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I'm trying to calculate the mean NDVI for a polygon in an urban area. I have a code that takes Landsat7 images, clip them according to the geometry and knows to select only images with certain number of pixels.

The next step is that I want to take those images that are ranging from 1999-2013, and for each year to create one mean image. so in the end instead of for example 67 images, I'll have one image per year. I have faced two problems:

  1. I have tried to use :
var img2000=withNDVI.filter(ee.Filter.calendarRange,(2000,2001,'year')).mean();

in order to get one mean image from 2000, but I get the next error message:

Received too many arguments to function filter(). Expected at most 1 but got 2.

and I'm also not sure that if I choose 2000-2001, if it knows that I want only 2000 or it will take also 2001.

  1. I'm lazy. I don't want to write this line so many times and I want to have function that does it -that knows to calculate mean for each year but not sure how to do it, should I start with defining a variable for the function?

Here is the code I have:

var geometry=Osaka;
Map.centerObject(geometry,12);
//create Landsat7 database
/**
 * Function to mask clouds based on the pixel_qa band of Landsat SR data.
 * @param {ee.Image} image Input Landsat SR image
 * @return {ee.Image} Cloudmasked Landsat image
 */
var cloudMaskL457 = function(image) {
  var qa = image.select('pixel_qa');
  // If the cloud bit (5) is set and the cloud confidence (7) is high
  // or the cloud shadow bit is set (3), then it's a bad pixel.
  var cloud = qa.bitwiseAnd(1 << 5)
                  .and(qa.bitwiseAnd(1 << 7))
                  .or(qa.bitwiseAnd(1 << 3));
  // Remove edge pixels that don't occur in all bands
  var mask2 = image.mask().reduce(ee.Reducer.min());
  return image.updateMask(cloud.not()).updateMask(mask2).divide(10000)
  .copyProperties(image, ['system:time_start']);
};

//Create LANDSAT7 dataset

var dataset = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR')
                  .filterDate('1999-01-01', '2013-04-29')
                  .select('B3','B4','pixel_qa')
                  .filterBounds(geometry)
                  .map(cloudMaskL457);

//clip the dataset according to the geometry
var clippedCol=dataset.map(function(im){ 
  return im.clip(geometry);
});

// Get the number of images.
var count = clippedCol.size();
print('Count: ',count);

//function to calculate NDVI in LANDSAT7

var addNDVI = function(image) {
  var NDVI = image.normalizedDifference(['B4', 'B3'])
  .rename('NDVI')
  .copyProperties(image,['system:time_start']);
  return image.addBands(NDVI);

};



//NDVI to the clipped image collection
var withNDVI = clippedCol.map(addNDVI).select('NDVI');

var NDVIcolor = {
  min: 0,
  max:1,
  palette: ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
    '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
    '012E01', '011D01', '011301'],
};

var img2000=withNDVI.filter(ee.Filter.calendarRange,(2000,2001,'year')).mean();

1 Answer 1

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You could set up a list of years that you're interested in, then map a function over that list which grabs the relevant imagery from your withNDVI collection and applies the relevant reducer. Note that the output for this is an imageCollection with 15, single-band images, one for each year.

// Declare years of interest
var years = ee.List.sequence(1999, 2013);
// Map a function to select data within the year and apply mean reducer
var byYear = ee.ImageCollection.fromImages(
    years.map(function(y) {
      return withNDVI
        .filter(ee.Filter.calendarRange(y, y, 'year'))
        .reduce(ee.Reducer.mean())
        .set('year', y);
    })
  );
// Look at your output: 15 elemenets suggest we're on the right track
print(byYear, "byYear");
// Add 1999 to the map - looks good!
Map.addLayer(byYear.first(), NDVIcolor, "img1999");
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