Update usage of raster data

episodes/05-access-data.md

@@ -66,7 +66,7 @@
 ::: 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
@@ -96,7 +96,7 @@
 api_url = "https://earth-search.aws.element84.com/v1"
 ```
 
-You can query a STAC API endpoint from Python using the [`pystac_client` library](https://pystac-client.readthedocs.io/en/stable/api.html#pystac_client). 
+You can query a STAC API endpoint from Python using the [`pystac_client` library](https://pystac-client.readthedocs.io/en/stable/api.html#pystac_client).
 To do so we will first import `Client` from `pystac_client` and use the [method open from the Client object](https://pystac-client.readthedocs.io/en/stable/quickstart.html):
 
 ```python
@@ -105,11 +105,11 @@
 client = Client.open(api_url)
 ```
 
-For this episode we will focus at scenes belonging to the `sentinel-2-l2a` collection. 
-This dataset is useful for our case and includes Sentinel-2 data products pre-processed at level 2A (bottom-of-atmosphere reflectance). 
+For this episode we will focus at scenes belonging to the `sentinel-2-l2a` collection.
+This dataset is useful for our case and includes Sentinel-2 data products pre-processed at level 2A (bottom-of-atmosphere reflectance).
 
-In order to see which collections are available in the provided `api_url` the 
-[`get_collections`](https://pystac-client.readthedocs.io/en/stable/api.html#pystac_client.Client.get_collections) method can be used on the Client object. 
+In order to see which collections are available in the provided `api_url` the
+[`get_collections`](https://pystac-client.readthedocs.io/en/stable/api.html#pystac_client.Client.get_collections) method can be used on the Client object.
 
 ```python
 collections = client.get_collections()
@@ -136,7 +136,7 @@
 As said, we want to focus to the `sentinel-2-l2a` collection. To do so, we set this collection into a variable:
 
 ```python
-collection_sentinel_2_l2a = "sentinel-2-l2a"  
+collection_sentinel_2_l2a = "sentinel-2-l2a"
 ```
 
 The data in this collection is stored in the Cloud Optimized GeoTIFF (COG) format and as JPEG2000 images. In this episode we will focus at COGs, as these offer useful functionalities for our purpose.
@@ -153,10 +153,10 @@
 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. 
+In order to get data for a specific location you can add longitude latitude coordinates (World Geodetic System 1984 EPSG:4326) in your request.
 In order to do so we are using the `shapely` library to define a geometrical point.
 Below we have included a center point for the island of Rhodes, which is the location of interest for our case study (i.e. Longitude: 27.95 | Latitude 36.20).
 
@@ -329,7 +329,17 @@
 items.save_object("rhodes_sentinel-2.json")
 ```
 
-This creates a file in GeoJSON format, which we will reuse here and in the next episodes. Note that this file contains the metadata of the files that meet out criteria. It does not include the data itself, only their metadata.
+This creates a file in GeoJSON format, which we can reuse here and in the next episodes. Note that this file contains the metadata of the files that meet out criteria. It does not include the data itself, only their metadata.
+
+To load the saved search results as a `ItemCollection` we can use [`pystac.ItemCollection.from_file()`](https://pystac.readthedocs.io/en/stable/api/item_collection.html). Through this, we are instructing Python to use the `from_file` method of the `ItemCollection` class from the `pystac` library to load data from the specified GeoJSON file:
+
+```python
+import pystac
+items_loaded = pystac.ItemCollection.from_file("../data/stac_json/rhodes_sentinel-2.json")
+```
+
+The loaded item collection (`items_loaded`) is equivalent to the one returned earlier by `search.item_collection()` (`items`). You can thus perform the same actions on it: you can check the number of items (`len(items_loaded)`), you can loop over items (`for item in items_loaded: ...`), and you can access individual elements using their index (`items_loaded[0]`).
+
 
 ## Access the assets
 
@@ -552,7 +562,7 @@
 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: