I am exporting imagery from Google Earth Engine and the exported data are not aligned properly in space. I tried defining the export crs as per Nicholas Clinton's advice [here][1] but to no avail. I discovered the issue after exporting both the raster and vector data and projecting simultaneously in R. They do not align (note that the vector ROI drops off the lower-left side of the plotting window):

[![VectorRasterMismatch][2]][2]

I verified that it is not a problem with the vector data by opening it separately in Google Earth Pro - the vector data are projected fine. And I believe the actual data in the raster are correct; low-quality pixels are unmasked with the value set to 255, and we can clearly see the outline of Mono Lake, which is the focus of this toy ROI. Thus, I am led to believe that the issue is in the proj information for the raster dataset.

Reproducible example (first the GEE data acquisition and export):

    //// ROI
    var Sample = ee.Geometry.Polygon([
      [[-119.22, 38.06], [-119.23, 37.97], [-119.01, 37.91], 
      [-118.89, 37.93], [-118.85, 38.02], [-119.01, 38.09],
      [-119.22, 38.06]]
    ]);
    
    // Generate Region of Interest
    var ROI = ee.FeatureCollection(Sample);
    
    // visualize
    Map.centerObject(ROI, 7); // Center on the Grand Canyon.
    Map.addLayer(ROI, {color: '6a0dad', opacity:0.99}, 'ROI');
    
    //// Data filters
    // Create a QA mask + clipping function
    var masker = function(image){ 
      var mask = image.select('NDSI_Snow_Cover_Basic_QA').lte(1);
      var maskedImage = image.updateMask(mask);
      return maskedImage.unmask(255).clip(ROI);
    };
    
    //// Acquire data
    // Compile the data
    var dataset = ee.ImageCollection('MODIS/006/MOD10A1')
                      .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
                      .map(masker);
    print("MOD10A1 Image Collection", dataset);
    
    // Select the data you're interested in
    var ndsi = dataset.select('NDSI_Snow_Cover');
    print("MOD10A1 NDSI Collection",ndsi);
    
    // Plot the data
    Map.addLayer(ndsi, {}, 'NDSI');
    
    
    //// PREPARE DATA FOR EXPORT
    // Thanks Tyler Erickson for the stacking function
    // https://gis.stackexchange.com/a/254778/67264
    // The code below is slightly modified from Erickson's
    // approach in order to rename using dateString in
    // stackCollection function
    var stackCollection = function(collection) {
      // Create an initial image.
      var first = ee.Image(collection.first()).select([]);
    
      // Write a function that appends a band to an image.
      var appendBands = function(image, previous) {
          var dateString = ee.Date(image.get('system:time_start')).format('yyyy-MM-dd');
          return ee.Image(previous).addBands(image.rename(dateString));
      };
      return ee.Image(collection.iterate(appendBands, first));
    };
    var ndsi_img = stackCollection(ndsi);
    print("NDSI stacked collection", ndsi_img);
    
    
    //// Export the data
    // Export table for plotting
    Export.table.toDrive({
      collection: ROI,
      description: 'SampleROI',
      folder: "AlignmentTroubleshooting",
      fileFormat: 'SHP'
    });
    
    // Export imagery
    // Specify a crs as per Nicholas Clinton's advice
    // https://gis.stackexchange.com/a/257647/67264
    // Export a cloud-optimized GeoTIFF.
    // See https://developers.google.com/earth-engine/exporting
    Export.image.toDrive({
      image: ndsi_img,
      description: 'NDSI_2019',
      folder: "AlignmentTroubleshooting",
      scale: 500,
      region: ROI,
      crs: ndsi_img.select(0).projection(),
      fileFormat: 'GeoTIFF',
      formatOptions: {
        cloudOptimized: true
      }
    });

Reproducible part II (R import and plotting):

    library(raster)
    library(rgdal)
    
    # Raster
    ras = raster::stack("NDSI_2019.tif")
    proj4string(ras)
    
    # ROI
    roi = readOGR(".",
                  "SampleROI")
    proj4string(roi)
    roi = spTransform(roi, CRSobj = crs(ras))
    
    # Plot
    plot(ras[[150]])
    plot(roi, add = T)


  [1]: https://gis.stackexchange.com/a/257647/67264
  [2]: https://i.sstatic.net/xsiZc.jpg