Environmental Data Platform

Normalized Difference Vegetation Index - 231m 8 day Maximum Value Composite

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The Normalized Difference Vegetation Index (NDVI) is based on 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-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The NDVI is calculated using the formula NDVI = (NIR - Red) / (NIR + Red). The NDVI expresses the vitality of vegetation. The data is provided as 8 day measures. The time series is starting from 2001. The NDVI values range from -1 - 1, whereas high values correspond to healthy vegetation.

collection, normalized difference vegetation index, ndvi, modis, ADO project, ADO, Terra, Land use, Land cover

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


2001-01-01T00:00:00Z 2021-01-03T00:00:00Z

WGS-84 (3035:EPSG)

Grid

mapDigital

Imagery base maps earth cover

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$load_collection(id = "ADO_NDVI_MODIS_231m_3035", 
                                             spatial_extent = list(west = 3.995373,
                                                                                 east = 17.523924,
                                                                                 south = 42.873494,
                                                                                 north = 50.326362),
                                             temporal_extent = list("2001-01-01T00:00:00Z", "2021-01-03T00:00:00Z"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
                             format = "netCDF",
                             output_file = "ADO_NDVI_MODIS_231m_3035.nc", 
                             con = con)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "ADO_NDVI_MODIS_231m_3035",
                                   description = "ADO_NDVI_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_NDVI_MODIS_231m_3035",spatial_extent={'west':3.995373,'east':17.523924,'south':42.873494,'north':50.326362},temporal_extent=["2001-01-01T00:00:00Z", "2021-01-03T00:00:00Z"])

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

# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)
result.download("ADO_NDVI_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_NDVI_MODIS_231m_3035",description = "ADO_NDVI_MODIS_231m_3035",out_format = "netCDF")
jobId = job.job_id
job.start_job()

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