Monthly leaf area index generated using Sentinel-2 for Bavarian Forest National Park in 2019

DOI

Monthly Leaf Area Index (LAI) products with a 20-meter resolution were generated using Sentinel-2 data for 2019 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: 30-03-2019

Date: 24-04-2019

Date: 28-06-2019

Date: 23-07-2019

Date: 27-08-2019

Date: 21-09-2019

Date: 03-10-2019

Date: 30-12-2019

Date Submitted: 2023-04-06

Identifier
DOI https://doi.org/10.17026/dans-xz4-jeb9
Metadata Access https://lifesciences.datastations.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.17026/dans-xz4-jeb9
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; 2063; 2209; 2022; 2046; 2199; 2224; 2179; 2000; 530; 531; 108; 121962; 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