Mosaicing images across orbits in one AOI

I am using Sentinel Hub’s Python SDK to download large-scale Sentinel 2/1 imagery. This works great but we still have some issues at the edges of Sentinel orbits, as we are downloading imagery for specific dates as ~5-6 km tiles.

The Python request looks something like this:

image_request = WcsRequest(
            bbox=box, time=dates,
            image_format = MimeType.TIFF,
            maxcc=1., resx='10m', resy='10m',
            custom_url_params = {constants.CustomUrlParam.DOWNSAMPLING: 'BICUBIC',
                                constants.CustomUrlParam.UPSAMPLING: 'BICUBIC'},

Setting time_difference to 48 hours typically allows sentinel-hub to collect images from both orbits, rather than returning one orbit, and no data outside of the orbit bounds.

Here is my setup function in the evalscript, where I have set mosaicking to orbit:

function setup() {
  return {
    input: [{
      bands: [
    output: {
      bands: 4,
      mosaicking: "ORBIT"

This works insofar as all pixels have data but it appears that the images are superimposed, leaving artifacts. Is it possible to average them? (Or perhaps is there a better way to combine them?) I have tried per the example here but with no success: … e.g. this evaluatePixel only returns 0.

function evaluatePixel(samples, scenes, inputMetadata, customData, outputMetadata) {
  //Average value of band B02 based on the requested scenes
  var sumOfValidSamplesB02 = 0
  var sumOfValidSamplesB03 = 0
  var sumOfValidSamplesB04 = 0
  var sumOfValidSamplesB08 = 0
  var numberOfValidSamples = 0
  var factor = 65535
  for (i = 0; i < samples.length; i++) {
    var sample = samples[i]
    if (sample.dataMask == 1){
     sumOfValidSamplesB02 += sample.B02
     sumOfValidSamplesB03 += sample.B03
     sumOfValidSamplesB04 += sample.B04
     sumOfValidSamplesB08 += sample.B08
     numberOfValidSamples += 1
  return [(sumOfValidSamplesB02 / numberOfValidSamples) * factor,
          (sumOfValidSamplesB03 / numberOfValidSamples) * factor,
         (sumOfValidSamplesB04 / numberOfValidSamples) * factor,
         (sumOfValidSamplesB08 / numberOfValidSamples) * factor]

function updateOutputMetadata(scenes, inputMetadata, outputMetadata) {
  outputMetadata.userData = {
    "inputMetadata": inputMetadata
  outputMetadata.userData["orbits"] = scenes.orbits

Solved this! By taking the minimum of each sample rather than the mean.

Great! Happy that you solved it John! Thanks for sharing hoe you solved it with the community! :+1: :clap: