Corrija o seguinte erro no script do google earth engine:
o Erro:

Line 35: Required argument (kernel) missing to function: Image.reduceNeighborhood(image, reducer, kernel, inputWeight, skipMasked, optimization)

Applies the given reducer to the neighborhood around each pixel, as determined by the given kernel. If the reducer has a single input, it will be applied separately to each band of the collection; otherwise it must have the same number of inputs as the input image has bands.
The reducer output names determine the names of the output bands: reducers with multiple inputs will use the output names directly, while reducers with a single input will prefix the output name with the input band name (e.g., '10_mean', '20_mean').
Reducers with weighted inputs can have the input weight based on the input mask, the kernel value, or the smaller of those two.

Args:

  image (Image): The input image.
  reducer (Reducer): The reducer to apply to pixels within the neighborhood.
  kernel (Kernel): The kernel defining the neighborhood.
  inputWeight (String, optional): One of 'mask', 'kernel', or 'min'.
  skipMasked (Boolean, optional): Mask output pixels if the corresponding input pixel is masked.
  optimization (String, optional): Optimization strategy. Options are 'boxcar' and 'window'. The 'boxcar' method is a fast method for computing count, sum or mean. It requires a homogeneous kernel, a single-input reducer and either MASK, KERNEL or no weighting. The 'window' method uses a running window, and has the same requirements as 'boxcar', but can use any single input reducer. Both methods require considerable additional memory.

O script: 

// Function to mask clouds using the quality band of Landsat 8.
var maskL8 = function (image) {
  // Bit 0 - Fill
  // Bit 1 - Dilated Cloud
  // Bit 2 - Unused
  // Bit 3 - Cloud
  // Bit 4 - Cloud Shadow
  var qaMask = image.select('QA_PIXEL')
  //.eq(21824)
  .bitwiseAnd(parseInt('11111', 2)).eq(0);
  var saturationMask = image.select('QA_RADSAT').eq(0);

  // Apply the scaling factors to the appropriate bands.
  var opticalBands = image.select('B.')//.multiply(0.0000275).add(-0.2);
  //var thermalBand = image.select('ST_B.*').multiply(0.00341802).add(149.0);

  // Replace the original bands with the scaled ones and apply the masks.
  return image.addBands(opticalBands, null, true)
      //.addBands(thermalBand, null, true)
      .updateMask(qaMask)
      .updateMask(saturationMask);
}

var kernel = ee.Kernel.square({
  radius: 90, 
  units: 'meters'
});

var pansharpen1 = function(scene, kernel) {
  // Compute the per-pixel means of the unsharpened bands.
  var bgr = scene.select(['B7', 'B5', 'B4']);
  var pan = scene.select('B8');
  var bgr_mean = bgr.reduce('mean').rename('mean');
  // Compute the aggregate mean of the unsharpened bands and the pan band.
  var mean_values = pan.addBands(bgr_mean).reduceNeighborhood({
    reducer: ee.Reducer.mean(), 
    kernel: kernel,
  });
  var gain = mean_values.select('mean_mean').divide(mean_values.select('B8_mean'));
  return bgr.divide(bgr_mean).multiply(pan).multiply(gain);
};


// Map the function over one year of Landsat 8 TOA data and take the median.
var L8 = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA")
    .filterDate('2024-01-01', '2024-12-31')
    .filterBounds(geometry)
    .map(maskL8)
    .map(pansharpen1)
    
var L9 = ee.ImageCollection("LANDSAT/LC09/C02/T1_TOA")
    .filterDate('2024-01-01', '2028-12-31')
    .filterBounds(geometry)
    .map(maskL8)
    .map(pansharpen1)

var composite = L8.merge(L9)
    
print(composite, 'composite')
    


var sharp1 = pansharpen1(composite, kernel);
Map.addLayer(sharp1, {bands: ['B4', 'B3', 'B2'], max: 0.3}, 'sharp1');