Rice Phenology Retrieval Based on Growth Curve Simulation and Multi-Temporal Sentinel-1 Data

Bo Wang, Yu Liu, Qinghong Sheng, Jun Li, Jiahui Tao and Zhijun Yan
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Bo Wang: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Yu Liu: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Qinghong Sheng: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Jun Li: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Jiahui Tao: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Zhijun Yan: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Sustainability, 2022, vol. 14, issue 13, 1-24

Abstract: The accurate estimation and monitoring of phenology is necessary for modern agricultural industries. For crops with short phenology occurrence times, such as rice, Sentinel-1 can be used to effectively monitor the growth status in different phenology periods within a short time interval. Therefore, this study proposes a method to monitor rice phenology based on growth curve simulation by constructing a polarized growth index (PGI) and obtaining a polarized growth curve. A recursive neural network is used to realize the classification of phenology and use it as prior knowledge of rice phenology to divide and extract the phenological interval and date of rice in 2021. The experimental results show that the average accuracy of neural network phenological interval division reaches 93.5%, and the average error between the extracted and measured phenological date is 3.08 days, which proves the application potential of the method. This study will contribute to the technical development of planning, management and maintenance of renewable energy infrastructure related to phenology.

Keywords: photovoltaic agriculture; rice phenology; synthetic aperture radar; multi-temporal Sentinel-1 data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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