Status for: MAIAC (MCD19)

General Accuracy Statement

The MODIS MAIAC products are currently considered to be at validation stage 1

The Collection 6 MAIAC land surface reflectance suite (MCD19A1) provides spectral bi-directional reflectance factor (BRF), or surface reflectance (SR), daily at 500m and 1km, and 8-day spectral BRDF and albedo for MODIS bands 1-8 (MCD19A3) at 1km.

MAIAC surface reflectance products have been extensively evaluated by the MAIAC team during algorithm development and testing. The test criteria were visual inspection for the lack of cloud/cloud shadow/aerosol artifacts, the realism of RGB images, and the consistency of the time series [Lyapustin et al., 2012]. Our current large-scale analysis is nearing completion and will be reported here soon. Initial results show highly accurate BRF from improved cloud detection, aerosol retrievals and the use of a Green’s function solution, instead of a Lambertian approximation, to derive BRF. This study also found that MAIAC BRF has a low sensitivity to variations in AOD. In cloudy conditions, we found MAIAC provides more high-quality retrievals (up to a factor of 3), confirming an earlier analysis by Hilker et al. [2012] in the highly cloudy Amazon. These results are consistent with those from Chen et al. [2017], based on a limited set of data showing an increase in spatial coverage of the best quality, high-precision LAI retrievals, using MAIAC input.

MAIAC surface reflectance and BRDF-normalization to the fixed view geometry was most thoroughly investigated and used over the tropics by the land community, particularly over the often cloudy Amazon basin (Hilker et al. [2012; 2014; 2015]; Bi et al. [2016]; Maeda et al. [2016] and others). A global inter-comparison with standard MODIS products is pending.

In addition to the reflectance layers, the MCD19A1 includes a Snow QA bit mask, as well as sub-pixel snow fraction and snow grain size layers at 1km. The MAIAC snow mask was evaluated by Cooper et al. [2018] over North America in 2015 against the ground Global Historical Climatology Network-Daily (GHCN-D) database of over 10,000 measurements. The study reported an 82% F-score for MAIAC (the F-score provides a balanced performance assessment combining accuracy and precision, or false positives and false negatives), and an average improvement of 30% in snow detection compared to the NOAA Near-real-time Ice and Snow Extent (NISE) and standard MODIS snow products in 2015.

The MAIAC snow grain size layer was validated against ground measurements in Alaska and Japan (2001-2005) in the case of pure snow (Lyapustin et al., 2009). The retrievals correlate well with measurements in the range of radii ˜0.1–1 mm, although retrieved optical diameter may be about a factor of 1.5 lower than the physical measured diameter.

The Snow Fraction layer (sub-pixel, at 1km) has thus far only been visually evaluated, as part of the MAIAC development and assessment, using new snowfalls, periods of melting and snow seasonality. A validation using high-resolution Landsat data is pending.

Product status updated:  January 2020
Product version:  Collection 6
Supporting Studies:

Title: Assessing snow extent data sets over North America to inform and improve trace gas retrievals from solar backscatter
Author: Cooper, M. J., Martin, R. V., Lyapustin, A. I., and McLinden, C. A.
Source: Atmos. Meas. Tech., 11, 2983-2994
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Title: Prototyping of LAI and FPAR Algorithm with MODIS MultiAngle Implementation of Atmospheric Correction (MAIAC) data
Author: Chen, C., Y. Knyazikhin, T. Park, K. Yan, A. Lyapustin, Y. Wang, B. Yang and R. B. Myneni
Source: Rem. Sensing, 2017, 9, 370; doi:10.3390/rs9040370
View Abstract and Access Publication
Title: Amazon forests' response to droughts: a perspective from the MAIAC product
Author: Bi, J., R. Myneni, A. Lyapustin, Y. Wang, T. Park, C. Chen, K. Yan, Y. Knyazikhin
Source: Remote Sens., 2016, 8, 356; doi:10.3390/rs8040356
View Abstract and Access Publication
Title: Consistency of vegetation index seasonality across the Amazon rainforest
Author: Maeda, E.E., Mendes Moura, Y., Wagner,F., Hilker, T., Lyapustin, A.I., Wang, Y., Chave, J., Mõttus,M., Aragão, L.E.O.C., Shimabukuro Y.
Source: Int.J. Appl. Earth Observation & Geoinformation, 52, 42-53, 2016
View Abstract and Access Publication
Title: On the measurability of change in Amazon vegetation from MODIS
Author: Hilker, T., A. I. Lyapustin, Y. Wang, F. G. Hall, C. J. Tucker, P. J. Sellers
Source: Remote Sens. Environ., 166, 233-242, 2015
View Abstract and Access Publication
Title: Vegetation dynamics and rainfall sensitivity of the Amazon
Author: Hilker, T., A. I. Lyapustin, C. J. Tucker, F. G. Hall, R. B. Myneni, Y. Wang, J. Bi, Y. M. de Moura, P. J. Sellers
Source: PNAS, 111 (45), 16041-16046, doi:10.1073/pnas.1404870111
View Abstract and Access Publication
Title: Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter
Author: Hilker, T., A. I. Lyapustin, C. J. Tucker, P. J. Sellers, F. G. Hall, Y. Wang
Source: Rem. Sens. Environ.,
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Title: Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction
Author: Lyapustin, A., Y. Wang, I. Laszlo, T. Hilker, F. Hall, P. Sellers, J. Tucker, S. Korkin
Source: Rem. Sens. Environ. (2012),
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Title: Analysis of snow bidirectional reflectance from ARCTAS Spring-2008 Campaign
Author: Lyapustin, A., Gatebe, C. K., Kahn, R., Brandt, R., Redemann, J., Russell, P., King, M. D., Pedersen, C. A., Gerland, S., Poudyal, R., Marshak, A., Wang, Y., Schaaf, C., Hall, D., Kokhanovsky, A.
Source: Atmos. Chem. Phys., 10, 4359–4375, 2010
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Title: Retrieval of snow grain size over Greenland from MODIS
Author: Lyapustin, A., Tedesco, M., Wang, Y., Aoki, T., Hori, M., Kokhanovsky, A.
Source: Remote Sensing of Environment, Volume 113, Issue 9, Pages 1976-1987
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