Gross Primary Productivity (GPP) from the MYD17A2H product over central South America on August 29 - September 5, 2018.View full-size image
The MYD17A2H Version 6 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP minus the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.
|File Size||~4.16 MB|
|Temporal Extent||2002-07-04 to Present|
|Geographic Dimensions||1200 km x 1200 km|
|Number of Science Dataset (SDS) Layers||3|
|Columns/Rows||2400 x 2400|
|Pixel Size||500 m|
|SDS Name||Description||Units||Data Type||Fill Value||No Data Value||Valid Range||Scale Factor|
|Gpp_500m||Gross Primary Productivity||kg C/m²||16-bit signed integer||32761 to 32767||N/A||0 to 30000||0.0001|
|PsnNet_500m||Net Photosynthesis||kg C/m²||16-bit signed integer||32761 to 32767||N/A||-30000 to 30000||0.0001|
|Psn_QC_500m||Quality Control Indicators||N/A||8-bit unsigned integer||255||N/A||0 to 254||N/A|
32761|Land cover assigned as "unclassified" or not able to determine| |32762|Land cover assigned as urban/built-up| |32763|Land cover assigned as "permanent" wetlands/inundated marshland| |32764|Land cover assigned as perennial snow, ice| |32765|Land cover assigned as barren, sparse veg (rock, tundra, desert)| |32766|Land cover assigned as perennial salt or inland fresh water| |32767|Fill Value|
The quality 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 this layer.
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.
The bit flags for the Psn_QC_500m Quality layer are provided in the MYD17A2H File Specification.
For complete information about the MYD17A2H known issues refer to the MODIS Land Quality Assessment website.