博碩士論文 110022001 詳細資訊




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姓名 徐庭偉(Ting-Wei Hsu)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 整合Sentinel-1 和 Sentinel-2 衛星影像進行水稻田土壤濕度監測—以桃園灌區為例
(Integrating Sentinel-1 and Sentinel-2 satellite images for soil moisture monitoring in paddy field—A case study of the Taoyuan irrigation district)
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摘要(中) 水稻是台灣最重要的作物,在農業種植面積和產量上都佔有相當大的比例,同時也是水資源用量最多的作物。然而隨著氣候變遷加劇,乾旱、水災等天然災害發生的頻率越來越高,這對台灣的經濟和糧食安全造成巨大的威脅。遙測的發展提供了在大範圍的情況下監測水稻的生長狀況,如何及時且準確地監測稻田土壤水分狀況能夠更好地配置水資源確保食物供應的穩定性,使有效的農業水資源應用成為桃園灌區的重要議題。
估計植被覆蓋下的土壤濕度狀況是目前遙測在農業應用上的重要難題,本研究使用水雲模型 (Water-Cloud Model) 來降低植被覆蓋對於土壤溼度估計的影響,該模型透過結合合成孔徑雷達(Synthetic Aperture Radar, SAR)和光學資訊來模擬不同植被覆蓋下的雷達散射狀況。水雲模型的應用需要根據分析SAR回波、光學植被指數和現場量測的地表土壤濕度之間的關係進行校準,因此本研究蒐集了台灣桃園市桃園灌區2018年至2022年的Sentinel-1雷達影像和Sentinel-2光學影像和中央大學大氣水文觀測站所量測的土壤濕度,並考量了模型變數的影響分別測試了不同類型的雷達極化和針對不同環境變化進行校正的植生指數對於水雲模型校準的性能差異,最後利用精度最高的模型監測桃園灌區水稻田的土壤濕度狀況。
研究結果顯示,在不受到降雨所影響的條件下使用同極化VV搭配耐大氣植生指數 (Atmospherically Resistant Vegetation Index, ARVI) 時的效果最好 (R2=0.55, RMSE=4.16) ,然而,由於缺乏不同條件的土壤濕度量測資料和農業活動的不確定性,本研究所使用的方法需要進一步的分析和實驗以改進模型的性能。
摘要(英) Paddy rice is the crop with the largest planting area and the most significant irrigation water demand in Taiwan, and timely and accurate monitoring of soil moisture in paddy rice fields can make a better allocation of water resources and secure the stability of the food supply. Previous studies have focused on rice mapping using remote sensing techniques. However, irrigation and soil moisture which also significantly influence the rice yield has not yet been fully considered in the rice production estimation.
In this study, to monitor soil moisture in paddy rice fields, this study uses the modified water-cloud model (WCM) which is able to estimate surface moisture in different vegetation covers by integrating Synthetic Aperture Radar (SAR) and optical information. The model application requires calibrating work that is based on analyzing the relationships between SAR backscattering, optical vegetation index, and measured surface moisture content. Specifically, this study collected Sentinel-1 and Sentinel-2 satellite images to perform the soil estimation, and ground measurements from 2018 to 2022 were also obtained for calibrating and validating estimation results. The soil moisture content is measured and recorded using Frequency-domain sensors (FDR) by National Central University (NCU) Atmosphere and Hydrology Observation station. To calibrate the model, this study used the SAR backscattering data from Sentinel-1 and tested various vegetation indexes calculated by using Sentinel-2 imagery. Among them, the highest R2 value of 0.55 can be obtained when the Atmospherically Resistant Vegetation Index (ARVI) is applied. However, the model can be further improved by calibrating the model with more soil moisture observations from various conditions of vegetation covers.
關鍵字(中) ★ 土壤濕度
★ 水稻
★ Sentinel 1&2
★ 水雲模型
★ 合成孔徑雷達
關鍵字(英) ★ Soil moisture
★ Paddy rice
★ Sentinel 1&2
★ Water Cloud Model
★ SAR
論文目次 摘要 I
ABSTRACT II
目錄 III
圖目錄 VI
表目錄 IX
第一章 緒論 1
1.1 研究背景及動機 1
1.2 研究目的 4
1.3 論文架構 4
第二章 文獻回顧 6
2.1 水稻生長概述 6
2.2 土壤濕度估計方法 10
2.2.1 光學估計方法 13
2.2.2 雷達估計方法 17
2.2.3 整合雷達及光學之估計方法 21
第三章 研究方法 23
3.1 研究架構 23
3.2 水雲模型 (Water-Cloud Model) 24
3.3 植生指數 27
3.3.1 常態化差值植生指數 (NDVI) 28
3.3.2 土壤校正植生指數 (SAVI) 29
3.3.3 轉換土壤校正植生指數 (TSAVI) 29
3.3.4 優化土壤校正植生指數 (OSAVI) 30
3.3.5 耐大氣植生指數 (ARVI) 30
3.3.6 增強植生指數 (EVI) 31
3.3.7 常態化水體指數 (NDWI) 32
3.4 模式評估 32
第四章 研究對象及材料 34
4.1 研究流程 34
4.2 研究範圍 36
4.3 研究資料 38
4.3.1 Sentinel-1 40
4.3.2 Sentinel-2 43
4.3.3中央大學大氣水文觀測站 45
4.4 影響資料選取與處理 47
第五章 研究結果 59
5.1 水雲模型校準 59
5.1.1 不同極化下的土壤濕度估計性能 59
5.1.2 不同植生指數下的模型校準性能 60
5.2 土壤濕度觀測值及估計值的比較 63
5.3 桃園灌區各期土壤濕度 69
5.4 水稻田對於土壤濕度的物候變化 75
第六章 研究討論 79
6.1 水雲模型對於降低植被覆蓋影響的效果 79
6.2 不同模型變數對於水雲模型校準的影響 79
6.3 土壤濕度估計效能和過去研究之比較 81
6.4 土壤濕度在稻田中時間及空間上的變化 83
6.5 研究限制及應用 85
第七章 結論及建議 87
7.1 結論 87
7.2 建議 88
參考文獻 90
附錄一 水雲模型衛星影像搭配表 104
附錄二 水雲模型效準之詳細公式 107
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指導教授 姜壽浩(Shou-Hao Chiang) 審核日期 2023-7-27
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