Monthly leaf area index generated using Sentinel-2 for Bavarian Forest National Park in 2020

DOI

Monthly Leaf Area Index (LAI) products with a 20-meter resolution were generated using Sentinel-2 data for 2020 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).

Date: 08-02-2020

Date: 24-03-2020

Date: 23-04-2020

Date: 18-05-2020

Date: 22-07-2020

Date: 21-08-2020

Date: 20-09-2020

Date: 29-11-2020

Date Submitted: 2023-04-06

Identifier
DOI https://doi.org/10.17026/dans-z23-uxpz
Metadata Access https://lifesciences.datastations.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.17026/dans-z23-uxpz
Provenance
Creator E Neinavaz ORCID logo
Publisher DANS Data Station Life Sciences
Contributor M Th Koelen; A K Skidmore (Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente); Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente
Publication Year 2023
Rights CC0 1.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Contact M Th Koelen (Faculty of Geo-Information Science and Earth Observation)
Representation
Resource Type Dataset
Format application/octet-stream; application/zip; text/xml; application/pdf
Size 32; 47042; 308; 2048; 2131; 2195; 2281; 2176; 2193; 2218; 2148; 530; 108; 121968; 11241090; 6364
Version 1.0
Discipline Agricultural Sciences; Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Earth and Environmental Science; Environmental Research; Forestry; Geosciences; Life Sciences; Natural Sciences