The VNP14 thermal anomalies product over the western United States from August 20, 2018.View full-size image
The Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies (VNP14) Version 1 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the Suomi National Polar Orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies.
The VNP14 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16.
Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14 product is also used to generate higher-level fire data products.
Use of the VNP03MODLL data product is required to apply accurate geolocation information to the VNP14 Science Datasets (SDS).
Validation at stage 1 has been achieved for the VIIRS Thermal Anomalies & Fire product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.
|Collection||Suomi NPP VIIRS|
|File Size||~0.75 MB|
|Temporal Resolution||< Daily|
|Temporal Extent||2012-01-19 to Present|
|Geographic Dimensions||3060 km x 3060 km|
|Number of Science Dataset (SDS) Layers||31|
|Columns/Rows||3200 x 3200|
|Pixel Size||750 m|
|SDS Name||Description||Units||Data Type||Fill Value||No Data Value||Valid Range||Scale Factor|
|CMG_day¹||Day flag||N/A||16-bit unsigned integer||N/A||N/A||N/A||N/A|
|CMG_night¹||Night flag||N/A||16-bit unsigned integer||N/A||N/A||N/A||N/A|
|FP_AdjCloud||Number of adjacent cloud pixels||N/A||8-bit unsigned integer||N/A||N/A||0 to 8||N/A|
|FP_AdjWater||Number of adjacent water pixels||N/A||8-bit unsigned integer||N/A||N/A||0 to 8||N/A|
|FP_CMG_col||Climate modeling grid column||N/A||16-bit signed integer||N/A||N/A||N/A||N/A|
|FP_CMG_row||Climate modeling grid row||N/A||16-bit signed integer||N/A||N/A||N/A||N/A|
|FP_MAD_DT||Background M13-M15 brightness temperature difference mean absolute deviation||Kelvin||32-bit floating point||0||N/A||~ > 0 to 20||N/A|
|FP_MAD_R7||Background M7 reflectance mean absolute deviation||N/A||32-bit floating point||-1||N/A||~ > 0 to 0.2||N/A|
|FP_MAD_T13||Background M13 brightness temperature mean absolute deviation||Kelvin||32-bit floating point||0||N/A||~ > 0 to 20||N/A|
|FP_MAD_T15||Background M15 brightness temperature mean absolute deviation||Kelvin||32-bit floating point||0||N/A||~ > 0 to 20||N/A|
|FP_MeanDT||Mean background brightness temperature difference||Kelvin||32-bit floating point||0||N/A||~ > -10 to 40||N/A|
|FP_MeanR7||Background M7 reflectance||N/A||32-bit floating point||-1||N/A||~ > 0 to 0.6||N/A|
|FP_MeanT13||M13 brightness temperature of background||Kelvin||32-bit floating point||0||N/A||260 to 340||N/A|
|FP_MeanT15||M15 brightness temperature of background||Kelvin||32-bit floating point||0||N/A||260 to 340||N/A|
|FP_NumValid||Number of valid background pixels||Number||16-bit signed integer||N/A||N/A||N/A||N/A|
|FP_R7||M7 fire reflectance pixels||N/A||32-bit floating point||-1||N/A||~ > 0 to 0.35||N/A|
|FP_RelAzAng||Relative Azimuth Angle||Degree||32-bit floating point||N/A||N/A||-180 to 180||N/A|
|FP_SolZenAng||Solar Zenith Angle of fire pixel||Degree||32-bit floating point||N/A||N/A||0 to 180||N/A|
|FP_T13||M13 brightness temperature of fire pixel.||Kelvin||32-bit floating point||N/A||N/A||~ 300 to 634||N/A|
|FP_T15||M15 brightness temperature of fire pixel||Kelvin||32-bit floating point||N/A||N/A||~ 265 to 330||N/A|
|FP_ViewZenAng||View Zenith Angle of fire pixel||Degree||32-bit floating point||N/A||N/A||~ 0 to 70||N/A|
|FP_WinSize||Background Window Size||N/A||8-bit unsigned integer||N/A||N/A||5 to 21||N/A|
|FP_confidence||Detection confidence||Percent||8-bit unsigned integer||N/A||N/A||0 to 100||N/A|
|FP_land||Land pixel flag||N/A||8-bit unsigned integer||N/A||N/A||N/A||N/A|
|FP_latitude||Latitude of fire pixel||Degree||32-bit floating point||N/A||N/A||-90 to 90||N/A|
|FP_line||Fire pixel line||Number||16-bit signed integer||N/A||N/A||0 to (16 x N)-1||N/A|
|FP_longitude||Longitude of fire pixel||Degree||32-bit floating point||N/A||N/A||-180 to 180||N/A|
|FP_power||Fire radiative power||Megawatts||32-bit floating point||0||N/A||~ > 0 to 5000||N/A|
|FP_sample||Fire Pixel Sample||N/A||16-bit signed integer||N/A||N/A||0 to 3199||N/A|
|fire mask||Confidence of Fire||Class Flag||8-bit unsigned integer||N/A||N/A||0 to 9||N/A|
|fire_qa||Pixel quality indicators||Bit Field||32-bit unsigned integer||N/A||N/A||0 to 4294967295||N/A|
¹Additional Climate Modeling Grid (CMG) layers are also found among the SDSs. Those CMG layers contain information used for the generation of Level 4 products by the VIIRS Science Team.
|0||not processed (missing input data)|
|1||not processed (obsolete)|
|2||not processed (other reason)|
|3||non-fire water pixel|
|4||cloud (land or water)|
|5||non-fire land pixel|
|6||unknown (land or water)|
|7||fire (low confidence, land or water)|
|8||fire (nominal confidence, land or water)|
|9||fire (high confidence, land or water)|
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 Table 5 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.
For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.