Exploring MODIS Products in GEE: A Complete Guide

Learn how to access and use MODIS products in Google Earth Engine for NDVI, land surface temperature, snow, fire, and more. Scripts and use cases

 MODIS (Moderate Resolution Imaging Spectroradiometer), aboard NASA’s Terra and Aqua satellites, provides daily to 16-day composite data across the globe. With a history that spans over two decades, MODIS products are essential for monitoring vegetation, temperature, snow cover, fire, and more.

Google Earth Engine (GEE) hosts an extensive archive of MODIS data, giving users direct access to dozens of products with just a few lines of code. This guide will walk you through how to access, visualize, and apply key MODIS products in GEE, including sample scripts and use case ideas.

1. Why Use MODIS Data in Google Earth Engine?

Feature	Advantage
📅 Temporal coverage	Data from 2000 to present
🛰️ Spatial coverage	Global, consistent
🕒 Frequency	Daily to 16-day composites
📏 Resolution	250m–1km, depending on the product
🔄 Pre-processed	Cloud masking, corrections often applied

MODIS is ideal for trend analysis, climate research, and continental-scale studies.

2. How to Search MODIS Products Datasets in GEE

Use the GEE Data Catalog  and filter by:

• Provider: NASA, USGS

• Keyword: “MODIS”

• Categories: Vegetation, Climate, Surface Reflectance, etc.

You can also type in GEE's code editor:

// Search for MODIS datasets
var datasets = ee.data.getList({id: 'MODIS'});
print(datasets);

3. Top MODIS Products in Earth Engine

🔹 MOD13Q1 – NDVI / EVI (Vegetation Index)

• Spatial: 250m

• Temporal: 16-day composite

• Dataset ID: MODIS/006/MOD13Q1

var ndvi = ee.ImageCollection("MODIS/006/MOD13Q1")
             .select('NDVI')
             .filterDate('2023-01-01', '2023-12-31')
             .mean()
             .multiply(0.0001); // Scale factor
Map.addLayer(ndvi, {min: 0, max: 0.8, palette: ['white', 'green']}, 'NDVI');

---

🔹 MOD11A1 – Land Surface Temperature (LST)

• Spatial: 1km

• Temporal: Daily

• Dataset ID: MODIS/006/MOD11A1

var lst = ee.ImageCollection("MODIS/006/MOD11A1")
             .select('LST_Day_1km')
             .filterDate('2023-07-01', '2023-07-10')
             .mean()
             .multiply(0.02).subtract(273.15); // Kelvin to °C
Map.addLayer(lst, {min: 10, max: 40, palette: ['blue', 'yellow', 'red']}, 'LST °C');

---

🔹 MOD10A1 – Snow Cover

• Spatial: 500m

• Temporal: Daily

• Dataset ID: MODIS/006/MOD10A1

var snow = ee.ImageCollection("MODIS/006/MOD10A1")
               .select('NDSI_Snow_Cover')
               .filterDate('2023-01-01', '2023-01-31')
               .mean();
Map.addLayer(snow, {min: 0, max: 100, palette: ['gray', 'cyan', 'white']}, 'Snow Cover');

---

🔹 MCD64A1 – Burned Area (Fires)

• Spatial: 500m

• Temporal: Monthly

• Dataset ID: MODIS/006/MCD64A1

var fire = ee.ImageCollection("MODIS/006/MCD64A1")
               .select('BurnDate')
               .filterDate('2023-06-01', '2023-07-01')
               .mean();
Map.addLayer(fire, {min: 0, max: 365, palette: ['black', 'orange', 'red']}, 'Burned Area');

---

🔹 MOD16A2 – Evapotranspiration (ET)

• Spatial: 500m

• Temporal: 8-day

• Dataset ID: MODIS/006/MOD16A2

var et = ee.ImageCollection("MODIS/006/MOD16A2")
             .select('ET')
             .filterDate('2023-05-01', '2023-05-31')
             .mean()
             .multiply(0.1); // Scale factor
Map.addLayer(et, {min: 0, max: 100, palette: ['white', 'blue', 'green']}, 'ET mm');

4. Working with MODIS Scale Factors

Many MODIS bands require scaling:

• NDVI: × 0.0001

• LST: × 0.02

• ET: × 0.1

• QA flags: Bitmasks to apply filtering (optional but recommended)

📏 Always read the MODIS product documentation or Earth Engine dataset description to apply correct units.

5. Common Applications of MODIS in GEE

Application	Relevant Products
Vegetation health monitoring	MOD13Q1, MOD09GA
Drought and water stress	MOD16A2, MOD11A1
Fire detection and mapping	MCD64A1
Snow and ice monitoring	MOD10A1
Surface temperature trends	MOD11A1, MOD21A1
Climate variability	MOD17A2 (GPP), MOD16A2 (ET)

🔁 Combine multiple products for richer environmental analysis.

 

6. Tips for Working with MODIS in GEE

• Temporal aggregation: MODIS offers dense time series—use mean(), median(), or mosaic() for smoothing.

• Mask clouds and outliers using quality assurance (QA) bands or thresholds.

• Use Region Reduction (reduceRegion) to extract statistics.

• Export time series charts with ui.Chart.image.series.

7. How to Visualize MODIS Time Series

Example: NDVI over time for a region:

var region = ee.Geometry.Point([36.8, -1.3]); // Nairobi
var ndviSeries = ee.ImageCollection("MODIS/006/MOD13Q1")
                    .select('NDVI')
                    .filterBounds(region)
                    .filterDate('2022-01-01', '2023-01-01');

var chart = ui.Chart.image.series({
  imageCollection: ndviSeries,
  region: region,
  scale: 250,
  xProperty: 'system:time_start'
}).setOptions({title: 'NDVI Time Series'});
print(chart);

8. Exporting MODIS Data from GEE

To export an NDVI layer:

Export.image.toDrive({
  image: ndvi,
  description: 'MODIS_NDVI_2023',
  region: region,
  scale: 250,
  maxPixels: 1e13
});

📁 You can also export as .CSV or to Google Drive/Cloud Storage.

Conclusion

MODIS Products is a cornerstone dataset in Earth Engine, providing rich, multi-year, multi-variable data that supports global environmental research, land cover mapping, and climate monitoring. By mastering MODIS products, you unlock a toolbox for powerful, scalable geospatial analysis.

Explore it, script it, visualize it—MODIS in GEE is the gateway to understanding our dynamic planet.

🔗 Related Articles:

• Top 10 Free Satellite Datasets Available in Google Earth Engine

• Smoothing land cover with morphological

• Compute EVI using expressions in GEE Google Earth Engine

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Written by: Jamal Chaaouan | GEE Academy