Using NDVI, climate data and machine learning to estimate yield in the Douro wine region

Barriguinha, A., Jardim, B., De Castro Neto, M., & Gil, A. (2022). Using NDVI, climate data and machine learning to estimate yield in the Douro wine region. International Journal of Applied Earth Observation and Geoinformation, 114(November), 1-14. [103069]. https://doi.org/10.1016/j.jag.2022.10...

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Detalhes bibliográficos
Autor principal: Barriguinha, André (author)
Outros Autores: Jardim, Bruno (author), Neto, Miguel de Castro (author), Gil, Artur (author)
Formato: article
Idioma:eng
Publicado em: 2022
Assuntos:
Remote sensing
Vineyard
Yield
Prediction
NDVI
Climate
machine learning
Global and Planetary Change
Earth-Surface Processes
Computers in Earth Sciences
Management, Monitoring, Policy and Law
SDG 13 - Climate Action
Texto completo:http://hdl.handle.net/10362/145107
País:Portugal
Oai:oai:run.unl.pt:10362/145107
Descrição
Resumo:Barriguinha, A., Jardim, B., De Castro Neto, M., & Gil, A. (2022). Using NDVI, climate data and machine learning to estimate yield in the Douro wine region. International Journal of Applied Earth Observation and Geoinformation, 114(November), 1-14. [103069]. https://doi.org/10.1016/j.jag.2022.103069 -- Funding: The authors gratefully acknowledge: IVDP - Instituto dos Vinhos do Douro e do Porto, IP (Institute of Douro and Port Wines) (https://www.ivdp.pt/en), for providing historical data related to wine grape production for the entire DDR at the parish level; IPMA - Instituto Portuguˆes do Mar e da Atmosfera, IP (Portuguese Institute for Sea and Atmosphere)

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