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Downscaling Advanced Microwave Scanning Radiometer 2 Surface Soil Moisture Using Normalized Multi-Band Drought Index And Leaf Area Index

机译:缩小高级微波扫描辐射计2表面土壤水分使用归一化多带干旱指数和叶面积指数

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Soil moisture is one of the most significant variables for various applications in meteorology, climatology, hydrology, and ecology. To monitor surface soil moisture (SSM) for large scale, Advanced Microwave Scanning Radiometer 2 (AMSR2) provides SSM data with a spatial resolution of 10 km and 25 km. Experiments from previous studies have revealed that SSM can be possibly functionalized by normalized multi-band drought index (NMDI) and leaf area index (LAI). Since NMDI and LAI are both acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) data, with resolutions of 0.5 km and 1 km respectively, a downscaling method by incorporating AMSR2 and MODIS data is therefore expected to generate SSM data with a finer resolution of 1 km. The main objective of this study is to formulate relationships between AMSR2 SSM and MODIS-derived NMDI and LAI. The study area is located in Central America, and we mainly focus on the dry season, which extends from January to April. The period of acquisition for image data of AMSR2 and MODIS is from January to February, 2014. The study was conducted by first generating a transformation function based on the observations each day, and the results confirmed the validity of the method for AMSR2 SSM downscaling. Furthermore, the method is expected to develop the analysis for the rainy season in order to finalize the method, and is also expected to be transferable to other regions to obtain the SSM data in finer scale.
机译:土壤水分是气象,气候,水文和生态学中各种应用中最重要的变量之一。为了监控大规模的表面土壤水分(SSM),先进的微波扫描辐射计2(AMSR2)提供SSM数据,空间分辨率为10公里和25公里。来自先前研究的实验表明,SSM可以通过标准化的多频带干旱指数(NMDI)和叶面积指数(LAI)来官能化。由于NMDI和LAI都是从中等分辨率的成像光谱辐射器(MODIS)数据,因此分别的分辨率分别为0.5公里和1 km,因此预计通过结合AMSR2和MODIS数据的缩小方法将产生具有1的更精细分辨率的SSM数据km。本研究的主要目的是制定AMSR2 SSM和MODIS衍生的NMDI和LAI之间的关系。该研究区位于中美洲,主要关注旱季,从1月至4月延伸。 AMSR2和MODIS的图像数据的收购时间是2014年1月至2月。通过首先根据每天的观察结果产生转变函数,结果证实了AMSR2 SSM级较低的方法的有效性。此外,该方法预计将为雨季开发分析,以便最终确定该方法,并且还预计将可转移到其他区域以获得更精细的SSM数据。

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