Combined MODIS Leaf Area Index (LAI) data from the MCD15A2H product over Lake Titicaca, Bolivia, August 13 - 20, 2018.View full-size image
The MCD15A2H Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites from within the 8-day period.
LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.
|File Size||~4.3 MB|
|Temporal Extent||2002-07-04 to 2023-02-17|
|Geographic Dimensions||1200 km x 1200 km|
|Number of Science Dataset (SDS) Layers||6|
|Columns/Rows||2400 x 2400|
|Pixel Size||500 m|
|SDS Name||Description||Units||Data Type||Fill Value||No Data Value||Valid Range||Scale Factor|
|Fpar_500m¹||Fraction of Photosynthetically Active Radiation||Percent||8-bit unsigned integer||249 to 255||N/A||0 to 100||0.01|
|Lai_500m¹||Leaf Area Index||m²/m²||8-bit unsigned integer||249 to 255||N/A||0 to 100||0.1|
|FparLai_QC||Quality for FPAR and LAI||Class Flag||8-bit unsigned integer||255||N/A||0 to 254||N/A|
|FparExtra_QC||Extra detail Quality for FPAR and LAI||Class Flag||8-bit unsigned integer||255||N/A||0 to 254||N/A|
|FparStdDev_500m²||Standard deviation of FPAR||Percent||8-bit unsigned integer||248 to 255||N/A||0 to 100||0.01|
|LaiStdDev_500m²||Standard deviation of LAI||m²/m²||8-bit unsigned integer||248 to 255||N/A||0 to 100||0.1|
¹The FPAR and LAI fill value legends are provided in the User Guide on page 10.
²The FPAR and LAI standard deviation fill value legends are provided in the User Guide on page 11.
Quality assurance information should be considered when determining the usability of data for a particular science application. The ArcGIS MODIS-VIIRS Python Toolbox contains tools capable of decoding quality data layers while producing thematic quality raster files for each quality attribute.
The QC layer is stored in an efficient bit-encoded manner. The unpack_sds_bits executable from the LDOPE Tools is available to the user community to help parse and interpret the QC layers.
In addition to data access and transformation processes, AppEEARS also has the capability to unpack and interpret the quality layers.
The QC bitmap for the QC layer is provided in the User Guide on pages 8 and 9.
For complete information about the MCD15A2H known issues refer to the MODIS Land Quality Assessment website.