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Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction

机译:MODIS(MAIAC)的大气校正的多角度实现:3.大气校正

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This paper describes the atmospheric correction (AC) component of the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) which introduces a new way to compute parameters of the Ross-Thick Li-Sparse (RTLS) Bi-directional reflectance distribution function (BRDF), spectral surface albedo and bidirectional reflectance factors (BRF) from satellite measurements obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS). MAIAC uses a time series and spatial analysis for cloud detection, aerosol retrievals and atmospheric correction. It implements a moving window of up to 16. days of MODIS data gridded to 1. km resolution in a selected projection. The RTLS parameters are computed directly by fitting the cloud-free MODIS top of atmosphere (TOA) reflectance data stored in the processing queue. The RTLS retrieval is applied when the land surface is stable or changes slowly. In case of rapid or large magnitude change (as for instance caused by disturbance), MAIAC follows the MODIS operational BRDF/albedo algorithm and uses a scaling approach where the BRDF shape is assumed stable but its magnitude is adjusted based on the latest single measurement. To assess the stability of the surface, MAIAC features a change detection algorithm which analyzes relative change of reflectance in the Red and NIR bands during the accumulation period. To adjust for the reflectance variability with the sun-observer geometry and allow comparison among different days (view geometries), the BRFs are normalized to the fixed view geometry using the RTLS model. An empirical analysis of MODIS data suggests that the RTLS inversion remains robust when the relative change of geometry-normalized reflectance stays below 15%. This first of two papers introduces the algorithm, a second, companion paper illustrates its potential by analyzing MODIS data over a tropical rainforest and assessing errors and uncertainties of MAIAC compared to conventional MODIS products.
机译:本文介绍了大气校正算法(MAIAC)的多角度实现中的大气校正(AC)组件,该组件介绍了一种计算Ross-Thick Li-Sparse(RTLS)双向反射率分布函数(BRDF)参数的新方法),中分辨率成像光谱仪(MODIS)从卫星测量获得的光谱表面反照率和双向反射系数(BRF)。 MAIAC使用时间序列和空间分析进行云探测,气溶胶检索和大气校正。它在选定的投影中实现了长达16天的MODIS数据移动窗口,网格分辨率为1. km。通过拟合存储在处理队列中的无云MODIS大气顶(TOA)反射率数据,可以直接计算RTLS参数。当陆地表面稳定或变化缓慢时,将应用RTLS检索。如果发生快速或大幅度变化(例如,由干扰引起),MAIAC会遵循MODIS的BRDF /反照率算法,并采用缩放方法,假定BRDF形状稳定,但其幅度会根据最新的单次测量进行调整。为了评估表面的稳定性,MAIAC提供了一种变化检测算法,该算法可以分析累积期间红色和近红外波段的反射率的相对变化。为了根据太阳观察者的几何形状调整反射率的可变性并允许在不同日期之间进行比较(视图几何形状),使用RTLS模型将BRF标准化为固定视图几何形状。 MODIS数据的经验分析表明,当几何归一化反射率的相对变化保持在15%以下时,RTLS反转仍然很可靠。两篇文章中的第一篇介绍了该算法,第二篇随附的论文通过分析热带雨林上的MODIS数据并评估与传统MODIS产品相比MAIAC的误差和不确定性来说明其潜力。

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