Losing data when add a raster to a geocube

I don’t know if it is a bug or my misunderstanding, but I’m following this example without fortune.

vector_in = geopandas.read_file(input_vector)
raster_in = rioxarray.open_rasterio(input_raster)

out_grid = make_geocube(
    vector_data=vector_in,
    measurements=["id"],
    like=raster_in,
)

When I use out_grid["process_value"] = raster_in and later out_grid.to_netcdf('mydata.nc') I see that the output has a lot of nodata. But neither vector_in or raster_in have this amount of nodata.

Here there is a QGIS project with my data.

[snowman2]

I was able to reproduce the issue you demonstrated. I think it has to do with decimal precision of the coordinates when adding the DataArray to the Dataset. That being said, I checked that the x coordinate and y coordinates were the exact same for raster_in and out_grid. This is likely an issue with xarray and not geocube.

For a workaround, this got it working without the gaps for me:

import geopandas
import rioxarray
from geocube.api.core import make_geocube

vector_in = geopandas.read_file("vector_data.shp")
raster_in = rioxarray.open_rasterio("raster_data.tif", masked=True).sel(band=1).drop("band")

out_grid = make_geocube(
    vector_data=vector_in,
    measurements=["id"],
    like=raster_in,
)

out_grid["process_value"] = (raster_in.dims, raster_in.values, raster_in.attrs, raster_in.encoding)

[snowman2]

Are you sure that there isn’t a bug inside your code?

Will need to dig into it later to see what is going on. Thanks for the images, that is helpful.