Monthly leaf area index generated using Sentinel-2 for Bavarian Forest National Park in 2016

Monthly Leaf Area Index (LAI) products with a 20-meter resolution were generated using Sentinel-2 data for 2016 over Bavarian Forest National Park, Germany, applying an enhanced vegetation index (EVI)-based model as an empirical approach. The LAI products were utilised as an input for estimating Net Primary Productivity using the LPJ-GUESS model as the deliverable for the EO4Diversity project funded by the European Space Agency (ESA).

Identifier
DOI https://doi.org/10.17026/dans-245-nkdw
PID https://nbn-resolving.org/urn:nbn:nl:ui:13-5q-u4j6
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:280101
Provenance
Creator Neinavaz, E ORCID logo
Publisher Faculty Geo-Information Science and Earth Observation (ITC)
Contributor Skidmore, A K; Prof. Dr A K Skidmore (Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente )
Publication Year 2023
Rights info:eu-repo/semantics/openAccess; License: http://creativecommons.org/publicdomain/zero/1.0; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Representation
Language English
Resource Type Image
Format image/tiff; Raster Data
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Forestry; Life Sciences
Spatial Coverage DEU; Bavarian Forest National Park