Active Fire Data

Let us compare the current data which we can find on the websites of INPE Brazil with the NASA Active Fire Data on firms.modaps.eosdis.nasa.gov.

How does a Brazilian prediction of the fire risk compare to an actual observation by a satellite?

Brazilian data

Fire risk index map for 16-03-2020 (1 day prediction)

NM_ESTADO NM_REGIAO CD_GEOCUF geometry
0 RONDÔNIA NORTE 11 POLYGON ((-62.86662 -7.97587, -62.86017 -7.982...
1 ACRE NORTE 12 POLYGON ((-73.18253 -7.33550, -73.05413 -7.381...
2 AMAZONAS NORTE 13 POLYGON ((-67.32609 2.02971, -67.31682 2.00125...
3 RORAIMA NORTE 14 POLYGON ((-60.20051 5.26434, -60.19828 5.26045...
4 PARÁ NORTE 15 MULTIPOLYGON (((-46.06095 -1.09470, -46.06666 ...

check the predicted risk of the max. FRP : POINT (-60.68428 4.21427)

Spots160320 =brazil[brazil.geometry.str.contains("-60.68428")] OK 0.07

datahora satelite pais estado municipio bioma diasemchuv precipitac riscofogo latitude longitude frp geometry
192 2020/03/16 01:42:23 METOP-B Brasil RORAIMA MUCAJAI Amazonia 5 5.19 0.0 2.55700 -61.390999 NaN POINT (-61.39100 2.55700)
193 2020/03/16 01:42:23 METOP-B Brasil RORAIMA MUCAJAI Amazonia 5 5.19 0.0 2.55530 -61.398602 NaN POINT (-61.39860 2.55530)
194 2020/03/16 05:00:30 NOAA-20 Brasil MATO GROSSO PONTES E LACERDA Amazonia 4 2.94 0.1 -15.68063 -59.286200 NaN POINT (-59.28620 -15.68063)
195 2020/03/16 05:00:30 NOAA-20 Brasil MATO GROSSO PONTES E LACERDA Amazonia 4 2.94 0.1 -15.67667 -59.289310 NaN POINT (-59.28931 -15.67667)
196 2020/03/16 05:00:30 NOAA-20 Brasil MATO GROSSO PONTES E LACERDA Amazonia 4 2.94 0.1 -15.67606 -59.293000 NaN POINT (-59.29300 -15.67606)
datahora satelite pais estado municipio bioma diasemchuv precipitac riscofogo latitude longitude frp geometry
192 2020/03/16 01:42:23 METOP-B Brasil RORAIMA MUCAJAI Amazonia 5 5.19 0.0 2.55700 -61.390999 NaN POINT (-61.39100 2.55700)
193 2020/03/16 01:42:23 METOP-B Brasil RORAIMA MUCAJAI Amazonia 5 5.19 0.0 2.55530 -61.398602 NaN POINT (-61.39860 2.55530)
194 2020/03/16 05:00:30 NOAA-20 Brasil MATO GROSSO PONTES E LACERDA Amazonia 4 2.94 0.1 -15.68063 -59.286200 NaN POINT (-59.28620 -15.68063)
195 2020/03/16 05:00:30 NOAA-20 Brasil MATO GROSSO PONTES E LACERDA Amazonia 4 2.94 0.1 -15.67667 -59.289310 NaN POINT (-59.28931 -15.67667)
196 2020/03/16 05:00:30 NOAA-20 Brasil MATO GROSSO PONTES E LACERDA Amazonia 4 2.94 0.1 -15.67606 -59.293000 NaN POINT (-59.29300 -15.67606)
datahora satelite pais estado municipio bioma diasemchuv precipitac riscofogo latitude longitude frp geometry
203 2020/03/16 05:48:00 NPP-375 Brasil RORAIMA SAO JOAO DA BALIZA Amazonia 7 31.86 0.00 1.05432 -59.93779 2.1 POINT (-59.93779 1.05432)
204 2020/03/16 05:54:00 NPP-375 Brasil MATO GROSSO COLNIZA Amazonia 0 0.30 0.00 -9.16697 -61.40875 2.7 POINT (-61.40875 -9.16697)
205 2020/03/16 13:40:00 TERRA_M-T Brasil MATO GROSSO GAUCHA DO NORTE Amazonia 0 0.05 0.00 -13.35700 -53.84600 11.0 POINT (-53.84600 -13.35700)
206 2020/03/16 13:40:00 TERRA_M-T Brasil MATO GROSSO GAUCHA DO NORTE Amazonia 0 0.11 0.00 -13.25900 -53.69700 10.8 POINT (-53.69700 -13.25900)
207 2020/03/16 13:40:00 TERRA_M-T Brasil MATO GROSSO NOVA MARINGA Amazonia 4 0.15 0.00 -12.95400 -57.24200 18.6 POINT (-57.24200 -12.95400)
... ... ... ... ... ... ... ... ... ... ... ... ... ...
324 2020/03/16 16:54:00 NPP-375 Brasil RORAIMA NORMANDIA Amazonia 0 0.10 0.07 4.11714 -60.35289 14.1 POINT (-60.35289 4.11714)
325 2020/03/16 16:54:00 NPP-375 Brasil RORAIMA PACARAIMA Amazonia 0 0.50 0.07 4.19654 -60.67044 3.5 POINT (-60.67044 4.19654)
326 2020/03/16 16:54:00 NPP-375 Brasil RORAIMA PACARAIMA Amazonia 0 0.92 0.07 4.19833 -60.59929 6.4 POINT (-60.59929 4.19833)
327 2020/03/16 16:54:00 NPP-375 Brasil RORAIMA PACARAIMA Amazonia 17 5.31 0.07 4.21954 -60.65201 10.3 POINT (-60.65201 4.21954)
328 2020/03/16 16:54:00 NPP-375 Brasil RORAIMA UIRAMUTA Amazonia 11 15.91 0.07 4.97648 -60.42573 19.9 POINT (-60.42573 4.97648)

52 rows × 13 columns

diasemchuv precipitac riscofogo latitude longitude frp
count 52.000000 52.000000 52.000000 52.000000 52.000000 52.000000
mean 5.403846 2.646538 0.070769 -3.089322 -57.659039 12.786538
std 7.222676 5.235419 0.147247 7.568805 3.132128 12.294906
min 0.000000 0.010000 0.000000 -14.787040 -61.408750 1.500000
25% 0.000000 0.277500 0.000000 -12.315975 -60.309480 6.075000
50% 1.000000 0.720000 0.055000 1.583905 -58.876865 10.250000
75% 11.500000 2.095000 0.070000 3.724692 -54.890485 14.100000
max 18.000000 31.860000 0.770000 4.976480 -49.031590 64.400000

There are only 52 observations that contain frp-values. That makes 24,3% for that day of data.

datahora satelite pais estado municipio bioma diasemchuv precipitac riscofogo latitude longitude frp geometry
322 2020/03/16 16:54:00 NPP-375 Brasil RORAIMA PACARAIMA Amazonia 17 5.31 0.07 4.21427 -60.68428 64.4 POINT (-60.68428 4.21427)
datahora satelite pais estado municipio bioma diasemchuv precipitac riscofogo latitude longitude frp geometry
305 2020/03/16 16:48:00 NPP-375 Brasil MATO GROSSO NOVA UBIRATA Amazonia 0 0.01 777.70 -13.39025 -54.87175 8.4 POINT (-54.87175 -13.39025)
307 2020/03/16 16:48:00 NPP-375 Brasil MATO GROSSO DENISE Amazonia 0 0.52 0.63 -14.78704 -56.90749 10.2 POINT (-56.90749 -14.78704)
308 2020/03/16 16:48:00 NPP-375 Brasil MATO GROSSO TANGARA DA SERRA Amazonia 5 0.37 0.51 -14.75203 -57.70984 1.5 POINT (-57.70984 -14.75203)

At row 305 we find an outlier with a value of 777,7. I don't know yet if this is a mistype or a technical bug. I'll turn this value into 0.77 for better compliance with the other data.

standardize diasemchuv precipitac riscofogo frp

Fire risk index map for 16-03-2020 (actual observations)

Finding out the radiated power for a spot and the predicted fire risk index/value of an area.

In order to verify the (usefulness and) effectiveness of the predicted fire risk Risco de Fogo (RF), we need to link the predicted fire risk (area) to the actual observations of wild fires (point within the area) which have a FRP value. Lately I found out that there are at least 2 fire risk indici in use when you look at the 2020 datasets:

  • risco_fogo is a number (integer) between 0 and 5,
    • currently used for the predictions the next 1 to 3 days
  • riscofogo is used in the observation data and the 2019 report:
    • is a value between 0 and 1 (1 = maximum risk).
Risco Risco de Fogo (RF 2019) RF 2020 (predictions)
Mínimo RF ≤ 0,15 1
Baixo 0,15 ≤ RF ≤ 0,40 2
Médio 0,40 ≤ RF ≤ 0,70 3
Alto 0,70 ≤ RF ≤ 0,95 4
Crítico RF ≤ 0,95 5

NASA FIRMS Fire Information for Resource Management System Data

LATITUDE LONGITUDE BRIGHT_TI4 SCAN TRACK ACQ_DATE ACQ_TIME SATELLITE CONFIDENCE VERSION BRIGHT_TI5 FRP DAYNIGHT geometry
14654 -27.61700 -54.86763 337.1 0.53 0.42 2020-03-13 1736 N nominal 1.0NRT 299.2 7.8 D POINT (-54.86763 -27.61700)
36830 -13.39025 -54.87175 326.0 0.44 0.46 2020-03-16 1648 N low 1.0NRT 299.8 8.4 D POINT (-54.87175 -13.39025)
56423 -16.13545 -54.87363 335.3 0.35 0.57 2020-03-18 1748 N nominal 1.0NRT 293.2 3.8 D POINT (-54.87363 -16.13545)

For that particular outlier found in the Brazilian dataset, we found a match. It turns out that the NASA annotates its data with a confidence column. The confidence for this outlier has been marked as being "low".
Perhaps there is a reason for that strange value in the Brazilian dataset: to be able to filter it out more easily.

LATITUDE LONGITUDE BRIGHT_TI4 SCAN TRACK ACQ_DATE ACQ_TIME SATELLITE CONFIDENCE VERSION BRIGHT_TI5 FRP DAYNIGHT geometry
0 -28.10725 -48.77337 310.6 0.45 0.47 2020-03-12 0354 N nominal 1.0NRT 289.6 1.5 N POINT (-48.77337 -28.10725)
1 -26.32746 -56.65677 315.0 0.69 0.74 2020-03-12 0354 N nominal 1.0NRT 291.5 3.5 N POINT (-56.65677 -26.32746)
2 -26.33422 -56.65912 304.4 0.69 0.74 2020-03-12 0354 N nominal 1.0NRT 291.7 3.6 N POINT (-56.65912 -26.33422)
3 -26.25187 -57.01917 310.6 0.73 0.76 2020-03-12 0354 N nominal 1.0NRT 294.1 2.3 N POINT (-57.01917 -26.25187)
4 -26.25871 -57.02172 308.8 0.73 0.76 2020-03-12 0354 N nominal 1.0NRT 293.8 2.3 N POINT (-57.02172 -26.25871)
LATITUDE LONGITUDE BRIGHT_TI4 SCAN TRACK ACQ_DATE ACQ_TIME SATELLITE CONFIDENCE VERSION BRIGHT_TI5 FRP DAYNIGHT geometry
33966 -3.58094 -38.85745 305.3 0.49 0.40 2020-03-16 0412 N nominal 1.0NRT 277.4 1.9 N POINT (-38.85745 -3.58094)
33967 -12.39466 -38.34320 306.4 0.45 0.47 2020-03-16 0412 N nominal 1.0NRT 284.1 1.5 N POINT (-38.34320 -12.39466)
33968 -12.39899 -38.34378 313.0 0.45 0.47 2020-03-16 0412 N nominal 1.0NRT 284.7 1.5 N POINT (-38.34378 -12.39899)
33969 -15.56909 -47.85659 309.4 0.44 0.38 2020-03-16 0412 N nominal 1.0NRT 290.5 1.6 N POINT (-47.85659 -15.56909)
33970 -17.60978 -42.55631 302.6 0.45 0.39 2020-03-16 0412 N nominal 1.0NRT 286.9 1.0 N POINT (-42.55631 -17.60978)

finding the points contained within the borders of Brazil

  • GeoSeries crs mismatch: epsg:4326 and epsg:4674
  • so we set the correct CRS: Latitude - Longitude
  • now we can filter out the data lying within the Brazilian border
LATITUDE LONGITUDE BRIGHT_TI4 SCAN TRACK ACQ_DATE ACQ_TIME SATELLITE CONFIDENCE VERSION BRIGHT_TI5 FRP DAYNIGHT geometry
NM_ESTADO NM_REGIAO CD_GEOCUF geometry
0 RONDÔNIA NORTE 11 POLYGON ((-62.86662 -7.97587, -62.86017 -7.982...
1 ACRE NORTE 12 POLYGON ((-73.18253 -7.33550, -73.05413 -7.381...
2 AMAZONAS NORTE 13 POLYGON ((-67.32609 2.02971, -67.31682 2.00125...
3 RORAIMA NORTE 14 POLYGON ((-60.20051 5.26434, -60.19828 5.26045...
4 PARÁ NORTE 15 MULTIPOLYGON (((-46.06095 -1.09470, -46.06666 ...
NM_ESTADO NM_REGIAO CD_GEOCUF geometry index_right LATITUDE LONGITUDE BRIGHT_TI4 SCAN TRACK ACQ_DATE ACQ_TIME SATELLITE CONFIDENCE VERSION BRIGHT_TI5 FRP DAYNIGHT
2 AMAZONAS NORTE 13 POLYGON ((-67.32609 2.02971, -67.31682 2.00125... 36853 -3.31849 -58.04239 331.6 0.51 0.50 2020-03-16 1648 N nominal 1.0NRT 288.6 1.9 D
2 AMAZONAS NORTE 13 POLYGON ((-67.32609 2.02971, -67.31682 2.00125... 36854 -3.10486 -58.04660 346.6 0.51 0.49 2020-03-16 1648 N nominal 1.0NRT 290.1 11.2 D
2 AMAZONAS NORTE 13 POLYGON ((-67.32609 2.02971, -67.31682 2.00125... 36855 -3.10029 -58.04726 326.8 0.51 0.49 2020-03-16 1648 N nominal 1.0NRT 289.5 5.1 D
3 RORAIMA NORTE 14 POLYGON ((-60.20051 5.26434, -60.19828 5.26045... 36051 4.11714 -60.35289 342.6 0.57 0.52 2020-03-16 1654 N nominal 1.0NRT 295.6 14.1 D
3 RORAIMA NORTE 14 POLYGON ((-60.20051 5.26434, -60.19828 5.26045... 36054 4.19654 -60.67044 330.5 0.59 0.53 2020-03-16 1654 N nominal 1.0NRT 293.5 3.6 D

Background info for plot 16/03/2020

  • value 0412 on the x-axis is the time of the observation expressed in GMT Greenwich meantime 04:12:00 AM.
  • This would indicate a local time at Manaus in winter time of 00:12:00 AM
  • No data after 17:00 due to no coverage by any satellite, or bad atmosferical conditions.
geopandas.geodataframe.GeoDataFrame

FRP observations cumulated for 16/03/2020

We add up the FRP's during 1 day, so we can compare it to the average.

No handles with labels found to put in legend.
NM_ESTADO NM_REGIAO CD_GEOCUF geometry index_right LATITUDE LONGITUDE BRIGHT_TI4 SCAN TRACK ACQ_DATE ACQ_TIME SATELLITE CONFIDENCE VERSION BRIGHT_TI5 FRP DAYNIGHT
23 MATO GROSSO CENTRO-OESTE 51 POLYGON ((-57.93439 -7.65677, -57.93044 -7.657... 34003 -17.62227 -57.18470 309.9 0.48 0.65 2020-03-16 0412 N nominal 1.0NRT 271.2 2.3 N
23 MATO GROSSO CENTRO-OESTE 51 POLYGON ((-57.93439 -7.65677, -57.93044 -7.657... 33992 -17.58584 -57.12263 301.3 0.48 0.64 2020-03-16 0412 N nominal 1.0NRT 269.9 1.2 N
23 MATO GROSSO CENTRO-OESTE 51 POLYGON ((-57.93439 -7.65677, -57.93044 -7.657... 34004 -17.62326 -57.18012 311.8 0.48 0.65 2020-03-16 0412 N nominal 1.0NRT 268.8 1.7 N
23 MATO GROSSO CENTRO-OESTE 51 POLYGON ((-57.93439 -7.65677, -57.93044 -7.657... 34022 -17.66107 -57.07679 302.6 0.47 0.64 2020-03-16 0412 N nominal 1.0NRT 276.0 1.6 N
23 MATO GROSSO CENTRO-OESTE 51 POLYGON ((-57.93439 -7.65677, -57.93044 -7.657... 34011 -17.65772 -57.07812 309.7 0.47 0.64 2020-03-16 0412 N nominal 1.0NRT 274.9 1.4 N

In human terms: on March the 16th the observating satellites detected the emission of 3300 MegaWatts of energy in wavelengths that are known to quantify correctly the burning of diff. kind of vegetations. 3.300Megawatts=1.1261010BTU/Hour
At this moment of the year it is still the wet season in the Amazon.

FRP observations statewise 16/03/2020

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
DAYNIGHT D N
NM_ESTADO
AMAZONAS 18.2 0.0
MATO GROSSO 155.3 70.9
RORAIMA 336.6 2.2

So now we know the sums of the FRP for 3 states in Amozonia Legal at March 16th.

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

FRP plots of 2018

diasemchuva precipitacao riscofogo latitude longitude frp
count 1.806600e+06 1.806600e+06 1.739856e+06 1.806600e+06 1.806600e+06 1.079443e+06
mean 1.657343e+01 1.868359e+00 6.742996e-01 -9.316527e+00 -5.204725e+01 1.833236e+01
std 2.490910e+01 7.513667e+00 3.807454e-01 5.816046e+00 7.767577e+00 4.793838e+01
min 0.000000e+00 0.000000e+00 0.000000e+00 -3.366713e+01 -7.366103e+01 0.000000e+00
25% 3.000000e+00 0.000000e+00 3.100000e-01 -1.198000e+01 -5.776000e+01 3.600000e+00
50% 7.000000e+00 0.000000e+00 9.000000e-01 -9.100000e+00 -5.077000e+01 7.600000e+00
75% 1.700000e+01 1.000000e-01 1.000000e+00 -5.592528e+00 -4.588900e+01 1.700000e+01
max 1.200000e+02 2.216200e+02 1.000000e+00 5.157770e+00 -3.480641e+01 7.303400e+03

Filtering out the rows without frp or fire risk values.

Averaging 2018 data

satelite pais estado municipio bioma diasemchuva precipitacao riscofogo latitude longitude frp
datahora
2018-07-01 16:42:00 NPP-375 Brasil MINAS GERAIS ESTRELA DO SUL Cerrado 21 0.0 1.00 -18.73370 -47.69573 4.6
2018-07-01 16:48:00 NPP-375 Brasil PIAUI GUADALUPE Cerrado 41 0.0 1.00 -6.77950 -43.67107 11.9
2018-07-01 16:48:00 NPP-375 Brasil AMAZONAS MANAQUIRI Amazonia 6 0.0 0.04 -3.51727 -60.33548 45.8
2018-07-14 02:20:00 TERRA_M-M Brasil MATO GROSSO DO SUL ELDORADO Mata Atlantica 3 0.0 1.00 -23.61200 -54.26700 43.5
2018-07-08 01:20:00 TERRA_M-M Brasil PIAUI OEIRAS Caatinga 63 0.0 1.00 -6.96000 -42.06800 5.8
diasemchuva precipitacao riscofogo latitude longitude frp
datahora
2018-01-01 0.000000 2.854345 0.272163 -6.811750 -47.208722 35.388406
2018-01-02 0.000000 2.544623 0.259973 -5.723232 -45.557148 20.354636
2018-01-03 0.014124 2.263831 0.351915 -5.345643 -45.404175 29.863158
2018-01-04 0.000000 2.571179 0.237208 -6.472958 -48.624939 30.056180
2018-01-05 0.000000 3.209009 0.236678 -5.847425 -51.941709 27.093434

April 2018 new data became accessible with Suomi NPP.

Notice the sudden drop in average radiative power emitted starting from April 2018.

Starting April 2018 new data became accessible: the instrument Suomi NPP. The introduction of the Suomi NPP had 2 main reasons: the higher spatial resolution provided better location precision, and an extra orbiting satellite meant more coverage throughout 24 hours (mainly during the dark hours in this case).
But there is a drawback to witness here: adding much more numbers with lower values leads to lowering the average radiative power overall.
These gaps in observation windows are noticable in the fig. "FRP observations 16/03/2020".

Conclusions:

  • we should avoid to mix the data of Suomi NPP with other data without some weighting measures.
  • in order to use 2018 data for comparison, you'll need to split data in 2 series: before and after 01/04/2018.

Let's add up the numbers for FRP in 2018 cumulatively...

frp ... riscofogo
estado ACRE ALAGOAS AMAPA AMAZONAS BAHIA CEARA DISTRITO FEDERAL ESPIRITO SANTO GOIAS MARANHAO ... PIAUI RIO DE JANEIRO RIO GRANDE DO NORTE RIO GRANDE DO SUL RONDONIA RORAIMA SANTA CATARINA SAO PAULO SERGIPE TOCANTINS
satelite
AQUA_M-M 8.121053 46.695062 12.142553 16.571547 22.817534 17.455385 9.660000 19.871429 22.117291 17.832759 ... 0.977068 0.590000 0.916970 0.277500 0.531680 0.482706 0.080000 0.861374 1.000000 0.733481
AQUA_M-T 35.970555 32.289055 32.483499 53.151101 64.127805 47.769413 35.010227 21.812683 49.865080 50.945407 ... 0.962261 0.790840 0.947419 0.269614 0.524787 0.401716 0.368635 0.855376 0.914643 0.755595
GOES-13 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 0.006667 NaN 1.000000 0.284118 0.035000 0.327500 0.210000 0.028750 0.900000 0.027692
GOES-16 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 0.943200 0.945424 0.933801 0.310233 0.559441 0.487655 0.280546 0.886384 0.894220 0.682723
METOP-B NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 0.973059 0.821333 0.881622 0.255333 0.557126 0.535624 0.726667 0.782365 0.909245 0.731649
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
NOAA-19 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 0.963275 0.897761 0.971413 0.259290 0.581197 0.465461 0.452813 0.899841 0.949048 0.811942
NOAA-19D NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 0.997798 0.578214 NaN 0.245000 0.590487 0.523172 0.710000 0.898032 1.000000 0.809903
NPP-375 14.230987 8.147201 9.667108 14.002023 13.015254 12.208596 10.176424 7.592502 10.884208 11.533025 ... 0.961599 0.807781 0.917835 0.222463 0.502694 0.422693 0.358966 0.822693 0.927678 0.766300
TERRA_M-M 10.637736 36.962428 15.402158 21.779011 29.738022 17.355686 10.550000 13.688462 35.670936 19.672129 ... 0.964252 0.870000 0.927938 0.540476 0.532332 0.510300 0.407500 0.800504 0.921731 0.723652
TERRA_M-T 20.562085 22.688421 28.146604 32.095111 44.995097 29.321061 25.682979 28.490110 38.622431 33.277580 ... 0.950487 0.837679 0.926748 0.180330 0.570319 0.442698 0.310420 0.735581 0.936800 0.750569

15 rows × 54 columns

pais estado municipio bioma diasemchuva precipitacao riscofogo latitude longitude frp
satelite
AQUA_M-M 11346 11346 11346 11346 11346 11346 11346 11346 11346 11346
AQUA_M-T 126181 126181 126181 126181 126181 126181 126181 126181 126181 126181
NPP-375 812293 812293 812293 812293 812293 812293 812293 812293 812293 812293
TERRA_M-M 28211 28211 28211 28211 28211 28211 28211 28211 28211 28211
TERRA_M-T 54777 54777 54777 54777 54777 54777 54777 54777 54777 54777