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首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >A synergistic approach to atmospheric correction of NEON's airborne hyperspectral data utilizing airborne solar spectral flux radiometers, ground based radiometers, and airborne hyperspectral imagers
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A synergistic approach to atmospheric correction of NEON's airborne hyperspectral data utilizing airborne solar spectral flux radiometers, ground based radiometers, and airborne hyperspectral imagers

机译：利用机载太阳光谱通量辐射计，地基辐射计和机载高光谱成像仪，对NEON机载高光谱数据进行大气校正的协同方法

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A wide variety of critical information regarding bioclimate, biodiversity, and biogeochemistry is embedded in airborne hyperspectral imagery. Most, if not all of the primary signal relies upon first deriving the surface reflectance of land cover and vegetation from measured hyperspectral radiance. This places stringent requirements on terrain, and atmospheric correction algorithms to accurately derive surface reflectance properties. An observatory designed to measure bioclimate, biodiversity, and biogeochemistry variables from surface reflectance must take great care in developing an approach which chooses algorithms with the highest accuracy, along with providing those algorithms with data necessary to describe the physical mechanisms that affect the measured at sensor radiance. Such an approach is in development with the Airborne Observation Platform (AOP) part of the National Ecological Observatory Network (NEON). NEON is a continental-scale ecological observation platform designed to collect and disseminate data to enable the understanding and forecasting of the impacts of climate change, land use change, and invasive species on ecology [1].
机译：有关生物气候，生物多样性和生物地球化学的各种关键信息已嵌入到机载高光谱图像中。大部分（如果不是全部）主要信号依赖于首先从测得的高光谱辐射率推导土地覆盖物和植被的表面反射率。这对地形和大气校正算法提出了严格的要求，以精确得出表面反射率特性。设计用于从表面反射率测量生物气候，生物多样性和生物地球化学变量的天文台必须格外小心，以开发出一种方法，该方法选择具有最高准确度的算法，并为这些算法提供描述影响传感器测量结果的物理机制所必需的数据辐射。国家生态观测站网络（NEON）的机载观测平台（AOP）部分正在开发这种方法。 NEON是一个大陆性的生态观测平台，旨在收集和传播数据，以使人们能够了解和预测气候变化，土地利用变化和入侵物种对生态的影响[1]。

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