Sciliar_Catinaccio

FAIR Overall Score

Abstract

This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

Keywords

HYPERECOS, ASI, SENTINEL-2, SENTINEL2, S2, L2A

Legal constraints

CC-BY-4.0

FAIR Overall Score: 77%

Principle	Score	Earned	Level
Findable	57%	4 of 7	moderate
Accessible	100%	7 of 7	advanced
Interoperable	67%	4 of 6	moderate
Reusable	83%	5 of 6	moderate

Assessed 2026-07-22 (metrics 0.8)

Evaluated by F-UJI web service: Anusuriya Devaraju, & Robert Huber. (2020). F-UJI - An Automated FAIR Data Assessment Tool. Zenodo. https://doi.org/10.5281/zenodo.6361400

Snippet code

##### ----Explore and download STAC data with Python ----- #####

from pystac_client import Client

## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)

## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
print(f"Number of collections: {len(stac_collections)}")

## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
    print(f"- {collection.id}")
print("-------------------------")

## Retrieve a specific collection
collection = catalog.get_collection("ID_OfTheCollection")

## Search for items in the collection
collection_items = list(catalog.search(collections=['ID_OfTheCollection'], max_items=10).items())
print(collection_items)

## Retrieve a list of the first 10 items belonging to a specific collection
item = collection.get_item("NameOfTheItem")
#print(list(item.assets.items())[0:10])

##Print the band name and the href of a specific item retrieved from the previous list
print(item.assets["ID_OfTheCollection"].title)
print(item.assets["ID_OfTheCollection"].href)

## Print Item’s assets through the assets attribute, which is a dictionary
for asset_key in item.assets:
    asset = item.assets[asset_key]
    print("{}: {} ({})".format(asset_key, asset.href, asset.media_type))

## ------------- DONWLOAD COG files from a specific collection ------- ####
from pystac_client import Client
import requests

## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)

## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
# print(f"Number of collections: {len(stac_collections)}")

## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
	print(f"- {collection.id}")
print("-------------------------")

## Retrieve a specific collection
collection = catalog.get_collection("ID_OfTheCollection")

## Search for items in the collection
collection_items = list(catalog.search(collections=['ID_OfTheCollection'], max_items=10).items())
print(collection_items)

url_basepath = "https://eurac-eo.s3-eu-west-1.amazonaws.com/"
collection_url = url_basepath + "ID_OfTheCollection" + "/"
print(collection_url)
for item_id in collection_items:
	# item = item.assets[asset_key]
	print(item_id.id)
	cog_file = str(item_id.id + '.tif')
	url = collection_url + cog_file
	response = requests.get(url)
	with open(cog_file, "wb") as f:
		f.write(response.content)