I took two datasets – WorldCover 2020 for builtup (100m), and MODIS for LST (1km).

I then aggregated the builtup data to 1km resolution as given here.

Then I defined a threshold of 100,000 for the builtup and masked out the corresponding values for the LST image.

The problem is that after performing reduceRegion operation on this masked LST image, using a combined reducer, the values are shown as NULL.

What could I be doing wrong?

Following is the code:

var buffer = 
    /* color: #d63000 */
    /* displayProperties: [
      {
        "type": "rectangle"
      }
    ] */
    ee.Geometry.Polygon(
        [[[77.3771407683881, 13.160658695038297],
          [77.3771407683881, 12.795327445635392],
          [77.78500820002873, 12.795327445635392],
          [77.78500820002873, 13.160658695038297]]], null, false);



/* ----  Builtup image (WorldCover 2020) ---- */

var builtup = ee.ImageCollection('ESA/WorldCover/v100')
              .first()
              .clip(buffer);

// Show only the builtup class
var mask = builtup.eq(50)

builtup = builtup.updateMask(mask)


var projection = ee.Image(builtup).projection()

print(projection.nominalScale())

// Get the projection at required scale
var projectionAt100m = projection.atScale(100)
var projectionAt1k = projection.atScale(1000)
 
// Step1: 10m to 100m
var builtupAt100m = builtup
  .reduceResolution({
    reducer: ee.Reducer.sum().unweighted(),
    maxPixels: 1024
  })
  // Request the data at the scale and projection
  // of reduced resolution
  .reproject({
    crs: projectionAt100m
  });
  
// Step2: 100m to 1000m
var builtupAt1k = builtupAt100m
  .reduceResolution({
    reducer: ee.Reducer.sum().unweighted(),
    maxPixels: 1024
  })
  // Request the data at the scale and projection
  // of reduced resolution
  .reproject({
    crs: projectionAt1k
  });

var max_builtup = builtupAt1k
          .reduceRegion({
            reducer: ee.Reducer.max(),
            maxPixels: 1e4
          })


var min_builtup = builtupAt1k
          .reduceRegion({
            reducer: ee.Reducer.min(),
            maxPixels: 1e4
          })

print('max builtup', max_builtup)
print('min builtup', min_builtup)


var viz_builtup = {
  bands: ['Map'],
  min: 50,
  max: 670150,
  palette: ['cyan', 'red']
};




/* ----  Land surface Temperature image (MODIS) ---- */


function applyScaleFactors(img){
    
    img = img.multiply(0.02).subtract(273.15)
          .copyProperties(img, ['system:time_start','system:time_end','system:index'])
  
    return img

}

var lst = ee.ImageCollection('MODIS/061/MOD11A1')
          .select('LST_Day_1km')
          .filter(ee.Filter.date('2020-03-01', '2020-05-31'))
          .map(applyScaleFactors)
          .mean()
          .clip(buffer);
          
var mask_gte = builtupAt1k.gte(100000)
var mask_lt = builtupAt1k.lt(100000)

var masked_lst_gte = lst.updateMask(mask_gte)
var masked_lst_lt = lst.updateMask(mask_lt)


var reducers = ee.Reducer.mean().combine({
  reducer2: ee.Reducer.max(),
  sharedInputs: true
}).combine({
  reducer2: ee.Reducer.percentile([95]),
  sharedInputs: true
});


var reduced_lst_gte = masked_lst_gte.reduceRegion({
                        reducer: reducers
                      })

var reduced_lst_lt = masked_lst_lt.reduceRegion({
                        reducer: reducers
                     })
                
print('reducer for >= threshold', reduced_lst_gte)
print('reducer for < threshold', reduced_lst_lt)

The outputs for the last two print functions are as shown below: