Leaf Area Index (LAI ) data from the MYD15A2H product over Lake Titicaca, Bolivia, August 20 - 27, 2020.
View full-size imageThe MYD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the “best” pixel available from all the acquisitions of the Aqua sensor 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 nanometers (nm)) absorbed by the green elements of a vegetation canopy.
Science Datasets (SDS) in the Level 4 (L4) MYD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MYD15A2H granule.
The LAI product has attained stage 2 validation and the FPAR product has attained stage 1 validation. Further details regarding MODIS land product validation for the LAI/FPAR data products are available from the MODIS land team validation site.
| Characteristic | Description |
|---|---|
| Collection | Aqua MODIS |
| DOI | 10.5067/MODIS/MYD15A2H.061 |
| File Size | ~4 MB |
| Temporal Resolution | Multi-Day |
| Temporal Extent | 2002-07-04 to Present |
| Spatial Extent | Global |
| Coordinate System | Sinusoidal |
| Datum | N/A |
| File Format | HDF-EOS |
| Geographic Dimensions | 1200 km x 1200 km |
| Characteristic | Description |
|---|---|
| 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_500m | Fraction of Photosynthetically Active Radiation | Percent | 8-bit unsigned integer | 249 to 255 | N/A | 0 to 100 | 0.01 |
| Lai_500m | Leaf Area Index | m²/m² | 8-bit unsigned integer | 249 to 255 | N/A | 0 to 100 | 0.1 |
| FparLai_QC | Quality for LAI and FPAR | Class Flag | 8-bit unsigned integer | 255 | N/A | 0 to 254 | N/A |
| FparExtra_QC | Extra detail Quality for LAI and FPAR | Class Flag | 8-bit unsigned integer | 255 | N/A | 0 to 254 | N/A |
| FparStdDev_500m | Standard deviation of FPAR | Percent | 8-bit unsigned integer | 248 to 255 | N/A | 0 to 100 | 0.01 |
| LaiStdDev_500m | Standard deviation of LAI | m²/m² | 8-bit unsigned integer | 248 to 255 | N/A | 0 to 100 | 0.1 |
| Value | Description |
|---|---|
| 249 | Land cover assigned as "unclassified" or not able to determine |
| 250 | Land cover assigned as urban/built-up |
| 251 | Land cover assigned as "permanent" wetlands/inundated marshland |
| 252 | Land cover assigned as perennial snow, ice |
| 253 | Land cover assigned as barren, sparse vegetation (rock, tundra, desert) |
| 254 | Land cover assigned as perennial salt or inland fresh water |
| 255 | Fill |
| Value | Description |
|---|---|
| 248 | No standard deviation available, pixel produced using backup method |
| 249 | Land cover assigned as "unclassified" or not able to determine |
| 250 | Land cover assigned as urban/built-up |
| 251 | Land cover assigned as "permanent" wetlands / inundated marshland |
| 252 | Land cover assigned as perennial snow, ice |
| 253 | Land cover assigned as barren, sparse vegetation (rock, tundra, desert) |
| 254 | Land cover assigned as perennial salt or inland fresh water |
| 255 | Fill |
The quality layers are 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 these layers.
The bit flags for the FparLai_QC and FparExtra_QC layers are provided in section 6.1 of the User Guide.
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.
In addition to data access and transformation processes, AppEEARS also has the capability to unpack and interpret the quality layers.
For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.