Converting the output of a .map function into an image collection in Google Earth Engine using JavaScript

Question

I am trying to retrieve monthly Sentinel-2 band data using zonal statistics (mean) and point buffer for further analysis.

I have created monthly median composites from January to December of 2017 using the .map function. However, when I call the variable "S2A" (which has the monthly composites), I get an error which says that S2A is not defined. I tried printing the S2A variable, and I realise that it has 12 monthly images with no properties.

I want S2A as an image collection with properties like band names, band values and system time properties.

My code is below:

// Year
var year = 2017;

// List of months parameter
// Name is the name of the month
// End is the last day of the month
var months = [
  { name: 'January', end: 31 },
  { name: 'February', end: 28 },
  { name: 'March', end: 31 },
  { name: 'April', end: 30 },
  { name: 'May', end: 31 },
  { name: 'June', end: 30 },
  { name: 'July', end: 31 },
  { name: 'August', end: 31 },
  { name: 'September', end: 30 },
  { name: 'October', end: 31 },
  { name: 'November', end: 30 },
  { name: 'December', end: 31 }
];

// Composite and export per months
months.map(function(month, index){
  // Name of the image
  var name = year + '_' + month.name;
  
  // Create start and end date from the composite
  var start = ee.Date.fromYMD(year, index + 1, 1);
  var end = ee.Date.fromYMD(year, index + 1, month.end);

// https://geetools.readthedocs.io/en/latest/cloud_mask.html (Documentation)

// For Shumaila Mam


// SIAC atmospheric correction module
var siac = require('users/marcyinfeng/utils:SIAC');

// Hollstein cloud mask module
var cld = require('users/fitoprincipe/geetools:cloud_masks')

var S2A = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')
                  .filterDate(start, end)
                  // Pre-filter to get less cloudy granules.
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
                  .filterBounds(Ethiopia)
                   // Apply cloud-masking to each image
                  .map(cld.hollstein_S2(['shadow','cloud', 'cirrus']))
                  // Apply SIAC to each image
                  .map(siac.get_sur)
                 // Combine all masked, corrected images with a median
                  .median()
                  //.divide(10000);

Map.addLayer(S2A,{bands:"B4,B3,B2", min: 0.0, max: 0.3, gamma: 1.4},name, false)
Map.centerObject(S2A)
});

Map.addLayer(Points, {}, 'Points', false)
Map.centerObject(Ethiopia,6)

function zonalStats(ic, fc, params) {
  // Initialize internal params dictionary.
  var _params = {
    reducer: ee.Reducer.mean(),
    scale: null,
    crs: null,
    bands: [null],
    imgProps: null,
    imgPropsRename: null,
    datetimeName: 'datetime',
    datetimeFormat: 'YYYY-MM-dd HH:mm:ss'
  };

  // Replace initialized params with provided params.
  if (params) {
    for (var param in params) {
      _params[param] = params[param] || _params[param];
    }
  }

  // Set default parameters based on an image representative.
  var imgRep = ic.first();
  var nonSystemImgProps = ee.Feature(null)
    .copyProperties(imgRep).propertyNames();
  if (!_params.bands) _params.bands = imgRep.bandNames();
  if (!_params.bandsRename) _params.bandsRename = _params.bands;
  if (!_params.imgProps) _params.imgProps = nonSystemImgProps;
  if (!_params.imgPropsRename) _params.imgPropsRename = _params.imgProps;

  // Map the reduceRegions function over the image collection.
  var results = ic.map(function(img) {
    // Select bands (optionally rename), set a datetime & timestamp property.
    img = ee.Image(img.select(_params.bands, _params.bandsRename))
      .set(_params.datetimeName, img.date().format(_params.datetimeFormat))
      .set('timestamp', img.get('system:time_start'));

    // Define final image property dictionary to set in output features.
    var propsFrom = ee.List(_params.imgProps)
      .cat(ee.List([_params.datetimeName, 'timestamp']));
    var propsTo = ee.List(_params.imgPropsRename)
      .cat(ee.List([_params.datetimeName, 'timestamp']));
    var imgProps = img.toDictionary(propsFrom).rename(propsFrom, propsTo);

    // Subset points that intersect the given image.
    var fcSub = fc.filterBounds(img.geometry());

    // Reduce the image by regions.
    return img.reduceRegions({
      collection: fcSub,
      reducer: _params.reducer,
      scale: _params.scale,
      crs: _params.crs
    })
    // Add metadata to each feature.
    .map(function(f) {
      return f.set(imgProps);
    });
  }).flatten().filter(ee.Filter.notNull(_params.bandsRename));
  
  return results;
}


//Define parameters for the zonalStats function.
var params = {
  collection:Points,
  reducer: ee.Reducer.mean(),
  scale: 60,
  //crs: 'EPSG:5070',
  bands: [0, 1,2,3,4,5,6,7,8,9,10,11,12,13],
  bandsRename:['B1','B2', 'B3','B4','B5','B6','B7','B8','B8A','B11','B12','aot','tcwv','tco3'],
};



// Extract zonal statistics per point per image.
var ptsTopoStats = zonalStats(S2A, points, params);
print(ptsTopoStats);

Map.addLayer(ptsTopoStats)


Export.table.toDrive({
  collection: ptsTopoStats,
  folder: 'Etse_sent_1',
  description: 'S1_2018',
  fileFormat: 'CSV'
});

Here is the link to my code

Answer 1

A few observations about your code:

• When using map, assign the result to a variable.
• Load the modules outside the map.
• Don't mix client-side operations with server-side objects, such as using the + operator to concatenate strings.
• Don't use client-side methods inside a map, such as Map.addLayer(). The Code Editor only didn't show an error message because the map wasn't even executed – since the result wasn't stored in any variable.

The code below shows a simple way of computing monthly median composites, naming, and adding properties to the images.

// Load the modules here.
var siac = require('users/marcyinfeng/utils:SIAC');
var cld = require('users/fitoprincipe/geetools:cloud_masks');

var year = 2017;
var ethiopia = ee.FeatureCollection('users/S2967456/Ethiopia');

// Loads the collection and filters the images geographically and temporally.
var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')
  .filterBounds(ethiopia)
  .filter(ee.Filter.calendarRange(year, year, 'year'));

var months = ee.List.sequence(1, 12, 1);

var imageList = months.map(function(month) {
  // Filter images from month "month".
  var images = s2
    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
    .filter(ee.Filter.calendarRange(month, month, 'month'))
    // Use the functions of the "siac" and "cld" modules here.
    // .map(cld.hollstein_S2(['shadow','cloud', 'cirrus']))
    // .map(siac.get_sur)
  
  // Creates a median composite.
  var median = images.median();
  
  // Creates the string that will be used as the image name (id).
  var _y = ee.Number(year).format('%d');
  var _m = ee.Number(month).format('%d');
  
  var id = _y.cat('_').cat(_m);
  
  // Adds the properties to the composite.
  median = median.set({
    'year': year, 
    'month': month, 
    'system:id': id,
    'system:time_start': ee.Date.fromYMD(year, month, 1).millis()
  });
    
  return median;
});

// Builds a ImageCollection from the list of images.
var result = ee.ImageCollection.fromImages(imageList);
print('result', result);

https://code.earthengine.google.com/507027abccde12d32e788233c8ed1c11