Extracting date for each image as a string and previewing the data before exporting

Question

I am very new to Google Earth Engine. I manipulated this code to my use from my class mini project. I am trying to export a date associated with the image along side the ET value after I performer a Reducer.mean() for a every feature. For now, I have accomplished this by adding a separate band for Year, Month and Day for each image. But I feel this is a crude way of performing this task. Ideally, I would like to export the Date as a string in a single column for each image. So I manipulated my code a little bit.

In order to accomplish this I added line 107 and 108 where I get the date of each image and then add that as a dictionary with name "date_as_string". Having done this on loop, I call this dictionary later while exporting (line 153). But this give me an Error (given below). I am not sure what I am doing wrong.

If my approach is wrong, is there a efficient way to export the date of each image as string in separate column?

Also, what is the best way to preview my analysis data I want to export before I export to CSV? Any suggestions regarding this? Following are the relevant information:

Asset: https://code.earthengine.google.com/?asset=users/memugal33/studyarea__pnw

Code link: https://code.earthengine.google.com/56e46e8769ab2e05183079e8ffa58df8

Error I get:

Error in map(ID=0): Image.date: Image '0' does not have a 'system:time_start' property.

Code:

var counties = ee.FeatureCollection("users/memugal33/studyarea__pnw"),
    mod_ET = ee.ImageCollection("MODIS/006/MOD16A2");
    
var cnties = ee.FeatureCollection(counties); 
//print(cnties)
var start_date = '2001-01-01';
var end_date = '2020-12-31';
////////////////////////////////////////////////
/// add Date to an image
function addDate_to_image(image){

var d = (image.date().getRelative('day','month'))
var d2 = ee.Image.constant(d).uint16().rename('day')

var m = (image.date().getRelative('month','year'))
var m2 = ee.Image.constant(m).uint16().rename('month')

var y = image.date().format('YYYY')
y = ee.Number.parse(y)
var y2 = ee.Image.constant(y).uint16().rename('year')

  return image.addBands([d2,m2, y2]); /// The day, month and year is added as new bands
}
////////////////////////
///
/// add Date to an imageCollection/Time Series by using map(.) function
/// to recursively call the function addDate_to_image(.) defined above
//
//
function addDate_to_collection(collec){
  var C = collec.map(addDate_to_image);
  return C;
}


  function extract_ET(a_feature){
    var geom = a_feature.geometry();
    var newDict = {'original_polygon_1': geom};
        var imageC = mod_ET.filterDate(start_date, end_date)
                .select('ET')
                .filterBounds(geom)
                // Clip(.) function  is clipping the boundary of the given field.
                .map(function(image){return image.clip(geom)})
                // .filter('CLOUDY_PIXEL_PERCENTAGE < 90')
                .sort('system:time_start', true);
    imageC = addDate_to_collection(imageC);
    imageC = imageC.map(function(im){return(im.set(newDict))});           
    imageC = imageC.set({ 'original_polygon': geom,
                          'cnty_feat':a_feature});
 return imageC;
}  

//
// The function below (mosaic_and_reduce_IC_mean(.)) stiches
// the tiles that cover a polygon to create a complete image of a field
// and then reduces (i.e. takes averages of all) ET pixels values into one value for
// the field.
//
function mosaic_and_reduce_IC_mean(an_IC){
  an_IC = ee.ImageCollection(an_IC);
  
  var reduction_geometry = ee.Feature(ee.Geometry(an_IC.get('original_polygon')));
  var WSDA = an_IC.get('cnty_feat');
  var start_date_DateType = ee.Date(start_date);
  var end_date_DateType = ee.Date(end_date);
  //######**************************************
  // Difference in days between start and end_date
  var diff = end_date_DateType.difference(start_date_DateType, 'day');
  // Make a list of all dates
  var range = ee.List.sequence(0, diff.subtract(1)).map(function(day){
                                    return start_date_DateType.advance(day,'day')});
  // Funtion for iteraton over the range of dates
  function day_mosaics(date, newlist) {
    // Cast
    date = ee.Date(date);
    newlist = ee.List(newlist);

    // Filter an_IC between date and the next day
    var filtered = an_IC.filterDate(date, date.advance(1, 'day'));

    // Make the mosaic
    var image = ee.Image(filtered.mosaic());

    // Add the mosaic to a list only if the an_IC has images
    return ee.List(ee.Algorithms.If(filtered.size(), newlist.add(image), newlist));
  }

  // Iterate over the range to make a new list, and then cast the list to an imagecollection
  var newcol = ee.ImageCollection(ee.List(range.iterate(day_mosaics, ee.List([]))));
  //print("newcol 1", newcol);
  //######**************************************

  var reduced = newcol.map(function(image){
    
    var means = image.reduceRegions({
                                                        collection:reduction_geometry,
                                                        reducer:ee.Reducer.mean(), 
                                                        scale: 10  //Does this scale make any difference???
                                                      });
                                                      
        //// This two lines is intended to add Date as string in the image property //////     
        
        ///The idea is to name the dictionary name "date_as_string" and select
        ///"date_as_string" in the selectors while exporting to CSV
        means = means.map(function(f){return f.set({date_as_string: image.date().format("yyyy/MM/DD")})})
        return means.copyProperties(image);
        ////////////////////////////////////////////
                        
                          //return means;
                                          }
                        ).flatten();
                          
  reduced = reduced.set({ 'original_polygon': reduction_geometry,
                        'cnties':WSDA
                      });
  WSDA = ee.Feature(WSDA);
  WSDA = WSDA.toDictionary();
  
  // var newDict = {'WSDA':WSDA};
  reduced = reduced.map(function(im){return(im.set(WSDA))}); 
  return(reduced);
}
var Et_img_coll = cnties.map(extract_ET);
//print(Et_img_coll)

var allcnties = Et_img_coll.map(mosaic_and_reduce_IC_mean);
///////Visualizing For Preview purpose/////////
var allcnt = allcnties.first()
print(allcnt)

// var ET_chart = ui.Chart.feature.byFeature(allcnt, "ET")
// .setOptions({title: "ET MEAN",
// pointSize: 3});
// print(ET_chart);
////////////////////////////////////


var yourFileName = "ET_Final";
var FolderName = "rem_sens";
print(yourFileName)
Export.table.toDrive({
  collection: allcnties.flatten(),
  description: yourFileName,
  folder: FolderName,
  fileNamePrefix: yourFileName,
  fileFormat: 'CSV',
  // Provide the name of variables of interst to export.
  // Some of these names come from shapefile that we have added to imagecollection
  //
  ///Date_as_string is also selected
  selectors:["JURISDIC_2","name","day","month","year", "ET", "date_as_string"] 
});

Answer 1

This was a quite convoluted solution. Maybe you can simplify it like the below instead? See inline comments for explanations.

https://code.earthengine.google.com/fa6d784540a6c2c311914ed9e77ebd01

var startDate = '2001-01-01' // Pick a shorter date range while developing, to speed things up
var endDate = '2021-01-01' // Exclusive endDate
var etCollection = ee.ImageCollection("MODIS/006/MOD16A2") // Global images, no need to filter by bounds
  .filterDate(startDate, endDate) // Only include images in date range
  .select('ET') // Only care about ET
  
var featureCollection = ee.FeatureCollection("users/memugal33/studyarea__pnw")
  // .limit(2) // Just use a few features when developing, to speed things up
  .map(calculateMeans) // Calculate mean ET for every image in etCollection, for each feature
  .flatten() // Flattens the resulting collection of collections into a collection of features

print(featureCollection) // Print the collection before exporting, to "preview" it

Export.table.toDrive({
  collection: featureCollection,
  description: 'ET_Final',
  folder: 'rem_sens',
  fileNamePrefix: 'ET_Final',
  selectors: ['JURISDIC_2', 'et', 'date']
})


function calculateMeans(feature) {
  return etCollection
    .map(function (et) {
      return calculateMean(et, feature)
    })
    .filter(ee.Filter.notNull(['et'])) // Remove features without et
}

function calculateMean(et, feature) {
  var mean = et.reduceRegion({
    reducer: ee.Reducer.mean(), 
    geometry: feature.geometry(), 
    scale: 500, // Set the scale at same resolution as your images for maximum accuracy (smaller than that just makes it slower) 
    maxPixels: 1e13
  }).getNumber('ET')
  return feature.set({
    // Property name swill correspond to CSV column names
    // Tweak this for other date columns. 
    date: et.date().format('yyyy-MM-dd'), 
    et: mean
  })
}