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Consistency of Vegetation Index Seasonality Across the Amazon RainforestVegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.
Document ID
20170003721
Document Type
Reprint (Version printed in journal)
Authors
Maeda, Eduardo Eiji (Helsinki Univ. Helsinki, Finland)
Moura, Yhasmin Mendes (Instituto Nacional de Pesquisas Espacias Sao Jose dos Campos, Brazil)
Wagner, Fabien (Instituto Nacional de Pesquisas Espacias Sao Jose dos Campos, Brazil)
Hilker, Thomas (Oregon State Univ. Corvallis, OR, United States)
Lyapustin, Alexei I. (NASA Goddard Space Flight Center Greenbelt, MD United States)
Wang, Yujie (Maryland Univ. Baltimore County Catonsville, MD, United States)
Chave, Jerome (Centre National de la Recherche Scientifique Toulouse, France)
Mottus, Matti (Helsinki Univ. Helsinki, Finland)
Aragao, Luiz E.O.C. (Instituto Nacional de Pesquisas Espacias Sao Jose dos Campos, Brazil)
Shimabukuro, Yosio (Instituto Nacional de Pesquisas Espacias Sao Jose dos Campos, Brazil)
Date Acquired
April 20, 2017
Publication Date
June 9, 2016
Publication Information
Publication: International Journal of Applied Earth Observations and Geoinformation
Volume: 52
ISSN: 0303-2434
Subject Category
Earth Resources and Remote Sensing
Meteorology and Climatology
Report/Patent Number
GSFC-E-DAA-TN41824
Funding Number(s)
CONTRACT_GRANT: ANR-10-LABX-25-01
CONTRACT_GRANT: ANR-10-LABX-0041
CONTRACT_GRANT: ACAD-FIN-266393
CONTRACT_GRANT: NNX15AT34A
Distribution Limits
Public
Copyright
Other
Keywords
MAIAC
BRDF effect
MODIS