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Article

Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal

1
Institute of New Imaging Technologies, University Jaume I, 12071 Castellón de la Plana, Spain
2
Survey Department, Government of Nepal, Kathmandu NP-44600, Nepal
3
School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
*
Author to whom correspondence should be addressed.
Academic Editors: Pedro Latorre-Carmona and Qi Wang
Remote Sens. 2021, 13(7), 1391; https://doi.org/10.3390/rs13071391
Received: 23 February 2021 / Revised: 21 March 2021 / Accepted: 1 April 2021 / Published: 4 April 2021
(This article belongs to the Special Issue Machine Learning for Remote Sensing Image/Signal Processing)
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. View Full-Text
Keywords: Sentinel-2; rice-yield estimation; regression; deep learning; Nepal Sentinel-2; rice-yield estimation; regression; deep learning; Nepal
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MDPI and ACS Style

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/rs13071391

AMA Style

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 Sensing. 2021; 13(7):1391. https://doi.org/10.3390/rs13071391

Chicago/Turabian Style

Fernandez-Beltran, Ruben, Tina Baidar, Jian Kang, and Filiberto Pla. 2021. "Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal" Remote Sensing 13, no. 7: 1391. https://doi.org/10.3390/rs13071391

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