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I want to extract or detect water bodies in a selected region using the relationship between water and vegetation indices, and these water and vegetation indices were calculated by using the cloud and snow free Landsat TM, ETM+ & OLI surface reflectance images based on the spectral bands and equation of mNDWI, NDVI & EVI. How can I use this code to solve this task?

Import the "selectedCountry" boundary region

var countries = ee.FeatureCollection('ft:1tdSwUL7MVpOauSgRzqVTOwdfy17KDbw-1d9omPw');
Map.addLayer(selectedCountry);
// Load Footprint of Landsat WRS-2 grids
var wrs2_descending = ee.FeatureCollection('ft:1_RZgjlcqixp-L9hyS6NYGqLaKOlnhSC35AB5M5Ll');

// get the Country names
var names = countries.aggregate_array('Country');

// Initialize a selection field 
// Once a country has been selected, the redraw function is going
var select = ui.Select({items: names.getInfo(), onChange: redraw });

//select.setPlaceholder('Choose a country ...'); 

// Add the drop-down 'select' widget to the map
Map.add(select);
function redraw(key){

  // get the selected country
  //var selectedCountry = ee.Feature(countries.filter(ee.Filter.eq('Country', key)).first());
  Map.centerObject(selectedCountry);

  // store the name of the selected country
  var selectedCountry_Strg = ee.String(selectedCountry.get('Country'))

  // add the country geometry to the map
  var layer0 = ui.Map.Layer(selectedCountry, {color:'purple'}, 'Selected country');
  Map.layers().set(0, layer0);

  // filter the WRS-2 grids from the selected country footprint and add the grids to the map
  var wrs2_filtered = wrs2_descending.filterBounds(selectedCountry.geometry());
  var layer1 = ui.Map.Layer(wrs2_filtered,{color:'blue'}, 'WRS2 filtered');
  Map.layers().set(1, layer1);
  var a1 ='1986-01-01';
  var a2 ='1998-09-20';
  var a3 ='1998-09-23';
  var a4 ='2019-12-31';
  var b1 = ee.Filter.date(a1,a2);
  var b2 = ee.Filter.date(a3,a4);



var l4_coll = ee.ImageCollection('LANDSAT/LT4_L1T_TOA');  //Aug 22, 1982 - Dec 14, 1993
var l5_coll = ee.ImageCollection('LANDSAT/LT5_L1T_TOA').filter(ee.Filter.or(b1,b2));
var l7_coll = ee.ImageCollection('LANDSAT/LE7_L1T_TOA');  //Jan 1, 1999 - Apr 30, 2017
var l8_coll = ee.ImageCollection('LANDSAT/LC8_L1T_TOA');  //Apr 11, 2013 - Apr 30, 2017

var merged_collection = ee.ImageCollection(l4_coll.merge(l5_coll)
.merge(l7_coll).merge(l8_coll));

// filter the ImageCollection with the boundary of the selected country
var iC = merged_collection.filterBounds(selectedCountry.geometry());
iC = iC.map(function(img){
var year  = img.date().format("Y");            // get the acquisition year
var CC = img.get('CLOUD_COVER');
return img.set('year', ee.Number.parse(year)).set('clouds', ee.Number.parse(CC)); // 
});
}
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  • Hi - thanks for the question! In the future, please use the "Get Link" button at the top of the Earth Engine JavaScript Code Editor to get your script link for sharing. This is the preferred, most direct method. Jan 24, 2020 at 17:23
  • 1
    If you're not aware of it, you should also look at the Global Surface Water dataset which provides monthly-integrated global maps of all water pixels in the Landsat archive. It's on GEE already. Might not suit your needs. developers.google.com/earth-engine/…
    – Jon
    Jan 24, 2020 at 18:27

1 Answer 1

1

There are a number of ways you can do this.

  • If you use the Landsat surface reflectance collections, you can extract water from the CFmask QA band associated with each image. Here is a toy example:
// Define area of interest.
var aoi = ee.Geometry.Point([-95.82, 46.84]);

// Import Landsat 8 surface reflectance collection.
var l8SrCol = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR');

// Filter collection to area and date.
var img = l8SrCol
 .filterBounds(aoi)
 .filterDate('2019-06-01', '2019-10-01')
 // for this example, get the first least cloudy image.
 .sort('CLOUD_COVER')
 .first();

// Select the image's CFmask band and make all water pixels value 1, all else 0.
var mask = img.select('pixel_qa').bitwiseAnd(4).neq(0);

// Display results to map.
Map.centerObject(img, 8);
Map.addLayer(img, {bands: ['B6', 'B5', 'B4'], min:100, max:4000});
Map.addLayer(mask);

enter image description here

Code Editor script

  • Train a model to predict water pixels from your derived vegetation and water indices. See the Earth Engine Developer guide Supervised Classification page.

  • Use image band histogram slicing to distinguish water from non-water. See this Earth Engine UI example to understand the concept and help pick band threshold values that separate water from non-water.

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