地表土壤含水量是運用在氣象學、氣候學、水文學及生態學的重要參數之一。遙感探測感測器AMSR2 (Advanced Microwave Scanning Radiometer 2)提供10公里的地表土壤含水量影像，能應用於大尺度地表土壤含水量的監測，然而，低空間解析度將限制地表土壤含水量影像的應用性。過去實驗中，發現地表土壤含水量、常態化多波段乾旱指數(Normalized Multi-band Drought Index, NMDI)及葉面積指數(Leaf Area Index, LAI)共三項資料間存在著關聯性，低葉面積指數時，NMDI與地表土壤含水量呈現負相關的現象，而此關係於葉面積指數漸增的過程中逐漸消失。因此，期望能將地表土壤含水量、NMDI及葉面積指數進行迴歸分析並建立地表土壤含水量的推估模式。本研究主要目的為建立迴歸模式，再以1公里葉面積指數影像及NMDI影像推估1公里高空間解析度的地表土壤含水量影像。本研究研究區域為中美洲，且針對該區之乾季(12月至隔年5月)進行研究，而收集2013年至2015年1月至4月的AMSR2及MODIS (Moderate Resolution Imaging Spectroradiometer)資料。融合這些衛星資料，並得到迴歸分析結果，其平均絕對誤差(Mean Absolute Error, MAE)為1.8 % 至 2.6 %，經過誤差補償後，平均絕對誤差能下降0.1 % 至 1.0 %。這結果證實了過去研究所發現地表土壤含水量、NMDI及葉面積指數共三項資料間存在著關聯性，並利用此關聯降低AMSR2土壤含水量的空間解析度。本研究發展一個創新的方式融合AMSR2及MODIS，以期望AMSR2資料的應用性能有所增加。

Surface soil moisture (SSM) is one of the most significant variables for various applications in meteorology, climatology, hydrology, and ecology. To monitor SSM for large scale, Advanced Microwave Scanning Radiometer 2 (AMSR2) provides SSM data with a spatial resolution of 10 km. However, this coarse resolution limits the applicability of the SSM product. Experiments from previous studies have revealed a relationship between SSM, normalized multi-band drought index (NMDI) and leaf area index (LAI). In general, the NMDI represents a negative relation with SSM, when the LAI is low. The negative relationship between NMDI and SSM becomes less significant with the increase of LAI. Accordingly, a bivariate regression analysis is expected to formulize the relationship between NMDI, LAI and SSM. The main objective of this study is to generate 1-km resolution SSM data by using 1-km resolution LAI and 1-km resolution NMDI, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, after a regression model can be obtained. The study area is located in Central America, and the study mainly focuses on the dry season (December-May). The period of image acquisition of AMSR2 and MODIS is from January to April, 2013 to 2015. Fusing these satellite datasets to obtain the regression analysis model, and the mean absolute errors (MAE) are 1.8 % to 2.6 %. The MAEs are reduced 0.1 % to 1.0 % after error compensation. It confirmed the relationship described by the previous study, and therefore was then applied for AMSR2 SSM downscaling. The study developed an innovative method for data fusion among AMSR2 and MODIS, and is expected to enhance the applicability of the AMSR2 data.

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