VNP14 v001

VIIRS/NPP Thermal Anomalies/Fire 6-Min L2 Swath 750 m


PI: Wilfrid Schroeder, Louis Giglio

Historic reprocessing is underway for VIIRS Version 2 (Collection 2) data products. Due to Version 2 reprocessing of the historical time series, latency for VIIRS Version 1 data products has varied.

Description

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 NASA/NOAA 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).

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Characteristics

Product Maturity

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 and Granule

Collection

Characteristic Description
CollectionSuomi NPP VIIRS
DOI10.5067/VIIRS/VNP14.001
File Size~0.75 MB
Temporal Resolution< Daily
Temporal Extent2012-01-19 to Present
Spatial ExtentGlobal
Coordinate SystemN/A
DatumN/A
File FormatnetCDF-4
Geographic Dimensions3060 km x 3060 km

Granule

Characteristic Description
Number of Science Dataset (SDS) Layers31
Columns/Rows3200 x 3200
Pixel Size750 m

Layers / Variables

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.

VNP14 fire mask pixel classes

Value Description
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)

Product Quality

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.

Known Issues

For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.


About the image

The VNP14 thermal anomalies product over the western United States from August 20, 2018.

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Documentation

User Guide
Algorithm Theoretical Basis Document (ATBD)
File Specification

Using the Data

Access Data

Citation

DOI: 10.5067/VIIRS/VNP14.001