تحديد منطقة البحث مكانيا

var dataset = ee.FeatureCollection('FAO/GAUL/2015/level0')
var dataset = ee.FeatureCollection('FAO/GAUL/2015/level1')
var dataset = ee.FeatureCollection('FAO/GAUL/2015/level2')

مجموعات تصفية الصورة

في الواقع الوصول إلى البيانات وتصفيتها

//Step 1: Use the map panel to browse to your research area. Define an Area of Interest by defining a point or polygon geometrie, or upload a shapefile to your assets.
//In this example, we use the Global Administration Units Layer(GAUL) 2015 to access geometry data of Lebanon. Please check the official documentation to learn more about its table schema.
//Keep in mind that this will not clip the imagery to the extents of Lebanon, so we will have a lot of excess data!

var extent_lebanon = ee.FeatureCollection('FAO/GAUL/2015/level0')
                  .filter(ee.Filter.eq('ADM0_NAME', 'Lebanon')) //filter for entry that equals the UN country name 'Lebanon'


//Step 2: Access the Sentinel-2 Level-2A data and filter it for all the the images of the year 2020 that lie within the geometries boundaries. Keep only the relevant bands.
var s2a = ee.ImageCollection('COPERNICUS/S2_SR')
                  .filterBounds(extent_lebanon)
                  .filterDate('2020-01-01', '2020-12-31')
                  .select('B1','B2','B3','B4','B5','B6','B7','B8','B8A','B9','B11','B12')

//Print your ImageCollection to your console tab to inspect it
print(s2a, 'Image Collection 2020')

/*
You can now check the printed ImageCollection in your Console to see that we have 678 images that we can freely use for further analysis and processing. 
By clicking on the arrows, you can look at further details and properties of the single Images of the ImageCollection.
*/

//if we want to shrink this ImageCollection further down by reducing bloating images that are not of much use, we can filter for clouds as well.

var s2a_cloudfree = s2a
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10)) //filters for entries with less than 10% cloudy pixel percentage

print(s2a_cloudfree, 'Image Collection 2020 < 10% Cloud Cover')  // now, only 363 images are left
 
 
//if we are interested in getting the 20 least cloudy images of 2020, this can also be easily achieved:

var s2a_leastcloudy = s2a_cloudfree
                  .sort('CLOUD_COVERAGE_ASSESSMENT', true)
                  .limit(20);
                  
print(s2a_leastcloudy, 'Image Collection 2020 20 least cloudy images')
 
 
 
//Alternatively, add .mean() to generate a single image that is composed of the mean values of all images in the IC. 
var s2a_mean = ee.ImageCollection('COPERNICUS/S2_SR')
                  .filterBounds(extent_lebanon)
                  .filterDate('2020-01-01', '2020-12-31')
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',10))
                  .mean();

print(s2a_mean, 'Image Collection 2020 mean')