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Early mapping of industrial tomato in Central and Southern Italy with Sentinel 2, aerial and RapidEye additional data

Published online by Cambridge University Press:  01 June 2018

Gaia Vaglio Laurin Open the ORCID record for Gaia Vaglio Laurin [Opens in a new window] ,
Claudio Belli ,
Roberto Bianconi ,
Pietro Laranci  and
Dario Papale
Gaia Vaglio Laurin*
Affiliation:
Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, 01100 Viterbo, Italy
Claudio Belli
Affiliation:
Terrasystem s.r.l., Via Pacinotti, 5, 01100 Viterbo, Italy
Roberto Bianconi
Affiliation:
Terrasystem s.r.l., Via Pacinotti, 5, 01100 Viterbo, Italy
Pietro Laranci
Affiliation:
Terrasystem s.r.l., Via Pacinotti, 5, 01100 Viterbo, Italy
Dario Papale
Affiliation:
Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, 01100 Viterbo, Italy
*
Author for correspondence: Gaia Vaglio Laurin, E-mail: gaia.vl@unitus.it
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Abstract

Timely crop information, i.e. well before harvesting time and at first stages of crop development, can benefit farmers and producer organizations. The current case study documents the procedure to deliver early data on planted tomato to users, showing the potential of Sentinel 2 (S2) to map tomato at the very beginning of the crop season, which is a challenging task. Using satellite data, integrated with ground and aerial data, an initial estimate of area planted with tomato and early tomato maps were generated in seven main production areas in Italy. Estimates of the amount of area planted with tomato provided similar results either when derived from field surveys or from remote-sensing-based classification. Tomato early maps showed a producer accuracy >80% in seven cases out of nine, and a user accuracy >80% in five cases out of nine, with differences attributed to the varying agricultural characteristics and environmental heterogeneity of the study areas. The additional use of aerial data improved producer accuracy moderately. The ability to identify abrupt growth changes, such as those caused by natural hazards, was also analysed: S2 detected significant changes in tomato growth between a hailstorm-affected area and a control area. The study suggests that S2, with enhanced spectral capabilities and open data policy, represents very valuable data, allowing crop monitoring at an early development stage.

Keywords

Early classificationremote sensingtomato damage assessmenttomato mapping
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 156 , Issue 3 , April 2018 , pp. 396 - 407
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Copyright
Copyright © Cambridge University Press 2018 

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