Getting time series from different locations for various indexes

Question

I am trying to get the time series for different points of interest, but I can only get one point location. Many of the examples that are on the web use points that are created during the code but in my case, I uploaded them from a shp file and not sure if that is the problem. Regarding that, I have noticed that is taking too long to compute, is there any other method in case the points to compute are too much? This is the code so far: https://code.earthengine.google.com/3a88da917a801be33638a6fb83829624

Map.addLayer(table);
Map.centerObject(table,8);
var studyArea = ee.FeatureCollection(table).geometry();
print (studyArea);
var buffer = studyArea.buffer(500);
print(buffer);
// Add the buffer as a layer on the map
Map.addLayer(buffer, {}, 'Buffer');

// Function to remove cloud and snow pixels
function maskCloudAndShadows(image) {
  var cloudProb = image.select('MSK_CLDPRB');
  var snowProb = image.select('MSK_SNWPRB');
  var cloud = cloudProb.lt(5);
  var snow = snowProb.lt(5);
  var scl = image.select('SCL'); 
  var shadow = scl.eq(3); // 3 = cloud shadow
  var cirrus = scl.eq(10); // 10 = cirrus
  // Cloud probability less than 5% or cloud shadow classification
  var mask = (cloud.and(snow)).and(cirrus.neq(1)).and(shadow.neq(1));
  return image.updateMask(mask);
}

var sentinel_S2 = ee.ImageCollection("COPERNICUS/S2_SR");//HARMONIZED

var s2a = sentinel_S2.filterBounds(studyArea)
        .filterDate('2020-01-01','2023-02-28')
        //.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 50))
        .map(maskCloudAndShadows)
    //.filter(ee.Filter.bounds(points));

print('filtered_S2', s2a);
print('filtered_S2 size',s2a.size());

var s2a_median = s2a.median()
                    .clip(buffer);

Map.addLayer(s2a_median,VisParam,'true Color');

function radiometric_scale(image) {
  image =  ee.Image(image.multiply(0.0001).copyProperties(image, ['system:time_start']));
  var ndwi = image.normalizedDifference(['B3', 'B8']).rename('ndwi');
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
  var ndti = image.normalizedDifference(['B11', 'B12']).rename('ndti');
  var ndci = image.normalizedDifference(['B5','B4']).rename('ndci');
  var RED = image.select('B4');
  var NIR = image.select('B8');
  var spm = NIR.divide(RED).rename('spm');
  return image.addBands([ndwi, ndvi, ndti, ndci,spm]).updateMask(ndwi.gt(0.12));
}

var with_indices = s2a.map(radiometric_scale);
var s2a_median = with_indices.median()
                             .select(['ndwi', 'ndvi', 'ndti', 'ndci','spm'])
                             .clip(buffer);
print(s2a_median);
Map.addLayer(s2a_median,{},'Indexes');


var chart = ui.Chart.image.seriesByRegion({
    imageCollection: with_indices.select('ndvi'),
    regions: studyArea,
    reducer: ee.Reducer.mean()
});
print(chart);

Answer 1

I don't know how many points do you have but, I created two arbitrary points in your roi where ndvi is not masked. So, by using as regions your variable studyArea, I got following result:

In your roi, there is not segregated any area because the problem is probably related with your regions parameter. You are defining studyArea as follows and it is a Multipart geometry.

var studyArea = ee.FeatureCollection(table).geometry();

Defining studyArea as follows (for avoiding issues with buffer layer):

var studyArea2 = table;

after running the complete code in the GEE code editor, the new result is segregating each region as expected: