EP-3986 finetune ARD/SAR backscatter docs a bit

usecases/ard/index.md

@@ -1,12 +1,14 @@
 # Analysis-Ready Data (ARD)
 
-For certain use cases, the preprocessed data collections available in the openEO back-ends are not sufficient or simply not
-available. For that case, openEO supports a few very common preprocessing scenario:
+For certain use cases, the readily available preprocessed data collections in the openEO back-ends 
+are not sufficient or inappropriately preprocessed.
+openEO supports some processes to address very common preprocessing scenarios:
 
 - Atmospheric correction of optical data
 - SAR backscatter computation
 
-These processes also offer a number of parameters to customize the processing. There's also variants with a default
+These processes also offer a number of parameters to customize the processing. 
+There are also variants with a default
 parametrization that results in data that is compliant with [CEOS CARD4L specifications](https://ceos.org/ard/).
 
 We should note that these operations can be computationally expensive, so certainly affect overall processing time and

usecases/ard/sar/index.md

@@ -2,12 +2,21 @@
 
 Data from synthetic aperture radar sensors requires significant preprocessing to be calibrated and normalized for terrain.
 This is referred to as backscatter computation, and supported by
-[sar_backscatter](https://processes.openeo.org/draft/#sar_backscatter) and the CARD4L compliant variant
-[ard_normalized_radar_backscatter](https://processes.openeo.org/draft/#ard_normalized_radar_backscatter)
+- the [`sar_backscatter` process](https://processes.openeo.org/draft/#sar_backscatter) 
+- and its CARD4L compliant variant, the
+[`ard_normalized_radar_backscatter` process](https://processes.openeo.org/draft/#ard_normalized_radar_backscatter)
 
-The user should load a datacube containing raw SAR data, such as Sentinel-1 GRD. On the resulting datacube, the
-`openeo.rest.datacube.DataCube.sar_backscatter` Python method can be invoked. The CEOS CARD4L variant is:
-`openeo.rest.datacube.DataCube.ard_normalized_radar_backscatter` in Python. These processes are tightly coupled to
+The user should first load a datacube containing raw SAR data, such as "Sentinel-1 GRD" 
+and apply one of these processes to it.
+
+::: tip Python example
+The openEO Python client library provides the
+[`DataCube.sar_backscatter`](https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube.sar_backscatter)
+and [`DataCube.ard_normalized_radar_backscatter`](https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube.ard_normalized_radar_backscatter)
+methods for this.
+:::
+
+These processes are tightly coupled to
 metadata from specific sensors, so it is not possible to apply other processes to the datacube first,
 with the exception of specifying filters in space and time.
 
@@ -19,24 +28,33 @@ EODC supports sar_backscatter, based on the [Sentinel-1 toolbox](https://sentine
 
 ## Terrascope back-end
 
-When working with the Sentinelhub SENTINEL1_GRD collection, both sar processes can be used. The underlying implementation is
-provided by [Sentinelhub](https://docs.sentinel-hub.com/api/latest/data/sentinel-1-grd/#processing-options), and offers full
+
+### Sentinel Hub based collection
+
+When working with the Sentinel Hub based `SENTINEL1_GRD` collection, both SAR backscatter processes can be used.
+The underlying implementation is
+provided by [Sentinel Hub](https://docs.sentinel-hub.com/api/latest/data/sentinel-1-grd/#processing-options), and offers full
 CARD4L compliant processing options.
 
-This is an example of `openeo.rest.datacube.DataCube.ard_normalized_radar_backscatter` in Python:
+This is an example of [`DataCube.ard_normalized_radar_backscatter`](https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube.ard_normalized_radar_backscatter) in Python:
 
 ```python
-s1grd = connection.load_collection('SENTINEL1_GRD',
+s1grd = connection.load_collection(
+    'SENTINEL1_GRD',
     spatial_extent={'west':2.59003,'east':2.8949,'south':51.069,'north':51.2206},
     temporal_extent=['2019-10-10','2019-10-10'],
-    bands=['VH','VV'])
+    bands=['VH','VV']
+)
 
-job = s1grd.ard_normalized_radar_backscatter().execute_batch()
+normalized = s1grd.ard_normalized_radar_backscatter()
 
-for asset in job.get_results().get_assets():
-    asset.download()
+job = normalized.execute_batch()
+job.get_results().download_files("output")
 ```
 
+
+### Other GRD collections
+
 When working with other GRD data, an implementation based on Orfeo Toolbox is used:
 
 - [Orfeo docs](https://www.orfeo-toolbox.org/CookBook/Applications/app_SARCalibration.html)