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Improving the AVHRR Long Term Data Record BRDF Correction

NASA Goddard Space Flight Center 8800 Greenbelt Rd, Greenbelt, MD 20771, USA
Department of Geographical Sciences, University of Maryland College Park, 2181 LeFrak Hall, College Park, MD 20740, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 502;
Received: 24 January 2019 / Revised: 21 February 2019 / Accepted: 25 February 2019 / Published: 1 March 2019
(This article belongs to the Special Issue Remotely Sensed Albedo)
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The Long Term Data Record (LTDR) project has the goal of developing a quality and consistent surface reflectance product from coarse resolution optical sensors. This paper focuses on the Advanced Very High Resolution Radiometer (AVHRR) part of the record, using the Moderate Resolution Imaging Spectrometer (MODIS) instrument as a reference. When a surface reflectance time series is acquired from satellites with variable observation geometry, the directional variation generates an apparent noise which can be corrected by modeling the bidirectional reflectance distribution function (BRDF). The VJB (Vermote, Justice and Bréon, 2009) method estimates a target’s BRDF shape using 5 years of observation and corrects for directional effects maintaining the high temporal resolution of the measurement using the instantaneous Normalized Difference Vegetation Index (NDVI). The method was originally established on MODIS data but its viability and optimization for AVHRR data have not been fully explored. In this study we analyze different approaches to find the most robust way of applying the VJB correction to AVHRR data, considering that high noise in the red band (B1) caused by atmospheric effect makes the VJB method unstable. Firstly, our results show that for coarse spatial resolution, where the vegetation dynamics of the target don’t change significantly, deriving BRDF parameters from 15+ years of observations reduces the average noise by up to 7% in the Near Infrared (NIR) band and 6% in the NDVI, in comparison to using 3-year windows. Secondly, we find that the VJB method can be modified for AVHRR data to improve the robustness of the correction parameters and decrease the noise by an extra 8% and 9% in the red and NIR bands with respect to using the classical VJB inversion. We do this by using the Stable method, which obtains the volumetric BRDF parameter (V) based on its NDVI dependency, and then obtains the geometric BRDF parameter (R) through the inversion of just one parameter. View Full-Text
Keywords: AVHRR; BRDF; MODIS; VJB; LTDR; directional correction AVHRR; BRDF; MODIS; VJB; LTDR; directional correction

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Villaescusa-Nadal, J.L.; Franch, B.; Vermote, E.F.; Roger, J.-C. Improving the AVHRR Long Term Data Record BRDF Correction. Remote Sens. 2019, 11, 502.

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