Environmental Data Platform


Snow cover phenology

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The snow cover phenology dataset was generated by exploiting the snow product MOD10A1.061 and contains the following variables: first snow day (FSD), last snow day (LSD), yearly averaged snow cover area (SCA), snow cover duration (SCD).

https://doi.datacite.org/dois/10.48784/1ZVV-NW59

Notarnicola, C. (2024). Snow cover phenology (Version 1.0). Eurac Research. https://doi.org/10.48784/1ZVV-NW59

Collection, Snow cover phenology, FSD, LSD, SCA, SCD, MODIS, MODIS-Terra, Hydrography

CC BY 4.0 NC

eurac research - Institute for Earth Observation
bartolomeo.ventura@eurac.edu
Viale Druso, 1 / Drususallee 1, eurac research, Bolzano, Autonomous Province of Bolzano, 39100, Italy


2000-10-01T00:00:00Z 2023-09-30T00:00:00Z

4326 (4326:EPSG)

Grid

Climatology, meteorology, atmosphere


Snippet code
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##### ----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("FSD")

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

## Retrieve a list of the first 10 items belonging to a specific collection
item = collection.get_item("FSD2000-1")
#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["FSD"].title)
print(item.assets["FSD"].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("SCA")

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

url_basepath = "https://eurac-eo.s3-eu-west-1.amazonaws.com/"
collection_url = url_basepath + "FSD" + "/"
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)

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