Spatial resolution, temporal coverage, etc.]
var dataset1 = ee.ImageCollection('DATASET_ID_HERE')
.filterBounds(aoi)
.filterDate('START_DATE', 'END_DATE')
.sort('CLOUD_COVER')
.first();
Map.addLayer(dataset1, {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000}, 'Dataset 1');
// Repeat for Dataset 2 and Dataset 3
// ...Try It
Use the Data Catalog search to find dataset IDs. Each dataset page shows the exact collection ID to use.
Comparison Questions
After loading all three datasets, answer these questions in your script comments:
- Which dataset has the highest spatial resolution? What are the trade-offs?
- Which dataset provides the most frequent updates?
- Which dataset has the most spectral bands? How might this be useful?
- For your chosen location and research question, which dataset would you recommend? Why?
Check Your Understanding
- Why might you choose a 500m resolution dataset over a 10m dataset?
- What's the difference between temporal resolution and temporal coverage?
- Why would you care about the swath width of a satellite?
- When would radar imagery be preferable to optical imagery?
Pro Tips for Dataset Selection
- Check availability: Some datasets don't cover all areas (e.g., some are US-only)
- Consider your study period: Older studies may require Landsat (1984+) vs Sentinel (2015+)
- Balance resolution with coverage: Higher resolution = smaller swath = fewer images
- Read the documentation: Each dataset page has crucial information about bands, scaling, and known issues
Troubleshooting
Solution: Expand your date range or check if the dataset covers your area. Some datasets are regional, not global.
Solution: Your visualization parameters (min/max) don't match the data. Check the dataset documentation for recommended values.
Solution: The "Bands" section of each dataset page lists all available bands with their wavelength ranges and recommended visualizations.
📋 Lab Submission
Subject: Lab 7 - Challenge Finding Dataset - [Your Name]
Submit:
A document containing:
- URL to your GEE script with all three datasets loaded and visualized
- Screenshots of all three datasets showing your area of interest
- Dataset comparison table with characteristics (resolution, coverage, bands, etc.)
- Recommendation paragraph explaining which dataset is best for your site and why
- Answers to comparison questions above
Total: 50 Points