(GEE) Constant value in Chart after applying scaling factor to LANDSAT

Question

After applying a scaling factor to my LANDSAT collection 2 level 2 data the values seem to be off.

var addNDVI = function(image) {
      var ndvi = image.normalizedDifference(['SR_B4', 'SR_B5']).rename('NDVI');
      return image.addBands(ndvi);
    };


var collection1 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
    .filterBounds(sch)
    .filterDate('2013-01-01', '2023-01-01')
    .select(['SR_B4', 'SR_B5'])
    .map(addNDVI).select('NDVI');

collection1 = collection1.map(
  function(img){
    var rescaled_NDVI = img.select('NDVI').multiply(0.0000275).add(-0.2)
    .rename('NDVI_rescaled')
    return img.addBands(rescaled_NDVI);
  }
);
  
var series1 = ui.Chart.image.seriesByRegion(
    collection1, sch, ee.Reducer.mean(), 'NDVI_rescaled', 30, 'system:time_start', 'label')
        .setChartType('ScatterChart')
        .setOptions({
          title: 'L5_SCH_1984-01-01_1986-12-31',
          vAxis: {title: 'NDVI'},
          lineWidth: 1,
          pointSize: 4,
});

Map.addLayer(sch, {color: 'FF0000'});
print(series1);
//var palette = {min: -1, max: 1, palette: ['blue', 'white', 'green']};
Map.addLayer(collection1);

Answer 1

According to the chart, you seem to be getting a constant value. But that's not true. If you limit the axis, you will see the result clearly. To limit the axis, add viewWindow: {min: -0.20001, max: -0.19999} into vAxis option.

    .setOptions({
      title: 'L5_SCH_1984-01-01_1986-12-31',
      vAxis: {title: 'NDVI', viewWindow: {min: -0.20001, max: -0.19999}}, //HERE
      lineWidth: 1,
      pointSize: 4,

Result:

---

Old answer:
If you want to scale ndvi values [-1, 1] to, for example, [0, 1], use ndvi * 0.5 + 0.5.

var rescaled_NDVI = img.select('NDVI').multiply(0.5).add(0.5)

The result of the line above:

---

To scale a range [min, max] to [a, b] use this formula:

(b - a)(x - min)
----------------- + a
    max - min

The result for conversion [-1, 1] to [0, 1] is ndvi * 0.5 + 0.5. Rescaling formula and additional explanation can be found here.