Vegetation Condition Index - 231 m 8 days

Full metadata OpenEO repository

FAIR Overall Score

58%

Abstract

The Vegetation Condition Index (VCI) is based on the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VCI is calculated using the formula VCIi = (NDVIi - NDVImin,i)/(NDVImax,i - NDVImin,i) * 100. The VCI expresses anomalies of the NDVI. The data is provided as 8 day measures. The time series is starting from 2001. The VCI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.

Keywords

collection, vegetation condition index, vci, modis, ADO project, ADO, Terra, Land use, Land cover

Citation

Zellner, P., & Castelli, M. (2022). Vegetation Condition Index - 231 m 8 days (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/16367c6a-534a-11ec-b0a3-02000a08f41d

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

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

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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$load_collection(id = "ADO_VCI_MODIS_231m_3035", 
                                             spatial_extent = list(west = 3.995373,
                                                                                 east = 17.523924,
                                                                                 south = 42.873494,
                                                                                 north = 50.326362),
                                             temporal_extent = list("2001-01-01T00:00:00Z", "2022-08-29T00:00:00Z"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
                             format = "netCDF",
                             output_file = "ADO_VCI_MODIS_231m_3035.nc", 
                             con = con)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "ADO_VCI_MODIS_231m_3035",
                                   description = "ADO_VCI_MODIS_231m_3035",
                                   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_VCI_MODIS_231m_3035",spatial_extent={'west':3.995373,'east':17.523924,'south':42.873494,'north':50.326362},temporal_extent=["2001-01-01T00:00:00Z", "2022-08-29T00:00:00Z"])

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

# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)
result.download("ADO_VCI_MODIS_231m_3035.nc",format="netCDF")

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

job = result.create_job(title = "ADO_VCI_MODIS_231m_3035",description = "ADO_VCI_MODIS_231m_3035",out_format = "netCDF")
jobId = job.job_id
job.start_job()

jobResults = job.get_results()
jobResults.download_files('.')

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