title: I want to solve the "system:time_start" error when trying to output GLDAS monthly precipitation to CSV using Google Earth Engine
body: <p>I am using GoogleEarthEngine to look at precipitation data for the Mekong River basin. The GLDAS data are set every three hours per day. My goal is to extract the GLDAS data from 2000 to 2020 by adding up the data by month.</p>
  <p>I want to extract the total monthly precipitation in GLDAS using the Google Earth Engine, but I cannot extract the CSV due to the error</p>
  <blockquote>
  <p>Image.date: Image '120' has a 'system:time_start' property which is not a number: 2010-01-01T00:00:00</p>
  </blockquote>
  <p>I think I can extract the CSV by converting &quot;system:time_start&quot;, how should I change it?</p>
  <pre><code>var studyArea = ee.Geometry.Rectangle(102, 8.5, 107, 15);
  Map.centerObject(mekong, 9);
  
  // 解析対象年
  var years = ee.List.sequence(2000, 2019);
  var months = ee.List.sequence(1, 12);
  
  var early = ('2010-01-01');
  var late =  ('2011-01-01');
  
  // MOD11A1の取り出し
  var image = ee.ImageCollection('NASA/GLDAS/V021/NOAH/G025/T3H')
    .filterDate(early, late).filterBounds(mekong);
  //print(image);
  
  // mmに変換してmodLSTcに保存
  var gldas_precipitation = image.select('Rainf_f_tavg');
  var gldas_precipitation_mm = gldas_precipitation.map(function(img)
    {return img.multiply(10080.0).copyProperties(img, ['system:time_start'])});
  
  // 変数gldas_precipitation_mm_monthの中に月単位のメジアンデータを保存  ////////////////////////////////////////////
  var gldas_precipitation_mm_month = ee.ImageCollection.fromImages(
    years.map(function(y) {
      return months.map(function(m) {
        var monthly = gldas_precipitation_mm.filter(ee.Filter.calendarRange(y, y, 'year'))
          .filter(ee.Filter.calendarRange(m, m, 'month'))
          .sum()
          .rename('precipitation_mm_month');
      
      return monthly.set('year', y).set('system:time_start', ee.Date.fromYMD(y, 1, 1))
        .set('month', y).set('system:time_start', ee.Date.fromYMD(y, m, 1));
    });
  }).flatten());
  
  var gldas_precipitation_mm_month = gldas_precipitation_mm_month.filterBounds(mekong);
  
  // TSLのポリゴン ///////////////////////////////////////////////////////////////////////////////////
  var empty = ee.Image().byte();
  // Paint all the polygon edges with the same number and width, display
  var outline = empty.paint({
    featureCollection: mekong,
    color: 1,
    width: 2
  });
  Map.addLayer(outline, {palette: 'FF0000'}, 'TSL');
  
  //output_csv_precipitation
  //Create variables and extract data
  var scale = gldas_precipitation_mm_month.mean().projection().nominalScale().multiply(0.05); print(scale);
  var gldas = gldas_precipitation_mm_month.filter(ee.Filter.listContains('system:band_names', gldas_precipitation_mm.mean().bandNames().get(0)));
  var ft = ee.FeatureCollection(ee.List([]));
  
  //Function to extract values from image collection based on point file and export as a table 
  var fill = function(img, ini) {
  var inift = ee.FeatureCollection(ini);
  var ft2 = img.reduceRegions(mekong, ee.Reducer.mean(), scale);
  var date = img.date().format(&quot;YYYY/MM/dd&quot;);
  var ft3 = ft2.map(function(f){return f.set('month', date)});
  return inift.merge(ft3);
  };
  
  // Iterates over the ImageCollection
  var profile = ee.FeatureCollection(gldas_precipitation_mm_month.iterate(fill, ft));
  print(profile,'profile');
  </code></pre>