Batch Exports

Quick win: Export by region

Export NDVI for multiple parks:

// Define multiple regions
var parks = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Point([-81.0, 28.4]).buffer(10000), {name: 'Park_A'}),
  ee.Feature(ee.Geometry.Point([-82.0, 29.1]).buffer(10000), {name: 'Park_B'}),
  ee.Feature(ee.Geometry.Point([-80.5, 27.8]).buffer(10000), {name: 'Park_C'})
]);

// Create NDVI image
var image = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
  .filterDate('2023-06-01', '2023-08-31')
  .median()
  .multiply(0.0000275).add(-0.2);
var ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']).rename('NDVI');

// Get list of features to loop over
var parkList = parks.toList(parks.size());

// Export each region
for (var i = 0; i < 3; i++) {
  var park = ee.Feature(parkList.get(i));
  var name = park.get('name').getInfo();  // Get name client-side
  
  Export.image.toDrive({
    image: ndvi,
    description: 'NDVI_' + name + '_2023',
    folder: 'EarthEngine/Parks',
    region: park.geometry(),
    scale: 30,
    maxPixels: 1e9
  });
}

print('3 export tasks created - check Tasks panel!');

Export by time period

Create monthly composites and export each:

var roi = ee.Geometry.Rectangle([-82.5, 29.5, -82.0, 30.0]);
var year = 2023;

// Loop through months
for (var month = 1; month <= 12; month++) {
  var startDate = ee.Date.fromYMD(year, month, 1);
  var endDate = startDate.advance(1, 'month');
  
  // Get monthly composite
  var composite = ee.ImageCollection('MODIS/061/MOD13A2')
    .filterDate(startDate, endDate)
    .select('NDVI')
    .median();
  
  // Format month with leading zero
  var monthStr = (month < 10) ? '0' + month : '' + month;
  
  Export.image.toDrive({
    image: composite,
    description: 'NDVI_' + year + '_' + monthStr,
    folder: 'EarthEngine/Monthly',
    region: roi,
    scale: 250,
    maxPixels: 1e9
  });
}

print('12 monthly export tasks created!');

Export each image in a collection

Export individual images (not composites):

var roi = ee.Geometry.Point([-82.3, 29.6]).buffer(5000);

// Get collection
var collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
  .filterDate('2023-06-01', '2023-06-30')
  .filterBounds(roi);

// Get list of image IDs
var imageList = collection.toList(collection.size());
var count = collection.size().getInfo();

// Export each image
for (var i = 0; i < count; i++) {
  var img = ee.Image(imageList.get(i));
  var id = img.id().getInfo();
  var date = img.date().format('YYYY-MM-dd').getInfo();
  
  Export.image.toDrive({
    image: img.select(['SR_B4', 'SR_B3', 'SR_B2']).multiply(0.0000275).add(-0.2),
    description: 'L8_' + date,
    folder: 'EarthEngine/L8_Images',
    region: roi,
    scale: 30,
    maxPixels: 1e9
  });
}

print(count + ' image export tasks created!');

Export zonal statistics for many features

Calculate and export statistics for each polygon:

// Load a FeatureCollection (counties, watersheds, etc.)
var counties = ee.FeatureCollection('TIGER/2018/Counties')
  .filter(ee.Filter.eq('STATEFP', '12'))  // Florida
  .limit(10);  // Limit for demo

// Calculate NDVI
var ndvi = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
  .filterDate('2023-06-01', '2023-08-31')
  .filterBounds(counties)
  .median()
  .multiply(0.0000275).add(-0.2)
  .normalizedDifference(['SR_B5', 'SR_B4'])
  .rename('NDVI');

// Add mean NDVI to each county
var withStats = ndvi.reduceRegions({
  collection: counties,
  reducer: ee.Reducer.mean(),
  scale: 30
});

// Export as table
Export.table.toDrive({
  collection: withStats,
  description: 'County_NDVI_Stats',
  folder: 'EarthEngine',
  fileFormat: 'CSV'
});

print('Zonal stats export task created!');

Multi-year time series

Export annual composites for multiple years:

var roi = ee.Geometry.Rectangle([-82.5, 29.5, -82.0, 30.0]);

// Loop through years
for (var year = 2018; year <= 2023; year++) {
  var composite = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
    .filterDate(year + '-06-01', year + '-08-31')
    .filterBounds(roi)
    .median()
    .multiply(0.0000275).add(-0.2)
    .normalizedDifference(['SR_B5', 'SR_B4'])
    .rename('NDVI');
  
  Export.image.toDrive({
    image: composite,
    description: 'NDVI_Summer_' + year,
    folder: 'EarthEngine/TimeSeries',
    region: roi,
    scale: 30,
    maxPixels: 1e9
  });
}

print('6 annual export tasks created!');

Running all tasks

After running your script, you still need to click "Run" for each task in the Tasks panel. For many tasks, you can:

1. Use the browser console to auto-start tasks (advanced)
2. Use the Python API with task.start()
3. Click quickly through the list (practical for <20 tasks)

Next steps

• Export Best Practices
• Exporting Data
• Temporal Compositing