GCOM–C1/SGLI Fire power estimation algorithm
Algorithm Theoretical Basis Document
Masao Moriyama
Nagasaki University
Nagasaki, JAPAN.
Mar. 23, 2017

Contents
1 Introduction

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2 Fire power estimation algorithm
2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Physics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2 Validation of the constant brightness temperature assumption . . . . . . . . . . . . . . . .
2.2.3 Fire area proportion estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.4 Fire brightness tempearture estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3 Operation
3.1 Input and output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Data processing flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Figures
2.1
2.2
2.3

3.1

Simulated brightness temperature under the various atmospheric condition . . . . . . . . . . . .
Relationship between the TOA reflectance diﬀerence of SW3 and 4 and the fire fraction . . . . .
Fire are proportion estimation result(Left), Relationship between fire area proportion estimation
error and the fire brightness temperature (Right) . . . . . . . . . . . . . . . . . . . . . . . . . . .

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SGLI fire detection data processing flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Tables
2.1

Simulation condition for the constant brightness tempearture validation . . . . . . . . . . . . . .

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3.1

Input data for SGLI fire detection algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 1

Introduction
This document describes the fire detection algorithm for GCOM–C1/SGLI, it does not covered the burnt area(burn scar) detection. SGLI has the disadvantage which SGLI does not have 4 [µm] observation channels like
MODIS but has more fine spatial resolution, the algorithm mainly uses SWIR spectral channel such as 1.6 and2.2 [µm]. Also from SGLI dataset, the fire energy estimation on the basis of the fire area proportion and thefire brightness temperature estimation is made.
This documents shows the fire power estimation algorithm as well as the description of input/output dataset
and few of validation.

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Chapter 2Fire power estimation algorithm
2.1

Overview

MODIS fire product has not only the fire pixel but the fire power which is the product of fire temperature
and area proportion [Justice et al., 2002]. This product is considerd as the higher level product which shows
the how hot and wide the fire is within the pixel. The subpixel size temperature field eﬀect is originallyanalyzed by Dozier [Dozier, 1981] and MODIS fire detection algorithm uses this for the fire temperature andarea proportion retrieval [Justice et al., 2002]. Dozier’s method is necessay to make the atmospheric correctionof the fire contained pixel but over the flaming or smoldering area the accurate atmospheric condition cannot
be estimated, also SGLI has few spectral channels compared with MODIS, so that the least squere method will
not be used and the error propagation will be significant for the estimation. SGLI uses the Dozier’s method
but with some modification.

2.2

Algorithm

2.2.1

Physics

The fire contained pixel radiance I is expressed as Eq. (2.1).
Ii = pBi (T f ) + (1 − p)Ibi(2.1)

where p, T f subscript i and Ib are the fire area proportion, the fire brightness temperature, spectral channel
and the background radiance. In SGLI case, the fire area proportion and the fire brightness temperature are thesolution by using the multiple spectral channel to avoid the atmosphric correction. To solve the above equation,
the follwing assumptions are made.
1. The fire brightness temperature and the background radiance are constant over the pixel.
2. The background radiance is the same as the average of the surrounding pixel.
3. The fire brightness temperature is constant at each spectral channel.

As the output, the fire power Pf computed from the fire temperature T f and the fire area proportion p as
Eq. (2.2),
Pf = σT f 4 · p · (250)2 [W/m2 ](2.2)
σ is Stephan–Boltzmann constant 5.67 ×108 [W/m2 /K].

2.2.2

Validation of the constant brightness temperature assumption

To validate the assumption 3 of the previous section, the numerical simulation of the brightness temperature ofthe fire contained pixel is made. The simulation condition is listed in Table 2.1. The result is shown in Figure
2.1. Since the brightness temperature diﬀerence between SW3 and SW4 is up to 6 [K] in the moise atmosphericcondition, this auumption can be valid.

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Table 2.1: Simulation condition for the constant brightness tempearture validation
Atmosphere US standard, Subarctic winter, Tropic
Aerosol Biomass burning model
Surface visibility 7[km]Fire temperature 500–1200[K]Fire area proportion 1
Background Vegetation (6S)
Solar zenith 45 [deg.]
Observation zenith Nadir
Computation term Brightness temperature diﬀerence between SW3 and SW4Figure 2.1: Simulated brightness temperature under the various atmospheric condition2.2.3

Fire area proportion estimation

From the previous discussion, the simultaneous radiative transfer equation of SW3 and SW4 (cf. Eq. (2.1))
can be solved to estimate the fire area proportion a and fire brightness tempearture T f , however, this scheme
is so sensitive with the observation noise and the violation of the assumptions. So SGLI fire product uses thestepwise estimation [Miura and Moriyama, 2010] which estimates the fire area proportion first and after thatthe fire brightness tempearture is estimated. From the correctly detected 264 SGLI fire pixel with the truthof ASTER fire pixel listed in Table ??, the relationship between the fire area proportion which assumed theASTER fire pixel is coverd the complete fire and the diﬀerence of the SW3, 4 TOA reflectance (cf. Eq. (??)) is
found as in Figure 2.2. The fire area proportion p can be expressed as the logistic function of ∆ρ in Eq.(2.3).
p=exp(β0 + β1 ∆ρ)
1 + exp(β0 + β1 ∆ρ)(2.3)

where β’s are the regression coeﬃcients. From 262 detected SGLI fire pixel dataset, the regression coeﬃcientsare defined and the RMS error of the fire area proportion is 0.07. That is enough to use the SGLI fire algorithm.

3Figure 2.2: Relationship between the TOA reflectance diﬀerence of SW3 and 4 and the fire fraction

2.2.4

Fire brightness tempearture estimation

After the fier area proportion is estimated, the fire rightness temperature can be estimated from the simultaneousradiative transfer equation of SW 3 and 4 by suing ghe non–lienar least squere method to minimize the squere
sum of the diﬀerence between the observed and computed radiance as in Eq. (2.4).
J = [pBSW 3 (T f ) + (1 − p)IbSW 3 − ISW 3 ]2 + [pBSW 4 (T f ) + (1 − p)IbSW 4 − ISW 4 ]2(2.4)

From the 262 SGLI fire pixel, the fire area proportion and the fire brightness temperature are estimated. Theresults are shown in Figure 2.3

Figure 2.3: Fire are proportion estimation result(Left), Relationship between fire area proportion estimation
error and the fire brightness temperature (Right)
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The estimated fire brightness temperature looks lower than the actual case, this is because the assumptionof the ASTER fire pixel coverd by the complete fire. If the appropreate assumption is made for the fire area
proportion of the ASTER fire pixel, this result become more realistic, but the estimation scheme itself cannot
be changed.

5Chapter 3Operation
3.1

Input and output

SGLI fire detection algorithm use the swath Level 1B satellite detected radiance as an input, and the location
and brightness temperature and area proportion are the output. For the data processing, the latitude andlongitude of each pixel, cloud flag and the land cover especially inland water is necessary as the auxiliary data.
The input data is listed in Table 3.1. The output data will be combined with the fire detection results.
Table 3.1: Input data for SGLI fire detection algorithm
1 Level 1B data
2 Cloud flag
3 Latitude/Longitude of each pixel
4 Land/water mask

3.2

Data processing flow

SGLI fire detection data processing flow is shown in Figure 3.1.

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Level 1B
swath data

TOA reflectance (VN10, SW3, 4),
Brightness temperature (T1)

Cloud flag

Fire detection process

Latitude,Longitude

Fire are proportion estimation

Land/watermaskFire brightness temperature
estimation

Fire power

SGLI FIRE PRODUCTFigure 3.1: SGLI fire detection data processing flow

3.3

Validation

It is very diﬃcult to make the ground measurement based truth data for the fire. So the cross comparison
with the high spatial resolution data will be the validation source. In this time there are no high resolution
sensor which has VNIR/SWIR spectral channels, for the future, Next generation Thematic Mapper or some
hyperspectral sensor will be used as the validation sources.

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Bibliography[Dozier, 1981] Dozier, J., 1981. A method for satellite identification of surface temperature fields of subpixelresolution. Remote Sensing Env. 11, pp. 221–229.
[Justice et al., 2002] Justice, C. O., Giglio, L., Korontzi, S., Owens, J., Morisette, J. T., Roy, D., Descloitres,J., Alleaume, S., Perircolin, F. and Kaufman, Y. J., 2002. The modis fire products. Remote Sensing Env.
83, pp. 244–262.
[Miura and Moriyama, 2010] Miura, T. and Moriyama, M., 2010. Development of the forest fire detection algorithm for GCOM–C1/SGLI”, rature estimation. In: Proc. ISPRS Comm. 8 interim symposium, pp. 783–786.

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