Extracting pixel values from points and lines using Google Earth Engine JavaScript API

Question

I am quite new to both Google Earth Engine and JavaScript. I have an LST image for all the regions. I want to extract LST pixels from east to west and from north to south around POI. Where the black line indicates in the figure. (in the range of 5-10 km)

My goal is to draw the following graph with the extracted pixels.

How do I do it using the Google Earth Engine JavaScript API.

Here is my code so far.

Map.centerObject(py_point, 9.5);

// Select Landsat 8 TOA image (CLOUD_COVER_LAND is less than 5%)
var col_l8 = ee.ImageCollection(l8_TOA
  .filterBounds(py_point)
  .aside(print)
  .filterDate('2019-01-01', '2019-12-31')
  .filterMetadata('CLOUD_COVER', 'less_than', 5)
  .select(['B4', 'B5', 'B10'])); // 2019-05, 2019-09
print(col_l8, 'l8_2019');

// Define a function that will add an NDVI band to a Landsat 8 image.
var addNDVI = function(image) {
  var NDVI = image.normalizedDifference(['B5', 'B4']).rename('NDVI');
  return image.addBands(NDVI);
};

// Add an NDVI band 
var ndviAdded_2019 = ee.ImageCollection(col_l8
  .map(addNDVI));
print(ndviAdded_2019.first(), 'ndvi_2019');

// Emissivity

var addEM = function emissivity(image){
  var ndvi = image.select('NDVI');
  var e1 = ee.Number(0.995);
  var e2 = ee.Number(0.97);
  var e3 = image.expression(
    '1.0094 + 0.047*(log(NDVI))', {
      'NDVI': image.select(['NDVI'])
    });
  var e4 = ee.Number(0.99);


  var emis1 =  ee.Image(ndvi.lt(-0.185)).multiply(e1)
    .rename('emis1');
  var emis2 =  ee.Image(ndvi.gte(-0.185)).and(ndvi.lt(0.157)).multiply(e2)
    .rename('emis2');  
  var emis3 =  ee.Image(ndvi.gte(0.157)).and(ndvi.lt(0.727)).multiply(e3)
    .rename('emis3');
  var emis4 =  ee.Image(ndvi.gt(0.727)).multiply(e4)
    .rename('emis4');

    
  var em = image.expression(
    'emis1 + emis2 + emis3 + emis4', {
      'emis1': emis1.select('emis1'),
      'emis2': emis2.select('emis2'),
      'emis3': emis3.select('emis3'),
      'emis4': emis4.select('emis4')
  }).rename('EM');
  
  return image.addBands(em);
}

// Add an EM band 
var emAdded_2019 = ee.ImageCollection(ndviAdded_2019
  .map(addEM));
  
print(emAdded_2019.first(), 'em_2019');

// Calculate Emissivity Pow
var addEmPow = function(image){
  var pow = image.select('EM').pow(ee.Number(0.25))
  .rename('EM_P');
    
  return image.addBands(pow);
};

// Add an EM_P band 
var empAdded_2019 = ee.ImageCollection(emAdded_2019
  .map(addEmPow));
print(empAdded_2019.first(), 'emp_2019');

// Calculate LST(Celcius)
var addLST = function(image) {
  var lst = image.expression(
    '(T * EM_P) - 273.15', {
      'T' : image.select(['B10']),
      'EM_P': image.select(['EM_P'])}
    ).rename('LST');
  return image.addBands(lst); 
};
                         
var lstAdded_2019 = ee.ImageCollection(empAdded_2019
  .map(addLST));
print(lstAdded_2019.first(), 'lst_2019');

Map.addLayer(lstAdded_2019.first().select('LST'), {
  min: 13, 
  max: 35, 
  palette: ['040274', '040281', '0502a3', '0502b8', '0502ce', '0502e6',
            '0602ff', '235cb1', '307ef3', '269db1', '30c8e2', '32d3ef',
            '3be285', '3ff38f', '86e26f', '3ae237', 'b5e22e', 'd6e21f',
            'fff705', 'ffd611', 'ffb613', 'ff8b13', 'ff6e08', 'ff500d',
            'ff0000', 'de0101', 'c21301', 'a71001', '911003']
},
'LST');

https://code.earthengine.google.com/3f06a4423337c51894dc1b4eebe63ad3?accept_repo=users%2Fstacemaples%2FSGC-EE101

Answer 1

Here's a stab at solving this. For the east-west case (north-south is essentially the same): An image where each pixel represent the distance to the point is combined with the LST band, pixels not on the east-west line are masked out, and features with distance and LST are created for every non-masked out pixel.

var image = lstAdded_2019.first().select('LST')

var eastWestFeatures = extractEastWestFeatures(image, py_point, 5000)
var eastWestChart = ui.Chart.feature.byFeature({
  features: eastWestFeatures.sort('distance'), 
  xProperty: 'distance', 
  yProperties: ['LST']
})
print('east-west', eastWestFeatures)
print(eastWestChart)

var northSouthFeatures = extractNorthSouthFeatures(image, py_point, 5000)
var northSouthChart = ui.Chart.feature.byFeature({
  features: northSouthFeatures.sort('distance'), 
  xProperty: 'distance', 
  yProperties: ['LST']
})
print('north-south', northSouthFeatures)
print(northSouthChart)


function extractNorthSouthFeatures(image, point, radius) {
  var lon = point.coordinates().getNumber(0)
  var northSouthLine = ee.Geometry.LineString({
    coords: [[lon, -90], point.coordinates(), [lon, 90]]
  })
  return extractFeatures(image, point, radius, northSouthLine)
}


function extractEastWestFeatures(image, point, radius) {
  var lat = point.coordinates().getNumber(1)
  var eastWestLine = ee.Geometry.LineString({
    coords: [[-180, lat], point.coordinates(), [180, lat]], 
    geodesic: false
  })
  return extractFeatures(image, point, radius, eastWestLine)  
}


function extractFeatures(image, point, radius, geometry) {
  var mask = geometryToMask(geometry)
  var distance = distanceToGeometry(point)
  return ee.FeatureCollection(distance
    .addBands(image)
    .updateMask(mask)
    .reduceRegion({
      reducer: ee.Reducer.toCollection(ee.List(['distance']).cat(image.bandNames())), 
      geometry: point.buffer(radius, 30), 
      scale: 30, 
      maxPixels: 1e13
    })
    .get('features')
  )
}


function distanceToGeometry(geometry) {
  var pointMask = geometryToMask(geometry)
  var distancePixels = pointMask.fastDistanceTransform(1024)
    .sqrt() // fastDistanceTransform() return squared distance
  return distancePixels.multiply(ee.Image.pixelArea().sqrt()) // Convert to meters
}


function geometryToMask(geometry) {
  // Hack to convert a geometry to an image mask.
  // We don't care about the image values produced by reduceToImage(),
  // so we just add a 0 property and pick the first one when reducing.
  // Is there a cleaner way to do this?
  return ee.FeatureCollection([ee.Feature(geometry, {_: 0})])
    .reduceToImage(['_'], ee.Reducer.first())
    .mask()  
}

https://code.earthengine.google.com/59eea3a94950885281dae6b615c13914?accept_repo=users%2Fstacemaples%2FSGC-EE101