Environmental Data Platform

Daily evaporation product - Herault basin

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Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Hérault basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

Bartkowiak, P., Ventura, B., Jacob, A., & Castelli, M. (2023). Daily evaporation product - Herault basin (Version v1) [Data set]. Institute for Earth Observation. https://doi.org/10.48784/FB631817-189F-4B57-AF6A-38CEF217BAD3

Collection, Daily evaporation, TSEB, Sentinel, 4DMED project, 4DMED, Herault basin, cct, Hydrography

CC BY 4.0

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


2017-01-01T12:00:00Z 2021-12-31T12:00:00Z

4326 (4326:EPSG)

Grid

Imagery base maps earth cover

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("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)
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