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

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Article
English
2012

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

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0 Files

English
2012
Remote Sensing of Environment
Vol 127
DOI: 10.1016/j.rse.2012.09.002

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Compton Tucker
Compton Tucker

NASA Goddard Space Flight Center

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Alexei Lyapustin
Yujie Wang
István László
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Abstract

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 16days of MODIS data gridded to 1km 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.

How to cite this publication

Alexei Lyapustin, Yujie Wang, István László, Thomas Hilker, Forrest G. Hall, P. J. Sellers, Compton Tucker, Sergey Korkin (2012). Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction. Remote Sensing of Environment, 127, pp. 385-393, DOI: 10.1016/j.rse.2012.09.002.

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Publication Details

Type

Article

Year

2012

Authors

8

Datasets

0

Total Files

0

Language

English

Journal

Remote Sensing of Environment

DOI

10.1016/j.rse.2012.09.002

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