crossref fixes

docs/basics.rst

@@ -317,6 +317,7 @@ and observations are filtered out:
 
 .. image:: _static/images/basics/evi-masked-composite.png
 
+.. _aggregate-spatial-evi
 
 Aggregated EVI timeseries
 ===========================

docs/cookbook/job_manager.rst

@@ -1,3 +1,5 @@
+.. _job-manager
+
 ====================================
 Multi Backend Job Manager
 ====================================

docs/cookbook/sampling.md

@@ -19,7 +19,7 @@ Sampling can be done for points or polygons:
 
 Note that sampling many points or polygons may require to send a large amount of geometry, which sometimes makes the size
 of the requests too large when it is included inline as GeoJson. Therefore, we recommend to upload your vector data to a
-public url, and to load it in openEO using `openeo.rest.connection.Connection.load_url`.
+public url, and to load it in openEO using {py:meth}`openeo.rest.connection.Connection.load_url`.
 
 ## Sampling at point locations
 
@@ -28,7 +28,7 @@ commonly supported reducer like `min`, `max` or `mean` and will receive only one
 in edge cases, a point can intersect with up to 4 pixels. If this is not desirable, it might be worth trying to align 
 points with pixel centers, which does require more advanced knowledge of the pixel grid of your data cube.
 
-More information on `aggregate_spatial` is available :ref:`here<Aggregated EVI timeseries>`.
+More information on `aggregate_spatial` is available [here](_aggregate-spatial-evi).
 
 ## Sampling polygons as rasters
 
@@ -76,4 +76,4 @@ batch job. The recommendation here is to apply a spatial grouping to your sampli
 an area of around 100x100km. The optimal size of a group may be backend dependant. Also remember that when working with
 data in the UTM projection, you may want to avoid covering multiple UTM zones in a single group.
 
-See also :ref:`Managing many jobs<Multi Backend Job Manager>`.
+See also how to manage [multiple jobs](_job-manager).