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I tried to plot the NDVI Time Series graph with the following code. but got this error

Error:

Computation timed out.

Code:

// Importing the Shape File of Area:

var Dehradun = ee.FeatureCollection('projects/ee-omkarthesis2022/assets/India_Dist')
                .filter('NAME_2 == "Dehradun"');

Map.centerObject(Dehradun, 8)        
/*-------------------------------------- // CloudMask-----------------------------------------*/

function mask2Clouds(image){
  var QA = image.select('QA60')
   
  var cloudBitMask = (1 << 10);                            // Bits 10 and 11 cloud and cirrus 
  var cirrusBitMask = (1 << 11);
  
  var mask = QA.bitwiseAnd(cloudBitMask).eq(0)            // setting both to zero indictes clear condition
              .and(QA.bitwiseAnd(cirrusBitMask).eq(0));
  
  return image.updateMask(mask).divide(10000)
              .copyProperties(image)
              .set('system:time_start',
              image.get('system:time_start'));
}

var start_date= '2016-06-01'
var end_date= '2016-11-30'

var Sen2 = ee.ImageCollection('COPERNICUS/S2')
              .sort('CLOUDY_COVER', false)
              .filterDate(start_date, end_date)
              .filterBounds(Dehradun)
              .map(mask2Clouds)
              
/*----------------------------------- // Computation NDVI----------------------------------------*/
                   
var NDVI_Sen2 = Sen2.map (function(image) {
return image.addBands(image.normalizedDifference(['B8', 'B4']))
                           .addBands(image.metadata('system:time_start')
                          // .divide(1e18)
                           .rename('time'))
})


function smoother(t){
  // helper function to apply linear regression equation
  function applyFit(img){
      return img.select('time').multiply(fit.select('scale')).add(fit.select('offset'))
              .set('system:time_start',img.get('system:time_start')).rename('nd');
  }
  t = ee.Date(t);
  
  var window = NDVI_Sen2.filterDate(t.advance(-windowSize,'day'),t.advance(windowSize,'day'));
    
  var fit = window.select(['time','nd'])
    .reduce(ee.Reducer.linearFit());
    
  return window.map(applyFit).toList(5);
}

// function to reduce time stacked linear regression results
// requires that a variable 'fitIC' exists from the smooter function

function reduceFits(t){
  t = ee.Date(t);
  return fitIC.filterDate(t.advance(-windowSize,'day'),t.advance(windowSize,'day'))
              .mean().set('system:time_start',t.millis()).rename('nd');
}

var dates = NDVI_Sen2.aggregate_array('system:time_start');

var windowSize = 30; //days on either sides

var fitIC = ee.ImageCollection(dates.map(smoother).flatten());

var smoothed = ee.ImageCollection(dates.map(reduceFits));
// print('smoothed',smoothed)

// var smooth_mos = smoothed.mosaic()

// merge original and smoothed data into one image collection for plotting
// var joined = ee.ImageCollection(smoothed.select(['nd'],['smoothed'])
//                 .merge(NDVI_Sen2.select(['nd'],['original'])));

/*------------------------------------// Plot the Graph----------------------------------------*/

var chart = ui.Chart.image.series({
  imageCollection: smoothed,
  region: Dehradun,
  reducer: ee.Reducer.mean(),
  scale: 20
}).setOptions({title: 'NDVI over time'});

print(chart);

Code link: https://code.earthengine.google.co.in/eae1f20818b09f37a14b4232738aec0d

1 Answer 1

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It is an scale problem (the value of 20 is very low for smoothed; it has a value about of 100,000). It can be fixed in following code lines:

print(smoothed);
var scale = smoothed.first().projection().nominalScale();

print("scale", scale);

Map.addLayer(smoothed.first());

var chart = ui.Chart.image.series({
  imageCollection: smoothed,
  region: Dehradun,
  reducer: ee.Reducer.mean(),
  scale: scale
}).setOptions({title: 'NDVI over time'});

print(chart);

Assuming an arbitrary area for Dehradun (your asset was not available for me), after running complete code, I could get the chart in a few seconds as follows:

enter image description here

0

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