How to Make Your Google Earth Engine JavaScript Run Faster

Optimizing the Execution Time of Google Earth Engine Scripts

Google Earth Engine (GEE) is a powerful platform for analyzing large-scale geospatial data. However, users often encounter issues with the execution time, even when their scripts appear basic. A script that takes several minutes to run can impact productivity and delay insights.

In this case, a user has created a simple script to process Sentinel and Landsat 8 data. Despite its simplicity, the script takes about 3-5 minutes to execute. Understanding why this happens and how to optimize the script is essential for efficient data processing.

The performance of a GEE script depends on several factors, including data size, filtering, and computational complexity. Reducing the execution time involves identifying bottlenecks within the script, such as unnecessary operations or large datasets that slow down processing.

This article will explore the possible causes of slow execution times in GEE and provide practical tips for optimizing the given script. By implementing these strategies, users can significantly improve the speed and performance of their geospatial data analysis tasks.

Optimizing GEE Scripts for Improved Efficiency

In the provided script, the goal is to extract and process satellite imagery from two different sources: Sentinel and Landsat. The Google Earth Engine (GEE) platform allows users to access vast amounts of satellite data and apply various operations such as filtering, indexing, and visualization. One of the key features used in this script is the normalizedDifference() function, which is employed to calculate important indices like NDVI, NDWI, and NDSI. These indices are crucial for analyzing vegetation, water, and snow cover in the specified region. The script starts by defining a point of interest and centers the map on it using the coordinates provided.

The script applies multiple filters, such as filterDate() and filterBounds(), to reduce the amount of data being processed, thus improving execution time. For instance, filterBounds() ensures that only the images intersecting the area of interest are included, while filterDate() limits the images to a specific date range. This is essential for processing large datasets like Sentinel and Landsat imagery, as it minimizes the computational load. Additionally, the filter for cloud coverage helps to discard images that have too much cloud, ensuring better-quality analysis.

One important aspect of the script is the addBands() function, which adds calculated indices (NDVI, NDWI, NDSI) to the imagery, making them accessible for further analysis. The script also incorporates a permanent water mask using data from the JRC Global Surface Water dataset. The water mask helps in excluding areas with a high occurrence of water (greater than 80%), which could otherwise skew the results of the vegetation and snow analysis. This is done through the gt() and unmask() functions, which allow the script to isolate areas based on pixel values.

Finally, the script uses the reduce() function with the percentile reducer to generate a composite image that represents the 30th percentile of the selected pixel values. This composite image is then clipped to the region of interest and visualized on the map using the clip() function. The visual parameters are defined for both the Sentinel and Landsat composites, allowing the user to view them with appropriate color settings. By combining various image processing steps like filtering, masking, and composite generation, this script provides an efficient way to analyze satellite imagery, though further optimization could be applied to reduce execution times.

var pointJSP = ee.Geometry.Point([86.465263, 20.168076]);
Map.centerObject(pointJSP, 14);
// Combine date variables for flexibility
var startDate = '2024-02-01';
var endDate = '2024-03-01';
// Function to add NDVI, NDWI, NDSI
var addIndices = function(image) {
  var ndvi = image.normalizedDifference(['NIR', 'RED']).rename('NDVI');
  var ndwi = image.normalizedDifference(['NIR', 'SWIR1']).rename('NDWI');
  var ndsi = image.normalizedDifference(['SWIR1', 'SWIR2']).rename('NDSI');
  return image.addBands(ndvi).addBands(ndwi).addBands(ndsi);
};
// Use fewer data points by filtering for cloud-free pixels only once
var sentinel = ee.ImageCollection('COPERNICUS/S2_SR')
  .filterBounds(pointJSP)
  .filterDate(startDate, endDate)
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30));

var landsat8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
  .filterBounds(pointJSP)
  .filterDate(startDate, endDate)
  .filter(ee.Filter.lt('CLOUD_COVER', 30));
// Apply water mask for permanent water areas
var waterMask = ee.Image('JRC/GSW1_4/GlobalSurfaceWater').select('occurrence').gt(80).unmask();
// Add indices to Landsat 8 imagery
var landsatIndices = landsat8.map(addIndices);
var composite = landsatIndices.reduce(ee.Reducer.percentile([30])).clip(pointJSP).mask(waterMask.eq(0));
Map.addLayer(composite, {bands: ['RED', 'GREEN', 'BLUE'], min: 0, max: 3000}, 'Landsat Composite');
Map.addLayer(waterMask, {min: 0, max: 1, palette: ['white', 'blue']}, 'Water Mask', false);

Improving the Efficiency of Remote Sensing Scripts

One key consideration when working with Google Earth Engine (GEE) scripts is ensuring that operations are efficiently handled. While the use of large datasets such as Sentinel and Landsat is common in environmental analysis, the sheer volume of data can slow down script execution. One method to improve performance is to ensure that only the necessary data is being processed. Using commands like filterBounds() and filterDate() minimizes the dataset size, which helps streamline computations. Selecting specific date ranges and geographic regions can reduce execution time significantly.

Another aspect that impacts GEE script performance is how the data is processed. The script example uses a function to calculate important indices like NDVI, NDWI, and NDSI. These indices are added as bands to the image collections, which allows for a more thorough analysis. However, a common mistake is applying such functions to an entire dataset without filtering first. It's crucial to apply such operations after filtering to avoid unnecessary computations on irrelevant data.

Visualization is another element of the script that can be optimized. Adding too many layers or complex visualizations can bog down the processing time. The script uses predefined visual parameters to render the composites, but performance can be improved by disabling certain layers unless they are explicitly needed. This helps in keeping the script lightweight while maintaining its functionality. Using clip() commands also ensures that only the necessary area is rendered, reducing the overall processing burden.