Rice-yield prediction with multi-temporal sentinel-2 data and 3D CNN: A case study in Nepal
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Otros documentos de la autoría: Fernandez-Beltran, Ruben; Baidar, Tina; kang, jian; Pla, Filiberto
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INVESTIGACIONMetadatos
Título
Rice-yield prediction with multi-temporal sentinel-2 data and 3D CNN: A case study in NepalFecha de publicación
2021-04-04Editor
Multidisciplinary Digital Publishing InstituteISSN
2072-4292Cita bibliográfica
Fernandez-Beltran, R.; Baidar, T.; Kang, J.; Pla, F. Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal. Remote Sens. 2021, 13, 1391. https://doi.org/10.3390/rs 13071391Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/2072-4292/13/7/1391Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Crop yield estimation is a major issue of crop monitoring which remains particularly
challenging in developing countries due to the problem of timely and adequate data availability.
Whereas traditional agricultural ... [+]
Crop yield estimation is a major issue of crop monitoring which remains particularly
challenging in developing countries due to the problem of timely and adequate data availability.
Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available
multi-temporal and multi-spectral remote sensing images are excellent tools to support these vulnerable
systems by accurately monitoring and estimating crop yields before harvest. In this context, we
introduce the use of Sentinel-2 (S2) imagery, with a medium spatial, spectral and temporal resolutions,
to estimate rice crop yields in Nepal as a case study. Firstly, we build a new large-scale rice crop
database (RicePAL) composed by multi-temporal S2 and climate/soil data from the Terai districts of
Nepal. Secondly, we propose a novel 3D Convolutional Neural Network (CNN) adapted to these
intrinsic data constraints for the accurate rice crop yield estimation. Thirdly, we study the effect of
considering different temporal, climate and soil data configurations in terms of the performance
achieved by the proposed approach and several state-of-the-art regression and CNN-based yield
estimation methods. The extensive experiments conducted in this work demonstrate the suitability
of the proposed CNN-based framework for rice crop yield estimation in the developing country of
Nepal using S2 data. [-]
Publicado en
Remote Sensing, número 7, volum 13, abril 2021Código del proyecto o subvención
RTI2018-098651_B_C54 | GV/2020/167
Título del proyecto o subvención
Productos avanzados L3 y L4 para la misión Flex-S3 (FlexL3L4) | Fusión de datos multi-sensor y temporales para la misión espacial Flex
Derechos de acceso
info:eu-repo/semantics/openAccess
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