Environmental Data Platform

Temperature Condition Index - 231 m 8 days detail

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The Temperature Condition Index (TCI) is based on the Land Surface Temperature (LST) MODIS satellite data. The LST is based on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m after regridding from the original 1000 m resolution. The LST 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 TCI is calculated using the formula TCIi = (LSTmax,i - LSTi)/(LSTmax,i - LSTmin,i) * 100. The TCI expresses anomalies of the LST. The data is provided as 8 day measures. The time series is starting from 2001. The TCI values range from 0-100, whereas high values correspond to optimal vegetation conditions and low values indicate unfavorable vegetation conditions.

Land use, Land cover, collection, temperature condition index, tci, modis, ADO project, ADO, Terra

CC BY 4.0

eurac research - Institute for Earth Observation
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)


Snippet code
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#remotes::install_github(repo="Open-EO/openeo-r-client",ref="develop", dependencies=TRUE)

# login ----
euracHost = "https://openeo.eurac.edu"
conf = read.csv("./pwd/openeo_eurac_conf.csv") # adapt this path to where you store the conf file.
conf = list(client_id = conf$client_id, secret = conf$secret)
eurac = connect(host = euracHost)
prov = list_oidc_providers()
login(login_type = "oidc", provider = prov$Eurac_EDP_Keycloak, config = conf, con = eurac)

# load colleaction - save result ----
p = processes()
data = p$load_collection(id = "https://openeo.eurac.edu/collections/", 
                                             spatial_extent = list(west = 3.995373,
                                                                                 east = 17.523924,
                                                                                 south = 42.873494,
                                                                                 north = 50.326362),
                                             temporal_extent = list("2001-01-01T00:00:00Z", "2021-01-03T00:00:00Z"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
                             format = "netCDF",
                             output_file = "https://openeo.eurac.edu/collections/.nc", 
                             con = eurac)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "https://openeo.eurac.edu/collections/",
                                   description = "https://openeo.eurac.edu/collections/",
                                   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"
eurac = openeo.connect(euracHost).authenticate_oidc(client_id="openEO_PKCE")

# load collection - save result ----
data = eurac.load_collection("https://openeo.eurac.edu/collections/",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)

# or start a batch job (suitable for larger requests)

job = result.send_job(title = "https://openeo.eurac.edu/collections/",description = "https://openeo.eurac.edu/collections/",out_format = "netCDF")
jobId = job.job_id

jobResults = job.get_results()

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