The VNP14A1 thermal anomalies/fire product over the northwestern United States from August 19, 2018.View full-size image
The daily Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies/Fire (VNP14A1) Version 1 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VNP14 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite.
The VNP14A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format.
|Collection||Suomi NPP VIIRS|
|File Size||~0.55 MB|
|Temporal Extent||2012-01-19 to Present|
|Geographic Dimensions||1200 km x 1200 km|
|Number of Science Dataset (SDS) Layers||4|
|Columns/Rows||1200 x 1200|
|Pixel Size||1000 m|
|SDS Name||Description||Units||Data Type||Fill Value||No Data Value||Valid Range||Scale Factor|
|FireMask||Confidence of fire||Class||8-bit unsigned integer||N/A||N/A||0 to 9||N/A|
|MaxFRP||Maximum Fire Radiative Power||Megawatts||32-bit signed integer||0||N/A||N/A||0.1|
|QA||Pixel quality indicators||Bit Field||8-bit unsigned integer||N/A||N/A||0 to 6||N/A|
|sample||Sample number within a swath||N/A||16-bit signed integer||-1||N/A||0 to 3199||N/A|
|0||not processed (missing input data)|
|1||not processed (trim)|
|2||not processed (obsolete)|
|4||cloud (land or water)|
|7||fire (low confidence)|
|8||fire (nominal confidence)|
|9||fire (high confidence)|
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 the quality layer.
The Quality Assurance (QA) bit flags for the quality layer are provided in Section 2.4.1 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.