Worldwide sustainable development is threatened by current agricultural land change
trends, particularly by the increasing rural farmland abandonment and agricultural intensification
phenomena. In Mediterranean countries, these processes are a ecting especially traditional olive
groves with enormous socio-economic costs to rural areas, endangering environmental sustainability
and biodiversity. Traditional olive groves abandonment and intensification are clearly related
to the reduction of olive oil production income, leading to reduced economic viability. Most
promising strategies to boost traditional groves competitiveness—such as olive oil di erentiation
through adoption of protected denomination of origin labels and development of value-added
olive products—rely on knowledge of the olive varieties and its specific properties that confer their
uniqueness and authenticity. Given the lack of information about olive varieties on traditional groves,
a feasible and inexpensive method of variety identification is required. We analyzed leaf spectral
information of ten Portuguese olive varieties with a powerful data-mining approach in order to verify
the ability of satellite’s hyperspectral sensors to provide an accurate olive variety identification. Our
results show that these olive varieties are distinguishable by leaf reflectance information and suggest
that even satellite open-source data could be“Integrated protection of the Alentejo olive grove. Contributions
to its innovation and improvement against its key enemies” with the reference ALT20-03-0145-FEDER-000029.
co-financed by the European Union through the European Regional Development Fund. under the ALENTEJO 2020
(Regional Operational Program of the Alentejo).PTDC/ASP-PLA/30650/2017 (“Fundação
para a Ciência e Tecnologia”. FCT Portugal).National Funds through FCT-Foundation for
Science and Technology under the Project UIDB/05183/202
