Image_analysis

Map.centerObject(roi,10)

//Select dates 
var startW = ee.Date('2018-11-01')
var finishW = ee.Date('2018-12-31')
var startS = ee.Date('2018-03-01')
var finishS = ee.Date('2018-03-31')


////////////////////////                        ///////////////////////////////////     
////////////////////////          S2            ///////////////////////////////////     
////////////////////////                        /////////////////////////////////// 

var S2 = ee.ImageCollection('COPERNICUS/S2')
       // .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))
        .filterBounds(roi)   
  //      .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 25))
    //    .filterDate(startS, finishS)
        .filterDate('2018-03-29', '2018-03-30');
        
print(S2.count(), 'Number of S2 images 2 months in winter' )        

Map.addLayer(ee.Image(S2.min().clip(roi)), {bands: ['B4', 'B3', 'B2'], min:0, max:3000, gamma: 1.62}, 'S2 Spring');

//Load sentinel1 SAR data collection filtered by date and location
var S1 = ee.ImageCollection('COPERNICUS/S1_GRD')
        .filterBounds(roi);

////////////////////////                        ///////////////////////////////////     
////////////////////////          VV            ///////////////////////////////////     
////////////////////////                        ///////////////////////////////////     

//Sentinel1 SAR data filtered by metadata to get images with HH dual polarization and interferometric wide swath mode
var VVWinter = S1
        .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
        .filter(ee.Filter.eq('instrumentMode', 'IW'))
        .filterDate(startW, finishW)
        .select('VV');
        
var VVSpring = S1
        .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
        .filter(ee.Filter.eq('instrumentMode', 'IW'))
    //    .filterDate(startS, finishS)
        .filterDate('2018-03-29', '2018-03-30')
        .select('VV'); //not sure about this one

var VVSpringpast = S1
        .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
        .filter(ee.Filter.eq('instrumentMode', 'IW'))
        .filterDate('2018-02-27', '2018-02-28')
        .select('VV'); //not sure about this one

        
print(VVWinter.count(), 'Number of VV during 1 month winter')
print(VVSpring.count(), 'Number of VV during 1 month spring')
//Map.addLayer(ee.Image(VVWinter.max()).clip(roi), {palette:'black,white', min:-14, max:-3}, 'VV max Winter');
Map.addLayer(ee.Image(VVSpring.max()).clip(roi), {palette:'black,white', min:-22, max:-0.4}, 'VV max Spring');
Map.addLayer(ee.Image(VVSpringpast.max()).clip(roi), {palette:'black,white', min:-22, max:-0.4}, 'VV max Spring past');
////////////////////////                        ///////////////////////////////////     
////////////////////////          HH            ///////////////////////////////////     
////////////////////////                        ///////////////////////////////////   

//Sentinel1 SAR data filtered by metadata to get images with HH dual polarization and interferometric wide swath mode
var HHWinter = S1
        .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'HH'))
        .filter(ee.Filter.eq('instrumentMode', 'IW'))
        .filterDate(startW, finishW)
        .select('HH');
        
var HHSpring = S1
        .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'HH'))
        .filter(ee.Filter.eq('instrumentMode', 'IW'))
    //    .filterDate(startS, finishS)
        .filterDate('2018-03-29', '2018-03-30')
        .select('HH'); //not sure about this one
        
print(HHWinter.count(), 'Number of HH during 1 month winter')
print(HHSpring.count(), 'Number of HH during 1 month spring')
//Map.addLayer(ee.Image(HHWinter.max()).clip(roi), {palette:'black,white', min:-10, max:2}, 'HH max Winter');
//Map.addLayer(ee.Image(HHSpring.max()).clip(roi), {palette:'black,white', min:-10, max:2}, 'HH max Spring');

////////////////////////                        ///////////////////////////////////     
////////////////////////          VH            ///////////////////////////////////     
////////////////////////                        /////////////////////////////////// 

        
var VHWinter = S1
        .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
        .filter(ee.Filter.eq('instrumentMode', 'IW'))
        .filterDate(startW, finishW)
        .select('VH');
        
var VHSpring = S1
        .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
        .filter(ee.Filter.eq('instrumentMode', 'IW'))
    //    .filterDate(startS, finishS)
        .filterDate('2018-03-29', '2018-03-30')
        .select('VH'); 
        
print(VHWinter.count(), 'Number of VH during 1 month winter')
print(VHSpring.count(), 'Number of VH during 1 month spring')
//Map.addLayer(ee.Image(VHWinter.max()).clip(roi), {palette:'black,white', min:-10, max:2}, 'VH max Winter');
Map.addLayer(ee.Image(VHSpring.max()).clip(roi), {palette:'black,white', min:-27, max:-0.7}, 'VH max Spring');


////////////////////////                        ///////////////////////////////////     
////////////////////////    COMPOSITE           ///////////////////////////////////     
////////////////////////                        /////////////////////////////////// 


var VVpast = ee.Image(VVSpringpast.max()).clip(roi);
var VH = ee.Image(VHSpring.max()).clip(roi);
var VV = ee.Image(VVSpring.max()).clip(roi);
var VVVH = VV.divide(VH)
var VHVV = VH.divide(VV)
var VVdif = VVpast.subtract(VV);


//Synthetic blue band is the ratio of VV to VH (which changes to difference for decibels).


var composite1 = ee.Image.cat([
  VV,
  VH,
  VVVH
  ]).focal_median();
  
var composite2 = ee.Image.cat([
  VV,
  VH,
  VVVH
  ]).focal_median();
  
var composite3 = ee.Image.cat([
  VV,
  VH,
  VV
  ]).focal_median();
  
var composite4 = ee.Image.cat([
  VVdif,
  VV,
  VV
  ]).focal_median();  

Map.addLayer(composite1, {min: [-25, -20, -25], max: [0, -5, 15]}, 'VV,VH,VVVH', false);
Map.addLayer(composite2, {min: [-16, -27, 2.1045], max: [-2, -8, 11.303]}, 'VV,VH,VHVV ', false);
Map.addLayer(composite3, vis_vvvhvv, 'VV,VH,VV ');
Map.addLayer(composite4, {}, 'Composite different dates');
Map.addLayer(VVdif, vis_vvdif, 'VV dif');




////////////////////////                        ///////////////////////////////////     
////////////////////////  SUP CLASSIFICATION    ///////////////////////////////////     
////////////////////////                        /////////////////////////////////// 

var polygons = thinice.merge(thickice).merge(land).merge(water)
var bands = ['VV', 'VH'];
var sarimage = VV.addBands(VH);

// Creation of training data 
var training = sarimage.select(bands).sampleRegions({
  collection: polygons, 
  properties: ['landcover'], 
  scale: 30, 
  tileScale: 4
});



// Training a CART classifier with default parameters.
var trained = ee.Classifier.cart().train(training, 'landcover', bands);

// Classify the image with the same bands used for training.
var classified = sarimage.select(bands).classify(trained);


Map.addLayer(classified, {min: 0, max:3, palette: 'red,orange,green,blue'}, 'Classification', false);



////////////////////////                        ///////////////////////////////////     
////////////////////////       MASKING          ///////////////////////////////////     
////////////////////////                        ///////////////////////////////////


var WATER_MASK = ee.Image('UMD/hansen/global_forest_change_2015').select('datamask').neq(1);
var masked = sarimage.updateMask(WATER_MASK);
        //  } else if (masking == 'mask water') {
        //    transformed = transformed.updateMask(app.WATER_MASK.not());
Map.addLayer(masked, {bands: 'VV', min: -23, max: -4.7} , 'Mask test', false);

var VVdifmasked = VVdif.updateMask(WATER_MASK);
Map.addLayer(VVdifmasked, vis_vvdif, 'VVdif water')


////////////////////////                        ///////////////////////////////////     
////////////////////////  THRESHOLDING          ///////////////////////////////////     
////////////////////////                        /////////////////////////////////// 

var threshold = ee.Number(-10);
var watermap = sarimage.mask(sarimage.lt(-24));
Map.addLayer(watermap, {} , 'Threshold Water');

     
////////////////////////                        ///////////////////////////////////     
////////////////////////  UNSUP CLASSIFICATION ///////////////////////////////////     
////////////////////////                        /////////////////////////////////// 

// Make the training dataset S1
var trainingS1 = masked.select('VV').sample({
  region: roi,
  scale: 30, 
  numPixels: 5000,
  tileScale: 16
});


// Instantiate the clusterer and train it.
var clustererS1 = ee.Clusterer.wekaKMeans(3).train(trainingS1);

// Cluster the input using the trained clusterer.
var resultS1 = masked.cluster(clustererS1);

var smooth = resultS1.focal_median(5, 'circle', 'meters');
//Map.addLayer(smooth.clip(roi), {palette:'black,white', min:-27, max:-0.7}, 'VH max Spring Smoothed');

// Display the clusters with random colors.
Map.addLayer(resultS1.randomVisualizer(), {}, 'Unsupervised S1');
Map.addLayer(smooth, {min: 0, max:2, palette: 'green,red,orange'}, 'Unsupervised Smoothed S1');




//Sentinel1 SAR data filtered to get images of descending and ascending angle

//var HHascending = HH.filter(ee.Filter.eq('orbitProperties_pass', 'ASCENDING'));
//var HHdescending = HH.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'));

//Create a composite with the means of different look angles
//var composite = ee.Image.cat([
//  HHascending.select('HH').mean(),
//  ee.ImageCollection(HHascending.select('HH').merge(HHdescending.select('HH'))).mean(),
//  HHdescending.select('HH').mean()
//]).focal_median();
        
//Visualize the composite
//Map.addLayer(composite.clip(roi),{min: [-25, -20], max: [0, 10]}, 'Composite');