Terra MODIS fire data from the MOD14A1 product over the western United States, August 13, 2018.View full-size image
The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MOD14A1) Version 6 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file.
The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.
|File Size||0.55 MB|
|Temporal Extent||2000-02-18 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 Flag||8-bit unsigned integer||0||N/A||1 to 9||N/A|
|QA||Pixel quality indicators||Bit Field||8-bit unsigned integer||N/A||N/A||0 to 6||N/A|
|MaxFRP||Maximum Fire Radiative Power||Megawatts||32-bit unsigned integer||0||N/A||0 to 180000||0.1|
|Sample||Position of fire pixel within scan||Number||16-bit unsigned integer||N/A||N/A||0 to 1353||N/A|
|0||Not processed (missing input data)|
|1||Not processed (obsolete; not used since Collection 1)|
|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 Assessment (QA) bit flags for the quality layer are provided in Table 6 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.