Access data

episodes/05-access-data.md

@@ -17,11 +17,14 @@
 - Open satellite imagery as raster data and save it to disk.
 :::
 
+::::::::::::::::::::::::::::::::::::: instructor
 
-## Considerations for the position of this episode in the workshop
+### Considerations for the position of this episode in the workshop
 
 *When this workshop is taught to learners with limited prior knowledge of Python, it might be better to place this episode after episode 11 and before episode 12. This episode contains an introduction to working with APIs and dictionaries, which can be perceived as challenging by some learners. Another consideration for placing this episode later in the workshop is when it is taught to learners with prior GIS knowledge who want to perform GIS-like operations with data they have already collected or for learners interested in working with raster data but less interested in satellite images.*
 
+:::::::::::::::::::::::::::::::::::::
+
 ## Introduction
 
 A number of satellites take snapshots of the Earth's surface from space. The images recorded by these remote sensors
@@ -71,7 +74,7 @@
 ::: challenge
 ## Exercise: Discover a STAC catalog
 Let's take a moment to explore the Earth Search STAC catalog, which is the catalog indexing the Sentinel-2 collection
 that is hosted on AWS. We can interactively browse this catalog using the STAC browser at [this link](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1).
 
 1. Open the link in your web browser. Which (sub-)catalogs are available?
 2. Open the Sentinel-2 Level 2A collection, and select one item from the list. Each item corresponds to a satellite
@@ -160,7 +163,7 @@
 COGs typically include multiple lower-resolution versions of the original image, called "overviews", which can also be
 accessed independently. By providing this "pyramidal" structure, users that are not interested in the details provided
 by a high-resolution raster can directly access the lower-resolution versions of the same image, significantly saving
 on the downloading time. More information on the COG format can be found [here](https://www.cogeo.org).
 :::
 
 In order to get data for a specific location you can add longitude latitude coordinates (World Geodetic System 1984 EPSG:4326) in your request.
@@ -197,6 +200,17 @@
 You will notice that more than 500 scenes match our search criteria. We are however interested in the period right before and after the wildfire of Rhodes. In the following exercise you will therefore have to add a time filter to
 our search criteria to narrow down our search for images of that period.
 
+::::::::::::::: instructor
+
+### Extra attention for the following exercise
+
+- The exercise **Exercise: Search satellite scenes using metadata filters** needs extra attention. Its output `search.json` is required for the later episodes. Therefore we recommend:
+  - Do not skip this exercise;
+  - Think twice when you would like to change the query arguments in this exercise;
+  - Make sure all the audience have the output `search.json` before continuing.
+
+::::::::::::::::
+
 ::: challenge
 ## Exercise: Search satellite scenes with a time filter
 
@@ -566,12 +580,14 @@
 The authentication procedure for dataset with restricted access might differ depending on the data provider. For the
 NASA CMR, follow these steps in order to access data using Python:
 
 * Create a NASA Earthdata login account [here](https://urs.earthdata.nasa.gov);
-* Set up a netrc file with your credentials, e.g. by using [this script](https://git.earthdata.nasa.gov/projects/LPDUR/repos/daac_data_download_python/browse/EarthdataLoginSetup.py);
-* Define the following environment variables:
+* Set up a netrc file with your credentials, e.g. by using [earthaccess](https://earthaccess.readthedocs.io/en/latest/user/authenticate/), which can be executed interactively in a Jupyter session, and creates a `.netrc` file in your home directory;
+* Define the following environment variables in your Jupyter session:
 
 ```python
 import os
+# These variables are needed by rasterio.open_rasterio function, which calls GDAL in the background
+# GDAL needs the path where to store cookies
 os.environ["GDAL_HTTP_COOKIEFILE"] = "./cookies.txt"
 os.environ["GDAL_HTTP_COOKIEJAR"] = "./cookies.txt"
 ```

instructors/instructor-notes.md

@@ -4,13 +4,6 @@ title: Instructor Notes
 
 ## Instructor notes
 
-### Episode 1
-
-- The exercise **Exercise: Search satellite scenes using metadata filters** needs extra attention. Its output `search.json` is required for the later episodes. Therefore we recommend:
-  - Do not skip this exercise;
-  - Think twice when you would like to change the query arguments in this exercise;
-  - Make sure all the audience have the output `search.json` before continuing.
-
 ### Episode 3
 
 -  `brpgewaspercelen_definitief_2020_small.gpkg` was created because the original file was too large to download and load. Original file, which was ~500Mb could take several minutes to load, and could crash the Jupyter terminal.