Environmental Data Platform

Copernicus Surface Soil Moisture - 1km

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The Soil Water Index quantifies the moisture condition at various depths in the soil. It is mainly driven by the precipitation via the process of infiltration. Soil moisture is a very heterogeneous variable and varies on small scales with soil properties and drainage patterns. Satellite measurements integrate over relative large-scale areas, with the presence of vegetation adding complexity to the interpretation.

collection, surface soil moisture, ASCAT, Sentinel-1, ADO project, ADO, Sentinel-1 A/B; MetOp A/B, Land use, Land cover


Eurac Research - Institute for Earth Observation
Viale Druso, 1 / Drususallee 1, Eurac Research, Bolzano, Autonomous Province of Bolzano, 39100, Italy

2015-01-01T00:00:00Z 2020-04-19T00:00:00Z

WGS-84 (4326:EPSG)



Imagery base maps earth cover

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# login ----
host = "https://openeo.eurac.edu"
con = connect(host = host)

# check login ---

# load collection - save result ----
p = processes()
data = p$load_collection(id = "ADO_SWI_1km_4326", 
                                             spatial_extent = list(west = 3.691964,
                                                                                 east = 17.15625,
                                                                                 south = 42.995536,
                                                                                 north = 50.558036),
                                             temporal_extent = list("2015-01-01T00:00:00Z", "2020-04-19T00:00:00Z"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
                             format = "netCDF",
                             output_file = "ADO_SWI_1km_4326.nc", 
                             con = con)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "ADO_SWI_1km_4326",
                                   description = "ADO_SWI_1km_4326",
                                   format = "netCDF")
start_job(job = job_id)
result_list = list_results(job = job_id)
download_results(job = job_id, folder = ".")
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#pip install openeo
import openeo

# login ----
euracHost        = "https://openeo.eurac.edu"
conn = openeo.connect(euracHost).authenticate_oidc(client_id="openEO_PKCE")

# load collection - save result ----
data = conn.load_collection("ADO_SWI_1km_4326",spatial_extent={'west':3.691964,'east':17.15625,'south':42.995536,'north':50.558036},temporal_extent=["2015-01-01T00:00:00Z", "2020-04-19T00:00:00Z"])

result = data.save_result(format="NetCDF")

# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)

# or start a batch job (suitable for larger requests, e.g. when .download() timeouts)

job = result.create_job(title = "ADO_SWI_1km_4326",description = "ADO_SWI_1km_4326",out_format = "netCDF")
jobId = job.job_id

jobResults = job.get_results()

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