Classification of Different Irrigation Systems at Field Scale Using Time-Series of Remote Sensing Data
Author
Paolini, Giovanni
Escorihuela, Maria Jose
Merlin, Olivier
Pamies-Sans, Magí
Publication date
2022-11-17ISSN
1939-1404
Abstract
Maps of irrigation systems are of critical value for a better understanding of the human impact on the water cycle, while they also present a very useful tool at the administrative level to monitor changes and optimize irrigation practices. This study proposes a novel approach for classifying different irrigation systems at field level by using remotely sensed data at subfield scale as inputs of different supervised machine learning (ML) models for time-series classification. The ML models were trained using ground-truth data from more than 300 fields collected during a field campaign in 2020 across an intensely cultivated region in Catalunya, Spain. Two hydrological variables retrieved from satellite data, actual evapotranspiration ( ETa ) and soil moisture ( SM ), showed the best results when used for classification, especially when combined together, retrieving a final accuracy of 90.1±2.7% . All the three ML models employed for the classification showed that they were able to distinguish different irrigation systems, regardless of the different crops present in each field. For all the different tests, the best performances were reached by ResNET, the only deep neural network model among the three tested. The resulting method enables the creation of maps of irrigation systems at field level and for large areas, delivering detailed information on the status and evolution of irrigation practices.
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
631 - Agriculture in general
Pages
18
Publisher
IEEE
Is part of
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Citation
Paolini, Giovanni, Maria Jose Escorihuela, Olivier Merlin, Magí Pamies Sans, and Joaquim Bellvert. 2022. "Classification Of Different Irrigation Systems At Field Scale Using Time-Series Of Remote Sensing Data". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15: 10055-10072. doi:10.1109/JSTARS.2022.3222884
Grant agreement number
MICIU/Programa Estatal de I+D+I orientada a los retos de la sociedad/PCI2019-103649/ES/Managing water resources within Mediterranean agrosystems by accounting for spatial structures and connectivities/ALTOS
EC/H2020/823965/EU/Accounting for Climate Change in Water and Agriculture management/ACCWA
Program
Ús Eficient de l'Aigua en Agricultura
This item appears in the following Collection(s)
- ARTICLES CIENTÍFICS [2549]
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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/