I want to create a correlation chart between LST and NDVI (with trendlines and R2 value inside the chart)for an area in Google Earth Engine (GEE). I tried many solutions (adding both images to image collection, merging them). But nothing seems to work.

I am a newbie to GEE.

The code is as follows

//Landsat 5 Images as L5
// Study Region as roi
var image = L5.filterBounds(roi)

// LST Calculation
var radiance = image.expression(    //Radiance
  '((15.303-1.238)/(255-1)*(B6-1)+1.238)', {
    B6: image.select('B6')
var LST = image.expression(         //At Sensor Brightness
  '(K2 / (2.303 * (log10(K1 / L)))+ 1) -273.15', {
    K1: 607.76,       //Calibration Constant 1
    K2: 1260.56,     //Calibration Constant 2
    L: radiance
// Add LST to Map
Map.addLayer(LST, {min: 10, max: 40, palette: ['green','blue', 'orange','red']}, 'LST')

// Mean LST
var meanLST = LST.reduceRegion({
  reducer: ee.Reducer.mean(),
  geometry: roi.geometry(),
  scale: 30,
print('Mean LST:', meanLST)

// NDVI Calculation
var ndvi = image.normalizedDifference(['B4', 'B3'])

//Add NDVI to Map
Map.addLayer(ndvi, {min: -1, max: 1, palette: ['red','orange','yellow','green']}, 'NDVI')

// Mean NDVI
var meanndvi = ndvi.reduceRegion({
  reducer: ee.Reducer.mean(),
  geometry: roi.geometry(),
  scale: 30,
print('Mean NDVI:', meanndvi)

// Need to Create Correlation Plot (LST vs NDVI) with trendlines and R2 values inside the chart
  • Here says, R squared value can not be shown in the graph. For correlation chart, you can checkout this question.
    – Padmanabha
    Jul 22, 2021 at 13:00

1 Answer 1


This could be one approach: First do the linear regression with reduceRegion(), to get the coefficients of your trend line and R2. Combine your NDVI and LST into a single image and sample it. Map over the samples and create four features for each. One with the observation, one with the model value, one with the model value + n x R2 and finally one with the model value - n x R2. Assign a series property for each of these. Finally, do a scatter plot of these, grouping into series by the series property.


var reduced = ee.Image(1).rename('constant')
    reducer: ee.Reducer.linearRegression(2, 1),
    geometry: roi,
    scale: 30,
    bestEffort: true,
    maxPixels: 10
var coefficients = ee.Array(reduced.get('coefficients'))
var offset = coefficients.get([0, 0])
var slope = coefficients.get([1, 0])
var r2 = ee.Array(reduced.get('residuals')).get([0])
var errors = 3 // How many r2 away to highlight

var samples = ndvi
  .select(['ndvi', 'lst'])
    region: roi,
    scale: 30,
    numPixels: 500,
    geometries: true
  .map(function (sample) {
    var ndvi = sample.getNumber('ndvi')
    var model = ndvi.multiply(slope).add(offset)
    var upper = model.add(r2.multiply(errors))
    var lower = model.subtract(r2.multiply(errors))
    return ee.FeatureCollection([
      sample.set('series', 'observations'),
      ee.Feature(sample.geometry(), {
        ndvi: ndvi,
        lst: upper,
        series: 'upper'
      ee.Feature(sample.geometry(), {
        ndvi: ndvi,
        lst: model,
        series: 'model'
      ee.Feature(sample.geometry(), {
        ndvi: ndvi,
        lst: lower,
        series: 'lower'

var chart = ui.Chart.feature
    features: samples, 
    xProperty: 'ndvi', 
    yProperty: 'lst', 
    seriesProperty: 'series'
    // Order of seeries seems to be the order they are encountered
    // So controlled when creating the FeatureCollection
    series: {
      0: {pointSize: 1, color: 'blue'}, // observations
      1: {lineWidth: 1, color: 'lightgray', pointSize: 0}, // upper
      2: {lineWidth: 1, color: 'red', pointSize: 0}, // model
      3: {lineWidth: 1, color: 'lightgray', pointSize: 0} // lower

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