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Estimation of rice plant coverage using Sentinel-2 based on UAV-observed data
http://hdl.handle.net/10076/0002000849
http://hdl.handle.net/10076/0002000849bd81c641-4771-424c-bd7f-e26e8b00b669
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
2023ME0203.pdf (12.2 MB)
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||||
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| 公開日 | 2024-08-20 | |||||||
| タイトル | ||||||||
| タイトル | Estimation of rice plant coverage using Sentinel-2 based on UAV-observed data | |||||||
| 言語 | en | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | unmanned aerial vehicle (UAV) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Sentinel-2 multispectral instrument (MSI) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | paddy field | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | rice plant coverage | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | mixed pixel analysis | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_46ec | |||||||
| 資源タイプ | thesis | |||||||
| 著者 |
佐藤, 優気
× 佐藤, 優気
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | Vegetation coverage is a crucial parameter in agriculture, as it offers essential insights into crop growth and health conditions. The spatial resolution of spaceborne sensors is relatively limited, making it challenging to precisely measure vegetation coverage. Consequently, fine-resolution ground observation data becomes indispensable for establishing the correlation between remotely sensed reflectance and plant coverage. This study estimated rice plant coverage per pixel using time series Sentinel-2 Multispectral Instrument (MSI) data, which enables monitoring rice growth conditions over a wide area. Rice plant coverage was calculated using Unmanned Aerial Vehicle (UAV) data with a spatial resolution of 3 cm based on the spectral unmixing method. This plant coverage map was generated every two to three weeks throughout the rice growing season. Subsequently, the plant coverage was estimated at a 10 m resolution through the multiple linear regression, utilizing Sentinel-2 MSI reflectance data and these plant coverage maps. In this process, a geometric registration of MSI and UAV data was conducted to improve their spatial agreement. The coefficient of determination (R²) of the multiple linear regression model was 0.92 and 0.94 for the Level-1C and Level-2A products of Sentinel-2 MSI, respectively. The root mean squared error (RMSE) of the estimated rice plant coverage was 10.77% and 9.34%, respectively. This study highlights the potential of a satellite time series model for accurate estimation of rice plant coverage. | |||||||
| 内容記述 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 三重大学 大学院工学研究科 情報工学専攻 データサイエンス研究室 | |||||||
| 内容記述 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 37p | |||||||
| 書誌情報 |
発行日 2024-03 |
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| フォーマット | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | application/pdf | |||||||
| 著者版フラグ | ||||||||
| 出版タイプ | VoR | |||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||
| 出版者 | ||||||||
| 出版者 | 三重大学 | |||||||
| 出版者（ヨミ） | ||||||||
| 値 | ミエダイガク | |||||||
| 修士論文指導教員 | ||||||||
| 姓名 | 松岡, 真如 | |||||||
| 言語 | ja | |||||||
| 資源タイプ（三重大） | ||||||||
| 値 | Master's Thesis / 修士論文 | |||||||
