In Earth Engine I have a table asset of coordinates and dates (properties `LAT', 'LONG', 'DATE') for which I would like to return a pixel value from my ImageCollection dswe_ic, which gives a monthly DSWE (Dynamic Surface Water Extent) value.

// get a DSWE value (integers of 0, 1, 2, 3, 4, or 9)
var dswe_ic = ee.ImageCollection.fromImages(months.map(function(m){
  var start = start_date.advance(m, 'month'); // Advance start date by m months
  var end = start.advance(1, 'month');        // End date is start month +1 
  return img_indices_all.filterDate(start, end) 
          .reduce(ee.Reducer.min()) //  Produces band 'dswe_min'
          .set('date', start.format("YYYYMM")) // Sets yr/mo as property 'date'
          .cast({'dswe': 'uint8'})  // Adds empty band 'dswe'
          .remap([0,1,2,3,4,8,9], [0,1,2,3,4,9,9]).rename('dswe'); // remap takes the 1st band unless otherwise specified
          // return a monthly DSWE value, taking the lowest DSWE value (i.e. highest confidence value)
          // of the month if there are multiple DSWE values to pick from
          // (due to potential availability of more than 1 high quality Landsat scenes)

I can map over my coordinates of interest with dswe_ic easily enough and generate a CSV list of monthly pixel values from my start date (May 21, 2003) to my end date (Nov 17, 2016).

var results = points_of_interest.map(function(f) {
  return dswe_ic.map(function(i) {
    var mean = i.reduceRegion({
      geometry: f.geometry(),
      scale: 30,
      crs: 'EPSG:4326',
      reducer: ee.Reducer.first(),

    return f.setMulti(mean).set('date', i.get('date'))
results = results.flatten()

  collection: results,
  description: 'results_all_coords_allmonths_from_may2003_to_nov2016,
  fileFormat: 'CSV'

However, I am more interested in directly appending my original table with a new column for the pixel value from dswe_ic for each particular 'DATE' and coordinate. Unfortunately, my GEE skills are quite limited to do this. Any suggestions?

1 Answer 1


You can join your feature collection with your image collection. This script creates monthly images with a yearMonth property, and add a corresponding yearMonth property to each feature. These properties are then used as a condition when joining the collections. Obviously, it doesn't use neither your image collection or features, but you hopefully get the gist.

var features = ee.FeatureCollection([
    ee.Feature(ee.Geometry.Point([14.803, 41.314]), {date: '2019-01-01'}),
    ee.Feature(ee.Geometry.Point([11.858, 44.468]), {date: '2020-01-01'})
  .map(function (feature) {
    return feature.set('yearMonth', ee.Date(feature.getString('date')).format('yyyyMM'))

var startDate = ee.Date('2016-01-01')
var endDate = ee.Date(new Date())
var months = endDate.difference(startDate, 'months')
var monthOffsets = ee.List.sequence(0, months.subtract(1))

var monthlyImages = ee.ImageCollection(monthOffsets
  .map(function (monthOffset) {
    var start = startDate.advance(monthOffset, 'months')
    var end = start.advance(1, 'months')
    return ee.ImageCollection('COPERNICUS/S2')
      .filterDate(start, end)
      .set('yearMonth', start.format('yyyyMM'))

var featuresWithMean = ee.Join.saveFirst('image')
    primary: features, 
    secondary: monthlyImages, 
    condition: ee.Filter.equals({leftField: 'yearMonth', rightField: 'yearMonth'})
  .map(function (feature) {
    var image = ee.Image(feature.get('image'))
    var mean = image
        geometry: feature.geometry(),
        scale: 30,
        crs: 'EPSG:4326',
        reducer: ee.Reducer.mean(),
    return feature
      .set('mean', mean)
      .set('yearMonth', null).set('image', null) // Drop redundant properties




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