JAXA/EORC Joint PI Work Shop 2020JFY, Online, 21 January 2021

Wildﬁre detection and FRP retrieval
from SWIR data of SGLI
Yukio Kurihara (kurihara.yukio@jaxa.jp)
Japan Aerospace Exploration Agency (JAXA), Tsukuba, Ibaraki, Japan

250x250 (=FOVSW3 = 1/16 FOVSW4)

5.6 m2

Fire Temperature (K)

1. Introduction
Spatial resolution of 250 m of SGLI is expected to detect
wildﬁres with high accuracy. We investigated wildﬁre detection
and ﬁre radiative power (FRP) retrieval using SW3 (1.6 um) andSW4 (2.2 um) data from SGLI.

0˚range80˚Valid 120˚40˚90˚60˚

2. Hot spot detection
1.We assumed that SW3 (1.6 um) and SW4 (2.2 um) areinsensitive to the Earth surface temperatures.
2.Hot spots are detected by comparing the normalized standard
deviation (nσ, Eq. (1)) with a threshold.
4Algorithm
use
two thresholds, one (nf=4) to detect hot spots and3.We 4 Algorithm
another one (nf0=3) to ﬁnd likely hotspots in the neighborhood
4.1 Hot spot detectionof eachhotspot (Figs. 1-3, Tables 1-3).
4.1 detectedHot spotdetection

160˚

Out of the range−160˚of FRP−120˚−80˚retrieval−40˚90˚SW4ﬂaming(1000K)*60˚SW330˚0˚30˚smoldering(600K)*0˚
Out of the range of−30˚−30˚

wildﬁre detection

cooling(350K)*−60˚−60˚−90˚−90˚2*) Wooster,et. al 2003
Pixels, where LSW3 and/or LSW4 are larger than the three times of the noise level (3 e ), are selected
atArea(x0.0625km )−80˚ −40˚0˚40˚80˚Fire120˚160˚−160˚ −120˚Pixels,(b)in thewhere
flow. LSW3 and/or LSW4 are larger than the three times of the noise level (3 e ), are selected at(✓Fig. 1 Sensitivity to wildﬁre◆2 ✓◆2 )1/2(b) in the flow.nσ>nf(L✓SW 3 ◆ L✓SW 4 ◆ )1/2
2n =+2(1)Figure 3. Frequently detected hot spots.
(1)
LL
Table 3 Agreements
SWSW
3 3SWSW
4 4n =+(1) with Himawari-8. Detected hot spots were mapped onto 2.5x2.5-degree
SW 3grids and compared with those detected using TIR and MIR data of Himawari-8. The numberin the brackets shows the total number of 2.5x2.5 grids.

SW 4

Lstd,SW x(2)
SW x =
SNLRstd,SW
SW x x=(2)whereSW x
SW x
Then, selected pixels are divided into clusters at (c) SN
by Rgrouping
the data spatially connected.2019
Here,

nσ>nThen,selected
are divided
intoclustersat (c)
by andgrouping
data1. spatiallyHere,f0 pixels
themethoduses
by Lstd/SNR
by using
Lstd
SNR inthe
Table
The stepconnected.
(d) comparese determinedthemaximum
methodby Lstd/SNR
using Lofin Table 1.radiance
The step(d)Aprilcompares
theradiancein each cluster
withthe bythresholdthe maximum
exceeds
thestd4 andFig.uses
2 e determinede . IfSNR
the maximum
radiancein eachthresholdof 4 e . Ifradiance
exceeds
thresholdat SW3or at SW4,
allcluster
pixels within thetheclusterare detected
as thehotmaximum
spots. Figure
2 shows
the theTable 1 Conﬁdence level. A conﬁdence level is decided for each detected hotspot
fractiontherange
ofduring
thehot cluster
spot whichis(Fig.
detectableby SW3
SW4. 2Finally,
thresholdatandSW3
ortemperature
at SW4,
allpixels
in theare detected
spots.andFigureshows theMay
based onarea
thenσ andthedetection
frequency
2018-20193).as hota areaconfidence2) is decided
for eachatwhich(e). Land
cover types
are quoted
from Finally,thefractionlevel
and(Table
the temperature
rangeof thehot
hotspot
spot
is detectable
by SW3and SW4.
MODIS
Land Cover
Type Product.
Detection
frequency(Fig.at3)(e).
was Landgeneratedeach 0.05⇥0.05-degreea confidence
level (Table2) is decided
for each
hot spot
coverattypes
are quoted
from theJune
Level
Description
latitude-longitude
gridType
by using
hot spots
detectedfor 2018and3)2019.
Each threshold
arrived at
MODIS Land Cover
Product.
Detection
frequency(Fig.
was generated
at eachwas
0.05⇥0.05-degree
8σe (L32σe detectedfor water
empirically.
latitude-longitude grid L
by< usinghot< spots
forpixel),2018 and 2019. Each threshold was July
arrived at
1 empirically.
non vegetated (snow, ice, barren), or
detection frequency > ~50 250x250%
Apr. - Jul.
(=FOVSW3 = 1/16SW4)
4.2 FOV
FRP
5.6 m2
L < 32σe4

L < 64σe5L >= 64σe

FireTemperature(K)Fire Temperature (K)60˚30˚0˚−30˚

L < 16σe30˚90˚

5.6 m240˚80˚Valid range
120˚for FRP
160˚

250x250 (=FOVSW3 = 1/16 FOVSW4)−160˚ −120˚

−80˚

SW3

smoldering(600K)*
smoldering

SW42SW430˚(600K)*−30˚cooling(350K)*
cooling−60˚−60˚SW4(350K)*

Area (WF, x0.0625 km )2−90˚(185)0.66(146)20.65 34(196)50.67(142)

3Description
L < 8 for land pixel,0.67 e
0.76
L < 32 e for water pixel,(123)(62)
non vegetated (snow, ice, barren), or
0.70
detection
frequency0.74> 50%(93) L < 16 e (54)0.69 L < 32 e 0.75
L < 64 e(124)(72)
L = 64 e
0.76
0.85

0.64

(669)

(94)

(46)

0.70

0.77

(434)

(234)

45

0.85

1.00

0.79

0.92

0.81

0.71

0.88

1.00

0.83

0.88

(27)

(15)

(28)

(12)

(41)

(21)

(24)

(9)

(120)

Table 3. FRP coefficients(57)⇣ ⌘SW41650
100.254
3F RPSW IR =Lf,SW(5)IR700
SW465.9356
4290˚
Table 3. FRP coefficients
aSW43700
65.9356
5Here, is the Stefan-Boltzmann constant, "f is the fire emissivity over all wavelengths, "f,M IR is the fire
Table 4 FRP coeﬃcients60˚ for the MIR wavelength, a is a constant coefficient, and Lf,M IR denotes the radiance of fire atemissivity
Tf (K)/aQuality flag
SW3MIR. In application of (4) to SWIR, "f and "f,M IR were assumed to be 1.0, and Lf,M IR was replacedL< 3 e L<Lmax L=Lmax
SW31600
133.921
1by L30˚LSW4 ; thiswas basedon the assumptionthat SW3and SW4 are insensitive
to backgroundSW3 or SW3800
1639.2
2SW4L<3SW32e1temperatures.
Thecoefficient
was determinedby using numericallydata3). 3SW4650
100.254
3L<Lmax simulated
SW41SW4(TableSW4
12SW3(1000K)*

Level
1
0.61 %

22to SWIR data of SGLI (Fig. 4, Table 4).

−40˚SW4(1000K)*
ﬂaming1

Following Wooster et al. [2003], FRP is determined by performing the MIR radiance method on theSWIR data. The determination formula is
3. Fire Radiative Power (FRP)
Tf (K)/aQuality flag✓◆"fSW31Lf,M 600
133.921 et al.1 2003)
We applied the single
radiancemethod(WoosterF RPM IR=.
(4)IRa"f,M IR
SW38001639.2
2SW4Valid range forFRPﬂaming

SGLI−90˚

700
700

65.9356
65.9356

45

L=Lmax

SW32Conﬁdence level >1

SW3102
L< 3 e L<Lmax L=Lmax
Red: SGLI
Blue: Himawari-8
L< 3 eSW31L<LSW4
SW4SW43 an alert file is generated. The alert data consists omax
1 hot
2If thereisanyspot
detected,101
4L=Lmax
SW325Report (Alert)

Frequency (%)(%)
Frequency

2Table 2. Confidence levelpart and a data part. The header part is denoted by the ’#’ at the start of ea0˚40˚120˚ and160˚−160˚ range−120˚−40˚ with SW3 acquisition and processed date and time and parameter names are provided in the hFigure 80˚
2. The fraction
temperatureof hot−80˚spots
2 detectableArea(WF,x0.0625arekm) from Wooster et al.
andSW4.
Wildfirelevelsand
thosetemperatures
quoted10Fig. 3 Frequently detected hotspots for 2018-2019.
holds detected hot spots and determined
FRPs in the comma-separated values (C[2003].
red bottomlines denoterange
the detection
limit,detectable
and the toplines
FigureThe2. blue
The andfraction
and temperatureof hot spotswith
SW3
5 Report(Alert)
thesaturationlevels. levels and those temperatures
andSW4. Wildfire
are quoted
from Wooster et al.shows the parameters described in the data part. The hot center indicates the ho[2003].The
Thehotblueandred bottom
denotethe detection
Table 2 Preliminary result.
spot
detectionusinglines
SWIR
data wasexamined limit,
for its and the top linescluster. Each fire cluster can be specified by gathering the hot spots associated with
10capability using SWIR thedatasaturationfrom April levels.
to July 2019.
0100
200300If there is any hot spot detected, an alertNote
file isgenerated.
The
alert
data
consistsoftwoparts:
aheaderthat
thealert
file
isnotgenerated
if
thereisnohotspotdetected.
2FRP(Wm)
FRP
FRP (Wm )partandadatapart.
Theheaderpart
isdenoted
bythe’#’
atthestartof
eachrecord.
The data
sensitive
insensitive
saturate
SW4Table4. Datain data
the reportacquisition
andprocesseddateandtimeandparameternames
areprovided
intheheader.
Thepartinsensitivesensitive
saturatesensitive
SW3Fig. 4 SGLI vs. Himawari-8. FRPs retrieved from SGLI SWIR data were
holds
detectedhot
spotsanddetermined
FRPs in the comma-separated values (CSV) format. Table 4
1,690
15,21779
5,814
91
April 2019
compared with those from Himawari-8 which were determined by performingshows
the parametersdescribed in thedata part.method
The hot
centerindicatesthe parameterhottestpixelin thedescription
firecolumn
aBi-spectral(Dozier2918)
with
SWIRandMIR
data.
984
10,046
59
4,059
42May
3
1Hot-spot
IDcluster.
Eachfirecluster
canbespecified
bygathering
thehotspots
associatedwiththesame hot center.
322
4,340
21
2,049
30
June
2Year
4.
Summary
Note
that
thealert
file
isnotgenerated
if
there
is
nohot
spotdetected.
30−12

July

453

5,040

29

2,351

38

3
Monthusing
SWIR data of SGLI
1.More than 60 % of hotspots detected
4Day
Table 4. Data in the report
3,449
34,643
188
14,273201Apr. - Jul. 2019agreed with those detected from5 Himawari-8
MIRindata.
Time TIR and hhmn
UTC
6 dataLatitude
show good agreements
2.FRPs retrieved from SGLI SWIR
6.54
65.67
.36
27.06
.38%
7Longitude
columnparameter
description
with thoseretrieved fromHimawari-8
data.
8
Area
Area factor (1/cos(✓z ))1Hot-spot ID
9Volcano
0: no volcano, 1: volcanos
References
2Year
Reliability satellite and Table
2Wooster, M., Zhukov, B., and Oertel, D. (2003). Fire radiative energy for quantitative study
burning: derivation from the10bird experimental
comparison
to3 of biomass
Month11FRPWm 24Day
modes ﬁre products. Remote Sensing of Environment, 86(1):83-107.
12
Quality flag
Table 35Time Remote Sensing
hhmn inofUTC
Dozier, J. (1981). A method for satellite identiﬁcation of surface temperature ﬁelds of subpixel
resolution.
Environment,
11:221-229.
13Hot center (ID) Hottest pixel in the cluster
6Latitude
7Longitude