Terra MODIS fire data from the MOD14A2 product over the western United States, Aug 13 - 20, 2018.View full-size image
The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MOD14A2) Version 6 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition.
The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.
Validation at stage 3 has been achieved for all MODIS Thermal Anomalies and Fire products. Further details regarding MODIS land product validation for the MOD14 data product is available from the MODIS land team validation site.
|File Size||~0.37 MB|
|Temporal Extent||2000-02-18 to 2023-02-17|
|Geographic Dimensions||1200 km x 1200 km|
|Number of Science Dataset (SDS) Layers||2|
|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|
|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 Assurance (QA) 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 bit flags for the QA 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.
In addition to data access and transformation processes, AppEEARS also has the capability to unpack and interpret the quality layers.
Known issues are described on the MODIS Land Quality Assessment website and in Section 7.2 of the User Guide which covers Pre-November 2000 Data Quality, Detection Confidence, Flagging of Static Sources, and the August 2020 MODIS Aqua Outage