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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:使用归一化多波段干旱指数和叶面积指数缩小尺度的先进微波扫描辐射计2的表面土壤水分

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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:土壤湿度是气象,气候,水文学和生态学中各种应用中最重要的变量之一。为了大规模监测地表土壤水分(SSM),高级微波扫描辐射计2(AMSR2)提供了空间分辨率分别为10 km和25 km的SSM数据。先前研究的实验表明,通过标准化多波段干旱指数(NMDI)和叶面积指数(LAI),可以使SSM功能化。由于NMDI和LAI都是从中等分辨率成像光谱仪(MODIS)数据中获取的,分辨率分别为0.5 km和1 km,因此通过合并AMSR2和MODIS数据的降尺度方法有望产生分辨率为1的SSM数据。公里这项研究的主要目的是建立AMSR2 SSM与MODIS派生的NMDI和LAI之间的关系。研究区域位于中美洲,我们主要关注从1月到4月的旱季。 AMSR2和MODIS的图像数据采集时间为2014年1月至2014年2月。该研究是通过首先基于每天的观察结果生成转换函数进行的,结果证实了AMSR2 SSM降尺度方法的有效性。此外,该方法有望发展为雨季的分析,以最终确定该方法,并且有望将其转移到其他地区,以获得更精细的SSM数据。

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