init commit smap_gis.js for SMAP implementation

src/smap_gis.js

+// Import shelf files
+var table = ee.FeatureCollection("projects/ee-arachchigepramod/assets/Mallawapitiya");
+
+// SMAP(Soil Moisture Active Passive)soil moisture data
+
+// Extract & Visualize Monthly Time Series Analysis of SMAP soil moisture 
+// and export to your Google Drive as a CSV file
+
+// 1. Define countries boundary
+var Countries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
+// var roi = Countries.filter(ee.Filter.eq('country_na', 'South Sudan'));
+var roi = table;
+Map.addLayer(roi, {}, "roi");
+Map.centerObject(roi);
+
+// 2. List of years
+var years = ee.List.sequence(2016, 2021);
+var months = ee.List.sequence(1, 12);
+
+// 3. Load SMAP Data
+var coll = ee.ImageCollection('NASA_USDA/HSL/SMAP10KM_soil_moisture').select('ssm');
+print(coll.first());
+
+// 4. Set visualization parameter
+var soilVis = {
+  min: 0.0,
+  max: 28.0,
+  palette: ['0300ff', '418504', 'efff07', 'efff07', 'ff0303'],
+};
+// 5. Center and add SMAP layer
+Map.setCenter(17, 13, 2); // Zoom level ranges from 1 to 16
+Map.addLayer(coll.mean(), soilVis, 'Soil Moisture');
+Map.addLayer(coll.mean().clip(roi), soilVis, 'Mallwapitiya Soil Moisture');
+
+// 6. Summaries our SMAP (Soil Moisture Active Passive) soil moisture data by month and year
+var smap = coll.select('ssm')
+            .map(function(img){
+              var d = ee.Date(ee.Number(img.get('system:time_start')));
+              var m = ee.Number(d.get('month'));
+              var y = ee.Number(d.get('year'));
+              return img.set({'month':m, 'year':y});
+            });
+print(smap.first());
+
+// 7. Function generate monthly soil moisture data for each year
+var byYearMonth = ee.ImageCollection.fromImages(
+  years.map(function(y){
+    return months.map(function(m) {
+      return smap.filterMetadata('year', 'equals', y)
+      .filterMetadata('month', 'equals', m)
+      .select('ssm').mean()
+      .set('year', y)
+      .set('month', m)
+      .set('date', ee.Date.fromYMD(y,m,1));
+  });
+}).flatten()
+);
+print("monthlyCol", byYearMonth.first());
+
+// 8. Zonal statistics to sumarries SMAP soil moisture to specific study area (eg South Sudan)
+var smapSouth_Sudan = byYearMonth.map(function(img) {
+  var features = roi.map(function(f) {return f.set('date', img.get('date'), 'month', img.get('month'), 'year', img.get('year'))})
+  var proj = ee.Image(byYearMonth.first()).projection();
+  return img.reduceRegions(features, ee.Reducer.mean(), 1000, proj);
+}).flatten();
+print("SMAP Summary Mean", smapSouth_Sudan.limit(10));
+
+// 9. Export the resulting mean soil moisture as a table to Google Drive
+var selectors = "year, month, country_na, mean";
+Export.table.toDrive({
+    collection: smapSouth_Sudan,
+    description: 'SMAP_Timeseries', 
+    folder: 'earth_engine_data', 
+    fileNamePrefix: 'SMAP_Timeseries', 
+    fileFormat: 'CSV',
+    selectors: selectors
+});
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