[GIS] Downloading entire image collection from GEE with metadata

google-earth-engine

I develop code in python using Google Earth Engine Python API, to download the entire collection of NOAA LAI that is stored in GEE. Despite the fact that the whole downloading process is painfully slow, I noticed that the downloaded data does not come with the original metadata. Below is the code that I developed using the tool of Rodrigo E. Principe

Here is my code.

from geetools import batch
import ee

ee.Initialize()

collection = ee.ImageCollection('NOAA/CDR/AVHRR/LAI_FAPAR/V4').select('LAI')
reference = collection.filterDate('1981-12-31', '1982-12-31')
tasks = batch.ImageCollection.toDrive(reference, 'MyFolder', scale=5000)

How can I download the data in a faster way (if possible) with the original metadata?

Best Answer

Presence of metadata in the downloaded raster

I had to check before answering. After reading this and this and inspecting the metadata with rasterio, QGIS and gdalinfo, I can tell you that images downloaded from Google Earth Engine do not carry the metadata (called "tags" by rasterio). It's not an issue of geetools.

If you want to keep metadata information (called "properties" by Earth Engine), you can upload the raster to an Asset (Google give you 250GB of storage!). You can use geetools.batch.ImageCollection.toAsset

Download speed

The function batch.ImageCollection.toDrive of geetools package is just an iteration over each image of the collection to call ee.batch.Export.image.toDrive, after the task has started, the process goes on in Goolge's servers, so once again, geetools has nothing to do with it.

Related Solutions

[GIS] GEE – Image collection to multiband image

Casting may solve the error you encountered, but the function you're looking for is imageCollection.toBands().

Downloading entire image collection of a particular month from 1990-2020 in GEE platform

You can try converting the image collection to bands in a single image. I just tried the following code and it took around 10 minutes to export to Google Drive.

var era5_2mt = ee.ImageCollection('ECMWF/ERA5/DAILY')
                   .select('minimum_2m_air_temperature')
                   .filter(ee.Filter.calendarRange(1990,2020,'year'))
                   .filter(ee.Filter.calendarRange(11,11,'month'))
                   // clip to your study area
                   .map(function(image) {return image.clip(table)});


// if you actually need to subtract 273.15, you can map over the image collection, rather than a list of images
// uncomment the following block

// var study_area = era5_2mt.map(function(img){
//   var s_ts = img.get('system:time_start')    // get the 'system:time start' property from the image
//   img = ee.Image(img).subtract(273.15)
//                      .set({'system:time_start': s_ts});   // set the 'system:time_start' property from the output image
//   return(img);
  
// });


// flatten collection to a single image
var merged = era5_2mt.toBands();
// var merged = study_area.toBands();  // use this if you need the calculated images


// export image to Google Drive
Export.image.toDrive({
  image: merged,
  description: 'era5_2mt',
  scale: 20000,
  region: table
});