Factor soil texture
FAIR Overall Score
Abstract
USDA soil textural classes derived from clay, silt and sand maps. - Resolution: 500m - Geographical Coverage: Alpine space - Input data: LUCAS 2009 Topsoil- Model: Multivariate Additive Regression Splines (MARS) - Year: 2015- Soil texture is classified into 12 classes: 1: Clay, 2: Silty-Clay, 3: Silty Clay-Loam, 4: Sandy Clay, 5: Sandy Clay-Loam, 6: Clay-Loam, 7: Silt, 8: Silt-Loam, 9: Loam, 10: Sand, 11: Loam Sand, 12: Sandy Loam. For further information visit the website European soil data centre (ESDAC).
Keywords
Supplemental information
Additional information can be added here
Legal constraints
CC BY 4.0
Contact for metadata
Eurac Research - Institute for Earth Observation
bartolomeo.ventura@eurac.edu
Viale Druso, 1 / Drususallee 1, Eurac Research, Bolzano, Autonomous Province of Bolzano, 39100, Italy
FAIR Overall Score: 58%
| Principle | Score | Earned | Level |
|---|---|---|---|
| Findable | 43% | 3 of 7 | initial |
| Accessible | 57% | 4 of 7 | advanced |
| Interoperable | 67% | 4 of 6 | moderate |
| Reusable | 67% | 4 of 6 | moderate |
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
install.packages("openeo")
library(openeo)
# login ----
host = "https://openeo.eurac.edu"
con = connect(host = host)
login()
# check login ---
con$isConnected()
con$isLoggedIn()
describe_account()
# load collection - save result ----
p = processes()
data = p$load_collection(id = "ADO_factor_soil_texture",
spatial_extent = list(west = 4.008928,
east = 17.507985,
south = 42.885755,
north = 50.317601),
temporal_extent = list("STARTTIME", "ENDTIME"))
result = p$save_result(data = data, format="netCDF")
# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
format = "netCDF",
output_file = "ADO_factor_soil_texture.nc",
con = con)
# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
title = "ADO_factor_soil_texture",
description = "ADO_factor_soil_texture",
format = "netCDF")
start_job(job = job_id)
result_list = list_results(job = job_id)
download_results(job = job_id, folder = ".")#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_factor_soil_texture",spatial_extent={'west':4.008928,'east':17.507985,'south':42.885755,'north':50.317601},temporal_extent=["STARTTIME", "ENDTIME"])
result = data.save_result(format="NetCDF")
# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)
result.download("ADO_factor_soil_texture.nc",format="netCDF")
# or start a batch job (suitable for larger requests, e.g. when .download() timeouts)
job = result.create_job(title = "ADO_factor_soil_texture",description = "ADO_factor_soil_texture",out_format = "netCDF")
jobId = job.job_id
job.start_job()
jobResults = job.get_results()
jobResults.download_files('.')Related docs
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| 4 | OpenEO doc | Documentation for OpenEO API | Link | June 9, 2021 | OpenEO |
| 5 | Eurac - OpenEO | openEO endpoint | Link | April 28, 2021 | OpenEO |
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