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\begin{document}

\title{Research on the Fusion of Time-Series Sentinel-1 Data and Phenological
Features for Sugarcane Planting Distribution Extraction}



\author[1]{Senzheng Chen}%
\author[1]{Huichun Ye}%
\author[2]{Shanyu Huang}%
\author[3]{Longlong Zhao}%
\author[1]{Chaojia Nie}%
\author[4]{Weixia Hu}%
\affil[1]{Chinese Academy of Sciences Aerospace Information Research Institute}%
\affil[2]{Ministry of Agriculture and Rural Affairs of the People's Republic of China}%
\affil[3]{Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology}%
\affil[4]{JiangXi University of Science and Technology School of Science}%


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  \date{June 11, 2024}


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\begin{abstract}
The extraction of sugarcane planting distribution provides a scientific
basis and theoretical support for local sugarcane cultivation management
and the prediction of sugarcane yield. Sugarcane predominantly grows in
tropical and subtropical regions characterized by cloudy and rainy
conditions. Optical satellite remote sensing imagery is greatly affected
by cloud and rain interference. In contrast, synthetic aperture radar
(SAR) data exhibit strong penetration capabilities, enabling effective
imaging in overcast, rainy, and cloudy environments. Focusing on Fusui
County, Guangxi Province, China, this research utilizes Sentinel-1 radar
data and integrates the phenological features of sugarcane growth. A
sugarcane planting distribution extraction model is constructed using a
random forest classifier. The results demonstrate that the phenological
feature approach based on temporal radar scattering characteristics
achieves superior performance in sugarcane identification and
extraction. The overall accuracy surpasses 92.18\%, with a Kappa
coefficient of 0.89. This method exhibits a 4.95\% accuracy improvement
compared to single-period radar scattering feature methods. Therefore,
this radar-based method for extracting sugarcane planting distribution
can effectively and accurately extract sugarcane cultivation patterns in
regions with complex cloud and rain conditions, such as Guangxi
Province. It also serves as a methodological reference for extracting
crop planting distributions in cloudy and rainy areas.%
\end{abstract}%



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\textbf{Hosted file}

\verb`\begin{CJK}{UTF8}{gbsn}投稿新\end{CJK}\selectlanguage{english}Sugarcane Planting Distribution Extraction.docx`  available at \url{https://authorea.com/users/756375/articles/727340-research-on-the-fusion-of-time-series-sentinel-1-data-and-phenological-features-for-sugarcane-planting-distribution-extraction}

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