MOD14A1. Acquired August 5, 2010. Tile H22V10. Northern Madagascar.
The MOD14A1 version 6 product is produced every 8 days, but contains daily information. The MOD14A1 data file (HDF format) is a 4 dimensional file, which consists of 4 Science Dataset (SDS) layers. Within each SDS layer are 8 separate files consisting of complete per pixel information for each of the corresponding 8 days. The SDS layers include the Fire Mask, QA, MaxFRP, and Sample. The MOD/MYD14 products have achieved stage 3 validation.
Improvements/Changes from Previous Versions
Citation
PI Name: Louis Giglio
DOI: 10.5067/MODIS/MOD14A1.006
Characteristic | Description |
---|---|
Temporal Granularity | Daily |
Temporal Extent | February 2000 - Present |
Spatial Extent | Global |
File Size | ~0.55 MB |
Coordinate System | Sinusoidal |
Datum | N/A |
File Format | HDF-EOS |
Geographic Dimensions | 1200 km x 1200 km |
Characteristic | Description |
---|---|
Number of Science Dataset (SDS) Layers | 4 |
Rows/Columns | 1200 cols x 1200 rows |
Pixel Size | 1000 m |
SDS Layer Name | Description | Units | Data Type | Fill Value | Valid Range | Scaling Factor |
---|---|---|---|---|---|---|
Fire Mask | Confidence of fire | Fire Mask | 8-bit unsigned integer | 0 | 1 to 9 | N/A |
QA | Pixel quality indicators | Bit Field | 8-bit unsigned integer | N/A | 0 to 6 | N/A |
MaxFRP | Maximum Fire Radiative Power | Megawatts | 32-bit unsigned integer | 0 | 0 to 180000 | 0.1 |
sample | Position of fire pixel within scan | Number | 16-bit unsigned integer | N/A | 0 to 1353 | N/A |
MOD14/MYD14 fire mask pixel classes
The QC layer is bit encoded. We recommend the unpack_sds_bits executable from the LDOPE tools to unpack these layers.
The QC bitmaps for the QC layer is given in the User Guide on page 15.
For complete information about the MOD14A1 known issues please refer to the MODIS Land Quality Assessment website.
Title | Author | Links |
---|---|---|
MODIS Land Products Quality Assurance (QA) Tutorial: Part 1 | LP DAAC | Tutorial |
EarthExplorer Introduction from the Land Remote Sensing Data Access Workshop | LP DAAC and EROS | Presentation |
EarthExplorer Use Cases from the Land Remote Sensing Data Access Workshop | LP DAAC and EROS | Tutorial |
LP DAAC Introduction to MODIS Data | LP DAAC | Tutorial, Narrative |
Obtaining Remotely Sensed Imagery: A Guide to Online Resources for Satellite and Other Data | DOIRSWG and RSAC | Tutorial |
Diving into the NASA Data Pools with DAAC2Disk | LP DAAC | Webinar, Presentation |
Overview of LP DAAC Products | LP DAAC | Presentation |
LP DAAC Data Access through OPeNDAP and Web Services | LP DAAC | Presentation, Webinar |
Discover NASA Land Processes Data with Web Services | LP DAAC | Presentation, Webinar |
Using AppEEARS Quality Service to Extract Information from MODIS Quality Layers | LP DAAC | Tutorial, Jupyter |
Choosing a Data Access Tool: AppEEARS | LP DAAC | Video Tip |
Understanding Land Surface Temperature Dynamics | LP DAAC | Video |
Choosing a Data Access Tool: LP DAAC Data Pool and DAAC2Disk | LP DAAC | Video Tip |
Monthly MODIS Land Surface Temperature | LP DAAC | Video |
Using NASA Remote Sensing for Disaster Management | NASA ARSET | Webinar |
Advanced Webinar: Creating and Using Normalized Difference Vegetation Index (NDVI) from Satellite Imagery | NASA ARSET | Webinar |
Decoding MODIS Version 6 Quality Science Datasets using MODIS Python Toolbox for ArcGIS | LP DAAC | Tutorial |
An Introduction to MODIS Version 6 Data | LP DAAC | Video Tip |
Changes in Canopy Cover with NASA MODIS Leaf Area Index Data | LP DAAC | Video Tip |
Working with Land Remote Sensing Data in a GIS Environment | LP DAAC | Presentation |
R You Ready to Python? An Introduction to Working with Land Remote Sensing Data in R and Python | LP DAAC | Webinar, Presentation |
MODIS Observes Snowpack in the Sierra Nevada Mountain Range | LP DAAC | Video |
Getting Started with MODIS Version 6 Vegetation Indices Data Part 1: All About Accessing Data | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Vegetation Indices Data Part 2: Using the Data | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Vegetation Indices Data Part 3: Interpreting Quality Information | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Land Surface Temperature Data Part 1: All About Accessing Data | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Land Surface Temperature Data Part 2: Using the Data | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Land Surface Temperature Data Part 3: Interpreting Quality Information | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Thermal Anomalies and Fire Data Part 1: All About Accessing the Data. | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Thermal Anomalies and Fire Data Part 2: Using the Data | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Thermal Anomalies and Fire Data Part 3: Interpreting Quality Information | LP DAAC | Video Tip |
Getting Started with MODIS V6 Surface Reflectance Data Part 1: All About Accessing the Data | LP DAAC | Video Tip |
Getting Started with MODIS V6 Surface Reflectance Data Part 2: Using the Data | LP DAAC | Video Tip |
Getting Started with MODIS V6 Surface Reflectance Data Part 3: Interpreting Quality Information | LP DAAC | Video Tip |
Using NASA's AppEEARS to Slice and Dice Big Earth Data | LP DAAC | Presentation, Webinar |
Choosing a Data Access Tool: AppEEARS Area Sampler | LP DAAC | Video Tip |
Observing Land from Space: Interacting with Geospatial Data from NASA's LP DAAC | LP DAAC | YouTube Recording, Presentation |
Working with AppEEARS NetCDF-4 Output Data in R | LP DAAC | Tutorial, R Notebook |
Working with AppEEARS NetCDF-4 Output Data in Python | LP DAAC | Tutorial, Jupyter Notebook |
Masking, Visualizing, and Plotting AppEEARS Output GeoTIFF Time Series in Python | LP DAAC | Tutorial, Jupyter Notebook |
How to Access the LP DAAC Data Pool with R | LP DAAC | Tutorial |
How to Access the LP DAAC Data Pool with Python | LP DAAC | Tutorial |
How to Access LP DAAC Data from the Command Line | LP DAAC | Tutorial |