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Remote Sens. 2017, 9(4), 370; doi:10.3390/rs9040370

Prototyping of LAI and FPAR Retrievals from MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) Data

1
Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA 02215, USA
2
Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, MD 20771, USA
3
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
4
Beijing Key Lab of Spatial Information Integration & Its Applications, Institute of RS & GIS, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Received: 25 December 2016 / Revised: 3 April 2017 / Accepted: 13 April 2017 / Published: 15 April 2017
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Abstract

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation are key variables in many global models of climate, hydrology, biogeochemistry, and ecology. These parameters are being operationally produced from Terra and Aqua MODIS bidirectional reflectance factor (BRF) data. The MODIS science team has developed, and plans to release, a new version of the BRF product using the multi-angle implementation of atmospheric correction (MAIAC) algorithm from Terra and Aqua MODIS observations. This paper presents analyses of LAI and FPAR retrievals generated with the MODIS LAI/FPAR operational algorithm using Terra MAIAC BRF data. Direct application of the operational algorithm to MAIAC BRF resulted in an underestimation of the MODIS Collection 6 (C6) LAI standard product by up to 10%. The difference was attributed to the disagreement between MAIAC and MODIS BRFs over the vegetation by −2% to +8% in the red spectral band, suggesting different accuracies in the BRF products. The operational LAI/FPAR algorithm was adjusted for uncertainties in the MAIAC BRF data. Its performance evaluated on a limited set of MAIAC BRF data from North and South America suggests an increase in spatial coverage of the best quality, high-precision LAI retrievals of up to 10%. Overall MAIAC LAI and FPAR are consistent with the standard C6 MODIS LAI/FPAR. The increase in spatial coverage of the best quality LAI retrievals resulted in a better agreement of MAIAC LAI with field data compared to the C6 LAI product, with the RMSE decreasing from 0.80 LAI units (C6) down to 0.67 (MAIAC) and the R2 increasing from 0.69 to 0.80. The slope (intercept) of the satellite-derived vs. field-measured LAI regression line has changed from 0.89 (0.39) to 0.97 (0.25). View Full-Text
Keywords: MAIAC; MODIS; leaf area index (LAI); fraction of photosynthetically active radiation (FPAR); radiative transfer MAIAC; MODIS; leaf area index (LAI); fraction of photosynthetically active radiation (FPAR); radiative transfer
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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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MDPI and ACS Style

Chen, C.; Knyazikhin, Y.; Park, T.; Yan, K.; Lyapustin, A.; Wang, Y.; Yang, B.; Myneni, R.B. Prototyping of LAI and FPAR Retrievals from MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) Data. Remote Sens. 2017, 9, 370.

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