Image.constant: Parameter 'value' is required

Question

I am trying to calculate LST, for the time series, but it showing this error, "Image.constant: Parameter 'value' is required." My code link https://code.earthengine.google.com/d371816ad8d8307b8c632d0b3b13dec8

Map.centerObject(geometry);

//cloud mask
function maskL8sr(col) {
  // Bits 3 and 5 are cloud shadow and cloud, respectively.
  var cloudShadowBitMask = (1 << 3);
  var cloudsBitMask = (1 << 5);
  // Get the pixel QA band.
  var qa = col.select('pixel_qa');
  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
                 .and(qa.bitwiseAnd(cloudsBitMask).eq(0));
  return col.updateMask(mask);
}

//vis params
var vizParams = {
  bands: ['B5', 'B6', 'B4'],
  min: 642,
  max: 3307,
  gamma: [1, 0.9, 1.1]
};

var vizParams2 = {
  bands: ['B4', 'B3', 'B2'],
  min: 0,
  max: 3000,
  gamma: 1.4,
};

//load the collection:
var col = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
    .map(maskL8sr)
    .filterDate('2020-01-01','2020-12-31')
    .filterBounds(geometry)
    .map(function(image){return image.clip(geometry)});

print('collection', col);

var ndvi_func = function (image) {
  var ndvi_cal = image.normalizedDifference(['B5', 'B4']).rename('NDVI');
  return image.addBands(ndvi_cal);
};

var ndvi = col.map(ndvi_func);
var ndvi_chart=ndvi.select('NDVI')
print(ndvi)
var reduced = ndvi.map(function(image){
  return image.reduceRegions({
    collection:geometry , 
    reducer:ee.Reducer.mean(), 
    scale: 30,
  });
})
//imagen reduction

var image = col.median();
//print('image', image);

Map.addLayer(image, vizParams2);

//median
var ndvi1 = image.normalizedDifference(['B5', 'B4']).rename('NDVI');
var ndviParams = {min: 0.10554729676864096, max: 0.41295681063122924, palette: ['blue', 'white', 'green']};

//print('ndvi1', ndvi1);

//individual LST images

var col_list = col.toList(col.size());

var LST_col = col_list.map(function (ele) {
  
  var date = ee.Image(ele).get('system:time_start');

  var ndvi = ee.Image(ele).normalizedDifference(['B5', 'B4']).rename('NDVI');
  
  // find the min and max of NDVI
  var min = ee.Number(ndvi.reduceRegion({
    reducer: ee.Reducer.min(),
    geometry: geometry,
    scale: 30,
    maxPixels:19562870,
    bestEffort: true
  }).values().get(0));
  
  var max = ee.Number(ndvi.reduceRegion({
    reducer: ee.Reducer.max(),
    geometry: geometry,
    scale: 30,
    maxPixels:19562870,
   bestEffort: true
  }).values().get(0));
  
  var fv = (ndvi.subtract(min).divide(max.subtract(min))).pow(ee.Number(2)).rename('FV');
  
  var a= ee.Number(0.004);
  var b= ee.Number(0.986);
  
  var EM = fv.multiply(a).add(b).rename('EMM');

  var image = ee.Image(ele);

  var LST = image.expression(
    '(Tb/(1 + (0.00115* (Tb / 1.438))*log(Ep)))-273.15', {
      'Tb': image.select('B10').multiply(0.1),
      'Ep': fv.multiply(a).add(b)
  });

  return LST.set('system:time_start', date).float().rename('LST');
  
});

LST_col = ee.ImageCollection(LST_col);

print("LST_col", LST_col);

/////////////////

Map.addLayer(ndvi1, ndviParams, 'ndvi');

//select thermal band 10(with brightness tempereature), no calculation 
var thermal= image.select('B10').multiply(0.1);

var b10Params = {min: 306.4, max: 322.8, palette: ['blue', 'white', 'green']};

Map.addLayer(thermal, b10Params, 'thermal');

// find the min and max of NDVI
var min = ee.Number(ndvi1.reduceRegion({
  reducer: ee.Reducer.min(),
  geometry: geometry,
  scale: 30,
  maxPixels:19562870,
  bestEffort: true
}).values().get(0));

//print('min', min );

var max = ee.Number(ndvi1.reduceRegion({
  reducer: ee.Reducer.max(),
  geometry: geometry,
  scale: 30,
  maxPixels:19562870,
  bestEffort: true
}).values().get(0));

//print('max', max);

//fractional vegetation
var fv = ndvi1.subtract(min).divide(max.subtract(min)).pow(ee.Number(2)).rename('FV');
 

//print('fv', fv);

//Map.addLayer(fv);

//Emissivity
var a= ee.Number(0.004);
var b= ee.Number(0.986);
var EM = fv.multiply(a).add(b).rename('EMM');

var imageVisParam3 = {min: 0.9865619146722164, max:0.989699971371314};

//Map.addLayer(EM, imageVisParam3,'EMM');

//LST in Celsius Degree bring -273.15
//NB: In Kelvin don't bring -273.15
var LST = col.map(function (image){

  var date = image.get('system:time_start');
  
  var LST = image.expression(
    '(Tb/(1 + (0.00115* (Tb / 1.438))*log(Ep)))-273.15', {
    'Tb': thermal.select('B10'),
    'Ep': EM.select('EMM')
  }).float().rename('LST');
  
  return LST.set('system:time_start', date);
  
});

print(LST);

Map.addLayer(LST, {min: 34.22748947143555, max: 50.7606086730957, 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');

// merge the collections
var merged = LST_col.merge(ndvi_chart);
// plot the collection
var chart = ui.Chart.image.series({
  imageCollection: merged,
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 500
})
print(chart)
// Export the masked image as a GeoTIFF file
Export.image.toDrive({
  image: LST.mean().clip(geometry),
  description: 'LST',
  folder: 'GEE_Export',
  scale: 30,
  region: geometry,
  maxPixels:1e13
});
Export.image.toDrive({
  image: ndvi.select('NDVI').mean().clip(geometry),
  description: 'NDVI',
  folder: 'GEE_Export',
  scale: 30,
  region: geometry,
  maxPixels:1e13
});

Answer 1

The issue is that reduceRegion() returns null for some images. All pixels in your geometry are probably masked. One option to workaround this is to filter out images with null min/max.

var col = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
    .map(maskL8sr)
    .filterDate('2020-01-01','2020-12-31')
    .filterBounds(geometry)
    .map(addNdvi)
    .filter(ee.Filter.notNull(['NDVI_min'])) // Remove images where you have no min/max
    .map(toLST)


function addNdvi(image) {
  var ndvi = image
    .normalizedDifference(['B5', 'B4'])
    .rename('NDVI')
  var minMaxNdvi = ndvi.reduceRegion({
    reducer: ee.Reducer.minMax(),
    geometry: geometry,
    scale: 30,
    maxPixels:19562870,
    bestEffort: true
  })
  return image
    .addBands(ndvi)
    .set(minMaxNdvi)
}


function toLST(image) {
  var min = image.getNumber('NDVI_min')
  var max = image.getNumber('NDVI_max')
  var fv = image.select('NDVI')
    .subtract(min)
    .divide(max.subtract(min))
    .pow(ee.Number(2))
    .rename('FV')
  var a = ee.Number(0.004)
  var b = ee.Number(0.986)
  var EM = fv.multiply(a).add(b).rename('EMM')
  var LST = image.expression(
    '(Tb/(1 + (0.00115* (Tb / 1.438))*log(Ep)))-273.15', {
      'Tb': image.select('B10').multiply(0.1),
      'Ep': fv.multiply(a).add(b)
  })
  return LST
    .set('system:time_start', image.get('system:time_start'))
    .float()
    .rename('LST')  
}

https://code.earthengine.google.com/dcdf47573bd90401522e7d8e70b17d11

Other general efficiency tips:

• Minimize the number of reduceRegion() calls. In this specific case, you can use ee.Reducer.minMax(). In other cases, you can use ee.Reducer.combine().
• Try to avoid turning collections into lists.
• Only clip() when you really need to, typically at the end of your processing script when you create the image to put on a map or export.