Creating Proxy VIIRS Data From MODIS: Spectral Transformations for Mid- and Thermal-Infrared Bands
Creating Proxy VIIRS Data From MODIS: Spectral Transformations for Mid- and Thermal-Infrared Bands
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DOI：
10.1109/tgrs.2008.923320
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发表时间：
2008-11
影响因子：
8.2
通讯作者：
R. L. Vogel;J. Privette;Yunyue Yu
中科院分区：
文献类型：
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作者：
R. L. Vogel;J. Privette;Yunyue Yu
在发射新卫星之前，通常需要模拟传感器数据来开发和测试新传感器和算法。理想情况下，这些数据非常接近将在轨道上收集的数据。尽管辐射传输模型可以用于此目的，但所有模型都有偏差，并且没有一个模型可以完全模拟地球环境系统的复杂异质性。另一种方法是通过转换从过去或当前传感器收集的真实观测值来导出“代理”数据集。代理数据本质上包含自然地球辐射特性和传感器噪声，就像来自新传感器的数据一样。在为国家极轨运行环境卫星系统 (NPOESS) 和 NPOESS 准备项目任务做准备时，我们开发了一种为可见红外成像仪辐射计套件 (VIIRS) 的中红外和热红外波段创建代理数据的方法。具体来说，通过结合辐射传输模型和 NASA 大气红外探测器 (AIRS) 的数据，我们开发了光谱变换方程，将真实的中分辨率成像光谱辐射计 (MODIS) 数据转换为代理 VIIRS 数据。通过回归分析确定方程的函数形式。通常，给定 VIIRS 波段的最佳光谱变换方程是多个 MODIS 波段、传感器/太阳几何结构和表面类型的函数。所有变换方程均适用于晴空条件。我们的白天中红外变换方程的精度低于所有表面类型的预测传感器噪声。我们的陆地热红外方程对于植被覆盖来说是最准确的；我们的海洋方程对于大多数频段都是准确的。使用高级甚高分辨率辐射计对该方法进行的验证表明，该方法可以提供比其他方法更准确的代理数据。尽管使用 AIRS 的优点是其高光谱设计，允许模拟 MODIS 和 VIIRS 波段，但其粗空间分辨率在识别纯土地覆盖和无云像素以生成方程系数方面存在缺点。我们写这篇论文的主要目的是提供一种方法供其他传感器团队考虑。我们的临时 MODIS 到 VIIRS 光谱变换方程包含一些示例表面类型。
Prior to the launch of a new satellite, simulated sensor data are often desired to develop and test the new sensors and algorithms. Ideally, these data closely approximate the data that will be collected on-orbit. Although radiative-transfer models can be employed for this purpose, all models have biases, and none can completely mimic the complex heterogeneity of Earth's environmental system. An alternative approach is to derive ldquoproxyrdquo data sets by transforming real observations collected from past or current sensors. Proxy data inherently contain both natural Earth radiation characteristics and sensor noise as the data from the new sensor will. In preparation for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) and NPOESS Preparatory Project missions, we developed a methodology to create proxy data for the mid- and thermal-infrared bands of the Visible-Infrared Imager-Radiometer Suite (VIIRS). Specifically, by combining radiative-transfer modeling and data from NASA's Atmospheric Infrared Sounder (AIRS), we developed spectral transformation equations to convert real Moderate Resolution Imaging Spectroradiometer (MODIS) data into proxy VIIRS data. The functional forms of the equations were determined through regression analysis. Typically, the best spectral transformation equation for a given VIIRS band was a function of multiple MODIS bands, sensor/solar geometry, and surface type. All transformation equations are for clear-sky conditions. Our daytime midinfrared transformation equations have an accuracy that is below the predicted sensor noise for all surface types. Our thermal-infrared equations over land are most accurate for vegetated covers; our ocean equations are accurate for most bands. Validation of this approach with the Advanced Very High Resolution Radiometer suggests that this method may provide higher accuracy proxy data than other methods. Although the advantage in using AIRS is its hyperspectral design, allowing simulation of MODIS and VIIRS bands, its coarse spatial resolution presented a disadvantage in identifying pure land-cover and cloud-free pixels for generating the equation coefficients. Our primary intent with this paper is to offer a methodology for consideration by other sensor teams. Our provisional MODIS-to-VIIRS spectral transformation equations are included for some example surface types.