Extraction of time series simultaneously on different radius bufferpoint on Google Earth Engine

Question

I have this code that extracts the monthly time series of soil moisture, on radius defined by the buffer function:

function bufferPoints(radius, bounds) {
  return function(pt) {
    pt = ee.Feature(pt);
    return bounds ? pt.buffer(radius).bounds() : pt.buffer(radius);
  };
}
var ptsbuff = ee.FeatureCollection(point).map(bufferPoints(500, false));

// Create a chart for SoilMoi100_200cm_inst trend in point 1.
var dataset = ee.ImageCollection('NASA/GLDAS/V021/NOAH/G025/T3H')
          .filterBounds(point)
          .filterDate('2006-01-01', '2016-12-31')
          .select('SoilMoi100_200cm_inst');

var months = ee.List.sequence(1, 12);
var start_year = 2006;
var start_date = '2006-01-01';
var end_year = 2016;
var end_date = '2016-12-31';

var years = ee.List.sequence( start_year, end_year);


var byMonthYear =  ee.FeatureCollection(
  years.map(function (y) {
    return months.map(function(m) {
      var w = dataset.filter(ee.Filter.calendarRange(y, y, 'year'))
                    .filter(ee.Filter.calendarRange(m, m, 'month'))
                    .mean();
           
      var pointMean = w.reduceRegion({reducer:ee.Reducer.first(), geometry:ptsbuff,scale:500});  
      // set the dictionary as property and cast to an ee.Image, as setMulti returns an ee.Element.
      return ee.Feature(null).set("value",pointMean.get("SoilMoi100_200cm_inst")).set("year",y).set("month",m);
      

    });
  }).flatten()
);


// print the collection, point information
//print("feature collection",byMonthYear);

Export.table.toDrive({collection:byMonthYear,description:"csvExport"}) 

Since I am doing extractions on several radius (For example 250, 500, 1000, 1500, 2000...), I have to re-execute the code each time.
Is there a way to modify the code to extract the data simultaneously and automatically with several buffer radius.

Answer 1

You only have to define a buffers list object and modify your script as follows for mapping it. In this case, I only used two distances (250, 500) for saving compute time. I defined an arbitrary point in France for running complete script.

// Create a chart for SoilMoi100_200cm_inst trend in point 1.
var dataset = ee.ImageCollection('NASA/GLDAS/V021/NOAH/G025/T3H')
          .filterBounds(point)
          .filterDate('2006-01-01', '2016-12-31')
          .select('SoilMoi100_200cm_inst');

var radius_lst = ee.List([250, 500]);

var colByRadius = ee.List(radius_lst).map(function (radius) {

  var months = ee.List.sequence(1, 12);
  var start_year = 2006;
  var start_date = '2006-01-01';
  var end_year = 2016;
  var end_date = '2016-12-31';

  var years = ee.List.sequence( start_year, end_year);

  var ptsbuff = ee.FeatureCollection(point).map(function (radius, bounds) {
    return function(pt) {
      pt = ee.Feature(pt);
    return bounds ? pt.buffer(radius).bounds() : pt.buffer(radius);
  };
  }(radius, false));

  var byMonthYear =  years.map(function (y) {
      return months.map(function(m) {
        var w = dataset.filter(ee.Filter.calendarRange(y, y, 'year'))
                    .filter(ee.Filter.calendarRange(m, m, 'month'))
                    .mean();
           
        var pointMean = w.reduceRegion(
          {reducer:ee.Reducer.first(), 
          geometry:ptsbuff, scale:500});  
      
      // set the dictionary as property and cast to an ee.Image, as setMulti returns an ee.Element.
      return ee.Feature(null).set("value",
                                  pointMean
                                    .get("SoilMoi100_200cm_inst"))
                                    .set("year",y)
                                    .set("month",m)
                                    .set("radius", radius);
      

      });
    }).flatten();

  return byMonthYear;

}).flatten();

// print the collection, point information
//print("feature collection", colByRadius);

Export.table.toDrive({collection:ee.FeatureCollection(colByRadius), description:"csvExport"}) 

After running above script in GEE code editor, I got a CSV file with 264 rows as follows. Transition for different radius can be observed inside red rectangle.

Editing Note:

Subsequently, I ran the script with following extended list (250, 500, 1000, 1500, 2000) and result (with 660 rows) was obtained as expected in 6 minutes.