Google Earth Engine – Download Timeseries of Pixel Values for Multiple Bands

Question

I need an automated process for downloading a timeseries across multiple bands for a large number of pixels in the Google Earth Engine.

For one pixel, I have this piece of code:

var input_point = [4.666901, 50.649889]
var disp = .0005
  
Map.setCenter(input_point[0], input_point[1], 15)

var ee_input_point = ee.Geometry.Point(input_point)
Map.addLayer(ee_input_point,{}, 'SamplePoint')

var start = '2021-03-01'
var end = '2021-10-31'

var s2_sr = ee.ImageCollection('COPERNICUS/S2_SR').filterDate(start, end)
s2_sr = s2_sr.filter(ee.Filter.bounds(ee_input_point)).select(['B1', 'B2', 'B3'])
var s2_sr_p = s2_sr.map(function(img){return img.clip(ee_input_point)})

var chart = ui.Chart.image.series(s2_sr_p, ee_input_point)
print(chart)

The chart shows precisely what I need, and I can manually download the data as CSV. However, I would like to convert this code to Python and have it process a large number (1e3) of points. I can't figure out how to generate and download the chart data as a CSV (to drive). In Python, ui is not available.

Update
This produces a CSV which I can process further. If someone has a cleaner solution or one, which can handle multiple points without having to queue thousands of jobs in the queue, I am still very interested.

var stacked = s2_sr_p.toBands()
var means = stacked.reduceRegions(ee_input_point, ee.Reducer.mean().forEachBand(stacked), 10)
Export.table.toDrive(means, 'test1', 'px', 'test', 'CSV')

Answer 1

You can use an ee.FeatureCollection of points. You don't need to add the points individually as I have done in the example, you can upload a shapefile of multiple points and it will be converted to a FeatureCollection automatically.

This script should be translatable to Python:

var points = ee.FeatureCollection([
                   ee.Geometry.Point([4.666501, 50.649889]), 
                   ee.Geometry.Point([4.666901, 50.649289]), 
                   ee.Geometry.Point([4.666921, 50.649369])
             ]);

Map.addLayer(points);

// you can use `centerObject` to avoid having to get the coordinates for `setCenter`
Map.centerObject(points);

var start = '2021-03-01'
var end = '2021-10-31'

var s2_sr = ee.ImageCollection('COPERNICUS/S2_SR').filterDate(start, end)
                    // `filterBounds(...)` is shorthand for `filter(ee.Filter.bounds(...)`
                    .filterBounds(points).select(['B1', 'B2', 'B3'])

// no real need to clip because `reduceRegions` gets individual pixels from the point locations
// var s2_sr_p = s2_sr.map(function(img){return img.clip(points)})

var stacked = s2_sr.toBands();
var means = stacked.reduceRegions(points, ee.Reducer.mean().forEachBand(stacked), 10);
Export.table.toDrive(means, 'test', 'px', 'test', 'CSV');

Where "system:index" is the point ID number.