Iterate over Image Collection Google Earth Enigne

Question

I have written functions that processes all Landsat imagery and calculates NDVI. However, I have 59 GPS points, and I want a NDVI time series output per GPS point. After running my code, it seems that the resulting NDVI values are not per point, but a single value per image. I am guessing that some sort of bounding-box was automatically created and used for the calculation, instead of using the GPS points. Thus, I need to iterate my function over all 59 points, and save output into a table.

The GPS file is a ESRI points shape file.

What is the best way to do this?

Here is some of my code:

// import GPS locations from asset
GPS = GPS.geometry();

// Calculates the median NDVI and add value to image properties.
var meanNDVI = ndviLandsatCol.map(function(img) {
  var obs = img.reduceRegion({
    geometry: GPS,
    reducer: ee.Reducer.median(),
    scale: 30
  });
  return img.set('NDVI', obs.get('NDVI'));
});

The ndviLandsatCol variable is a pre-processed image collection with NDVI added as a band. I am still new to coding, and Google Earth Engine. Can someone advise on how to iterate this process over all my GPS points? How should I read in the GPS file, dictionary? And how can I save this into a .CSV without plotting the points and downloading accompanying file.

Any help would be appreciated.

Answer 1

Here is the final solution:

// Collect GPS, image, NDVI triplets.
var triplets = NDVILandsatCol.map(function(image) {
  return image.select('NDVI').reduceRegions({
    collection: GPS.select(['Site_ID']), 
    reducer: ee.Reducer.mean(), 
    scale: 30
  }).filter(ee.Filter.neq('mean', null))
    .map(function(f) { 
      return f.set('imageId', image.id());
    });
}).flatten();
print(triplets.first());

// Format a table of triplets into a 2D table of rowId x colId.
var format = function(table, rowId, colId) {
  var rows = table.distinct(rowId);
  var joined = ee.Join.saveAll('matches').apply({
    primary: rows, 
    secondary: table, 
    condition: ee.Filter.equals({
      leftField: rowId, 
      rightField: rowId
    })
  });

  return joined.map(function(row) {
      var values = ee.List(row.get('matches'))
        .map(function(feature) {
          feature = ee.Feature(feature);
          return [feature.get(colId), feature.get('mean')];
        });
      return row.select([rowId]).set(ee.Dictionary(values.flatten()));
    });
};

// Export table
var table1 = format(triplets, 'imageId', 'Site_ID');
var desc1 = 'Table_demo'; 
Export.table.toDrive({
  collection: table1, 
  description: desc1, 
  fileNamePrefix: desc1,
  fileFormat: 'CSV'
});