我國航遙測技術推動始於農林業調查，歷經幾十年來的發展已漸成熟，每年的稻作栽培面積調查與稻米產量預測乃是國內遙測技術最重要的應用項目之一。自1980年開始前臺灣省政府糧食局即利用航攝影像進行臺灣全區之稻作面積調查，因為當時衛星影像取得不易且資源衛星影像解析度較低，航攝影像相對較為簡捷便利。時至今日，固然航攝影像的應用仍有低時間解析度、高花費及資料處理時間冗長等缺點，國內農業資源調查與監測仍以航攝影像為主，惟近幾年來利用不同類型衛星影像來替代航攝影像的努力仍方興未艾，然多侷限於稻米產量預測。福爾摩沙二號衛星(簡稱福衛二號)係我國完全自主控制之資源與科學用途衛星，由於福衛二號與其他資源衛星相比較之最大優勢在於色彩真實、高空間與高時間解析度、每日再現性等，正好解決目前遙測科技應用於農業遭逢的難題，亦能夠提高農作物生長估測及產量預測的準度與精度。本篇論文研究採用福衛二號之可見光和近紅外光光譜資料，配合田間取樣調查及近地面量測植被高解析反射光譜，發展一套模組(algorithm)來監測水稻植株之生長並預估收穫時之產量表現。田間試驗係在臺中縣霧峰鄉農委會農業試驗所農場進行，於2006年一期稻作(3-6月)及二期稻作(8-11月)以輻射分光光譜儀(spectroradiometer, model GER-2600, Geophysical & Environmental Research Corp., NY, USA)量測近地面之水稻植被高解析反射光譜(350-2400 nm)，並每隔2-3週取樣調查葉面積指數(leaf area index, LAI)與生育進展，再於收穫時割取小區產量。在福衛二號資料方面，總計選用了36張經過錯位修正、輻射校正之衛星影像，這些衛星影像擷取之植被光譜資訊適時反映了水稻植被顏色及形態上的變化，並由此計算出標準差植被指數(或稱正規化植生指數; normalized difference vegetation index, NDVI)。依據由近地面量測之植被高解析反射光譜計算之NDVInear ground與實測LAImeasured建立的指數函數關係，可將不同生長時期計算之福衛二號NDVIFORMOSAT-2數值估算相對應LAIFORMOSAT-2數值。再藉由最佳時期累加之實測LAImeasured與實測產量之迴歸方程式，進而推估由福衛二號影像資料預測之水稻產量。研究結果顯示，由福衛二號之衛星影像所構建遙測-水稻產量模組具有監測水稻生長與預測產量之應用潛力，對於霧峰地區2006年一期稻作和二期稻作獲得合理產量預估。本文研究構建之遙測-水稻產量模組經納入多年期多地點資料以增進對環境變異的適用性，同時改進必要修正與校正程序後，預料將能提升對水稻生長監測與產量預測的準確度，並進一步發展為大區域之遙測應用。

Estimating the annual yield of rice is one of the most important applications of remote sensing in Taiwan. In the mid 1980s’, at least half of the gross domestic product contributed by agriculture in Taiwan came from rice. The demand of an efficient approach to investigate and estimate crops yield in a large scale, particularly for rice crop, initiates the development of remote sensing techniques afterwards. Nowadays, taking the aerial photos of rice paddy over the island of Taiwan has become a regular task operated once or twice per year. The application of these aerial photos in estimating crop yield is limited by their low temporal resolution, expensive cost and time-consuming data processing. Attempts have therefore been made to use various satellite images to replace the aerial photos in the recent years, with the sacrifice of spatial resolution, yet the same limitations still impede the application of remote sensing in yield estimation. The successful operation of FORMOSAT-2 satellite proved the concept that the temporal resolution of a remote sensing system can be much improved by deploying a high spatial resolution sensor in a daily revisit orbit. Therefore, the aforementioned limitations of remote sensing in estimating crop yield can be completely removed by employing the FORMOSAT-2 high-temporal and high-spatial imagery. This research follows the approach proposed by Chen and Yang (2005) to integrate the FORMOSAT-2 observations with a comprehensive dataset collected in the field, with the intention to monitor growth and estimate yield of rice crop. The field experiments were conducted at Taiwan Agricultural Research Institute Experimental Farm at Wufeng in the first and the second cropping seasons of 2006. The leaf area index (LAI), developmental stage, yield at harvest and the near ground canopy hyperspectral reflectance (R) were collected at the intervals of two to three weeks for rice plants (Oryza sativa L. cv. TNG 67) grown under eight planting densities. A total of thirty-six multispectral images of the study area taken by FORMOSAT-2 during the growing periods were processed by band-to-band coregistration, spectral preserved pan-sharpening, automatic orthorectification, multitemporal imagery matching and radiometric normalization. These FORMOSAT-2 images provided us the information of NDVI, and hence the LAI of rice paddy at different stages of growth. Finally, the yields of both crops were estimated by accumulating FORMOSAT-2-derived LAI and compared to the actual amounts of yields at harvest. Results demonstrated the potential of FORMOSAT-2 high-temporal and high-spatial images in monitoring rice growth and estimating crop yield.

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