MODIS Collection 6 Active Fire Product User’s Guide
Revision B

Louis Giglio
University of Maryland
Wilfrid Schroeder
National Oceanic and Atmospheric Administration
Joanne V. Hall
University of Maryland
Christopher O. Justice
University of Maryland
December 2018

Technical Contacts
Topic
Algorithm and products
Product validation

Contact
Louis Giglio (lgiglio@umd.edu)
Wilfrid Schroeder (wilfrid.schroeder@noaa.gov)

Document Change History
RevisionA
B

Date
3/2015
12/2018

Description
Original version.
Documented changes to version-2 MCD14ML product. Updated URLs
and description of LP-DAAC ordering interface. Updated references. Minor typographical corrections.

2Contents
1 Introduction

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2 Summary of Collection 6 Algorithm and Product Changes

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3 Overview of the MODIS Active Fire Products
3.1 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 Granules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.2 Tiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.3 Climate Modeling Grid (CMG) . . . . . . . . . . . . . . . . . . . . . . .
3.1.4 Collections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Level 2 Fire Products: MOD14 (Terra) and MYD14 (Aqua) . . . . . . . . . . . . .
3.3 Level 2G Daytime and Nighttime Fire Products . . . . . . . . . . . . . . . . . . .
3.4 Level 3 8-Day Daily Composite Fire Products . . . . . . . . . . . . . . . . . . . .
3.5 Level 3 8-Day Summary Fire Products: MOD14A2 (Terra) and MYD14A2 (Aqua)
3.6 Climate Modeling Grid Fire Products (MOD14CMQ, MYD14CMQ, etc.) . . . . .
3.7 Global Monthly Fire Location Product (MCD14ML) . . . . . . . . . . . . . . . .
3.8 Near Real-Time MODIS Imagery and Fire Products . . . . . . . . . . . . . . . . .
3.9 LDOPE Global Browse Imagery . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4 Obtaining the MODIS Active Fire Products
4.1 LAADS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 LP-DAAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 University of Maryland ftp Server . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 MODIS CMG Active-Fire Products . . . . . . . . . . . . . . . . . . . . .
4.3.2 MODIS Monthly Fire Location Product . . . . . . . . . . . . . . . . . . .
4.3.3 Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.4 Pre-Collection 6 Products . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.5 VIRS Monthly Fire Product . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 LANCE Worldview and Rapid Response . . . . . . . . . . . . . . . . . . . . . . .
4.5 LANCE FIRMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5 Detailed Product Descriptions
5.1 MOD14 and MYD14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.1 Fire Mask . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.2 Collection 6 Water Processing . . . . . . . . . . . . . . . . . . . . . . . .
5.1.3 Detection Confidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.4 Algorithm Quality Assessment Bits . . . . . . . . . . . . . . . . . . . . .
5.1.5 Fire Pixel Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.6 Metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.7 Example Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 MOD14A1 and MYD14A1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.1 Fire Mask . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.2 QA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.3 Maximum FRP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.4 Scan Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5.2.5 Metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.6 Level 3 Tile Navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.7 Example Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MOD14A2 and MYD14A2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.1 Fire Mask . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.2 QA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.3 Level 3 Tile Navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.4 Example Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
CMG Fire Products (MOD14CMQ, MYD14CMQ, etc.) . . . . . . . . . . . . . . .
5.4.1 CMG Naming Convention . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4.2 Data Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4.3 Global Metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4.4 Climate Modeling Grid Navigation . . . . . . . . . . . . . . . . . . . . .
5.4.5 Example Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Global Monthly Fire Location Product (MCD14ML) . . . . . . . . . . . . . . . .
5.5.1 Naming Convention . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.2 Versions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.3 Example Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6 Validation of the MODIS Active Fire Products
6.1 Validation Based on ASTER Imagery . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Other Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 Caveats and Known Problems
7.1 Caveats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 Fire Pixel Locations vs. Gridded Fire Products . . . . . . . . . . . . . . .
7.2 Collection 6 Known Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2.1 Pre-November 2000 Data Quality . . . . . . . . . . . . . . . . . . . . . .
7.2.2 Detection Confidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2.3 Flagging of Static Sources . . . . . . . . . . . . . . . . . . . . . . . . . .
7.3 Collection 5 Known Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.3.1 False Alarms in Small Forest Clearings . . . . . . . . . . . . . . . . . . .
7.3.2 False Alarms During Calibration Maneuvers . . . . . . . . . . . . . . . .

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8 Frequently Asked Questions
8.1 Terra and Aqua Satellites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.1.1 Where can I find general information about the Terra and Aqua satellites? .
8.1.2 When were the Terra and Aqua satellites launched? . . . . . . . . . . . . .
8.1.3 How can I determine overpass times of the Terra and Aqua satellites for aparticular location? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2 General MODIS Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2.1 Where can I find Algorithm Technical Basis Documents (ATBDs) for the
MODIS land products? . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2.2 Do the MODIS sensors have direct broadcast capability? . . . . . . . . . .
8.3 General Fire Product Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3.1 How are the fires and other thermal anomalies identified in the MODIS fireproducts detected? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5.3

5.4

5.5

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8.4

8.5

8.6

What is the smallest fire size that can be detected with MODIS? What about
the largest? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3.3 Why didn’t MODIS detect a particular fire? . . . . . . . . . . . . . . . . .
8.3.4 How well can MODIS detect understory burns? . . . . . . . . . . . . . . .
8.3.5 Can MODIS detect fires in unexposed coal seams? . . . . . . . . . . . . .
8.3.6 How do I obtain the MODIS fire products? . . . . . . . . . . . . . . . . .
8.3.7 What validation of the MODIS active fire products has been performed? . .
8.3.8 I don’t want to bother with strange file formats and/or an unfamiliar orderinginterface and/or very large data files. Can’t you just give me the locationsof fire pixels in plain ASCII files and I’ll bin them myself? . . . . . . . . .
8.3.9 I want to estimate burned area using active fire data. What effective areaburned should I assume for each fire pixel? . . . . . . . . . . . . . . . . .
8.3.10 Why are some of the MODIS fire products not available prior to November
2000? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3.11 Why then are the Level 2 swath and Level 3 tiled fire products available
before November 2000? . . . . . . . . . . . . . . . . . . . . . . . . . . .
Level 2 Fire Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.4.1 Why do the Level 2 product files vary in size? . . . . . . . . . . . . . . . .
8.4.2 How should the different fire detection confidence classes be used? . . . .
8.4.3 How are the confidence values in the “FP confidence” SDS related to theconfidence classes assigned to fire pixels? . . . . . . . . . . . . . . . . . .
8.4.4 How can I take data from the fire-pixel-table SDSs (i.e., the one-dimensional
SDSs with the prefix “FP ”) and place the values in the proper locations ofa two-dimensional array that matches the swath-based “fire mask” and “algorithm QA” SDSs? . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.4.5 Why are the values of the fire radiative power (FRP) in the Collection 4
Level 2 product inconsistent with those in the Collection 5 Level 2 product?
8.4.6 What is the area of a MODIS pixel at the Earth’s surface? . . . . . . . . .
8.4.7 Can I use cloud pixels identified in the Level 2 fire product as a generalpurpose cloud mask for other applications? . . . . . . . . . . . . . . . . .
Level 3 Tiled Fire Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.5.1 Why do coastlines in the tile-based Level 3 products looked so warped? . .
8.5.2 Is there an existing tool I can use to reproject the tiled MODIS products intoa different projection? . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.5.3 Why do some MOD14A1 and MYD14A1 product files have fewer thaneight daily data layers? . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.5.4 How can I determine the date associated with each daily composite in the
MOD14A1 and MYD14A1 products when fewer than eight days of data arepresent? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.5.5 How do I calculate the latitude and longitude of a grid cell in the Level 3
products? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.5.6 How do I calculate the tile and grid cell coordinates of a specific geographic
location (latitude and longitude)? . . . . . . . . . . . . . . . . . . . . . .
8.5.7 What size are the grid cells of Level 3 MODIS sinusoidal grid? . . . . . .
Level 3 CMG Fire Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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I need to reduce the resolution of the 0.25◦ CMG fire product to grid cells
that are a multiple of 0.25◦ in size. How do I go about doing this? . . . . . 52
8.6.2 Why don’t you distribute a daily CMG fire product? . . . . . . . . . . . . 54
8.6.3 Why don’t you distribute the CMG fire products as plain binary (or ASCII)
files? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
8.6.4 Where can I find information about the FITS file format? . . . . . . . . . . 54
8.6.5 What software libraries are available for reading FITS files? . . . . . . . . 54
8.6.6 How can I display images in FITS files? . . . . . . . . . . . . . . . . . . . 54
8.6.7 Does the last 8-day CMG product for each calendar year include data fromthe first few days of the following calendar year? . . . . . . . . . . . . . . 58
8.6.8 Where can I find details about the different corrections performed on someof the data layers in the CMG fire products? . . . . . . . . . . . . . . . . . 58
8.6.9 Are persistent hot spots filtered out of the MODIS CMG fire products? . . 58
8.6.10 Is there an easy way to convert a calendar date into the ordinal dates (dayof-year) used in the file names of the 8-day fire products? . . . . . . . . . . 58
Global Monthly Fire Location Product . . . . . . . . . . . . . . . . . . . . . . . . 60
8.7.1 Can I use the MCD14ML fire location product to make my own gridded firedata set? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
8.7.2 How many lines are in each MCD14ML product file? . . . . . . . . . . . . 60
8.7.3 Are persistent hot spots filtered out of the fire location product? . . . . . . 60
8.7.4 The MCD14ML ASCII product files have fixed-width, space-delimited fields.
Is there an easy way to convert these to comma-separated values (CSV) files? 60
8.7.5 How can I compute the scan angle given the sample number in the MCD14ML
product? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Hierarchical Data Format (HDF) . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
8.8.1 What are HDF files? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
8.8.2 How do I read HDF4 files? . . . . . . . . . . . . . . . . . . . . . . . . . . 61
8.8.3 Can’t I just skip over the HDF header and read the data directly? . . . . . . 61
8.8.4 How can I list the contents of HDF4 files? . . . . . . . . . . . . . . . . . . 61
8.8.5 How can I display images in HDF4 files? . . . . . . . . . . . . . . . . . . 61
8.6.1

8.7

8.8

9 References

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10 Relevant Web and FTP Sites

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List of Tables
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MODIS fire product availability. . . . . . . . . . . . . . . . . . . . . . . . . . . .
MOD14/MYD14 fire mask pixel classes. . . . . . . . . . . . . . . . . . . . . . . .
Summary of Level-2 fire product pixel-level QA bits. . . . . . . . . . . . . . . . .
Collection 6 Level 2 fire product SDSs comprising the “fire pixel table”. MAD =“mean absolute deviation”. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MODIS Level 2 fire product metadata stored as standard global HDF attributes. . .
QA values in the Collection 6 MODIS Level 3 tiled active fire products. . . . . . .
MOD14A1 and MYD14A1 fire product metadata stored as standard global HDF
attributes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary of data layers in the CMG fire products. . . . . . . . . . . . . . . . . . .
Summary of columns in the MCD14ML fire location product. . . . . . . . . . . .
Fire-pixel confidence classes associated with the confidence level C computed foreach fire pixel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Sizes of grid cells in Level 3 tiled MODIS sinusoidal grid. . . . . . . . . . . . . .
Calendar dates (month/day) corresponding to the day-of-year (DOY) beginningeach 8-day time period for which the 8-day fire products are generated. Dates for
non-leap years and leap years are shown separately. . . . . . . . . . . . . . . . . .

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1 Introduction
This document contains the most current information about the Collection 6 Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) fire products. It is intended to provide theend user with practical information regarding their use and misuse, and to explain some of the more
obscure and potentially confusing aspects of the fire products and MODIS products in general.

2 Summary of Collection 6 Algorithm and Product Changes
1. Processing has been extended to oceans and other large water bodies to detect offshore gas
flaring.
2. Reduced incidence of false alarms caused by small forest clearings.
3. Improved detection of small fires.
4. Expanded sun-glint rejection.
5. Slightly improved cloud masking.
6. Slight adjustment of detection confidence calculation.
7. FRP retrieval now performed using Wooster et al. (2003) approach.
8. Expanded fire pixel table in Level 2 product.
9. Additional granule-level metadata in Level 2 product.
10. Simplified layer date information in 1-km Level 3 daily fire product.
11. Simplified QA layer in 1-km Level 3 8-day and daily fire products.
12. 0.25◦ CMG products (available late 2018).
13. Improved elimination of static hot-spot sources from CMG products.
14. New hot-spot type attribute and day/night flag in MCD14ML fire location product.

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3 Overview of the MODIS Active Fire Products
Here we provide a general overview of the MODIS active fire products. More detailed descriptionsof these products and example ingest code can be found in Section 4.

3.1

Terminology

Before proceeding with a description of the MODIS fire products, we briefly define the terms granule, tile, and collection, and climate modelling grid in the context of these products.
3.1.1 Granules
A granule is an unprojected segment of the MODIS orbital swath containing about 5 minutes ofdata. MODIS Level 0, Level 1, and Level 2 products are granule-based.
3.1.2 Tiles
MODIS Level 2G, Level 3, and Level 4 products are defined on a global 250-m, 500-m, or 1-km
sinusoidal grid (the particular spatial resolution is product-dependent). Because these grids are unmanageably large in their entirety (43200 × 21600 pixels at 1 km, and 172800 × 86400 pixels at
250 m), they are divided into fixed tiles approximately 10◦ × 10◦ in size. Each tile is assigned ahorizontal (H) and vertical (V) coordinate, ranging from 0 to 35 and 0 to 17, respectively (Figure 1).
The tile in the upper left (i.e. northernmost and westernmost) corner is numbered (0,0).

Figure 1: MODIS tiling scheme.

93.1.3 Climate Modeling Grid (CMG)
MODIS Level 3 and Level 4 products can also be defined on a coarser-resolution climate modelling
grid (CMG). The objective is to provide the MODIS land products at consistent low resolution spatial and temporal scales suitable for global modeling. In practice, there is a fair amount of variationin the spatial and temporal gridding conventions used among the MODIS land CMG products.
3.1.4 Collections
Reprocessing of the entire MODIS data archive is periodically performed to incorporate better calibration, algorithm refinements, and improved upstream data into all MODIS products. The updated
MODIS data archive resulting from each reprocessing is referred to as a collection. Later collections
supersede all earlier collections.
For the Terra MODIS, Collection 1 consisted of the first products generated following launch.
Terra MODIS data were reprocessed for the first time in June 2001 to produce Collection 3. (Notethat this first reprocessing was numbered Collection 3 rather than, as one would expect, Collection 2.) Collection 3 was also the first version produced for the Aqua MODIS products. Collection 4 reprocessing began in December 2002 and was terminated in December 2006. Productionof the Collection 5 products, which commenced in mid-2006, will continue until the Collection 6
reprocessing is complete. Production of the “Tier-1” Collection 6 MODIS products, which includes
the active fire products, commenced in February 2015.

3.2

Level 2 Fire Products: MOD14 (Terra) and MYD14 (Aqua)

This is the most basic fire product in which active fires and other thermal anomalies, such as volcanoes, are identified. The Level 2 product is defined in the MODIS orbit geometry covering an areaof approximately 2340 × 2030 km in the along-scan and along-track directions, respectively. It is
used to generate all of the higher-level fire products, and contains the following components:• An active fire mask that flags fires and other relevant pixels (Figure 2);• a pixel-level quality assurance (QA) image that includes 19 bits of QA information about eachpixel;• a fire-pixel table which provides 27 separate pieces of radiometric and internal-algorithm
information about each fire pixel detected within a granule;• extensive mandatory and product-specific metadata;• a grid-related data layer to simplify production of the Climate Modeling Grid (CMG) fireproduct (Section 3.6).
Product-specific metadata within the Level 2 fire product includes the number of cloud, water,
non-fire, fire, unknown, and other pixels occurring within a granule to simplify identification of
granules containing fire activity.

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Figure 2: Example MOD14 (Terra MODIS) swathlevel fire mask for granule acquired 5 September
2002 at 07:20 UTC, with water shown in blue,
clouds in purple, non-fire land pixels in grey, andfire pixels in white. The along-track direction points
toward the bottom of the page. The large land masson the right is Madagascar.

3.3

Level 2G Daytime and Nighttime Fire Products: MOD14GD/MOD14GN (Terra)and MYD14GD/MYD14GN (Aqua)

The Level 2 active fire products sensed over daytime and nighttime periods are binned without
resampling into an intermediate data format referred to as Level 2G. The Level 2G format provides
a convenient geocoded data structure for storing granules and enables the flexibility for subsequent
temporal compositing and reprojection. The Level 2G fire products are a temporary, intermediate
data source used solely for producing the Level 3 fire products and are consequently not available
from the permanent MODIS data archive.

3.4

Level 3 8-Day Daily Composite Fire Products: MOD14A1 (Terra) and MYD14A1(Aqua)The MODIS daily Level 3 fire product is tile based, with each product file spanning one of the 460
MODIS tiles, 326 of which contain land pixels. The product is a 1-km gridded composite of fire
pixels detected in each grid cell over each daily (24-hour) compositing period. For convenience,eight days of data are packaged into a single file.
Figure 3 shows the Terra fire mask for day 7 (4 September 2001) of the 29 August–5 September
2001 daily Level 3 fire product. The tile is located in Northern Australia (h31v10).

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Figure 3: Example of 4 September 2001 Collection 6 MOD14A1 daily fire mask for tile
h31v10, located in Northern Australia. Water is shown in blue, clouds in violet, non-fireland grid cells in grey, and fire grid cells inwhite. Grid cells lacking data are shown inblack.

3.5

Level 3 8-Day Summary Fire Products: MOD14A2 (Terra) and MYD14A2 (Aqua)

The MODIS daily Level 3 8-day summary fire product is tile-based, with each product file spanningone of the 460 MODIS tiles, of which 326 contain land pixels. The product is a 1-km gridded
composite of fire pixels detected in each grid cell during the 8-day compositing period.
Figure 4 shows the 8-day summary fire mask from the 8-day Level 3 Terra fire product spanning
29 August–5 September 2001. As in the previous example, the tile is located in Northern Australia(h31v10). The 8-day composite is the maximum value of the individual Level 2 pixel classes thatfell into each 1-km grid cell over the entire 8-day compositing period.

Figure 4: Example of 8-day MOD14A2
summary fire mask for MODIS tile h31v10
spanning 29 August–5 September 2001. The
color scale is the same as that of Figure 3.

12

3.6

Climate Modeling Grid Fire Products (MOD14CMQ, MYD14CMQ, etc.)

The CMG fire products are gridded statistical summaries of fire pixel information intended for use
in regional and global modeling. The products are now generated at 0.25◦ spatial resolution for time
periods of one calendar month (MOD14CMQ and MYD14CMQ) and eight days (MOD14C8Q and
MYD14C8Q). An example of the corrected fire pixel count layer of the product is shown in Figure 5.

Figure 5: Example of the corrected fire pixel count data layer from the January 2001 Terra MODIS
monthly CMG fire product.

3.7

Global Monthly Fire Location Product (MCD14ML)

For some applications it is necessary to have the geographic coordinates of individual fire pixels.
New for Collection 5 is the global monthly fire location product (MCD14ML), which contains thisinformation for all Terra and Aqua MODIS fire pixels in a single monthly ASCII file.

13

3.8

Near Real-Time MODIS Imagery and Fire Products

The Land Atmosphere Near Real-time Capability for EOS (LANCE) Rapid Response System produces near-real time global imagery including true- and false-color corrected reflectance superimposed with fire locations (Figure 6), Normalized Difference Vegetation Index (NDVI), and land
surface temperature. Near-real time locations of Terra and Aqua MODIS fire pixels are also available as text files.

Figure 6: MODIS Rapid Response true color imagery of fires and smoke in southeast Australia (10December 2006, 03:45 UTC).

14

3.9

LDOPE Global Browse Imagery

The MODIS Land Data Operational Product Evaluation (LDOPE) provides interactive daily global
browse imagery of many MODIS land products from the MODIS Land Global Browse Images web
site1 in near-real time (Figure 7). For most products (including the fire products) the browse imagery is generated using only the daytime overpasses. The site allows you to arbitrarily zoom into
any region of the globe and examine features of interest in more detail.

Figure 7: Example Collection-6 Aqua MODIS active fire global browse image for 28 December
2002 showing all daytime overpasses. Fire pixels are shown in red, cloud pixels are shown in light
blue, and areas lacking data are shown in white. Browse image courtesy of the LDOPE.

1

http://landweb.nascom.nasa.gov/cgi-bin/browse/browse.cgi

15

4 Obtaining the MODIS Active Fire Products
All MODIS products are available to users free of charge through several different sources (Table 1).
Not all products are available from each source.
Table 1: MODIS fire product availability.
Product
SourceLevel 2 and Level 3 fire products:
LAADS (Section 4.1) and LP-DAAC
MOD14, MYD14(Section 4.2)
MOD14A1, MYD14A1
MOD14A2, MYD14A2
CMG fire products:
MOD14CMQ, MYD14CMQ
MOD14C8Q, MYD14C8Q

University of Maryland (Section 4.3)

Global fire location product:
MCD14ML

University of Maryland (Section 4.3)

Near real-time fire and corrected reflectance imagery.

LANCE Worldview and Rapid Response(Section 4.4)

Geographic subsets of near real-time active fire
locations in various GIS-compatible formats.

LANCE FIRMS (Section 4.5)

4.1

LAADS

The MODIS Level 1, atmosphere, and land products may be obtained from the Level 1 and Atmosphere Archive and Distribution System (LAADS), available here:
http://ladsweb.nascom.nasa.gov/index.html

4.2

LP-DAAC

Most of the MODIS land products may be obtained from the Land Processes Distributed Active
Archive Center (LP-DAAC), available here:
https://lpdaac.usgs.gov/

16

4.3

University of Maryland ftp Server

At present the active-fire CMG products and the MCD14ML product are distributed from an ftp
server at the University of Maryland. Log in using the following information:
Server: fuoco.geog.umd.edu
Login name: fire
Password: burnt
Once connected, you will have access to the following directory tree:.
|-- db|‘-- MCD64A1|-- gfed -> gfed4|-- gfed3|‘-- monthly|‘-- hdf|-- gfed4||-- daily|‘-- monthly|-- modis||-- C4||-- C5|||-- cmg||||-- 8day|||||-- fits||||‘-- hdf|||‘-- monthly||||-- fits|||‘-- hdf||‘-- mcd14ml||-- C6|||-- cmg||||-- 8day|||||-- fits||||‘-- hdf|||‘-- monthly||||-- fits|||‘-- hdf||‘-- mcd14ml|‘-- docs‘-- virs‘-- monthly

17

4.3.1 MODIS CMG Active-Fire Products
The current MODIS CMG fire products are located in the directory modis/C6/cmg. For convenience, these products are distributed in multiple, standard data formats. Currently HDF andFlexible Image Transport System (FITS) files are available; additional formats may be produced in
the future.
4.3.2 MODIS Monthly Fire Location ProductThe current MCD14ML product is located in the directory modis/C6/mcd14ml.
4.3.3 Documentation
The most recent version of the Active Fire Product User’s Guide for each Collection is archived in
the directory modis/docs.
4.3.4 Pre-Collection 6 Products
To help ensure traceability and replicability the server provides an archive of the older Collection 4
and Collection 5 CMG fire products in the directories modis/C4 and modis/C5, respectively.
These products are obsolete and should not be used for new research or analyses.
4.3.5 VIRS Monthly Fire Product
Although unrelated to MODIS, the ftp server also hosts an archive of the 0.5◦ Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) monthly fire product in the directoryvirs/monthly. See the documentation in the directory virs for details.

4.4

LANCE Worldview and Rapid Response

Near real-time fire and corrected reflectance imagery are available from the NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Worldview and Rapid Response systems,
located here:
https://earthdata.nasa.gov/lance
Note: In general, the near real-time MODIS products should not be used for time series analyses orlong-term studies.

4.5

LANCE FIRMS

Near real-time MODIS fire locations are available in a variety of formats (ASCII, shapefile, KML,
or WMS) from the LANCE Fire Information for Resource Management System (FIRMS):
https://earthdata.nasa.gov/data/near-real-time-data/firms
Note: In general, the near real-time MODIS fire locations should not be used for time series analyses
or long-term studies; for such purposes the standard MCD14ML product is more appropriate. By
special request, FIRMS will distribute spatial subsets of MODIS fire locations extracted from thestandard MCD14ML product via the Archive Download Tool.
18

5 Detailed Product Descriptions
5.1

MOD14 and MYD14

MOD14/MYD14 is the most basic fire product in which active fires and other thermal anomalies,
such as volcanoes, are identified. The Level 2 product is defined in the MODIS orbit geometry
covering an area of approximately 2340 by 2030 km in the across- and along-track directions, respectively. It is used to generate all of the higher-level fire products.
5.1.1 Fire Mask
The fire mask is the principle component of the Level 2 MODIS fire product, and is stored as an
8-bit unsigned integer Scientific Data Set (SDS) named “fire mask”. In it, individual 1-km pixels
are assigned one of nine classes. The meaning of each class is listed in Table 2.
Table 2: MOD14/MYD14 fire mask pixel classes.
Class
0
1
2
3
4
5
6
7
8
9

Meaning
not processed (missing input data)
not processed (obsolete; not used since Collection 1)
not processed (other reason)
non-fire water pixel
cloud (land or water)
non-fire land pixel
unknown (land or water)
fire (low confidence, land or water)
fire (nominal confidence, land or water)
fire (high confidence, land or water)

5.1.2 Collection 6 Water Processing
For Collection 6, oceans and other large water bodies are now processed to detect offshore gas
flaring. As such, the cloud, unknown, and fire pixel classes can now occur over water. While it
was safe to use the fire mask as a rudimentary land/water mask prior to Collection 6, users must
now appeal to bits 0–1 of the pixel-level algorithm QA layer (Section 5.1.4) to unambiguously
discriminate land from water pixels.
5.1.3 Detection Confidence
A detection confidence intended to help users gauge the quality of individual fire pixels is includedin the Level 2 fire product. This confidence estimate, which ranges between 0% and 100%, is used toassign one of the three fire classes (low-confidence fire, nominal-confidence fire, or high-confidencefire) to all fire pixels within the fire mask.
In some applications errors of commission (or false alarms) are particularly undesirable, andfor these applications one might be willing to trade a lower detection rate to gain a lower false alarm
rate. Conversely, for other applications missing any fire might be especially undesirable, and one
19might then be willing to tolerate a higher false alarm rate to ensure that fewer true fires are missed.
Users requiring fewer false alarms may wish to retain only nominal- and high-confidence fire pixels,
and treat low-confidence fire pixels as clear, non-fire, land pixels. Users requiring maximum fire
detectability who are able to tolerate a higher incidence of false alarms should consider all threeclasses of fire pixels.
5.1.4 Algorithm Quality Assessment Bits
Pixel-level QA is stored in a 32-bit unsigned integer SDS named “algorithm QA”, with individual
fields stored in specific bits (Table 3). For details, please see the MODIS Level 2 Fire Product filespecification.
Table 3: Summary of Level-2 fire product pixel-level QA bits.
Bit(s)
0-1
2
3
4
5
6
7-10
11-16
17-19
20
21
22-23
24-28
29-31

Meaning
land/water state (00 = water, 01 = coast, 10 = land, 11 = unused)
3.9 µm high-gain flag (0 = band 21, 1 = band 22)
atmospheric correction performed (0 = no, 1 = yes)
day/night algorithm (0 = night, 1 = day)
potential fire pixel (0 = false, 1 = true)
spare (set to 0)
background window size parameter
individual detection test flags (0 = fail, 1 = pass)spare (set to 0)
adjacent cloud pixel (0 = no, 1 = yes)adjacent water pixel (0 = no, 1 = yes)
sun glint level (0–3)
individual rejection test flags (0 = false, 1 = true)
spare (set to 0)20

5.1.5 Fire Pixel Table
The fire pixel table is simply a collection of SDSs containing relevant information about individual
fire pixels detected within a granule. Due to HDF file format and library limitations, the Fire Pixel
Table is stored as 27 separate SDSs. A brief summary of these SDSs is provided in Table 3.
Table 4: Collection 6 Level 2 fire product SDSs comprising the “fire pixel table”. MAD = “meanabsolute deviation”.
SDS Name
FP line
FP sample
FP latitude
FP longitude
FP R2

Data Type
int16
int16
float32
float32
float32

Units
degreesdegrees-

FP T21

float32

K

FP T31FP MeanT21

float32
float32KK

FP MeanT31
FP MeanDTFP MAD T21
FP MAD T31
FP MAD DT
FP power
FP AdjCloud
FP AdjWater
FP WinSize
FP NumValid
FP confidence
FP landFP MeanR2
FP MAD R2
FP ViewZenAng
FP SolZenAng
FP RelAzAng
FP CMG row
FP CMG col

float32
float32
float32
float32
float32
float32
uint8
uint8
uint8
int16
uint8
uint8
float32
float32
float32
float32
float32
int16
int16

K
K
K
K
K
MW
%
degreesdegreesdegrees-

Description
Granule line of fire pixel.
Granule sample of fire pixel.
Latitude at center of fire pixel.
Longitude at center of fire pixel.
Near-IR (band 2) reflectance of fire pixel (daytime
only).
Channel 21/22 brightness temperature of fire
pixel.
Channel 31 brightness temperature of fire pixel.
Background channel 21/22 brightness temperature.
Background channel 31 brightness temperature.
Background brightness temperature difference.

Fire radiative power.
Number of adjacent cloud pixels.
Number of adjacent water pixels.
Background window size.
Number of valid background pixels.
Detection confidence estimate.
Land flag (0 = water pixel, 1 = land pixel).
Background channel 2 reflectance.
Background channel 2 reflectance MAD.
View zenith angle.
Solar zenith angle.
Relative azimuth angle.
CMG row.
CMG column.

21

5.1.6 Metadata
Every MODIS product carries with it ECS-mandated metadata stored in the HDF global attributes
CoreMetadata.0 and ArchiveMetadata.0. Each attribute is an enormous string of ASCII charactersencoding many separate metadata fields in Parameter Value Language (PVL). Among other information, the ArchiveMetadata.0 attribute usually contains product-specific metadata included at thediscretion of the PI. However, since the PVL is awkward to read and tedious to parse, we have stored
many of the product-specific metadata fields as standard HDF global attributes. These are summarized in Table 5. Descriptions of the product-specific metadata stored in the ECS ArchiveMetadata.0
attribute may be found in the MOD14/MYD14 Fire Product file specification (see Section 10).
Table 5: MODIS Level 2 fire product metadata stored as standard global HDF attributes.
Attribute Name
FirePix
MissingPix
LandPix
WaterPix
CoastPix
WaterAdjacentFirePix
CloudAdjacentFirePix
UnknownLandPix
UnknownWaterPix
LandCloudPix
WaterCloudPix
GlintPix
GlintRejectedPix
CoastRejectedPix
HotSurfRejectedPix
ClearingRejectedPix
CoastRejectedWaterPix
DayPixNightPix
Satellite
ProcessVersionNumber
MOD021KM input fileMOD03 input file

Description
Number of fire pixels detected in granule.
Number of pixels in granule lacking valid data for processing.
Number of land pixels in granule.
Number of water pixels in granule.
Number of coast pixels in granule.
Number of fire pixels that are adjacent to one or more water pixels.
Number of fire pixels that are adjacent to one or more cloud pixels.
Number of land pixels assigned a class of unknown.
Number of water pixels assigned a class of unknown.
Number of land pixels obscured by cloud in granule.
Number of water pixels obscured by cloud in granule.
Number of pixels in granule contaminated with sun glint.
Number of tentative fire pixels that were rejected due to apparent
sun glint contamination.
Number of tentative fire pixels that were rejected due to apparent
water contamination of the contextual neighborhood.
Number of tentative fire pixels that were rejected as apparent hot
desert surfaces.
Number of tentative fire pixels rejected as apparent forest clearings.
Number of tentative fire pixels rejected due to apparent land contamination of contextual background.
Number of daytime pixels in granule.
Number of nighttime pixels in granule.
Name of satellite (“Terra” or “Aqua”).
Production code version string (e.g. “6.2.3”).
File name of MOD021KM (Terra) or MYD021KM (Aqua)
Level 1B radiance input granule.
File name of MOD03 (Terra) or MYD03 (Aqua) geolocation input
granule.

22

5.1.7 Example Code
Example 1: IDL code for reading the “fire mask” SDS in the MODIS Level 2 fire product.
mod14_file = ’MOD14.A2002177.1830.005.2008192223417.hdf’; open the HDF file for reading
sd_id = HDF_SD_START(mod14_file, /READ); find the SDS index to the MOD14 fire mask
index = HDF_SD_NAMETOINDEX(sd_id, ’fire mask’); select and read the entire fire mask SDS
sds_id = HDF_SD_SELECT(sd_id, index)
HDF_SD_GETDATA, sds_id, fire_mask; finished with SDS
HDF_SD_ENDACCESS, sds_id; finished with HDF file
HDF_SD_END, sd_id

23

5.2

MOD14A1 and MYD14A1

The MOD14A1 and MYD14A1 daily Level 3 fire products are tile-based, with each product filespanning one of the 460 MODIS tiles, of which 326 contain land pixels. The product is a 1-km
gridded composite of fire pixels detected in each grid cell over each daily (24-hour) compositing
period. For convenience, eight days of data are packaged into a single file.
5.2.1 Fire Mask
The fire mask is stored as an 8 (or less) × 1200 × 1200, 8-bit unsigned integer SDS named “FireMask”. (For historical reasons this layer was named “most confident detected fire” prior to Collection 5.) The SDS contains eight successive daily fire masks for a specific MODIS tile. Each of thesedaily masks is essentially a maximum value composite2 of the Level 2 fire product pixel classes(Table 2) for those swaths overlapping the MODIS tile during that day. Product files containing lessthan eight days of data will occasionally be encountered during time periods of missing data andshould be handled in ingest software.
5.2.2 QA
Each of the daily fire masks has a corresponding simple QA layer. Each layer is a 1200 × 1200
8-bit unsigned integer array. Only seven unique QA values are possible, with the meanings shownin Table 6. Note that for missing-data grid cells (bit pattern 11 in bits 0-1), bit 2 will always be clear.
Table 6: QA values in the Collection 6 MODIS Level 3 tiled active fire products.
Bit(s)
0-1
2

Meaning
land/water state (00 = water, 01 = coast, 10 = land, 11 = missing data)
day/night observation (0 = night, 1 = day)

5.2.3 Maximum FRP
The maximum fire radiative power of all fire pixels falling within each grid cell is provided on adaily basis in the “MaxFRP” SDS. Here the FRP values have been scaled by a factor of 10 andstored as a 32-bit signed integer. Multiply these scaled values by 0.1 to retrieve the maximum FRP
in MW.
5.2.4 Scan Sample
For all grid cells assigned one of the fire pixel classes (values 7, 8, or 9), the position of the fire pixel
within the scan is recorded on a daily basis in a 1200 × 1200 16-bit unsigned integer SDS named“sample”. Sample values have a range of 0 to 1353. All grid cells assigned one of the non-fireclasses in the “FireMask” SDS will be filled with a sample value of 0.
2
Due to the introduction of water processing in Collection 6, the original maximum value compositing scheme had tobe modified slightly to prevent cloud-obscured water grid cells from having precedence over cloud-free water grid cells.

245.2.5 Metadata
As with the Level 2 fire products, the MOD14A1 and MYD14A1 products contain global
metadata stored in the ECS CoreMetadata.0 and ArchiveMetadata.0 global attributes. Also
like the Level 2 products, a subset of these metadata are written as standard HDF global
attributes for convenience (see Table 7). Here are example values for the product file“MOD14A1.A2002297.h31v10.006.2015058030918.hdf”, listed using ncdump (see Section
8.8.4)::FirePix = 482, 319, 329, 386, 660, 739, 1096, 477 ;:CloudPix = 76933, 83531, 61942, 67212, 36943, 6980, 46727,
15949 ;
:UnknownPix = 450, 168, 113, 96, 711, 190, 192, 225 ;:MissingPix = 53920, 155798, 4, 163180, 0, 163250, 0, 149596 ;:Dates = "2002-10-24 2002-10-25 2002-10-26 2002-10-27
2002-10-28 2002-10-29 2002-10-30 2002-10-31" ;:MaxT21 = 425.2746f ;:ProcessVersionNumber = "6.0.1" ;:StartDate = "2002-10-24" ;:EndDate = "2002-10-31" ;:HorizontalTileNumber = 31s ;:VerticalTileNumber = 10s ;Notice that the first four fields (FirePix, CloudPix, UnknownPix, and MissingPix) are
one-dimensional arrays (or vectors) nominally having eight elements. Each element corresponds toa single day in the 8-day time period covered by the product. Note that the Scientific Data Sets inthe product file (“FireMask”, “MaxFRP”, etc.) will contain fewer than eight planes when there areno valid MODIS observations during one or more days spanned by the product. In such cases, thevector metadata fields will have fewer than eight elements.

25

Table 7: MOD14A1 and MYD14A1 fire product metadata stored as standard global HDF attributes.
Attribute Name
FirePix
CloudPix
UnknownPix
MissingPix
Dates
MaxT21
ProcessVersionNumber
StartDate
EndDateHorizontalTileNumber
VerticalTileNumber

Description
Number of 1-km tile cells containing fires (8-element array).
Number of 1-km tile cells assigned a class of cloud after compositing (8-element array).
Number of 1-km tile cells assigned a class of unknown after compositing (8-element array).
Number of 1-km tile cells lacking valid data (8-element array).
Date of each plane in three-dimensional fire mask array.
Maximum band 21 brightness temperature (K) of all fire pixels intile.
Production code version string (e.g. “6.0.1”).
Start date of 8-day time period spanned by product (YYYY-MMDD).
End date of 8-day time period spanned by product (YYYY-MMDD).
Horizontal tile coordinate (H).
Vertical tile coordinate (V).

26

5.2.6 Level 3 Tile Navigation
Navigation of the tiled MODIS products in the sinusoidal projection can be performed using the forward and inverse mapping transformations described here. We’ll first need to define a few constants:
R = 6371007.181 m, the radius of the idealized sphere representing the Earth;
T = 1111950 m, the height and width of each MODIS tile in the projection plane;
xmin = -20015109 m, the western limit of the projection plane;ymax = 10007555 m, the northern limit of the projection plane;
w = T /1200 = 926.62543305 m, the actual size of a “1-km” MODIS sinusoidal grid cell.

Forward Mapping
Denote the latitude and longitude of the location (in radians) as φ and λ, respectively. First computethe position of the point on the global sinusoidal grid:x = Rλ cos φ(1)

y = Rφ.

(2)

Next compute the horizontal (H) and vertical (V ) tile coordinates, where 0 ≤ H ≤ 35 and 0 ≤V ≤ 17 (Section 3.1.2):
x − xminTymax − y
,
=
T

H =
V





(3)
(4)

where ⌊⌋ is the floor function. Finally, compute the row (i) and column (j) coordinates of the gridcell within the MODIS tile:(ymax − y) mod T
− 0.5
w


(x − xmin ) mod T− 0.5 .
j =
w
i =





(5)
(6)

Note that for the 1-km MOD14A1 and MYD14A1 products (indeed, all 1-km MODIS products on
the sinusoidal grid) 0 ≤ i ≤ 1199 and 0 ≤ j ≤ 1199.

27

Inverse Mapping
Here we are given the row (i) and column (j) in MODIS tile H, V . First compute the position of
the center of the grid cell on the global sinusoidal grid:x = (j + 0.5)w + HT + xmin

(7)

y = ymax − (i + 0.5)w − V T(8)

Next compute the latitude φ and longitude λ at the center of the grid cell (in radians):
φ =
λ =

y
R

x
.
R cos φ(9)
(10)

Applicability to 250-m and 500-m MODIS Products
With the following minor changes the above formulas are also applicable to the higher resolution
250-m and 500-m MODIS tiled sinusoidal products.
250-m grid: Set w = T /4800 = 231.65635826 m, the actual size of a “250-m” MODIS sinusoidal
grid cell. For 250-m grid cells 0 ≤ i ≤ 4799 and 0 ≤ j ≤ 4799.
500-m grid: Set w = T /2400 = 463.31271653 m, the actual size of a “500-m” MODIS sinusoidal
grid cell. For 500-m grid cells 0 ≤ i ≤ 2399 and 0 ≤ j ≤ 2399.

5.2.7 Example Code
Example 2: MATLAB code to read the Level 3 MODIS daily fire mask, using the MATLAB routine
hdfread. This is probably the easiest way to read individual HDF SDSs in MATLAB.
mod14a1_file = ’MOD14A1.A2008281.h31v10.005.2008292070548.hdf’% read entire "FireMask" SDS in one shot
fire_mask = hdfread(mod14a1_file, ’FireMask’);% display fire mask for the first day in MOD14A1/MYD14A1% note how image is transposed so that North appears on top
imagesc(fire_mask(:,:,1)’);28

Example 3: IDL code to read some of the global attributes and SDSs in the Level 3 daily fireproduct.
mod14a1_file = ’MOD14A1.A2007241.h08v05.005.2007251120334.hdf’
sd_id = HDF_SD_START(mod14a1_file, /READ); read "FirePix" and "MaxT21" attributes
attr_index = HDF_SD_ATTRFIND(sd_id, ’FirePix’)
HDF_SD_ATTRINFO, sd_id, attr_index, DATA=FirePix
attr_index = HDF_SD_ATTRFIND(sd_id, ’MaxT21’)
HDF_SD_ATTRINFO, sd_id, attr_index, DATA=MaxT21
index = HDF_SD_NAMETOINDEX(sd_id, ’FireMask’)sds_id = HDF_SD_SELECT(sd_id, index)
HDF_SD_GETDATA, sds_id, FireMask
HDF_SD_ENDACCESS, sds_id
index = HDF_SD_NAMETOINDEX(sd_id, ’MaxFRP’)
sds_id = HDF_SD_SELECT(sd_id, index)
HDF_SD_GETDATA, sds_id, MaxFRP
HDF_SD_ENDACCESS, sds_id
HDF_SD_END, sd_id
help, FirePix
print, FirePix, format = ’(8I8)’
print, MaxT21, format = ’("MaxT21:",F6.1," K")’
help, FireMask, MaxFRP

The code produces the following output:
FIREPIX
LONG
1848
MaxT21: 468.1 K
FIREMASKBYTEMAXFRP
LONG

19= Array[8]
1

18

11

= Array[1200, 1200, 8]= Array[1200, 1200, 8]

29

100

32

5.3

MOD14A2 and MYD14A2

The MOD14A2 (Terra) and MYD14A2 (Aqua) daily Level 3 8-day summary fire products are tilebased, with each product file spanning one of the 460 MODIS tiles, 326 of which contain land
pixels. The product is a 1-km gridded composite of fire pixels detected in each grid cell over each
8-day compositing period.
5.3.1 Fire Mask
The fire mask is stored as a 1200 × 1200 8-bit unsigned integer SDS named “FireMask”. (Forhistorical reasons this layer was named “most confident detected fire” prior to Collection 5.) Thissummary fire mask is essentially a maximum value composite of the Level 2 fire product pixel
classes (Table 2) for those swaths overlapping the MODIS tile during the eight-day compositing
period.
5.3.2 QAThe QA layer contains pixel-level quality assessment information stored in a 1200 × 1200 8-bit
unsigned integer image. The possible QA values are the same as those for the MOD14A1 and
MYD14A1 products (see Table 6).
5.3.3 Level 3 Tile Navigation
Forward and inverse mapping of the MODIS sinusoidal tile grid used for the MOD14A2 andMYD14A2 products is the same as for the MOD14A1 and MYD14A1 products. See Section 5.2.6
for details.
5.3.4 Example Code
Example 4: MATLAB code to read the Level 3 MODIS 8-day fire mask, using the MATLAB routine
hdfread. This is probably the easiest way to read individual HDF SDSs in MATLAB.
mod14a2_file = ’MYD14A2.A2004193.h08v08.005.2007207151726.hdf’% read entire "FireMask" SDS in one shot
fire_mask = hdfread(mod14a2_file, ’FireMask’);% display fire mask (transposed so that North appears on top)
imagesc(fire_mask’);30

Example 5: Longer version of MATLAB code to read the Level 3 MODIS 8-day fire mask. This isprobably the better approach to use if multiple subsets of an SDS will be read in sequence since the
HDF file will be opened and closed only once. (The shorter approach using hdfread requires thatthe file be opened and closed for each read.)
mod14a2_file = ’MYD14A2.A2004193.h08v08.005.2007207151726.hdf’
sd_id = hdfsd(’start’, mod14a2_file, ’DFACC_RDONLY’);
sds_index = hdfsd(’nametoindex’, sd_id, ’FireMask’);
sds_id = hdfsd(’select’, sd_id, sds_index);% prepare to read entire SDS (always 1200 x 1200 pixels in size)
start = [0,0];edges = [1200,1200];[fire_mask, status] = hdfsd(’readdata’, sds_id, start, [], edges);status = hdfsd(’endaccess’, sds_id);
status = hdfsd(’end’, sd_id);% display fire mask (transposed so that North appears on top)
imagesc(fire_mask’);31

Example 6: C code for reading Level 3 MODIS 8-day fire mask using HDF library functions.
#include <stdio.h>#include <stdlib.h>#include "mfhdf.h"#define ROWS 1200
#define COLS 1200
main(int argc, char **argv){
int32 sd_id, sds_index, sds_id;
int32 rank, data_type, nattr, dim_sizes[MAX_VAR_DIMS];
int32 start[2], int32 edges[2];char *infile;
int i, j;long nfire;uint8 fire_mask[ROWS][COLS];

infile = "MOD14A2.A2008265.h31v10.005.2008275132911.hdf";
if ((sd_id = SDstart(infile, DFACC_READ)) == FAIL) exit(1);start[0] = start[1] = 0;edges[0] = ROWS;
edges[1] = COLS;
if ((sds_index = SDnametoindex(sd_id, "FireMask")) == FAIL) exit(2);if ((sds_id = SDselect(sd_id, sds_index)) == FAIL) exit(3);if (SDgetinfo(sds_id, (char *) NULL, &rank, dim_sizes, &data_type,&nattr) == FAIL) exit(4);/* check rank and data type */if (rank != 2) exit(5);if (data_type != DFNT_UINT8) exit(6);if (SDreaddata(sds_id, start, NULL, edges,(void *) fire_mask) == FAIL) exit(7);if (SDendaccess(sds_id) == FAIL) exit(8);if (SDend(sd_id) == FAIL) exit(9);/* simple example: count grid cells containing fires */nfire = 0;
for (i = 0; i < ROWS; i++) {for (j = 0; j < COLS; j++)if (fire_mask[i][j] >= 7) nfire++;}printf("%d grid cells containing fires.\n", nfire);exit(0);
}

32

5.4

CMG Fire Products (MOD14CMQ, MYD14CMQ, etc.)

The CMG fire products are gridded statistical summaries of fire pixel information intended for use inregional and global modeling, and other large scale studies. For Collection 6, the products are generated at 0.25◦ spatial resolution for time periods of one calendar month (MOD14CMQ/MYD14CMQ)
and eight days (MOD14C8Q/MYD14C8Q).
At present the CMG products are distributed from the University of Maryland via anonymous
ftp (see Section 4.3). For convenience, these products are distributed in multiple, standard data
formats. Currently, HDF and Flexible Image Transport System (FITS) files are available.
5.4.1 CMG Naming Convention
Monthly CMG fire products. The file names of the monthly CMG product files have the structure
M?D14CM?.YYYYMM.CCC.VV.XXX, where M?D14CM? is a prefix3 encoding the satellite
and product spatial resolution (see Figure 8), YYYY is the four-digit product year, MM is thetwo-digit calendar month, CCC denotes the Collection (see Section 3.1.4), VV denotes the
product version within a Collection, and XXX is a suffix indicating the file format.
Eight-day CMG fire products. The file names of the 8-day CMG product files have the structure
M?D14C8?.YYYYDDD.CCC.VV.XXX, where M?D14C8? is a prefix encoding the satellite
and product spatial resolution (see Figure 8), YYYY is the four-digit product year, DDD is thetwo-digit calendar month, CCC denotes the Collection (see Section 3.1.4), VV denotes the
product version within a Collection, and XXX is a suffix indicating the file format.

Satellite‘O’ = Terra‘Y’ = Aqua
‘C’ = combined Terra/Aqua

M?D14C??
Temporal Resolution‘M’ = monthly‘8’ = 8 days

Spatial Resolution‘H’ = 0.5˚‘Q’ = 0.25˚Figure 8: MODIS CMG fire product naming prefix (ESDT) convention.
3

In MODIS-speak this prefix is usually referred to as an Earth Science Data Type (ESDT).

33

5.4.2 Data Layers
The CMG fire products contain eight separate data layers summarized in Table 8. For the 0.25◦ products each layer is a 1440 × 720 numeric array.
Table 8: Summary of data layers in the CMG fire products.
Layer Name
CorrFirePix
CloudCorrFirePix

Data Type
int16
int16

Units
-

MeanCloudFraction
RawFirePix
CloudPix
TotalPix
MeanFRP
NumPixFRP

int8
int16
int32
int32
float32
int16

MW
-

Description
Corrected number of fire pixels.
Corrected number of fire pixels, with an additional correction for cloud cover.
Mean cloud fraction.
Uncorrected count of fire pixels.
Number of cloud pixels.
Total number of pixels.
Mean fire radiative power.
Number of fire pixels used to compute mean FRP.

5.4.3 Global Metadata
Global metadata are stored as global attributes in the HDF product files, and primary-HDU keywords in the FITS product files.
5.4.4 Climate Modeling Grid Navigation
Forward navigation. Given the latitude and longitude (in degrees) of a point on the Earth’s surface,
the image coordinates (x,y) of the 0.25◦ CMG grid cell containing this point are computed as follows:y = floor((90.0 - latitude) / 0.25)
x = floor((longitude + 180.0) / 0.25),
where floor is the floor function, e.g., floor(2.2) = 2. These equations yield image coordinatessatisfying the inequalities 0 ≤ x ≤ 1439, 0 ≤ y ≤ 719, which are appropriate for programming
languages using zero-based array indexing such as C and IDL; for languages using one-based arrayindexing (e.g. Fortran, MATLAB) add 1.
Inverse navigation. Given coordinates (x,y) of a particular grid cell in the 0.25◦ CMG fire products,
the latitude and longitude (in degrees) of the center of the grid cell may be computed as follows:
latitude = 89.875 - 0.25 × ylongitude = -179.875 + 0.25 × x

34

Here, x and y are again zero-based image coordinates; for one-based image coordinates first subtract
1 from both x and y.
5.4.5 Example Code
Example 7: IDL code for reading the cloud-corrected fire pixel layer within the Collection 6 MODIS
CMG monthly and 8-day fire products (HDF4 format).
; read "CloudCorrFirePix" array in CMG product (HDF4 format)
cmg_file = ’MYD14CMQ.200412.006.01.hdf’
sd_id = HDF_SD_START(cmg_file, /READ)
index = HDF_SD_NAMETOINDEX(sd_id, ’CloudCorrFirePix’)
sds_id = HDF_SD_SELECT(sd_id, index)
HDF_SD_GETDATA, sds_id, CloudCorrFirePix
HDF_SD_ENDACCESS, sds_id
HDF_SD_END, sd_id

Example 8: IDL code for reading the cloud-corrected fire pixel layer within the Collection 6 MODIS
CMG monthly and 8-day fire products (FITS format).
; read "CloudCorrFirePix" array in CMG product (FITS format)
cmg_file = ’MYD14CMQ.200412.006.01.fits’
FITS_OPEN, cmg_file, fcb
ihdu = FITS_FIND_HDU(fcb, ’CloudCorrFirePix’)
FITS_READ_ARRAY, fcb, ihdu, CloudCorrFirePix, ndims, dims
FITS_CLOSE, fcb

35

5.5

Global Monthly Fire Location Product (MCD14ML)

The monthly fire location product contains the geographic location, date, and some additional information for each fire pixel detected by the Terra and Aqua MODIS sensors on a monthly basis. For
convenience, the product is distributed as a plain ASCII (text) file with fixed-width fields delimitedwith spaces. The first line of each file is a header containing the abbreviated names of each column(field). As an example, here are the first ten lines of the December 2008 product file:YYYYMMDD HHMM sat lat lon T21 T31 sample FRP conf type dn
20081201 0051 T -12.0288 143.0191 321.6 289.6 681
11.520081201 0051 T -12.0301 143.0282 317.8 287.9 682
7.820081201 0051 T -12.0391 143.0269 356.8 289.1 682
67.4
20081201 0051 T -12.0481 143.0255 346.6 286.7 682
46.2
20081201 0051 T -12.0552 141.9690 320.8 291.4 571
11.3
20081201 0051 T -12.9809 143.4871 330.7 301.2 752
16.8
20081201 0051 T -12.9999 143.4275 339.1 300.0 746
31.620081201 0051 T -13.0012 143.4368 327.6 300.5 747
14.020081201 0051 T -13.0079 143.4203 345.0 298.5 746
42.8

53
42
0
0
38
80
88
76
92

0 D
0 D
0 D
0 D
0 D
0 D
0 D
0 D
0 D

A brief description of each data column is provided in Table 9.
Table 9: Summary of columns in the MCD14ML fire location product.
Column
1
2
3
4
5
6
7
8
9
10
11

12

Name
YYYYMMDD
HHMM
sat
lat
lon
T21
T31
sample
FRP
conf
type

Units
degreesdegrees
K
K
MW
%
-

dn

-

Description
UTC year (YYYY), month (MM), and day (DD).
UTC hour (HH) and minute (MM).
Satellite: Terra (T) or Aqua (A).
Latitude at center of fire pixel.
Longitude at center of fire pixel.
Band 21 brightness temperature of fire pixel.
Band 31 brightness temperature of fire pixel.
Sample number (range 0-1353).
Fire radiative power (FRP).
Detection confidence (range 0-100).
Inferred hot spot type:
0 = presumed vegetation fire
1 = active volcano
2 = other static land source
3 = offshore
Day/night algorithm flag: day (D) or night (N).

Hot-spot types 0–2 are reserved exclusively for land pixels; hot spots detected over water (presumably offshore gas flares) will always be assigned a type of 3.
5.5.1 Naming Convention
The names of the MCD14ML product files have the structure MCD14ML.YYYYMM.CCC.VV.txt,
where YYYY is the four-digit product year, MM is the two-digit calendar month, CCC denotes the Col36

lection (see Section 3.1.4), and VV denotes the product version (currently “02”) within a Collection.
5.5.2 Versions
The version 2 MCD14ML product has an additional digit of precision in the latitude and longitude
fields, and a new column indicating the day/night state of each fire pixel.
Version 2:
YYYYMMDD HHMM sat lat lon T21 T31 sample FRP conf type dn
20081201 0051 T -12.0288 143.0191 321.6 289.6 681
11.5

53

Version 1:
YYYYMMDD HHMM sat lat lon T21 T31 sample FRP conf type20081201 0051 T -12.029 143.019 321.8 289.6 681
15.1

37

0

1

0 D

5.5.3 Example Code
Example 9: IDL code for reading a single monthly fire location product file while still compressed(note the COMPRESS keyword when the file is opened).
infile = ’MCD14ML.200904.006.02.txt.gz’
header = ’’year = 0month = 0B
day = 0B
hour = 0B & minute = 0B
sat = ’’
lat = 0.0 & lon = 0.0
T21 = 0.0 & T31 = 0.0
sample = 0
FRP = 0.0
confidence = 0B
type = 0B
dnstr = ’’fmt = ’(I4.4,2I2,1X,2I2,1X,A1,F9.4,F10.4,2F6.1,I5,F8.1,I4,I3,1X,A1)’
openr, 2, infile, /COMPRESS; skip header
readf, 2, header
while ˜EOF(2) do begin
readf, 2, year, month, day, hour, minute, sat, $lat, lon, T21, T31, sample, FRP, confidence, type, $dnstr, FORMAT = fmt; do something with values here
endwhile
close, 238

Example 10: R/S-Plus code for reading a single monthly fire location product file and plotting
separate histograms of band 21 brightness temperature for Terra and Aqua fire pixels.
# comment.char argument is not necessary but makes the read.table()# call somewhat fasterz <- read.table("MCD14ML.200711.006.02.txt.gz", header=TRUE,
comment.char="")# two plots on pagepar(mfrow=c(2,1))
xstr <- "Band 21 Brightness Temperature"# Terra fire pixels
hist(z$T21[z$sat == "T"], xlab = xstr)# Aqua fire pixels
hist(z$T21[z$sat == "A"], xlab = xstr)

39

6 Validation of the MODIS Active Fire Products
In this section we provide a brief overview of the validation status of the MODIS active fire products. A more detailed overview may be found in the active fire section of the MODIS Land Team
Validation web site4 .

6.1

Validation Based on ASTER Imagery

Validation of the Terra MODIS active fire product has primarily been performed using coincident,high resolution fire masks derived from Advanced Spaceborne Thermal Emission and Reflection
Radiometer (ASTER) imagery. See Morisette et al. (2005a,b), Csiszar et al. (2006), and Schroeder
et al. (2008) for details. A very brief (though now somewhat obsolete) discussion of the generalvalidation procedure, with some early results, can be found in Justice et al. (2002). For information
about the methodology for producing the ASTER fire masks, see Giglio et al. (2008).
More recent work described has achieved Stage 3 validation of the Level 2 Terra MODIS fireproduct using 2500 ASTER scenes distributed globally and acquired from 2001 through 2006 (Figure 9). See Giglio et al. (2016) for details.

Figure 9: Spatial coverage and distribution of ASTER scenes (red patches) used in the Stage 3
validation of MOD14.

6.2

Other Validation

Independent validation of the Collection 5 Terra and Aqua MODIS active fire products without
ASTER has been performed by de Klerk (2008) and Hawbaker et al. (2008). These approaches
have at least two advantages over ASTER-based methods: 1) They can be applied to both MODIS
sensors (not just the Terra MODIS), and 2) they are not restricted to the near-nadir portion of the
MODIS swath.
4

http://landval.gsfc.nasa.gov/ProductStatus.php?ProductID=MOD1440

7 Caveats and Known Problems
7.1

Caveats

7.1.1 Fire Pixel Locations vs. Gridded Fire Products
We urge caution in using fire pixel locations in lieu of the 1-km gridded MODIS fire products. The
former includes no information about cloud cover or missing data and, depending on the sort ofanalysis that is being performed, it is sometimes possible to derive misleading (or even incorrect)
results by not accounting for these other types of pixels. It is also possible to grossly misuse fire
pixel locations, even for regions and time periods in which cloud cover and missing observations
are negligible. Some caveats to keep in mind when using MODIS fire pixel locations:• The fire pixel location files allow users to temporally and spatially bin fire counts arbitrarily.
However, severe temporal and spatial biases may arise in any MODIS fire time series analysis
employing time intervals shorter than about eight days.
• Known fires for which no entries occur in the fire-pixel location files are not necessarily
missed by the detection algorithm. Cloud obscuration, a lack of coverage, or a misclassification in the land/sea mask may instead be responsible, but with only the information provided
in the fire location files this will be impossible to determine.

7.2

Collection 6 Known Problems

7.2.1 Pre-November 2000 Data Quality
Prior to November 2000, the Terra MODIS instrument suffered from several hardware problems
that adversely affected all of the MODIS fire products. In particular, some detectors were rendereddead or otherwise unusable in an effort to reduce unexpected crosstalk between many of the 500 m
and 1 km bands. The dead detectors are known to introduce at least three specific artifacts in the
pre-November 2000 fire products: striping, undetected small fires, and undetected large fires. In
some very rare instances severe miscalibration of band-21 in the first weeks of the MODIS dataarchive (February and March 2000) will cause entire scan lines to be identified as fire.
7.2.2 Detection Confidence
A detection confidence intended to help users gauge the quality of individual fire pixels is includedin the Level 2 fire product. This confidence estimate, which ranges between 0% and 100%, isused to assign one of the three fire classes (low-confidence fire, nominal-confidence fire, or highconfidence fire) to all fire pixels within the fire mask. In the Collection 4 fire product this confidence
estimate did not adequately identify highly questionable, low confidence fire pixels. Such pixels,
which by design should have a confidence close to 0%, were too often assigned much higher confidence estimates of 50% or higher. While an adjustment implemented in the Collection 5 code
partially mitigated this problem, some highly questionable fire pixels are still classified as nominalconfidence fires. A second minor adjustment was implemented for Collection 6 to help correct thisproblem.

41

7.2.3 Flagging of Static Sources
Some obvious, non-fire static hot-spot sources (both industrial and volcanic) are not properly flagged
as such in the type field of the MCD14ML product. This problem will be corrected in a forthcoming
reprocessed version of the product.

7.3

Collection 5 Known Problems

The frequency of certain problems known to affect the Collection 5 MODIS fire products was reduced for Collection 6. Two such problems are described here.
7.3.1 False Alarms in Small Forest Clearings
Extensive validation of the Collection-5 Level-2 Terra MODIS fire product by Schroeder et al. (2008)
found that small clearings within rainforest were a source of persistent false alarms in the Amazon.
An example is shown in Figure 10. For Collection 6, the frequency of this type false alarm was
reduced using an additional rejection test.

Figure 10: Example false alarm (red square with cross) in the Collection 5 product from 23
May 2002 (14:03 UTC) in an Amazonian rainforest clearing, with approximate edges of 1-km
MODIS pixels (black grid) superimposed on a high resolution ASTER image. Source: Schroeder
et al. (2008).

42

7.3.2 False Alarms During Calibration Maneuvers
A bug in the Collection-5 Level 1B calibrated-radiance production code occasionally produced
spurious radiance values in the thermal bands during lunar roll calibration maneuvers. This in turnproduced spurious stripes of fire pixels across the entire swath in up to ∼20 scans during these
periods. The bug caused similar striping in several other MODIS products, in particular the cloudmask.
While most of the affected Level 2 granules were deleted from the Collection 5 archive, a small
number were missed during quality assurance and subsequently propagated “arcs” of fire pixels into
the Collection 5 CMG and MCD14ML fire products. An example for the Aqua MODIS is shownin Figure 11. The bug was fixed in late 2009, and the corrected Level 1B production code is now
being used for the Collection 6 reprocessing.

Figure 11: Example of a spurious arc of false fire pixels (red dots) in the Collection-5 8 December
2008 Aqua daily global browse imagery caused by spurious mid-infrared radiance values in theLevel 1B input data during a lunar calibration maneuver at 22:35 UTC. Cloud pixels are shown inlight blue, and areas lacking data are shown in white. Browse image courtesy of the LDOPE.

43

8 Frequently Asked Questions
8.1

Terra and Aqua Satellites

8.1.1 Where can I find general information about the Terra and Aqua satellites?
See NASA’s Terra and Aqua web sites for a start:
http://terra.nasa.gov/
http://aqua.nasa.gov/
8.1.2 When were the Terra and Aqua satellites launched?
18 December 1999 and 4 May 2002, respectively.
8.1.3 How can I determine overpass times of the Terra and Aqua satellites for a particularlocation?
Both historical and predicted orbit tracks for Terra and Aqua are available from the University of
Wisconsin-Madison Space Science and Engineering Center (SSEC)5 .

8.2

General MODIS Questions

8.2.1 Where can I find Algorithm Technical Basis Documents (ATBDs) for the MODIS land
products?
ATBDs for all of the MODIS land products are available from MODARCH6 . Note that some are
not up to date and predate the launch of both the Terra and Aqua satellites.
8.2.2 Do the MODIS sensors have direct broadcast capability?
Yes, and there is a large community of MODIS direct broadcast data users. More information isavailable from the NASA Direct Readout Laboratory7 .

8.3

General Fire Product Questions

8.3.1 How are the fires and other thermal anomalies identified in the MODIS fire products
detected?
Fire detection is performed using a contextual algorithm (Giglio et al., 2003; Giglio et al., 2016)that exploits the strong emission of mid-infrared radiation from fires (Dozier, 1981; Matson andDozier, 1981). The algorithm examines each pixel of the MODIS swath, and ultimately assigns toeach one of the following classes: missing data, cloud, water, non-fire, fire, or unknown.
Pixels lacking valid data are immediately classified as missing data and excluded from further
consideration. Cloud and water pixels are identified using cloud and water masks, and are assigned
the classes cloud and water, respectively. Processing continues on the remaining clear land pixels.
5

http://www.ssec.wisc.edu/datacenter/
http://modarch.gsfc.nasa.gov/data/atbd/7
http://directreadout.sci.gsfc.nasa.gov/
6

44

A preliminary classification is used to eliminate obvious non-fire pixels. For those potential fire pixels that remain, an attempt is made to use the neighboring pixels to estimate the radiometric signalof the potential fire pixel in the absence of fire. Valid neighboring pixels in a window centered on
the potential fire pixel are identified and are used to estimate a background value. If the backgroundcharacterization was successful, a series of contextual threshold tests are used to perform a relative
fire detection. These look for the characteristic signature of an active fire in which both 4 µm brightness temperature and the 4 and 11 µm brightness temperature difference depart substantially fromthat of the non-fire background. Relative thresholds are adjusted based on the natural variability ofthe background. Additional specialized tests are used to eliminate false detections caused by sun
glint, desert boundaries, errors in the water mask, and small forest clearings. Candidate fire pixels
that are not rejected in the course of applying these tests are assigned a class of fire. Pixels for which
the background characterization could not be performed, i.e. those having an insufficient number ofvalid pixels, are assigned a class of unknown.
See Giglio et al. (2016) for a detailed description of the Collection 6 detection algorithm.
8.3.2 What is the smallest fire size that can be detected with MODIS? What about thelargest?
MODIS can routinely detect both flaming and smoldering fires ∼1000 m2 in size. Under very good
observing conditions (e.g. near nadir, little or no smoke, relatively homogeneous land surface, etc.)
flaming fires one tenth this size can be detected. Under pristine (and extremely rare) observing
conditions even smaller flaming fires ∼50 m2 can be detected.
Unlike most contextual fire detection algorithms designed for satellite sensors that were neverintended for fire monitoring (e.g. AVHRR, VIRS, ATSR), there is no upper limit to the largest
and/or hottest fire that can be detected with MODIS.
8.3.3 Why didn’t MODIS detect a particular fire?
This can happen for any number of reasons. The fire may have started and ended in between satellite
overpasses. The fire may be too small or too cool to be detected in the 1 km2 MODIS footprint.
Cloud cover, heavy smoke, or tree canopy may completely obscure a fire. Occasionally the MODIS
instruments are inoperable for extended periods of time (e.g. the Terra MODIS in September 2000)
and can of course observe nothing during these times.
8.3.4 How well can MODIS detect understory burns?
The likelihood of detection beneath a tree canopy is unknown but probably very low. Understory
fires are usually small, which already makes MODIS less likely to detect them, but with the additionof a tree canopy to obstruct the view of a fire, detection becomes very unlikely.
8.3.5 Can MODIS detect fires in unexposed coal seams?
In general, no. The detection algorithm is not tuned to look for the subtle temperature changes in
the overlying soil that is characteristic of such fires.
8.3.6 How do I obtain the MODIS fire products?
See Section 4.
45

8.3.7 What validation of the MODIS active fire products has been performed?
Validation of the Terra MODIS active fire product has primarily been performed using coincident,high resolution fire masks derived from Advanced Spaceborne Thermal Emission and Reflection
Radiometer (ASTER) imagery. See Section 6.
8.3.8 I don’t want to bother with strange file formats and/or an unfamiliar ordering interface
and/or very large data files. Can’t you just give me the locations of fire pixels in plain
ASCII files and I’ll bin them myself?
You can use the MCD14ML monthly fire location product, or obtain MODIS fire pixel locations
via the Web Fire Mapper, but this doesn’t necessarily mean that fire pixel locations are the mostappropriate source of fire-related information. The fire pixel location files include no information
about cloud cover or missing data, and depending on the sort of analysis you are performing, it
is sometimes possible to derive misleading (or even incorrect) results by effectively ignoring theseother types of pixels. In many cases it is more appropriate to use one of the 1-km Level 3 or CMG
fire products. See Section 7.1.1 for more information about this issue.
8.3.9 I want to estimate burned area using active fire data. What effective area burned
should I assume for each fire pixel?
Pulling this off to an acceptable degree of accuracy is generally not possible due to nontrivial spatial and temporal sampling issues. For some applications, however, acceptable accuracy can beachieved, although the effective area burned per fire pixel is not simply a constant, but rather varies
with respect to several different vegetation- and fire-related variables. See Giglio et al. (2006b) for
more information.
8.3.10 Why are some of the MODIS fire products not available prior to November 2000?
Although the Terra MODIS first began acquiring data in February 2000, crosstalk and calibration
remained problematic until early November 2000 (see Section 7.2.1). Among other problems, thiscompromises the integrity and consistency of the earliest MODIS fire products, in particular the
CMG fire products which are almost always used for time series analyses. For this reason we do not
distribute those products (specifically, the CMG and fire-location products) which were renderedparticularly inconsistent during the pre-November 2000 time period.
8.3.11 Why then are the Level 2 swath and Level 3 tiled fire products available before November 2000?
Because these products are not totally useless despite the early calibration problems. In addition,these products are less often used for time series analysis, where a lack of consistency is likely to
be more problematic.

8.4

Level 2 Fire Products

8.4.1 Why do the Level 2 product files vary in size?
Level 2 granules can contain slightly different numbers of scans. More importantly, internal HDF
compression is used to reduce the size of the files.
46

8.4.2 How should the different fire detection confidence classes be used?
Three classes of fire pixels (low confidence, nominal confidence, high confidence) are provided in
the fire masks of the MODIS Level 2 and Level 3 fire products. Users requiring fewer false alarms
may wish to consider only nominal- and high-confidence fire pixels, and treat low-confidence fire
pixels as clear, non-fire, land pixels. Users requiring maximum fire detectability, who are able totolerate a higher incidence of false alarms, should consider all three classes of fire pixels.
8.4.3 How are the confidence values in the “FP confidence” SDS related to the confidenceclasses assigned to fire pixels?
The confidence class assigned to a fire pixel (low, nominal, or high) is determined by thresholding
the confidence value (C) calculated for the fire pixel. These thresholds are listed in Table 10.
Table 10: Fire-pixel confidence classes associated with the confidence level C computed for eachfire pixel.
Range
0% ≤ C < 30%
30% ≤ C < 80%
80% ≤ C ≤ 100%

Confidence Classlownominalhigh

8.4.4 How can I take data from the fire-pixel-table SDSs (i.e., the one-dimensional SDSs withthe prefix “FP ”) and place the values in the proper locations of a two-dimensional
array that matches the swath-based “fire mask” and “algorithm QA” SDSs?
1. Open a MOD14/MYD14 Level 2 granule for reading using your favorite programming language.
2. Determine the number of fire pixels in the granule. The easiest way to do this is to readthe global HDF attribute “FirePix”. (If you are a masochist you can read and parse the ECSCoreMetadata.0 string for the product specific attribute FIREPIXELS instead.) If the number of fire pixels is zero, all of the “FP ” SDSs will have length zero, and there’s nothing left
to process, so close the file and go on to whatever else you’d like to do.
3. Find the number of lines in the granule. Call this number nlines. In the product thisquantity corresponds to the dimension number of scan lines. Since it is difficult todetermine the value of a named dimension directly with the HDF library, you must instead determine the dimensions of an SDS for which the named dimension applies. You
can use either the “fire mask” or “algorithm QA” SDSs for this as they both have dimensions number of scan lines by pixels per scan line. The HDF library function
SDgetinfo returns this information (in IDL use HDF SD GETINFO). You can determine thenumber of samples as well (pixels per scan line), if you like, but the value of thisdimension will always be 1354.
4. Read the the “FP line” and “FP sample” SDSs in their entirety. These arrays contain pixel coordinates within the granule for all of the quantities in the other “FP ” SDSs. Hereafter we’ll
47assume these have been read and stored in internal arrays named FP line and FP sample,
respectively.
5. Create a 2-D array to hold whatever “FP ” quantity it is that you’d like to use. Assuming you
want the band 21/22 brightness temperature (“FP T21”), then in IDL you could do this:T21 = fltarr(nlines, 1354)
6. Read the entire “FP ” SDS that you’d like to use. In the above example this is “FP T21”.
Following our earlier convention, we’ll assume this SDS is read into an internal array named
FP T21.
7. Populate pixels in the T21 array by indexing it with FP line and FP sample. In IDL you
would do this in one shot:
T21 = FP_T21[FP_line, FP_sample]
In non-vector-based languages you’d have to write an explicit loop. In C, for example, do
this:for (i = 0; i < num_fire_pixels; i++)
T21[FP_line[i]][FP_sample[i]] = FP_T21[i];Note that the coordinates in “FP line” and “FP sample” are zero-based. In a language like
Fortran (with the first array element numbered 1) you’d have to add 1 to all values in FP line
and FP sample.
8. Do whatever you want with the 2-D T21 array – it can now be indexed just like the fire mask
and QA SDSs would be if you had read them from the file. Note, though, that the newly
created T21 array will only contain data in those pixels where fires were detected. This istrue for 2-D arrays created from any of the other “FP ” SDSs as well.
9. Go back to step 4 for the remaining “FP ” quantities you want to use.
10. Close the HDF file.
8.4.5 Why are the values of the fire radiative power (FRP) in the Collection 4 Level 2 productinconsistent with those in the Collection 5 Level 2 product?
The “FP power” SDS in the Collection 4 Level 2 product actually contained radiative power per
unit area, despite the fact that the units attribute of this SDS is assigned a value of “megawatts”(this is an error). These values had to be multiplied by the appropriate pixel area (at the surface of
the Earth) to obtain the FRP, like this:
FRP (MW) = power values stored in the Collection 4 Level 2 product × pixel area (km2 )Note that the area of a MODIS pixel varies with its position in the MODIS scan; see the next question for details. Note also that starting with Collection 5 the Level 2 products have this multiplication
performed during processing and therefore contain the correct FRP.
48

8.4.6 What is the area of a MODIS pixel at the Earth’s surface?
The area of a MODIS pixel is nominally 1 km2 but grows away from nadir. To find the approximate
pixel area, calculate the along-scan and along-track pixel dimensions (∆S and ∆T , respectively).
The pixel area is then the product ∆S × ∆T . General formulas for the pixel dimensions (in km)
can be found in Ichoku and Kaufman (2005) and are reproduced here:∆S = Re s  q
∆T



cos θ
(Re /r)2 − sin2 θ= rs cos θ −

q



− 1

(Re /r)2 − sin2 θ



(11)
,

(12)

where Re = 6378.137 km (Earth radius), r = Re +h, h = 705 km (satellite altitude), s = 0.0014184397,
and θ is the scan angle. The scan angle (in radians) can be calculated from the granule sample SDS(“FP sample”) included in the Level 2 fire product as follows:θ = s × (sample − 676.5)(13)Note that the errors in the above approximations are smaller than the error entailed by treating the
pixel as having sharp edges.
8.4.7 Can I use cloud pixels identified in the Level 2 fire product as a general-purpose cloudmask for other applications?
Cloud pixels are identified in the Level 2 fire products using simple, fixed brightness-temperatureand reflectance thresholds. While adequate for identifying optically thick cloud cover, this scheme
often fails to identify cloud edges and thin cirrus. It is also likely to misclassify snow and sometimes
desert as cloud. While adequate for the fire detection algorithm, which can tolerate these limitations
but cannot tolerate fires being mislabeled as cloud, these characteristics probably render the internal
cloud mask inadequate for most other applications.

49

8.5

Level 3 Tiled Fire Products

8.5.1 Why do coastlines in the tile-based Level 3 products looked so warped?
The tile-based Level 3 products are defined on a global sinusoidal grid which preserves areas but
greatly distorts the shape of land masses at longitudes far from the prime meridian.
8.5.2 Is there an existing tool I can use to reproject the tiled MODIS products into a different
projection?
The MODIS Reprojection Tool (MRT) can reproject the tiled MODIS products into many different
projections; see Section 10.
8.5.3 Why do some MOD14A1 and MYD14A1 product files have fewer than eight daily data
layers?
Days for which no MODIS data was acquired at all will not have a “plane” in the three-dimensional
Scientific Data Sets included in the MOD14A1 and MYD14A1 products.
8.5.4 How can I determine the date associated with each daily composite in the MOD14A1
and MYD14A1 products when fewer than eight days of data are present?
There are two ways to do this. Let the number of days of valid data (nominally 8) in the product be
Ndays .
Method 1: Using the product-specific Dates global attribute.
The Dates attribute (a string) will contain the date for each plane of the three-dimensional
SDS, with each of the Ndays dates encoded as 10-character substring (format YYYY-MM-DD),
separated by a space. Here’s an example for the normal case of Ndays = 8:
"2002-10-24 2002-10-25 2002-10-26 2002-10-27 2002-10-28 2002-10-29 2002-10-30 2002-10-31"

Method 2: Using the ECS ArchiveMetadata.0 global attribute.
You may alternatively search the ArchiveMetadata.0 global attribute for the DAYSOFYEAR
entry. This field will contain exactly Ndays dates, with each date corresponding to the date of eachplane in the three-dimensional Scientific Data Sets. For example, a time period having no days of
100% missing data will contain eight dates in the DAYSOFYEAR entry, like this:"""OBJECT
NUM_VALVALUE"

END_OBJECT= DAYSOFYEAR\n",= 1\n",= \"2009-08-29, 2009-08-30, 2009-08-31,
2009-09-01, 2009-09-02, 2009-09-03,
2009-09-04, 2009-09-05\"\n",= DAYSOFYEAR\n",And here’s an example when only six days of data are present:

50

"
"
"

OBJECT
NUM_VALVALUE"

END_OBJECT= DAYSOFYEAR\n",= 1\n",= \"2001-06-10, 2001-06-11, 2001-06-12,
2001-06-13, 2001-06-14,
2001-06-15\"\n",= DAYSOFYEAR\n",8.5.5 How do I calculate the latitude and longitude of a grid cell in the Level 3 products?
You can use the online MODLAND Tile Calculator8 , or perform the calculation as described in
Section 5.2.6.
8.5.6 How do I calculate the tile and grid cell coordinates of a specific geographic location(latitude and longitude)?
You can use the online MODLAND Tile Calculator8 , or perform the calculation as described in
Section 5.2.6.
8.5.7 What size are the grid cells of Level 3 MODIS sinusoidal grid?
The Level 3 MODIS products generated on the MODIS sinusoidal grid are colloquially referred toas having “1 km”, ”500 m”, and “250 m” grid cells, but the actual cell sizes are shown in Table 11.
Table 11: Sizes of grid cells in Level 3 tiled MODIS sinusoidal grid.
Colloquial Size
“1 km”“500 m”“250 m”

8

Actual Size (m)926.62543305
463.31271653
231.65635826

http://landweb.nascom.nasa.gov/cgi-bin/developer/tilemap.cgi

51

8.6

Level 3 CMG Fire Products

8.6.1 I need to reduce the resolution of the 0.25◦ CMG fire product to grid cells that are a
multiple of 0.25◦ in size. How do I go about doing this?
For all pixel-count data layers simply sum the values of the 0.25◦ grid cells that lie within the larger
grid cells. Be sure to handle grid cells flagged with the missing data value of -1. At the very
least this entails excluding the negative missing data values from the resulting sum, but, depending
upon the application, it may be more appropriate to flag the coarser grid cell as lacking valid data
entirely. When coarsening the mean fire radiative power layer (MeanPower) you should weightthe individual 0.25◦ mean FRP values by the corrected fire pixel counts (CorrFirePix), handling(by at least excluding) missing FRP values of 0 in the process. A few examples are shown in thefollowing figures:

0.25˚ CorrFirePix
100

0.5˚ CorrFirePix

200
1000

300

400

Rebinning corrected fire pixel counts from 0.25◦ grid cells (left) to a 0.5◦ grid cell (right). The result
is simply the sum of the pixel counts of the four 0.25◦ grid cells (100 + 200 + 300 + 400 = 1000
fire pixels) nested within the 0.5◦ grid cell.

52

0.25˚ CorrFirePix-1

0.5˚ CorrFirePix

-1
-1

300

400

Rebinning corrected fire pixel counts from 0.25◦ grid cells (left) to a 0.5◦ grid cell (right) when
missing data values of -1 are present. In this case we flag the entire 0.5◦ grid cell as lacking valid
data which is appropriate when, for example, we are going to compare independent gridded fireproducts that won’t generally have missing data values in exactly the same grid cells. For otherapplications it would be sufficient to simply exclude the missing values from the sum, yielding a
result of 300 + 400 = 700 fire pixels in the 0.5◦ grid cell.

0.25˚ MeanPower (MW)10

0.5˚ MeanPower (MW)

20
30

30

40

Rebinning the mean FRP from 0.25◦ grid cells (left) to a 0.5◦ grid cell (right). The result is theaverage of the FRP in the four 0.25◦ grid cells nested within the 0.5◦ grid cell, weighted by theirindividual corrected fire pixel counts. Using the corrected fire pixel counts from the first example(above) this yields:
10 MW×100 + 20 MW×200 + 30 MW×300 + 40 MW×400= 30 MW
100 + 200 + 300 + 400

53

8.6.2 Why don’t you distribute a daily CMG fire product?
Because a MODIS product at daily temporal resolution will be plagued by extremely large samplingbias errors. At most latitudes a single MODIS instrument simply does not sample the Earth’s surface
adequately in time periods shorter than about 8 days to “average out” most of the sampling bias.
8.6.3 Why don’t you distribute the CMG fire products as plain binary (or ASCII) files?
1) The number of files one must deal with balloons since most users request that individual data
layers be written to separate files; 2) it is difficult to include useful metadata without writing separate
header files, increasing the total number of files to handle even further; 3) it is possible for data andits accompanying metadata to become separated; and 4) production, ingest, and analysis software ismuch more likely to break when changes are made to the product.
8.6.4 Where can I find information about the FITS file format?
Extensive information about FITS is available from NASA’s FITS Support Office9 . For a moregeneral overview see the FITS entry on Wikipedia10 . You may also find the TRMM VIRS Monthly
Fire Product User’s Guide (Giglio and Kendall, 2003) helpful, particularly Appendix 2.
8.6.5 What software libraries are available for reading FITS files?
An extensive list, which includes packages for C, Fortran, IDL, Java, Perl, Python, and R, is maintained by the FITS Support Office11 .
8.6.6 How can I display images in FITS files?
Two good choices are SAOimage12 (Figure 12) and DS913 (Figure 13). Use the “-ul” switch with
SAOimage and the “-orient y” switch with DS9 to orient North upwards, otherwise the grid
will appear upside down. If you’re willing to tolerate an upside-down orientation (i.e., South ontop), then Fv14 (Figure 14) is another good choice.

9

http://fits.gsfc.nasa.gov/
http://en.wikipedia.org/wiki/FITS11
http://fits.gsfc.nasa.gov/fits libraries.html
12
http://tdc-www.harvard.edu/software/saoimage.html
13
http://hea-www.harvard.edu/RD/ds9/
14
http://heasarc.nasa.gov/docs/software/ftools/fv/
1054

Figure 12: SAOimage displaying the CorrFirePix data layer in the February 2003 Collection 5
MOD14CMH product.

55

Figure 13: DS9 displaying the CorrFirePix data layer in the February 2003 Collection 5
MOD14CMH product. The longitude and latitude, respectively, at the center of the pixel beneaththe cursor is shown in the upper left hand corner of the window, in the two numeric fields to theright of the word “LINEAR”.

56

Figure 14: Fv displaying the CorrFirePix data layer in the January 2009 Collection 5
MYD14CMH product. Note the “upside down” orientation of the global image.

57

8.6.7 Does the last 8-day CMG product for each calendar year include data from the firstfew days of the following calendar year?
Yes. The last 8-day CMG product for each calendar year, which begins on day 361, includes thefirst three days (two days for leap years) of the following calendar year. For example, the 8-day
Aqua MODIS CMG product MYD14C8Q.2004361.006.01.hdf is produced using observations from days 361–366 of 2004 and days 1–2 of 2005.
8.6.8 Where can I find details about the different corrections performed on some of the data
layers in the CMG fire products?
See Giglio et al. (2006a).
8.6.9 Are persistent hot spots filtered out of the MODIS CMG fire products?
Yes, static, persistent hot spots are excluded during production of the CMG fire products (Figure 15).
See Giglio et al. (2006a) for details. For Collection 6, the static source database will be updatedbi-annually since the Terra MODIS data record now spans more than 15 years in duration.

Figure 15: Locations of static hot spots excluded from the Collection 5 CMG fire products.

8.6.10 Is there an easy way to convert a calendar date into the ordinal dates (day-of-year)
used in the file names of the 8-day fire products?
Yes. Try the Unix (or Linux) cal command (with the -j switch), or use Table 12.

58

Table 12: Calendar dates (month/day) corresponding to the day-of-year (DOY) beginning each 8day time period for which the 8-day fire products are generated. Dates for non-leap years and leap
years are shown separately.

DOY
1
9
17
25
33
41
49
57
65
73
81
89
97
105
113
121
129
137
145
153
161
169
177

Non-Leap
Date
01/01
01/09
01/17
01/25
02/02
02/10
02/18
02/26
03/06
03/14
03/22
03/30
04/07
04/15
04/23
05/01
05/09
05/17
05/25
06/02
06/10
06/18
06/26

Leap
Date
01/01
01/09
01/17
01/25
02/02
02/10
02/18
02/26
03/05
03/13
03/21
03/29
04/06
04/14
04/22
04/30
05/08
05/16
05/24
06/01
06/09
06/17
06/25

DOY
185
193
201
209
217
225
233
241
249
257
265
273
281
289
297
305
313
321
329
337
345
353
361

59

Non-Leap
Date
07/04
07/12
07/20
07/28
08/05
08/13
08/21
08/29
09/06
09/14
09/22
09/30
10/08
10/16
10/24
11/01
11/09
11/17
11/25
12/03
12/11
12/19
12/27

Leap
Date
07/03
07/11
07/19
07/27
08/04
08/12
08/20
08/28
09/05
09/13
09/21
09/29
10/07
10/15
10/23
10/31
11/08
11/16
11/24
12/02
12/10
12/18
12/26

8.7

Global Monthly Fire Location Product8.7.1 Can I use the MCD14ML fire location product to make my own gridded fire data set?
Yes, but please see the caveats in Section 7.1.1 first. If done carelessly you may end up with severe
temporal and spatial biases in your gridded data.
8.7.2 How many lines are in each MCD14ML product file?
This size of each product file depends on the number of fire pixels detected each month but typicallyvaries between 200,000 and 500,000 lines.
8.7.3 Are persistent hot spots filtered out of the fire location product?
No. Unlike the CMG fire product, static, persistent hot spots are not removed from the MCD14ML
product. For Collection 6, you can use the new type attribute to identify such fire pixels in the
MCD14ML product.
8.7.4 The MCD14ML ASCII product files have fixed-width, space-delimited fields. Is there
an easy way to convert these to comma-separated values (CSV) files?
Yes. In Unix, Linux, or Mac OS-X you can use the tr command to do this. Here’s an example:
tr -s ’ ’ , < MCD14ML.200805.006.01.txt > MCD14ML.200805.006.01.csv
8.7.5 How can I compute the scan angle given the sample number in the MCD14ML product?
The scan angle θ (in radians) can be calculated from the value in the sample column as follows:θ = s × (sample − 676.5),(14)

where s = 0.0014184397. (See section 8.4.6 if you are also interested in the approximate size of the
pixel at the Earth’s surface.)

60

8.8

Hierarchical Data Format (HDF)

8.8.1 What are HDF files?
The Hierarchical Data Format (HDF), developed at the National Center for Supercomputing, is oneof various file formats used to portably archive and distribute scientific data. HDF files are more orless “self-describing” in that they can include extensive metadata about the data stored within the
file. Note that there are two incompatible flavors of HDF in use: HDF4, the format in which all
MODIS products are stored, and HDF5, which is actually a completely different file format that is
not backwards-compatible with HDF4. See the NCSA HDF web site for more information15 .
8.8.2 How do I read HDF4 files?
If you are writing your own software in a “traditional” programming language such as C or Fortran,
you will need obtain the HDF4 library from NCSA16 . Some commercial software packages, however, including MATLAB, IDL, and ENVI, have the HDF library built-in, in which case you will
not need to install the library.
8.8.3 Can’t I just skip over the HDF header and read the data directly?
Put any thought of reading or writing HDF files without the HDF library out of your head. HDF
was intended to be not so much a physical file format, but instead an “application interface”. As
such, the format is fairly complicated (and has in fact changed over time) and it would be very time
consuming (and risky) to roll your own HDF ingest code. The physical file format is nothing likethe typical header-followed-by-data common to many other formats, and it is not easy to simply
skip over the metadata fragments in an HDF file.
8.8.4 How can I list the contents of HDF4 files?
The NCSA HDF4 Library includes a utility named ncdump which will do this. Be sure to use theswitch -h otherwise you will be inundated with ASCII dumps of all numeric arrays in the file.
8.8.5 How can I display images in HDF4 files?
Commercial software packages that can display the data layers in most MODIS products (which are
generally stored as HDF4 “Scientific Data Sets”) include ENVI17 and ERDAS Imagine18 .
Freely available display software includes HDFView19 and the older HDFLook20 (pre-compiled
binaries only).

15

http://hdf.ncsa.uiuc.edu/
http://hdf.ncsa.uiuc.edu/hdf4.html
17
http://www.ittvis.com/ProductServices/ENVI.aspx
18
http://www.erdas.com/
19
http://www.hdfgroup.org/hdf-java-html/hdfview/20
http://www-loa.univ-lille1.fr/Hdflook/hdflook gb.html
1661

9 References
Csiszar, I., Morisette, J. T., and Giglio, L., 2006, Validation of active fire detection from moderate
resolution satellite sensors: the MODIS example in Northern Eurasia. IEEE Transactions on Geosciences and Remote Sensing, 44:1757-1764, doi:10.1109/TGRS.2006.875941.
Giglio, L., Csiszar, I., and Justice, C. O., 2006a, Global distribution and seasonality of active fires as
observed with the Terra and Aqua MODIS sensors. Journal of Geophysical Research, 111:G02016,
doi:10.1029/2005JG000142.
Giglio, L., Csiszar, I., Restás, Á., Morisette, J. T., Schroeder, W., Morton, D., and Justice, C. O.,
2008, Active fire detection and characterization with the Advanced Spaceborne Thermal Emission
and Reflection Radiometer (ASTER). Remote Sensing of Environment, 112:3055-3063.
Giglio, L., Descloitres, J., Justice, C. O., and Kaufman, Y. J., 2003, An enhanced contextual fire
detection algorithm for MODIS. Remote Sensing of Environment, 87:273-282.
Giglio, L., and Kendall, J., 2003, TRMM VIRS Monthly Fire Product User’s Guide, Revision B.
(ftp://lake.nascom.nasa.gov/data/TRMM/VIRS Fire/docs/VIRS Fire Users Guide B.pdf).
Giglio, L., Schroeder, W., and Justice, C. O., 2016, The collection 6 MODIS active fire detection
algorithm and fire products. Remote Sensing of Environment, 178:31-41.
Giglio, L., van der Werf, G. R., Randerson, J. T., Collatz, G. J., and Kasibhatla, P., 2006b, Global estimation of burned area using MODIS active fire observations. Atmospheric Chemistry and Physics,
6:957-974.
Ichoku, C., and Kaufman, Y. J., 2005, A method to derive smoke emission rates from MODIS fireradiative energy measurements. IEEE Transactions on Geoscience and Remote Sensing, 43:26362649.
Justice, C. O., Giglio, L., Korontzi, S., Owens, J., Morisette, J., Roy, D., Descloitres, J., Alleaume,
S., Petitcolin, F., and Kaufman, Y. J., 2002, The MODIS fire products. Remote Sensing of Environment, 83:244-262.
Kaufman, Y. J., Ichoku, C., Giglio, L., Korontzi, S., Chu, D. A., Hao, W. M., Li, R.-R., and Justice, C. O., 2003, Fires and smoke observed from the Earth Observing System MODIS instrument– products, validation, and operational use. International Journal of Remote Sensing, 24:1765-1781.
Kaufman, Y. J., Justice, C. O., Flynn, L. P., Kendall, J. D., Prins, E. M., Giglio, L., Ward, D. E.,
Menzel, W. P., and Setzer, A. W., 1998, Potential global fire monitoring from EOS-MODIS. Journalof Geophysical Research, 103:32215-32238.
Hawbaker, T. J., Radeloff, V. C., Syphard, D., Zhu, Z., and Stewart, S., 2008, Detection rates of the
MODIS active fire product in the United States. Remote Sensing of Environment, 112:2656-2664.

62

de Klerk, H., 2008, A pragmatic assessment of the usefulness of the MODIS (Terra and Aqua) 1-km
active fire (MOD14A2 and MYD14A2) products for mapping fires in the fynbos biome. International Journal of Wildland Fire, 17:166-178.
Morisette, J. T., Giglio, L., Csiszar, I., and Justice, C. O., 2005a, Validation of the MODIS Active fire product over Southern Africa with ASTER data. International Journal of Remote Sensing,
26:4239-4264.
Morisette, J. T., Giglio, L., Csiszar, I., Setzer, A., Schroeder, W., Morton, D., and Justice, C. O.,
2005b, Validation of MODIS active fire detection products derived from two algorithms. Earth Interactions, 9(9):1-25.
Roy, D. P., Borak, J. S., Devadiga, S., Wolfe, R. E., Zheng, M., and Descloitres, J., 2002, The
MODIS Land product quality assessment approach. Remote Sensing of Environment, 83:62-76.
Schroeder, W., Prins, E., Giglio, L., Csiszar, I., Schmidt, C., Morisette, J. T., and Morton, D., 2008,
Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data. Remote Sensing of Environment, 112:2711-2726.
Wooster, M. J., Zhukov, B., and Oertel, D., 2003, Fire radiative energy for quantitative study ofbiomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fireproducts, Remote Sensing of Environment, 86:83-107.

10

Relevant Web and FTP Sites• MODIS Fire and Thermal Anomalies: General information about the MODIS Fire (Thermal Anomalies) and Burned Area products.
http://modis-fire.umd.edu/• MODIS File Specifications: Detailed file descriptions of all MODIS land products.
ftp://modular.nascom.nasa.gov/pub/LatestFilespecs/collection5• MODIS Land Team Validation: Information concerning the validation status of all MODIS
land products.
http://landval.gsfc.nasa.gov/• MODIS LDOPE Tools: A collection of programs, written by members of the Land Data Operational Product Evaluation (LDOPE) group, to assist in the analysis and quality assessmentof MODIS Land (MODLAND) products.
https://lpdaac.usgs.gov/tools/ldope tools• MODIS Reprojection Tool (MRT), Release 4.1: Software for reprojecting tiled MODIS
Level 3 products into many different projections.
https://lpdaac.usgs.gov/tools/modis reprojection tool

• MODLAND Tile Calculator: Online tool for performing forward and inverse mapping of
MODIS sinusoidal tiles.
http://landweb.nascom.nasa.gov/cgi-bin/developer/tilemap.cgi

63• LANCE FIRMS Web Fire Mapper: The Fire Information for Resource Management System (FIRMS) Web Fire Mapper generates custom maps of active fires detected by the Terra
and Aqua MODIS instruments. Users can also active fire locations in ESRI shape file and
ARC/INFO formats.
http://firms.modaps.eosdis.nasa.gov/firemap/• LANCE Rapid Response System: Access to near-real time Terra and Aqua MODIS reflectance, fire, vegetation index, and land surface temperature imagery. Includes a multi-yeararchive.
http://earthdata.nasa.gov/data/near-real-time-data/rapid-response• NASA Earthdata Search: The current primary distribution site for most MODIS products.
Formerly Reverb, and before that the Warehouse Inventory Search Tool (WIST), and before
that the EOS Data Gateway (EDG).
https://search.earthdata.nasa.gov/• MODIS Land Product Quality Assessment: Product quality-assessment (QA) related information, including a very complete archive of known land-product issues with descriptions
and examples.
http://landweb.nascom.nasa.gov/cgi-bin/QA WWW/newPage.cgi• NASA Direct Readout Laboratory: Free information and software to acquire, process, andanalyze MODIS Direct Broadcast data.
http://directreadout.sci.gsfc.nasa.gov/• SSEC Terra and Aqua Orbit Tracks: Orbit tracks for various polar orbiting satellites, including Terra and Aqua, from the University of Wisconsin-Madison Space Science and Engineering Center (SSEC).
https://www.ssec.wisc.edu/datacenter/orbit tracks.html

64