2

I want to create the LST in GEE for my region. I'd like to analyse differences. I found this code below, but the processed image is just one color. I'm not sure if it's right or if I have to change something for a better result. Therefore I want to export the image for Visualization in QGIS, but I don't get the code right. I get the error: Error: Image.clipToBoundsAndScale, argument 'input': Invalid type. Expected type: Image. Actual type: ImageCollection. (Error code: 3)

https://code.earthengine.google.com/aaa52c0e86905a2ed119cff68508f10a

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('2018-03-01','2021-10-1')
    .filterBounds(geometry)
    .map(function(image){return image.clip(geometry)});

print('coleccion', col);

//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: 1e9
  }).values().get(0));
  
  var max = ee.Number(ndvi.reduceRegion({
    reducer: ee.Reducer.max(),
    geometry: geometry,
    scale: 30,
    maxPixels: 1e9
  }).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 ee.Algorithms.If(min, LST.set('system:time_start', date).float().rename('LST'), 0);

}).removeAll([0]);

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: 1e9
}).values().get(0));

//print('min', min );

var max = ee.Number(ndvi1.reduceRegion({
  reducer: ee.Reducer.max(),
  geometry: geometry,
  scale: 30,
  maxPixels: 1e9
}).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');

print(
      ui.Chart.image.series({
        imageCollection: LST_col, 
        region: geometry, 
        scale: 30, // nominal scale Landsat imagery 
        xProperty: 'system:time_start' // default
      }));

//export NDMI
 
 var export_map = LST_col.select (['LST']);
 
 Export.image.toDrive ({image: export_map, description: 'LST', scale: 20}); ```

1 Answer 1

2

A few things:

First, since the geometry you point to isn't accessible, I used a county in Maine as a stand-in ROI. Second, your "thermal" map layer is all blue. Since your visualization parameters have blue on the low end of the color ramp, that suggests that all of the values for that band are less than the minimum value you specify. Indeed, when I spread the bounds of the visualization parameters by changing min to 200 and max to 400, we can see some variation in the image. So, consider what defines the bounds for b10 * 0.1. Similarly, by changing the range in the LST visualization parameters to [-50,50], we see some variation in the plotted image. As a last preliminary aside, I changed the scale parameter in your plots and exports to 10000 to save file space for people trying to reproduce the issue.

Finally, to the root of your exporting problem, the object LST is an ImageCollection with 108 elements. Export is a function for ee.Images, but not ImageCollections. To export an Image, you need to either reduce your ImageCollection to a single Image, or select a single Image from the ImageCollection. As an example, I added a step to reduce the ImageCollection with a mean reducer (producing a map of "average" LST from the time series). The other alternative is to convert your ImageCollection to one many-banded Image (in this case, 108 bands) using the ee.ImageCollection.toBands() function (also shown below).

var geometry = ee.FeatureCollection('TIGER/2016/Counties')
  .filter(ee.Filter.eq('NAME', 'Waldo'));
print(geometry);
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('2018-03-01','2021-10-1')
    .filterBounds(geometry)
    .map(function(image){return image.clip(geometry)});

print('coleccion', col);

//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: 1e9
  }).values().get(0));
  
  var max = ee.Number(ndvi.reduceRegion({
    reducer: ee.Reducer.max(),
    geometry: geometry,
    scale: 30,
    maxPixels: 1e9
  }).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 ee.Algorithms.If(min, LST.set('system:time_start', date).float().rename('LST'), 0);

}).removeAll([0]);

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: 200, max: 400, 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: 1e9
}).values().get(0));

//print('min', min );

var max = ee.Number(ndvi1.reduceRegion({
  reducer: ee.Reducer.max(),
  geometry: geometry,
  scale: 30,
  maxPixels: 1e9
}).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: -50, max: 50, 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');

print(
      ui.Chart.image.series({
        imageCollection: LST_col, 
        region: geometry, 
        scale: 10000, // nominal scale Landsat imagery 
        xProperty: 'system:time_start' // default
      }));

//export NDMI
 
var export_Collection = LST_col.select (['LST']).toBands();
// As a "flattened" image
print("export_Collection map", export_Collection);
Export.image.toDrive ({
   image: export_Collection, 
   description: 'LST_collection', 
   scale: 10000}); 
// As a reduced Image
var export_Image = LST_col.reduce(ee.Reducer.mean());
print("export_Image map", export_Image);
Export.image.toDrive ({
   image: export_Image, 
   description: 'LST_image', 
   scale: 10000}); 

 

Here's the "thermal" image: Thermal map

And here's "LST": LST map

5
  • Thanks for the improvements to the code. I have still a problem with the image of the mean values. Instead of the computed image just an image with 4 big pixels in the whole area is created.
    – L.J.E.
    Jan 25, 2022 at 19:39
  • 1
    Seems like you didn't adjust the scale parameter. I set scale to 10000 for ease of export and preliminary processing; if you want finer-scale data use e.g. 30 (beware of setting a finer scale than the raw data) Jan 25, 2022 at 20:02
  • thanks. that was it :)
    – L.J.E.
    Jan 25, 2022 at 21:28
  • I tried to modified the code for creating a LST map with the median values and a chart with the median values. But it did'nt work. I also want to export the chart with the median values direct to google Drive. How to manage? code.earthengine.google.com/…
    – L.J.E.
    Jan 27, 2022 at 10:55
  • Sounds like it’s time to make a new question Jan 27, 2022 at 16:33

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