This lesson is in the early stages of development (Alpha version)

Reproject Raster Data with Rioxarray


Teaching: 60 min
Exercises: 20 min
  • How do I call a DataArray to print out its metadata information?

  • How do I work with raster data sets that are in different projections?

  • Reproject a raster in Python using rasterio.

  • Accomplish the same task with rioxarray and xarray.

Things You’ll Need To Complete This Episode

See the lesson homepage for detailed information about the software, data, and other prerequisites you will need to work through the examples in this episode.

Sometimes we encounter raster datasets that do not “line up” when plotted or analyzed. Rasters that don’t line up are most often in different Coordinate Reference Systems (CRS), otherwise known as “projections”. This episode explains how to line up rasters in different, known CRSs.

Raster Projection in Python

If you loaded two rasters with different projections in QGIS or ArcGIS, you’d see that they would align since these software reproject “on-the-fly”. But with R or Python, you’ll need to reproject your data yourself in order to plot or use these rasters together in calculations.

For this episode, we will be working with the Harvard Forest Digital Terrain Model (DTM). This differs from the surface model data we’ve been working with so far in that the digital surface model (DSM) includes the tops of trees, while the digital terrain model (DTM) shows the ground level beneath the tree canopy.

Our goal is to get these data into the same projection with the rioxarray.reproject_match() function so that we can use both rasters to calculate tree canopy height, also called a Canopy Height Model (CHM).

First, we need to read in the DSM and DTM rasters.

import rioxarray

surface_HARV = rioxarray.open_rasterio("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif")
terrain_HARV = rioxarray.open_rasterio("data/NEON-DS-Airborne-Remote-Sensing/HARV/DTM/HARV_dtmCrop_WGS84.tif")

xarray.DataArray band: 1, y: 1367, x: 1697
[2319799 values with dtype=float64]
band       (band)   int64    1
 y          (y)    float64   4.714e+06 4.714e+06 ... 4.712e+06
 x          (x)    float64   7.315e+05 7.315e+05 ... 7.331e+05
spatial_ref ()      int64    0
STATISTICS_MAXIMUM: 416.06997680664
STATISTICS_MEAN: 359.85311802914
STATISTICS_MINIMUM: 305.07000732422
STATISTICS_STDDEV: 17.83169335933
_FillValue: -9999.0
scale_factor: 1.0
add_offset: 0.0
grid_mapping: spatial_ref

To read the spatial reference in the output you click on the icon “Show/Hide attributes” on the right side of the spatial_ref row. You can also print the Well-known Text projection string.
'PROJCS["WGS 84 / UTM zone 18N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-75],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32618"]]'

We can see the datum and projection are UTM zone 18N and WGS 84 respectively. UTM zone 18N is a regional projection with an associated coordinate system to more accurately capture distance, shape and/or area around the Harvard Forest.
'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]'

We see the DTM is in an unprojected geographic coordinate system, using WGS84 as the datum and a coordinate system that spans the entire planet (i.e. latitude and longitude). This means that every location on the planet is defined using the SAME coordinate system and the same units. Geographic coordinate reference systems are best for global analysis but not for capturing distance, shape and/or area on a local scale.

We can use the CRS attribute from one of our datasets to reproject the other dataset so that they are both in the same projection. The only argument that is required is the reproject_match argument, which takes the CRS of the result of the reprojection.

terrain_HARV_UTM18 =

xarray.DataArray band: 1, y: 1492, x: 1801
array([[[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.]]])
x            (x)       float64       7.314e+05 7.314e+05 ... 7.332e+05
y            (y)       float64       4.714e+06 4.714e+06 ... 4.712e+06
band       (band)       int64        1
spatial_ref  ()         int64        0
scale_factor: 1.0
add_offset: 0.0
grid_mapping: spatial_ref
_FillValue: -9999.0

In one line reproject_match does a lot of helpful things:

  1. It reprojects terrain_HARV from WGS 84 to UTM Zone 18.
  2. Where terrain_HARV has data values and surface_HARV does not, the result terrain_HARV_UTM18 is clipped. Where surface_HARV has data values and terrain_HARV does not, the result terrain_HARV_UTM18 is padded with no data values to match the extent.
  3. It sets the no data value of terrain_HARV to the no data value for surface_HARV

Code Tip

There also exists a method called reproject(), which only reprojects one raster to another projection. If you want more control over how rasters are resampled, clipped, and/or reprojected, you can use the reproject() method and other rioxarray methods individually.

We can also save our DataArray that we created with rioxarray to a file.

reprojected_path = "data/NEON-DS-Airborne-Remote-Sensing/HARV/DTM/HARV_dtmCrop_UTM18.tif"


Inspect the metadata for terrain_HARV_UTM18 and surface_HARV. Are the projections the same? Are the extents the same? Are the no data values the same? How might projections, extents, and no data values effect calculations we make between arrays?


# view crs for DTM

# view crs for DSM

Good, the CRSs are the same. But, before we plot or calculate both of these DataArrays together, we should make sure they have the same nodata value.

# view noddata value for DTM

# view nodata value for DSM

Furthermore, let’s make sure both of these DataArrays have the same shape (i.e. extent).

# view shape for DTM

# view shape for DSM
(1, 1367, 1697)
(1, 1367, 1697)

The shapes and projections are the same which means these data cover the same locations. The no data values are also the same. This means we can run calculations on these two DataArrays.

Let’s plot our handiwork so far! We can use the xarray.DataArray.plot function to show the DTM. But if we run the following code, something doesn’t look right …


plot of chunk unnamed-chunk-5


Whoops! What did we forget to do to the DTM DataArray before plotting?


Our array has a nodata value, -9999.0, which causes the color of our plot to be stretched over too wide a range. We’d like to only display valid values, so before plotting we can filter out the nodata values using the where() function and the .rio.nodata attribute of our DataArray.

terrain_HARV_UTM18_valid = terrain_HARV_UTM18.where(
    terrain_HARV_UTM18  !=

plot of chunk unnamed-chunk-5 If we had saved terrain_HARV_UTM18 to a file and then read it in with open_rasterio’s masked=True argument, the raster’s nodata value would be masked and we would not need to use the where() function to do the masking before plotting.

Plotting Tip

There are many ways to improve this plot. Matplotlib offers lots of different functions to change the position and appearance of plot elements. To plot with Matplotlib, you need to import the pyplot module. Something that would really improve our figure is adding a title. This can be done with the plt.title() function.

Try importing Matplotlib and adding a title to the figure.

Importing pyplot and adding a title

Here’s how we can use pyplot functions to modify elements in our graph.

import matplotlib.pyplot as plt
plt.title("Harvard Forest Digital Terrain Model")

plot of chunk unnamed-chunk-5

Because xarray has Matplotlib under the hood, we don’t need to modify our original plotting method.

Customizing plots with Matplotlib

Now that you’ve added a title to your plot, look for other ways to customize your plot with Matplotlib. One possible way to quickly customize a plot is with the function. You can check available styles with

Another useful function for the plots we are making is plt.ticklabel_format(style="plain"). This will ensure that our ticks are not truncated, making our plot nicer.

Try customizing your plot with the functions above or any other pyplot parameter.

Styles and formatting

Here is the result of using a ggplot like style for our digital terrain plot."ggplot")
plt.title("Harvard Forest Digital Terrain Model")

plot of chunk unnamed-chunk-5

Notice that comes before and both plt.title() and plt.ticklabel_format come after the .plot() function. This is because is a pyplot wide setting, while the latter two functions apply only to our current figure.

Quick tip: for all following plots in our lesson, use the plt.title and plt.ticklabel_format functions.

Challenge: Reproject, then Plot a Digital Terrain Model

Create 2 maps in a UTM projection of the San Joaquin Experimental Range field site, using theSJER_dtmCrop.tif and SJER_dsmCrop_WGS84.tif files. Use rioxarray and matplotlib.pyplot (to add a title). Reproject the data as necessary to make sure each map is in the same UTM projection and save the reprojected file with the file name “data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmCrop_WGS84.tif”.


If we read in these files with the argument masked=True, then the nodata values will be masked automatically and set to numpy.nan, or Not a Number. This can make plotting easier since only valid raster values will be shown. However, it’s important to remember that numpy.nan values still take up space in our raster just like nodata values, and thus they still affect the shape of the raster. Rasters need to be the same shape for raster math to work in Python. In the next lesson, we will examine how to prepare rasters of different shapes for calculations.

terrain_SJER = rioxarray.open_rasterio("data/NEON-DS-Airborne-Remote-Sensing/SJER/DTM/SJER_dtmCrop.tif", masked=True)
surface_SJER = rioxarray.open_rasterio("data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmCrop_WGS84.tif", masked=True)
reprojected_surface_model =
plt.title("SJER Reprojected Surface Model")"data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmCrop_WGS84.tif")
plt.title("SJER Terrain Model")

plot of chunk unnamed-chunk-5 plot of chunk unnamed-chunk-5

Key Points

  • In order to plot or do calculations with two raster data sets, they must be in the same CRS.

  • rioxarray and xarray provide simple syntax for accomplishing fundamental geospatial operations.

  • rioxarray is built on top of rasterio, and you can use rasterio directly to accomplish fundamental geospatial operations.