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Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

1, 1,2,*, 3, 1 and 1
1
School of Resources and Environment, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
2
Center for Information Geoscience, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
3
Department of Geography, Planning, and Environment, East Carolina University, Greenville, NC 27858, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 340; https://doi.org/10.3390/rs10020340
Received: 5 January 2018 / Revised: 8 February 2018 / Accepted: 18 February 2018 / Published: 23 February 2018
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Abstract

Accurate estimation and monitoring of rice phenology is necessary for the management and yield prediction of rice. The radar backscattering coefficient, one of the most direct and accessible parameters has been proved to be capable of retrieving rice growth parameters. This paper aims to investigate the possibility of monitoring the rice phenology (i.e., transplanting, vegetative, reproductive, and maturity) using the backscattering coefficients or their simple combinations of multi-temporal RADARSAT-2 datasets only. Four RADARSAT-2 datasets were analyzed at 30 sample plots in Meishan City, Sichuan Province, China. By exploiting the relationships of the backscattering coefficients and their combinations versus the phenology of rice, HH/VV, VV/VH, and HH/VH ratios were found to have the greatest potential for phenology monitoring. A decision tree classifier was applied to distinguish the four phenological phases, and the classifier was effective. The validation of the classifier indicated an overall accuracy level of 86.2%. Most of the errors occurred in the vegetative and reproductive phases. The corresponding errors were 21.4% and 16.7%, respectively. View Full-Text
Keywords: phenology; RADARSAT-2; rice; Synthetic Aperture Radar (SAR); decision tree phenology; RADARSAT-2; rice; Synthetic Aperture Radar (SAR); decision tree
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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He, Z.; Li, S.; Wang, Y.; Dai, L.; Lin, S. Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets. Remote Sens. 2018, 10, 340.

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