利用Sentinel-1 & 2遙測影像推估土壤含水量空間分佈－以桃園石門灌區為例

土壤含水量為一在氣象學、水文學、農業生態學等的重要參數，同時也影響全球水循環、植物蒸發散情形等機制運作。在農業中土壤含水量影響著農業配水的分配；在坡地災害中也作為土石流預警的一項重要參考指標。然而土壤含水量的量測常常受到量測儀器的時空間局限性，要浪費大量的人力物力才能取得大範圍及長時間的監測資訊。但若能使用衛星遙測技術的優點來進行土壤含水量的推估，將可取得大範面長時間的監測資訊，這必然會對自然環境有更進一步的了解。本文將應用歐洲太空總署ESA的哥白尼計畫-Sentinel系列任務之開放衛星資料—Sentinel-1合成孔徑雷達微波波段資料及Sentinel-2多光譜資料來進行土壤含水量的推估。合成孔徑雷達的成像資訊為後向散射係數，而後向散射係散與土壤含水量有直接的關係。因此本文以兩者關係的觀點角度切入，探討台灣地區兩者之間的關係，並以水雲模型來消除植被覆蓋所造成的影響。水雲模型的參數中，葉面積指數及植被含水量無法直接從Sentinel-1影像獲取，需要以Sentinel-2的影像反射率，配合PROSAIL模型反演得出葉面積指數及植被含水量。本文將以2017年全年的Sentinel-1及Sentinel-2資料，並與中央大學測站的土壤含水量進行比較，進而觀察後向散射係數與不同深度土壤含水量之相關性。結果顯示雷達後向散射係數與中央大學水文氣象測站的深度10公分的土壤含水量有比較好的相關性，相關係數R-square為0.53，並把回歸公式套用到桃園及石門灌區，觀察該區土壤含水量的時間變化，並以石門灌區內的皮寮溪為例作討論。

關鍵字

土壤含水量；合成孔徑雷達；衛星遙測；水雲模型； PROSAIL輻射傳輸模型

並列摘要

Soil moisture is one of the important parameters in meteorology, hydrology as well as agricultural, which also affect the operation of the mechanism such as global water cycle and plant evapotranspiration. Soil moisture is one of the factor which affect landslide as well as agriculture. However, the measurement of soil moisture is often limited by the spatial limitations of the observation instrument. If remote sensing data can be used to estimate soil moisture, it can have a large area and time series monitoring of soil moisture and that can increase the efficiency of environmental management and monitoring. This paper will use the Sentinel-1 C band SAR & Sentinel-2 multi-spectrum satellite data which come from Copernicus Programme of ESA (European Space Agency) to estimate soil moisture. The imaging principle of SAR is backscatter, which is directly related to soil moisture. Therefore, this paper will use the point of view of their relationship to estimate their situation in Taiwan region. Water-cloud model is used to reduce the vegetation cover affect. In water-cloud model, there are two parameters: LAI and canopy water content cannot be obtained directly from Sentinel-1 satellite. That’s why Sentinel-2 satellite reflectance data are used to do inverse calculation of LAI and canopy water content using PROSAIL. In this paper, Sentinel-1 & Sentinel-2 satellite data for the whole year 2017 are used and compare with the in situ soil moisture data which come from surface hydrology laboratory in National Central University, as to observe their relationship. Results show that C band radar backscatter coefficient the depth of 10cm soil moisture have a good correlation, the correlation coefficient R-square is 0.53. Therefore, apply their relationship to Taoyuan & Shihmen irrigation area, observe the time series change in 2017 of the area, and use Piliao river as a case study for discussion.