Integrating Remote Sensing and Mechanistic Modeling for Crop Monitoring and Yield Estimation

DOI

This dataset, collected in 2019 as part of Georgios Ntakos’ PhD thesis, combines field, laboratory, and regional-scale data for potato crop monitoring and yield estimation in the Netherlands.

It includes hyperspectral canopy reflectance, LAI, leaf chlorophyll and fluorescence parameters, detailed lab analyses of leaf samples, and region-wide remote sensing and weather data.

Field experiments covered two sites (Lelystad and Vredepeel), three potato varieties per site, and three nitrogen treatments under two levels of drip irrigation.

Identifier
DOI https://doi.org/10.17026/LS/V7JU7N
Metadata Access https://lifesciences.datastations.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.17026/LS/V7JU7N
Provenance
Creator G. Ntakos ORCID logo
Publisher DANS Data Station Life Sciences
Contributor Ntakos, Georgios; Ntakos Georgios; Prikaziuk Egor; Christiaan van der tol
Publication Year 2025
Rights DANS Licence; info:eu-repo/semantics/restrictedAccess; https://doi.org/10.17026/fp39-0x58
OpenAccess false
Contact Ntakos, Georgios (Agricultural University of Athens)
Representation
Resource Type Dataset
Format text/csv; text/tab-separated-values; text/plain
Size 26311449; 82601; 41739; 1697531; 9511
Version 1.0
Discipline Life Sciences; Medicine