Gee: (II) resampling the image

In the use of GEE When resampling an image , Mainly used .reproject(crs, crsTransform, scale) function , The parameters and usage of this function are introduced below .
img.reproject(crs, crsTransform, scale)
this:image (Image):
Images that need to be re projected
crs (Projection):
The coordinate system of re projection
crsTransform (List, default: null):
The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with the scale option, and replaces any transform already on the projection.
scale (Float, default: null):
The resolution can be set during re projection

There will be Sentinel-2 Image resampled to 100m As an example , stay GEE The implementation code is as follows ：

// De cloud function 
function maskS2clouds(image) {
    
  var qa = image.select('QA60');

  // Bits 10 and 11 are clouds and cirrus, respectively.
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;

  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(qa.bitwiseAnd(cirrusBitMask).eq(0));

  return image.updateMask(mask).divide(10000);
}

// Import polygon area 
var geometry = 
    ee.Geometry.Polygon(
        [[[17.86466765871711, 51.805755132803185],
          [17.86466765871711, 48.51354071088587],
          [24.85197234621711, 48.51354071088587],
          [24.85197234621711, 51.805755132803185]]], null, false);

// Import sentry 2 data 
var image = ee.ImageCollection('COPERNICUS/S2_SR')
                  .filterDate('2017-02-01', '2017-11-30')
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',20))
                  .map(maskS2clouds)
                  .median()
                  .clip(geometry);

// Output the coordinate system and resolution without re projection 
print('Proj and transform info of image:', image.projection().getInfo())
print('Pixel size in meters:', image.projection().nominalScale().getInfo())

// Output the coordinate system and resolution after re projection , Here, set the resolution to 100m
var reprojected = image.reproject('EPSG:4326',null,100); 
print('Proj and transform info after reprojection:', reprojected.projection());
print('Pixel size in meters:', reprojected.projection().nominalScale()); 

result ：

The above method is actually a re projection of the original image data , The resolution is reset during re projection , A resampling method is also provided on the official website , It uses .resample() function , The specific implementation code is as follows ：

//  load landsat image 
var landsat = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20160323');

//  Set display parameters 
Map.setCenter(-122.37383, 37.6193, 15);
var visParams = {
    bands: ['B4', 'B3', 'B2'], max: 0.3};

// Generally, the nearest pixel interpolation is used by default when loading images at the beginning 
Map.addLayer(landsat, visParams, 'original image');

//  Resample it with bicubic curve 
var resampled = landsat.resample('bicubic');

//  Show the resampled data 
Map.addLayer(resampled, visParams, 'resampled');

If in reality , We have 10m and 500m The data of , Need a unified scale , You need to be right about 10m Down sample the image of , Can be combined with .reduceResolution and .reproject Realization ：

// Load a MODIS EVI image.
var modis = ee.Image(ee.ImageCollection('MODIS/006/MOD13A1').first())
    .select('EVI');

// Display the EVI image near La Honda, California.
Map.setCenter(-122.3616, 37.5331, 12);
Map.addLayer(modis, {
    min: 2000, max: 5000}, 'MODIS EVI 500 m');

// Get information about the MODIS projection.
var modisProjection = modis.projection();
print('MODIS projection:', modisProjection);

// Load and display forest cover data at 30 meters resolution.
var forest = ee.Image('UMD/hansen/global_forest_change_2015')
    .select('treecover2000');
Map.addLayer(forest, {
    max: 80}, 'forest cover 30 m');

// Get the forest cover data at MODIS scale and projection.
var forestMean = forest
    // Force the next reprojection to aggregate instead of resampling.
    .reduceResolution({
    
      reducer: ee.Reducer.mean(),
      maxPixels: 1024
    })
    // Request the data at the scale and projection of the MODIS image.
    .reproject({
    
      crs: modisProjection
    });

// Display the aggregated, reprojected forest cover data.
Map.addLayer(forestMean, {
    max: 80}, 'forest cover at MODIS scale 500 m');