You can skip many of my tries, I the fifth one is almost there:
I am trying to generate a series of summaries for an image collection, where I get the mean, median, 90th quantile and 10th quantile for a series of polygons. My goal is to export either a shapefile with the attributes of mean, median, etc. I loaded this zipfile as assets which you can find here. This is what I have tried so far:
First try (export to raster)
var collection = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO')
.select('CO_column_number_density')
.filterDate('2019-06-01', '2019-06-11');
// clip to an asset
var clip = ee.FeatureCollection('projects/ee-my-derekcorcoran/assets/CitiesChile');
var clipped = collection.map(function(image) {
return image.clip(clip);
});
// get the mean of each feature in the collection
var mean = clipped.map(function(image) {
return image.reduceRegions({
collection: clip,
reducer: ee.Reducer.mean(),
scale: 500
});
});
// print the mean for each feature
print(mean);
//export the mean to a tif
Export.image.toDrive({
image: mean,
description: 'meanCO',
folder: 'EarthEngine',
fileNamePrefix: 'meanCO',
fileFormat: 'GeoTIFF',
formatOptions: {
cloudOptimized: true
},
skipEmptyTiles: true
});
This has seem to work until the last minute where it wont export it giving the following error:
ID: ZYUPAJJGL2U2PE4LLHGMXA5A
Phase: Failed
Runtime: 0s (started 2022-11-20 21:40:47 +0100)
Attempted 1 time
Error: Image.clipToBoundsAndScale, argument 'input': Invalid type. Expected type: Image<unknown bands>. Actual type: FeatureCollection. (Error code: 3)
Second try (export to shapefile)
Which led me to believe that this was generating a shapefile insted of a raster so I tried this:
var collection = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO')
.select('CO_column_number_density')
.filterDate('2019-06-01', '2019-06-11');
// clip to an asset
var clip = ee.FeatureCollection('projects/ee-my-derekcorcoran/assets/CitiesChile');
var clipped = collection.map(function(image) {
return image.clip(clip);
});
// get the mean of each feature in the collection
var mean = clipped.map(function(image) {
return image.reduceRegions({
collection: clip,
reducer: ee.Reducer.mean(),
scale: 500
});
});
// print the mean for each feature
print(mean);
//export the mean to a shp
Export.table.toDrive({
collection: mean,
description: 'meanCO',
folder: 'EarthEngine',
fileNamePrefix: 'meanCO',
fileFormat: 'SHP'
});
Which gave me the following error
Error: Unable to use a collection in an algorithm that requires a feature or image. This may happen when trying to use a collection of collections where a collection of features is expected; use flatten, or map a function to convert inner collections to features. Use clipToCollection (instead of clip) to clip an image to a collection. (Error code: 3)
Third try (flatten)
Because of the error I tried flatten.
var collection = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO')
.select('CO_column_number_density')
.filterDate('2019-06-01', '2019-06-11');
// clip to an asset
var clip = ee.FeatureCollection('projects/ee-my-derekcorcoran/assets/CitiesChile');
var clipped = collection.map(function(image) {
return image.clip(clip);
});
// get the mean of each feature in the collection
var mean = clipped.map(function(image) {
return image.reduceRegions({
collection: clip,
reducer: ee.Reducer.mean(),
scale: 500
});
});
// print the mean for each feature
print(mean);
var newTest = mean.flatten()
//export the mean to a tif
Export.table.toDrive({
collection: newTest,
description: 'meanCO',
folder: 'EarthEngine',
fileNamePrefix: 'meanCO',
fileFormat: 'SHP'
});
This again give me a new error:
Error: Shapefiles cannot contain multiple geometry types; found 'LineString', 'Polygon'. (Error code: 3)
But I know I loaded only polygons
Fourth try (based on another question)
I found this question so I tried to adapt it to my code, this is what I did:
var collection = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO')
.select('CO_column_number_density')
.filterDate('2019-06-01', '2019-06-15');
// clip to an asset
var clip = ee.FeatureCollection('projects/ee-my-derekcorcoran/assets/CitiesChile');
var createData = function(img){
var valueMean = img.reduceRegion({reducer: ee.Reducer.mean(), geometry: clip, scale: 30, maxPixels: 16016779});
var valueMedian = img.reduceRegion({reducer: ee.Reducer.median(), geometry: clip, scale: 30, maxPixels: 16016779});
var valueStdDev = img.reduceRegion({reducer: ee.Reducer.stdDev(), geometry: clip, scale: 30, maxPixels: 16016779});
var valueVariance = img.reduceRegion({reducer: ee.Reducer.variance(), geometry: clip, scale: 30, maxPixels: 16016779});
var ft = ee.Feature(null, {'Mean': valueMean.get('CO_column_number_density'),
'Median': valueMedian.get('CO_column_number_density'),
'stdDev': valueStdDev.get('CO_column_number_density'),
'Variance': valueVariance.get('CO_column_number_density'),
});
return ft;
};
// Apply the function to each image in dataset
var serie = collection.map(createData);
Export.table.toDrive({collection: serie,
folder: 'EarthEngine',
description: 'datatableCO',
fileFormat: 'CSV'})
This runned for 9 hours and no results
Fifth try:
this is almost there:
// Composite an image collection and clip it to a boundary.
// Load Landsat 7 raw imagery and filter it to April-July 2000.
var collection = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO')
.select('CO_column_number_density')
.filterDate('2019-06-01', '2019-12-12');
// Reduce the collection by taking the median.
var median = collection.median();
// Load a table of state boundaries and filter.
var fc = ee.FeatureCollection('projects/ee-my-derekcorcoran/assets/CitiesChile');
// Clip to the output image to the Nevada and Arizona state boundaries.
var clipped = median.clipToCollection(fc);
// Display the result.
Map.centerObject(fc);
var band_viz = {
min: 0,
max: 0.03,
palette: ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']
};
Map.addLayer(clipped, band_viz, 'S5P N02');
I can see the mean, now what is left to do is to summarise to transform each raster into the polygons as a mean for each, and export as shapefiles
