JRC Historical Flood Visualization and Download with Google Earth Engine

This is prepared with regard to the Google Earth Engine training in Vietnam.
The goal is to visualize the Joint Research Centre (JRC) Flood Occurrence Dataset and download the processed image. We will be using JRC Monthly Water History v1.0 (1984-2015). The highlight of this dataset is

To learn more about the dataset, please see this Nature, 2016 paper.

1. Let’s start by defining the useful image collection and feature collection. I have tried to keep the variable names as explanatory as possible.

   var jrcDataset = ee.ImageCollection('JRC/GSW1_0/MonthlyHistory');
   var countriesLayer = ee.FeatureCollection('ft:1tdSwUL7MVpOauSgRzqVTOwdfy17KDbw-1d9omPw');
   

2. Then we will define some variables for the output, like datetime, area, palette etc. Note we can define geometry in three ways:-
   1. You can have list of coordinates and generate Polygon using ee.Geometry.Polygon function.
   2. You can directly draw polygon using the draw tool.
   3. If you are focusing on country, you can filter from the countriesLayer

   var coordinates = [99.63, 14.37, 100.09, 14.77, 100.38, 14.33, 99.83, 14.05];
   var geometry = ee.Geometry.Polygon(coordinates);
   // or this can come directly as drawing polygon
   // or if you wanna focus on country, then
   var _country = ['Vietnam'];
   // filter the countriesLayer to include only the _country
   var geometry = countriesLayer.filter(ee.Filter.inList('Country', _country)).geometry();
   // Define start and end date
   var startDate = '2000-01-01';
   var endDate = '2004-01-01';
   // Define palette
   var palette = ['c10000,d742f4,001556,b7d2f7'];
   

3. Now filter the dataset to contain the required area and date.

   var filteredJRCDataset = jrcDataset.filterBounds(geometry).filterDate(startDate, endDate);
   

4. Since 0 is no data as explained before, we will focus on the valid data only. Set the properties for the valid image collection.

   var validFilteredJRCDataset = filteredJRCDataset.map(function (img) {
     var valid = img.gt(0);
     return valid.set('system:time_start', img.get('system:time_start'));
   });
   

5. Now get the image collection containing water data which is 2, and set the properties as before.

   var waterJRCDataset = filteredJRCDataset.map(function (img) {
     var waterImage = img.select('water').eq(2);
     return waterImage.set('system:time_start', img.get('system:time_start'));
   });
   

6. Let’s calculate the total water percentage image for the valid dataset for the given variables. The percentage is calculated as totalWater/totalValidData * 100

   var totalValidJRCDataset = validFilteredJRCDataset.sum().toFloat();
   var totalWaterJRCDataset = waterJRCDataset.sum().toFloat();
   var totalWaterPercentJRCDataset = totalWaterJRCDataset.divide(totalValidJRCDataset).multiply(100);
   

7. Since the totalWaterPercentJRCDataset also contains data from the totalValidJRCDataset, as we filtered image greater than 0, let’s use that as the mask on the totalWaterPercentJRCDataset and update it.

   var maskedWaterImage = totalWaterPercentJRCDataset.gt(1);
   var waterPercentImage = totalWaterPercentJRCDataset.updateMask(maskedWaterImage);
   

8. Filter to the geometry and add it to the map with the given palette

   waterPercentImage = waterPercentImage.clip(geometry);
   Map.centerObject(geometry);
   Map.addLayer(waterPercentImage, {'min': '0', 'max': '100', 'bands': 'water', 'palette': palette}, 'filtered');
   

9. Now let’s get the download URL using the getDownloadURL function

   var link = waterPercentImage.getDownloadURL({
     'name': 'water_extract',
     'scale': 30
   });
   print(link);
   

   Be careful with the large area. The maximum download is 1024 MB. You might need to adjust your scale for large area download.

If I had to do this on a server, it would take months to do the processing. A slight change in the parameter would require same time. But because of Earth Engine, this is possible within minutes to process the historical archive. Sincere gratitude to Google for providing the cloud processing.