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首页> 外文期刊>Geocarto international >Multispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data
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Multispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data

机译：用于从Hyperion数据生成高光谱植被指数的多光谱传感器光谱分辨率模拟

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The use of multispectral satellite sensors for generation of hyperspectral indices is restricted because of their coarse spectral resolutions. In this study, we attempted to synthesize a few of these hyperspectral indices, viz. RedEdge Normalized Difference Vegetation Index (NDVI705), Plant Senescence Reflectance Index (PSRI) and Normalized-Difference-Infrared-Index (NDII), for crop stress monitoring at regional scale using multispectral images, simulated from Hyperion data. The Hyperion data were resampled and simulated to corresponding spatial and spectral resolutions of AWiFS, OCM-2 and MODIS sensors using their respective filter function. Different possible combinations of two bands (i.e. simple difference, simple ratio and normalized difference) were computed using synthetic spectral bands of each sensor, and were regressed with NDVI705, PSRI and NDII. Models with highest correlation were selected and inverted on Hyperion data of another date to synthesize respective multispectral indices. Synthetic broad band indices of multispectral sensors with their respective narrow band indices of Hyperion were found to be in good agreement.
机译：由于其粗糙的光谱分辨率，限制了使用多光谱卫星传感器生成高光谱指数。在这项研究中，我们尝试合成其中一些高光谱指数。 RedEdge归一化差异植被指数（NDVI705），植物衰老反射率指数（PSRI）和归一化差异红外指数（NDII），用于使用来自Hyperion数据的多光谱图像在区域范围内监测作物胁迫。将Hyperion数据重新采样，并使用它们各自的滤波功能将其模拟为AWiFS，OCM-2和MODIS传感器的相应空间和光谱分辨率。使用每个传感器的合成光谱带计算两个波段的不同可能组合（即简单差异，简单比率和归一化差异），并分别用NDVI705，PSRI和NDII进行回归。选择具有最高相关性的模型，并在另一个日期的Hyperion数据上进行倒置，以合成各自的多光谱指数。发现多光谱传感器的合成宽带指数与它们各自的Hyperion窄带指数非常吻合。

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