I used the .combine command to convert two image collections into a two-band image collection (in the last line) to use in a function in the next step. This command is executed but writes 0 elements in the console. Where does this problem come from?
code link: https://code.earthengine.google.com/ed0992093ff830d926c7dd14403477c6
Code:
var ndvi = function(img){
var bands = img.select(['B2','B3','B4','B8']).multiply(0.0001)
.clip(geometry);
var index = bands.normalizedDifference(['B8','B4']).rename('NDVI_S2');
return index
.copyProperties(img,['system:time_start','system:time_end','system:index']);
};
var S2 = ee.ImageCollection('COPERNICUS/S2_SR')
.filterBounds(geometry)
.filterDate('2018-10-24','2019-06-30')
//.filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE',20))
.map(ndvi);
print(S2);
var START = '2018-10-24';
var END = '2019-06-30';
var DATES = [ '2018-12-19', '2018-12-29', '2019-01-23', '2019-02-12', '2019-03-04',
'2019-03-19', '2019-04-08', '2019-04-13', '2019-05-13', '2019-05-18', '2019-05-23',
'2019-05-28', '2019-06-02', '2019-06-07', '2019-06-12', '2019-06-17', '2019-06-22',
'2019-06-27'];
var addTime = function(x) {
return x.set('Date', ee.Date(x.get('system:time_start')).format("YYYY-MM-dd"))};
var Sentinel = ee.ImageCollection(S2)
.filter(ee.Filter.date(START, END))
.map(addTime)
.filter(ee.Filter.inList('Date',ee.List(DATES)));
print(Sentinel);
var PMODIS =
ee.Image('MODIS/006/MCD43A4/2018_12_19').select('Nadir_Reflectance_Band4');
var MODProjection = PMODIS.projection();
print('MODIS projection:', MODProjection);
var Viz = {min: 0, max: 1, palette: ['be6c44','ca3a3a','e4ae0c','565c04','819536']};
var S2_resampled = Sentinel.map(function(img){
var S2Mean = img
// Force the next reprojection to aggregate instead of resampling.
.reduceResolution({
reducer: ee.Reducer.mean(),
maxPixels: 2146
})
// Request the data at the scale and projection of the Sentinel image.
.reproject({
crs: MODProjection
});
return S2Mean
.copyProperties(img,['system:time_start','system:time_end','system:index']);
});
Map.addLayer(S2_resampled)
var M_ndvi = function(img){
var bands =
img.select(['Nadir_Reflectance_Band1','Nadir_Reflectance_Band5']).multiply(0.0001)
.clip(geometry);
var index=bands
.normalizedDifference(['Nadir_Reflectance_Band1','Nadir_Reflectance_Band5'])
.rename(
'NDVI_MOD');
return index
.copyProperties(img,['system:time_start','system:time_end','system:index']);
};
var MOD = ee.ImageCollection('MODIS/006/MCD43A4')
.filterBounds(geometry)
.filterDate('2018-10-24','2019-06-30')
.map(M_ndvi);
var MODIS = ee.ImageCollection(MOD)
.filter(ee.Filter.date(START, END))
.map(addTime)
.filter(ee.Filter.inList('Date',ee.List(DATES)));
print(MODIS);
var S2_and_MOD = S2_resampled.combine(MODIS, false);
print(S2_and_MOD);
var Diff = S2_and_MOD.map(function(img){
var clip = img.clip(geometry);
var Diffe = clip.expression('NDVI_S2 - NDVI_MOD',
{'NDVI_S2':clip.select('NDVI_S2') ,
'NDVI_MOD':clip.select('NDVI_MOD')}).rename('Diff');
return Diffe
.copyProperties(img,['system:time_start','system:time_end']); });
print(Diff);
ee.Image.combine() uses the system:ID property to join the 2 images. See the documentation here. Since your images do not match, the resulting collection has no images.
A solution that should fit your needs utilizes the ee.Join.inner() to take advantage of the Date property that you have created to join the 2 image collections. A similar question was answered here.
Using inner join, I was able to accomplish what appeared to be your goal of finding the difference in NDVI between the S2 and MODIS collections. The full working script can be found here: https://code.earthengine.google.com/dc45df1b7cf83723d53e9f7917975e2d
Code:
// Function - Calculate S2 NDVI
var ndvi = function(img){
var bands = img.select(['B2','B3','B4','B8']).multiply(0.0001)
.clip(geometry);
var index = bands.normalizedDifference(['B8','B4']).rename('NDVI_S2');
return index
.copyProperties(img,['system:time_start','system:time_end','system:index']);
};
// Get S2 NDVI images
var S2 = ee.ImageCollection('COPERNICUS/S2_SR')
.filterBounds(geometry)
.filterDate('2018-10-24','2019-06-30')
//.filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE',20))
.map(ndvi);
print('S2 NDVI ImageCollection',S2);
// Set Date Parameters
var START = '2018-10-24';
var END = '2019-06-30';
// Create Date List
var DATES = [ '2018-12-19', '2018-12-29', '2019-01-23', '2019-02-12', '2019-03-04',
'2019-03-19', '2019-04-08', '2019-04-13', '2019-05-13', '2019-05-18', '2019-05-23',
'2019-05-28', '2019-06-02', '2019-06-07', '2019-06-12', '2019-06-17', '2019-06-22',
'2019-06-27'];
// Function - Add 'Date' field to image
var addTime = function(x) {
return x.set('Date', ee.Date(x.get('system:time_start')).format("YYYY-MM-dd"))};
// Run addTime on S2 ImageCollection
var Sentinel = ee.ImageCollection(S2)
.filter(ee.Filter.date(START, END))
.map(addTime)
.filter(ee.Filter.inList('Date',ee.List(DATES)));
print('Date Added S2', Sentinel);
// Get MODIS Projection
var PMODIS = ee.Image('MODIS/006/MCD43A4/2018_12_19').select('Nadir_Reflectance_Band4');
var MODProjection = PMODIS.projection();
print('MODIS projection:', MODProjection);
// Set Visualization Parameters
var Viz = {min: 0, max: 1, palette: ['be6c44','ca3a3a','e4ae0c','565c04','819536']};
// Reproject S2 images to MODIS projection
var S2_resampled = Sentinel.map(function(img){
var S2Mean = img
// Force the next reprojection to aggregate instead of resampling.
.reduceResolution({
reducer: ee.Reducer.mean(),
maxPixels: 2146
})
// Request the data at the scale and projection of the Sentinel image.
.reproject({
crs: MODProjection
});
return S2Mean
.copyProperties(img,['system:time_start','system:time_end','system:index']);
});
print('S2_resampled',S2_resampled);
Map.addLayer(S2_resampled);
// Function - Calculate MODIS NDVI
var M_ndvi = function(img){
var bands = img.select(['Nadir_Reflectance_Band1','Nadir_Reflectance_Band5']).multiply(0.0001)
.clip(geometry);
var index = bands.normalizedDifference(['Nadir_Reflectance_Band1','Nadir_Reflectance_Band5']).rename('NDVI_MOD');
return index
.copyProperties(img,['system:time_start','system:time_end','system:index']);
};
// Get MODIS NDVI Images
var MOD = ee.ImageCollection('MODIS/006/MCD43A4')
.filterBounds(geometry)
.filterDate('2018-10-24','2019-06-30')
.map(M_ndvi);
// Run addTime on MODIS ImageCollection
var MODIS = ee.ImageCollection(MOD)
.filter(ee.Filter.date(START, END))
.map(addTime)
.filter(ee.Filter.inList('Date',ee.List(DATES)));
print('MODIS',MODIS);
// Combine MODIS and S2 Image Collections using Date
// Specify the join type
var join_type = ee.Join.inner();
// Set the join parameter
var filter = ee.Filter.equals({
leftField: 'Date',
rightField: 'Date'
});
// Execute the join
var inner_join = ee.ImageCollection(join_type.apply(MODIS,S2_resampled,filter));
// Flatten joined images into a single image with 2 bands
var S2_and_MOD = inner_join.map(function(feature) {
return ee.Image.cat(feature.get('primary'), feature.get('secondary'));
});
print('Combined S2 and MODIS Collection:',S2_and_MOD);
// Calculate the difference between S2 and MODIS NDVI values
var Diff = S2_and_MOD.map(function(img){
var clip = img.clip(geometry);
var Diffe = clip.expression('NDVI_S2 - NDVI_MOD',
{'NDVI_S2':clip.select('NDVI_S2') , 'NDVI_MOD':clip.select('NDVI_MOD')}).rename('Diff');
return Diffe
.copyProperties(img,['system:time_start','system:time_end']); });
print('NDVI Difference Collection',Diff);
I want to apply cloud masking to a MDNWI image but I get the error message "s2SR.map is not a function". I don't know how to resolve it.
var geometry=ee.Geometry.Polygon([[41.55427215633343,41.57962485896675],
[41.607143860434995,41.57962485896675],
[41.607143860434995,41.62429558047125],
[41.55427215633343,41.62429558047125],
[41.55427215633343,41.57962485896675]]);
var s2SR = ee.ImageCollection('COPERNICUS/S2_SR')
//filter start and end date
.filter(ee.Filter.calendarRange(2018,2018,'year'))
.filter(ee.Filter.calendarRange(9,10,'month'))
//filter according to drawn boundary
.filterBounds(geometry)
.filterMetadata('CLOUD_COVERAGE_ASSESSMENT', 'less_than',1)
.mean();
//print(typeof(s2SR))
//print("s2SR", s2SR);
//Map.addLayer(s2SR, {bands: ['B4', 'B3', 'B2'], min: 0, max: 2000}, 'Sentinel ');
Map.centerObject(geometry,10)
var Green = s2SR.select("B3");
var SWIR = s2SR.select("B11");
var mndwi = Green.subtract(SWIR).divide(Green.add(SWIR)).rename('MNDWI');
//Map.addLayer(mndwi, {min:0, max:1}, 'mndwı');
///// Cloud
var S2maskedVeg = function(image) {
var MNDWI = image.select(['MNDWI']);
return image.addBands(ee.Image(1).updateMask(mndwi.gte(0.95)).rename('MNDVI_mask'));
};
var S2collection = s2SR.map(mndwi).map(S2maskedVeg)
Map.addLayer(S2collection,{}, 'S2 NDWI mask');
(https://code.earthengine.google.com/6835ca74e985d40df906681039358ee1)
Here, you already calculate the MNDWI over the whole collection:
var mndwi = Green.subtract(SWIR).divide(Green.add(SWIR)).rename('MNDWI');
Therefore, this line does not make much sense:
var S2collection = s2SR.map(mndwi).map(S2maskedVeg)
map() requires a function as input, but it is an ImageCollection. You can just skip this part and map your S2maskedVeg() function over mndwi directly:
var S2collection = mndwi.map(S2maskedVeg)
How to get the difference image of this month and the previous month in Google Earth Engine with a smart way?
study area 25E-75E，5S-35N。
// study area 25E-75E，5S-35N。
var geometry =
ee.Geometry.Polygon(
[[[25, 35],
[25, 5],
[75, 5],
[75, 35]]], null, false);
var regions = ee.FeatureCollection([
ee.Feature(geometry)
]);
// imgCol
var now = ee.Date(Date.now());
var NDVICollection=ee.ImageCollection('MODIS/006/MOD13Q1')
.filterDate('2010-01-01',now)
.filterBounds(regions)
.select('NDVI');
var col = NDVICollection.map(function(img){
return img.multiply(0.0001)
.copyProperties(img,['system:time_start','system:time_end']);
});
// grouped by month
var months = ee.List([11,12,1,2]);
var byMonth = ee.ImageCollection.fromImages(
months.map(function (m) {
return col.filterDate('2019-11-01',now).filter(ee.Filter.calendarRange(m, m, 'month'))
.select('NDVI').mean()
.set('month', m);
}));
mask
var meanNDVI = byMonth.reduce(ee.Reducer.mean());
var mask = meanNDVI.gt(0.1);
Create difference image
**var img12 = byMonth.filter(ee.Filter.eq('month', ee.Number(12))).first().updateMask(mask);
var img11 = byMonth.filter(ee.Filter.eq('month', ee.Number(11))).first().updateMask(mask);
var img1 = byMonth.filter(ee.Filter.eq('month', ee.Number(1))).first().updateMask(mask);
var img2 = byMonth.filter(ee.Filter.eq('month', ee.Number(2))).first().updateMask(mask);
var ndviChange_12 = img12.subtract(img11).set('name','ndviChange_12');
var ndviChange_1 = img1.subtract(img12).set('name','ndviChange_1');
var ndviChange_2 = img12.subtract(img1).set('name','ndviChange_2');
var ndviChange = ee.ImageCollection([ndviChange_12,ndviChange_1,ndviChange_2]);**
I want make it much more smart, what should I do? make a function or something else?
Show the image
Map.centerObject(regions);
Map.addLayer(byMonth);
Map.addLayer(ndviChange);
I think the solution is to use iterate on your byMonth collection . Check this example in the documentations.
https://developers.google.com/earth-engine/ic_iterating
How I can extract index vegetation points over collections by adapting this beautiful code by #Rodrigo E. Principe:
Extract pixel values by points and convert to a table in Google Earth Engine
I try extract all values mas GEE is crashing, so only NDVI or EVI can works fine.
I did it with this tutorial https://developers.google.com/earth-engine/tutorial_api_06
// Dataset do sensor LS8
var dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')
.filterDate('2018-04-01', '2019-03-31')
.select('B5', 'B4')
.filterBounds(aoi6010)
.filter(ee.Filter.lt('CLOUD_COVER', 20));
var addNDVI = function(image) {
var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI');
return image.addBands(ndvi);
};
var withNDVI = dataset.map(addNDVI);
print(withNDVI);
// Empty Collection to fill
var ft = ee.FeatureCollection(ee.List([]))
var fill = function(img, ini) {
// type cast
var inift = ee.FeatureCollection(ini)
// gets the values for the points in the current img
var ft2 = img.reduceRegions(p601018, ee.Reducer.first(),30)
// gets the date of the img
var date = img.date().format()
// writes the date in each feature
var ft3 = ft2.map(function(f){return f.set("date", date)})
// merges the FeatureCollections
return inift.merge(ft3)
}
// Iterates over the ImageCollection
var newft = ee.FeatureCollection(withNDVI.iterate(fill, ft))
I want to use supervised classification to classify a pattern that has a clear temporal pattern. For example, identifying stands of deciduous trees in a coniferous forest. NDVI would change overtime in the deciduous stands in a regular pattern that should be easily detectable. I assume there's an easy method to flatten the temporal dataset into a single image so that the bands in that image can be used in a classification algorithm. Maybe using .map(....)?
Here's some code to build the answer from:
var startDate = '2016-05-01';
var endDate = '2016-09-01';
var lng = -122.3424; var lat = 37.9344; //SF
var region = ee.Geometry.Point(lng, lat);
//Image Import
var l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')
.filterBounds(region)
.filterDate(startDate,endDate);
// NDVI temporal
var ndvi = l8.map(function(image) {
var ndvi = image.normalizedDifference(['B5', 'B4']).rename("NDVI");
return ndvi;
});
Map.addLayer(ndvi,{},"NDVI Temporal"); // 8 images with 1 band
//NDVI FLATTENED??????? I want 1 image with 8 bands. The below code doesn't work...
var ndviFlat = ee.Image().addBands(ndvi.map(function(image){
var temp = image.select("NDVI");
return temp;
}));
From there, I will pass ndviFlat to .sampleRegions, which only works with Images not ImageCollections:
//Classification Model:
var points = ee.FeatureCollection([trainingPointsPos,trainingPointsNeg]).flatten();
var training = ndviFlat.sampleRegions({
collection: points,
properties: ['class'],
scale: 30
});
var trained = ee.Classifier.randomForest(20).train(training, 'class', bands);
classified = regLayers.select(bands).classify(trained);
Here's one way:
var startDate = '2016-05-01';
var endDate = '2016-09-01';
var lng = -122.3424;
var lat = 37.9344; //SF
var region = ee.Geometry.Point(lng, lat);
//Image Import
var l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')
.filterBounds(region)
.filterDate(startDate, endDate);
var empty = ee.Image();
// NDVI temporal
var ndvi = ee.Image(l8.iterate(function(image, previous) {
var name = ee.String('NDVI_').cat(image.id());
var ndvi = image.normalizedDifference(['B5', 'B4']).rename(name);
return ee.Image(previous).addBands(ndvi);
}, empty));
// Remove the annoying non-band
ndvi = ndvi.select(ndvi.bandNames().remove('constant'));
Map.centerObject(region, 13);
Map.addLayer(ndvi, {}, 'ndvi');