Processing large scale satellite imagery with openEO Platform and R

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Summary: openEO is an open source, community-based API for cloud-based processing of Earth Observation data. This blog introduces the R openeo client, and demonstrates a sample analysis using the openEO Platform for processing.


OpenEO is an open source project that tries to make large-scale, cloud-based satellite image processing easier and more open. This blog post shows how the openEO client, available from CRAN, can be used to explore datasets, compute on them, and download results.

Sample session

An openEO session is started with loading the library, connecting to a back-end (here:, and authenticating with the back-end:

con = connect("")

The login will prompt for an authentication ID which can come from your organisation, or an openID-based mechanism such as google or GitHub. For loggin on you need an account on the back-end, as cloud computing in general is not a free resource. The website has instructions on how to apply for a (ESA-) sponsored account with limited compute credits.

When these commands were carried out, and you are using RStudio as an IDE, RStudio will show an overview of the image collections available on this backend, which looks like this:

and which can help search for a particular collection. Image collections are collections of satellite images that are all processed in a uniform way. The set of image collections can also be obtained programmatically by

collections = list_collections(con)
names(collections) |> head()



## [1] 77

which can then be further processed

We will work with Sentinel level 2A data, available in the collection

collection = "SENTINEL2_L2A"
coll_meta = describe_collection(collection)

##  [1] "cube:dimensions" "description"     "extent"          "id"             
##  [5] "keywords"        "license"         "links"           "providers"      
##  [9] "stac_extensions" "stac_version"    "summaries"       "title"

information about the names and extents of data cube dimensions is for instance obtained by


## Dimension:    bands 
## Type:         bands 
## Values:       [B01,B02,B03,B04,B05,B06,B07,B08,B8A,B11,B12,SCL,relativeAzimuthAngles,sunZenithAngles,viewZenithAngles,B09,AOT,SNW,CLD,CLP,CLM,sunAzimuthAngles,viewAzimuthMean,viewZenithMean,dataMask]
## Dimension:    t 
## Type:         temporal 
## Extent:       [2015-07-06T00:00:00Z,NULL] 
## Dimension:    x 
## Type:         spatial 
## Axis:         x 
## Extent:       [-180,180] 
## Dimension:    y 
## Type:         spatial 
## Axis:         y 
## Extent:       [-56,83]

Next, we will select a spatial region, a set of spectral bands and a time period to work on, by specifying a few R objects:


## Linking to GEOS 3.10.2, GDAL 3.4.3, PROJ 8.2.1; sf_use_s2() is TRUE

bbox = st_bbox(c(xmin = 7, xmax = 7.01, ymin = 52, ymax = 52.01), crs = 'EPSG:4326')
bands = c("B04", "B08")
time_range = list("2018-01-01", "2019-01-01")

We can then start building a process graph, the object that contains the work to be done on the back-end side. First we load the available processes from the back-end:

p = openeo::processes(con)

and then we define the image collection as constrained by name, space, time, and bands (if no constraints are given, the full extent is used). We use a member function of p here, so that we cannot use processes that are not available on the back-end.

data = p$load_collection(id = collection, 
                         spatial_extent = bbox,
                         temporal_extent = time_range, 
                         bands = bands) 

We will compute NDVI, normalized differenced vegetation index, from the two selected bands, and use the NDVI function in reduce_dimension to reduce dimension bands:

ndvi = function(data, context) {
  red = data[1]
  nir = data[2]
calc_ndvi = p$reduce_dimension(data = data,
                               dimension = "bands",
                               reducer = ndvi)

Although ndvi is defined as an R function, in effect the openeo R client translates this function into openEO native processes. This cannot be done with arbitrarily complex functions, and passing on R functions to be processed by an R instance in the back-end is done using user-defined functions, the topic of a future blog post.

We will now process the NDVI values to a monthly series, by picking for each pixel the median value of all pixels over the month (Sentinel-2 has an image for roughly every 5 days). This is done by aggregate_temporal:

intervals = list(c('2018-01-01', '2018-02-01'),
                 c('2018-02-01', '2018-03-01'),
                 c('2018-03-01', '2018-04-01'),
                 c('2018-04-01', '2018-05-01'),
                 c('2018-05-01', '2018-06-01'), 
                 c('2018-06-01', '2018-07-01'), 
                 c('2018-07-01', '2018-08-01'),
                 c('2018-08-01', '2018-09-01'),
                 c('2018-09-01', '2018-10-01'), 
                 c('2018-10-01', '2018-11-01'),
                 c('2018-11-01', '2018-12-01'), 
                 c('2018-12-01', '2018-12-30'))
# and labels
labels = sapply(intervals, head, 1) # create labels from list

# add the process node
temp_period = p$aggregate_temporal(data = calc_ndvi,
                                   intervals = intervals,
                                   reducer = function(data, context){p$median(data)},
                                   labels = labels,
                                   dimension = "t")

Finally, we can define how we want to save results (which file format), by the save_result process

result = p$save_result(data = temp_period, format="NetCDF")

and request the results synchronously by compute_results:

# synchronous:
compute_result(result, format = "NetCDF", output_file = "", con = con)

## [1] ""

All commands before compute_result() can be executed without authentification; only compute_result asks for “real” computations on imagery, and requires authentication, so that the compute costs can be accounted for.

compute_result downloads the file locally, and we can now import it and plot it either by e.g. ggplot2


## Loading required package: abind

r = read_stars("")
ggplot() + geom_stars(data = r) +
        facet_wrap(~t) + coord_equal() +
        theme_void() +
        scale_x_discrete(expand = c(0,0)) +
        scale_y_discrete(expand = c(0,0)) +

or by mapview (where the “real” mapview obviously gives an interactive plot):


Batch jobs

The example above was deliberately kept very small; for larger jobs the synchronous call to compute_result will time out, and a batch job can be started with

job = create_job(graph = result, title = "", description = "ndvi 2018")
start_job(job = job$id) # use the id of the job (job$id) to start the job
job_list = list_jobs() # here you can see all your jobs and their status

The returned status is either queued, running or finished. When it is finished, then results can be downloaded by

dwnld = download_results(job = job$id, folder = "./") 

Further reading

The CRAN landing page has six (6!) vignettes on getting started, sample data retrieval, architecture, process graph building, and implementation details.


The use of R functions that can be sent to the back-end and executed there by an R engine is under development. This is a big milestone, as it would provide arbitrary R functionality (given that arbitrary R packages would also be provided by the back-end), and is something that for instance Google Earth Engine can not provide. The current form in which this works is still a bit user-unfriendly (for the curious: examples are found in this repo, and contain still a mix of Python and R code). When this has been cleaned up and undergone more testing it will be the topic of a follow-up blog post.

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