`// Load the shapefile asset var shapefile = ee.FeatureCollection('projects/ee-tilok-research/assets/CI_1500');

// Load Sentinel-2 SR image collection var sentinel = ee.ImageCollection('COPERNICUS/S2_SR');

// Filter by date and region var filtered = sentinel.filterDate('2022-05-01', '2023-05-31') .filterBounds(shapefile);

// Define a cloud mask function var cloudMask = function(image) { var qa = image.select('QA60'); var cloudBitMask = 1 << 10; var cirrusBitMask = 1 << 11; var mask = qa.bitwiseAnd(cloudBitMask).eq(0) .and(qa.bitwiseAnd(cirrusBitMask).eq(0)); return image.updateMask(mask).divide(10000); };

map.addLayer(shapefile);

// Apply the cloud mask function var masked = filtered.map(cloudMask);

// Define a NDVI function var ndvi = function(image) { return image.normalizedDifference(['B8', 'B4']).rename('NDVI'); };

// Apply the NDVI function var withNDVI = masked.map(ndvi);

// Reduce the collection to a single image or a time series // For example, you can use mean, median, max, min, etc. var reduced = withNDVI.mean();

// Export the NDVI values as a CSV file // Join the NDVI values with the existing attributes of the shapefile var joined = ee.Join.saveAll('ndvi').apply({ primary: shapefile, secondary: reduced.reduceRegions({ collection: shapefile, reducer: ee.Reducer.first(), scale: 10 // change this according to your needs }), condition: ee.Filter.equals({ leftField: '.geo', rightField: '.geo' }) });

// Export the joined feature collection to Google Drive Export.table.toDrive({ collection: joined, description: 'ndvi_values', // change this according to your needs folder: 'your_folder_name', // change this according to your needs // selectors: ['your_attribute_1', 'your_attribute_2', ..., 'ndvi'] // change this according to your needs });`

// Load the shapefile asset
var shapefile = ee.FeatureCollection('projects/ee-tilok-research/assets/CI_1500');

// Load Sentinel-2 SR image collection
var sentinel = ee.ImageCollection('COPERNICUS/S2_SR');

// Filter by date and region
var filtered = sentinel.filterDate('2022-05-01', '2023-05-31')
                       .filterBounds(shapefile);

// Define a cloud mask function
var cloudMask = function(image) {
  var qa = image.select('QA60');
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
               .and(qa.bitwiseAnd(cirrusBitMask).eq(0));
  return image.updateMask(mask).divide(10000);
};

map.addLayer(shapefile);

// Apply the cloud mask function
var masked = filtered.map(cloudMask);

// Define a NDVI function
var ndvi = function(image) {
  return image.normalizedDifference(['B8', 'B4']).rename('NDVI');
};

// Apply the NDVI function
var withNDVI = masked.map(ndvi);

// Reduce the collection to a single image or a time series
// For example, you can use mean, median, max, min, etc.
var reduced = withNDVI.mean();

// Export the NDVI values as a CSV file
// Join the NDVI values with the existing attributes of the shapefile
var joined = ee.Join.saveAll('ndvi').apply({
  primary: shapefile,
  secondary: reduced.reduceRegions({
    collection: shapefile,
    reducer: ee.Reducer.first(),
    scale: 10 // change this according to your needs
  }),
  condition: ee.Filter.equals({
    leftField: '.geo',
    rightField: '.geo'
  })
});

// Export the joined feature collection to Google Drive
Export.table.toDrive({
  collection: joined,
  description: 'ndvi_values', // change this according to your needs
  folder: 'your_folder_name', // change this according to your needs
  // selectors: ['your_attribute_1', 'your_attribute_2', ..., 'ndvi'] // change this according to your needs
});