classification.js

// set the path to the api.js script you copied to your GEE account:
var api = require('users/your_username/repository_name:utils/api.js');

/************* SETTINGS **************/

// set the output path for the classification results:
var outputCollection = 'users/your_username/MAPBIOMAS/C5/AGRICULTURE/SUGARCANE/RESULTS/RAW';

// set the years you want to classify:
var years = [2019];

var offset = 3;

var cloudCover = 90;

// set the WRS (path and row) you want to classify:
var tiles   = [[221, 75]];

var bands = [
  api.Band.BLUE, 
  api.Band.GREEN, 
  api.Band.RED, 
  api.Band.NIR, 
  api.Band.SWIR1, 
  api.Band.SWIR2, 
  api.Band.NDVI, 
  api.Band.NDWI
];

var reducers = [
  api.Reducer.MEDIAN
];

// set the periods of the region you want to classify
// more information about that you can read on the ATBD Agriculture Appendix
var periods = {
  'WET1': '(Y-1)-12-01,(Y)-01-31', 
  'WET2': '(Y)-02-01,(Y)-03-31', 
  'DRY1': '(Y)-04-01,(Y)-05-31',
  'DRY2': '(Y)-06-01,(Y)-07-31',
  'DRY3': '(Y)-08-01,(Y)-09-30',
  'WET3': '(Y)-10-01,(Y)-11-30',
};

var featureSpace = [
  'WET1_GREEN_median', 'WET1_RED_median', 'WET1_NIR_median', 'WET1_SWIR1_median', 'WET1_SWIR2_median', 'WET1_NDVI_median', 'WET1_NDWI_median',
  'WET2_GREEN_median', 'WET2_RED_median', 'WET2_NIR_median', 'WET2_SWIR1_median', 'WET2_SWIR2_median', 'WET2_NDVI_median','WET2_NDWI_median', 
  'DRY1_GREEN_median', 'DRY1_RED_median', 'DRY1_NIR_median', 'DRY1_SWIR1_median', 'DRY1_SWIR2_median', 'DRY1_NDVI_median', 'DRY1_NDWI_median', 
  'DRY2_GREEN_median', 'DRY2_RED_median', 'DRY2_NIR_median', 'DRY2_SWIR1_median', 'DRY2_SWIR2_median', 'DRY2_NDVI_median', 'DRY2_NDWI_median', 
  'DRY3_GREEN_median', 'DRY3_RED_median', 'DRY3_NIR_median', 'DRY3_SWIR1_median', 'DRY3_SWIR2_median', 'DRY3_NDVI_median', 'DRY3_NDWI_median',
  'WET3_GREEN_median', 'WET3_RED_median', 'WET3_NIR_median', 'WET3_SWIR1_median', 'WET3_SWIR2_median', 'WET3_NDVI_median', 'WET3_NDWI_median'
];

var imageCollection = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA");

// set the collection you want to use to create the mosaics:
var referenceCollection = ee.ImageCollection("users/your_username/MAPBIOMAS/C5/AGRICULTURE/SUGARCANE/REFERENCE_MAP");

var gridCollection = ee.FeatureCollection("users/agrosatelite_mapbiomas/COLECAO_5/PUBLIC/GRIDS/BRASIL");

var trainingSamples = 10000;

var randomForestTrees = 100;

/************* END SETTINGS **************/

/************* FUNCTIONS **************/

years.forEach(function(year){
  tiles.forEach(function(wrs){
    
    // Geração dos Mosaicos
    
    var filteredCollection = imageCollection
      .filterMetadata('WRS_PATH', 'equals', wrs[0])
      .filterMetadata('WRS_ROW', 'equals', wrs[1]);
      
    var roi = gridCollection
      .filterMetadata('PATH', "equals", wrs[0])
      .filterMetadata('ROW', "equals", wrs[1])
      .first()
      .geometry()
      .buffer(-4000);
     
    var images = [];
    
    for(var period in periods){
      var dates = periods[period];
      
      var filteredImages = ee.ImageCollection([]);
      
      for(var i=0; i<offset; i++){
        
        var apiImagesByPeriod = new api.ImageCollection(filteredCollection)
          .filterByPeriod(year, dates, offset, cloudCover)
          .applyBuffer(-4200)
          .removeClouds()
          .buildBands(bands);
          
        filteredImages = filteredImages.merge(apiImagesByPeriod.getEEImageCollection());
      }
      
      var apiFilteredImages = new api.ImageCollection(filteredImages);
      
      var apiImage = apiFilteredImages.applyReducers(reducers);
      
      var image = apiImage.getEEImage();
      
      image = image.clip(roi);

      image = image.rename(ee.Image(image).bandNames().map(
        function(band){
            return ee.String(period).cat('_').cat(band);
        }
      ))
      
      images.push(image);
    }
    
    var mosaic = ee.Image.cat(images)
      .clip(roi)
      .unmask(null);
    
    var filename = '' + wrs[0]+ wrs[1]  +  '_' + year;
    var mosaicFilename = filename + "_mosaic";
    
    Map.addLayer(mosaic, {bands: ['WET1_NIR_median', 'WET1_SWIR1_median', 'WET1_RED_median'], min: 0, max: 0.5}, mosaicFilename);
    Map.centerObject(roi);
    
    mosaic = mosaic.select(featureSpace).multiply(10000);
    
    // Sampling //
    
    var reference = referenceCollection
      .filterMetadata('year', 'equals', year)
      .mosaic()
      .clip(roi)
      .unmask(null);
      
    var train = mosaic
      .addBands(reference.select([0], ["class"]));
    
    var training = train.sample({
      'region': roi,
      'scale': 30,
      'numPixels': trainingSamples,
      'tileScale': 4
    });
    
    // Training // 
    
    var classifier = ee.Classifier
      .randomForest(randomForestTrees)
      .train(training, 'class', featureSpace);
    
    // Classification //
    
    var classified = mosaic.classify(classifier)
      .set('year', year)
      .rename(['classification'])

    // Visualizing results //
    
    var referenceFilename = filename + "_reference";
    var classificationFilename = filename + "_classification";	
	
    Map.addLayer(reference, {min: 0, max: 1}, referenceFilename, false);
    Map.addLayer(classified, {min: 0, max: 1}, classificationFilename, false);
    
    // Exporting Results //
    
    roi.evaluate(function(geometry){
      var filename = year + '_' + wrs[0] + '_' + wrs[1];
      
      Export.image.toAsset({
        image: classified.byte(), 
        description: 'SUGARCANE_' + filename, 
        assetId: outputCollection + '/' + filename, 
        region: geometry, 
        scale: 30, 
        maxPixels: 1.0E13
      });

    });
  });
});


/************* END FUNCTIONS **************/