Tile error Google Earth Engine "Some bands might require explicit casts"

Question

I'm getting this error when trying to map the result after calculating the seasonal median NDVI images for a time series with 7 different landsat tiles in Google Earth Engine:

Link to the code : https://code.earthengine.google.com/5ec4decebe224dcb8de73ed8c632c55f

Here is the code I'm using (sorry for the missing semi-colons):

var AOI_ZIM = ee.Geometry.Polygon(
     [[[25.02396766047741,-17.741728052677256],
     [26.56205359797741,-20.059388167694156],
     [28.28690711360241,-20.22441903223353],
     [28.42972937922741,-18.149348821775746],
     [25.02396766047741,-17.741728052677256]
     ]])    
//Image Collection//
var surfaceReflectanceL5 = ee.ImageCollection('LANDSAT/LT05/C01/T1_SR').select(['B3', 'B4', 'pixel_qa'])
  .filterDate('1990-01-01', '2002-12-31')
  .filterBounds(AOI_ZIM)
var surfaceReflectanceL7 = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR').select(['B3', 'B4', 'pixel_qa'])
  .filterDate('1990-01-01', '2002-12-31')
  .filterBounds(AOI_ZIM)
  
var combined_L5L7 = surfaceReflectanceL5.merge(surfaceReflectanceL7)
var filtered_L5L7 = combined_L5L7.filter(ee.Filter.or(ee.Filter.and(ee.Filter.eq('WRS_PATH', 173), ee.Filter.eq('WRS_ROW', 74)), ee.Filter.and(ee.Filter.eq('WRS_PATH', 174), ee.Filter.eq('WRS_ROW', 72))).not())

//Mask clouds and shadows with the QA band//
var cloudMask_L5L7 = function(image) {
  var qa = image.select('pixel_qa') 
  var R_NIR = image.select(['B3', 'B4'])
  var qa_thresholds = qa.bitwiseAnd(1 << 5)
          .and(qa.bitwiseAnd(1 << 6))
          .or(qa.bitwiseAnd(1 << 3))
  return R_NIR.updateMask(qa_thresholds.not())
}
var masked_L5L7 = filtered_L5L7.map(cloudMask_L5L7)

//Compute NDVI collection//
var calculateNDVI = function(scene) {
  var dateString = ee.Date(scene.get('system:time_start')).format('yyyyMMdd').cat('_').cat(ee.String(ee.Number(scene.get('WRS_PATH')).int())).cat(ee.String(ee.Number(scene.get('WRS_ROW')).int())).cat('_ndvi')
  var NDVI_band = scene.normalizedDifference(['B4', 'B3']).multiply(10000).int16()
  var add_band = scene.addBands(NDVI_band)
  return add_band.select('nd').rename(dateString)
}
var NDVIcollection = masked_L5L7.map(calculateNDVI)

//Calculate the median NDVI per season//
var years = ee.List.sequence(1990, 2002) //years
var list_path = ee.List.sequence(171, 173) //tile path ID
var list_row = ee.List.sequence(72, 74) //tile row ID

var stackList = function(collection) {
  var first = ee.Image(collection.get(0)).select([])
  var appendBands = function(image, previous) {
    return ee.Image(previous).addBands(image)
  }
  return ee.Image(collection.iterate(appendBands, first))
}

//Calculate median images
var median_NDVI = ee.ImageCollection.fromImages(years.map(function(y) {
  //filter by season
  var inSeason = NDVIcollection.filter(ee.Filter.calendarRange(y,y,'year')).filter(ee.Filter.calendarRange(10,4,'month'))
  var offSeason = NDVIcollection.filter(ee.Filter.calendarRange(y,y,'year')).filter(ee.Filter.calendarRange(5,9,'month'))
  
  //filter the in-season images by tile
  var path_list_in = function(path) {
    var filter_row_in = list_row.map(function(row) {
      var filter_tile_in = inSeason.filter(ee.Filter.and(ee.Filter.eq('WRS_PATH', path),ee.Filter.eq('WRS_ROW', row)))
      var inSeason_string = ee.String(ee.Number(y).int()).cat('_').cat(ee.String(ee.Number(path).int())).cat(ee.String(ee.Number(row).int())).cat('_inSeason')
      return filter_tile_in.median().rename(inSeason_string)
    })
    return filter_row_in
  }
  var medians_inSeason = list_path.map(path_list_in).flatten()
  var stack_inSeason = stackList(medians_inSeason)
  
  //filter the off-season images by tile
    var path_list_off = function(path) {
    var filter_row_off = list_row.map(function(row) {
      var filter_tile_off = offSeason.filter(ee.Filter.and(ee.Filter.eq('WRS_PATH', path),ee.Filter.eq('WRS_ROW', row)))
      var offSeason_string = ee.String(ee.Number(y).int()).cat('_').cat(ee.String(ee.Number(path).int())).cat(ee.String(ee.Number(row).int())).cat('_offSeason')
      return filter_tile_off.median().rename(offSeason_string)
    })
    return filter_row_off
  }
  var medians_offSeason = list_path.map(path_list_off).flatten()
  var stack_offSeason = stackList(medians_offSeason)
  
  return stack_inSeason.addBands(stack_offSeason).int16()
})) //returns an image collection with 13 images (elements), one per year, with 14 bands (1 band per season (x2) and per tile (x7))

var listMedians_coll = median_NDVI.toList(median_NDVI.size())
var Median_image = ee.Image(listMedians_coll.get(0))
Map.addLayer(Median_image, {bands: '1990_17173_inSeason', min:0, max:10000}, "Median")

Data types are consistent between all bands in the new image collection median_NDVI (signed int16) and between the original NDVIcollection and the new image collection, so I guess the problem must come from the fact that median_NDVI collection images have bands corresponding to different tiles, and therefore not spatially consistent.

What is the proper way to calculate these images?

Answer 1

It's the name that's giving you grief. Try rename()ing with a consistent string.

Answer 2

I got the same error and tried many ways. In the end, the only answer that saved me was that all bands should have the same name.