nirst land and sea surface temperature algorithms
TRANSCRIPT
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6th Aquarius/SAC-D Science Meeting
19-21 July 2010 Seattle, Washington, USA
NIRST Land and Sea Surface Temperature Algorithms
Derived Products Status
Marisa M. Kalemkarian CONAE
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
2 of 28 July 19-21, 2010
NIRST Land and Sea Surface Temperature Algorithms
FIRE MAPPING AND FIRE
RADIATIVE POWER
VOLCANIC ACTIVITY
MONITORING
SEA SURFACE
TEMPERATURE (SST)
LAND SURFACE
TEMPERATURE (LST)
NIRST DERIVED PRODUCTS (Level 2)
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
3 of 28 July 19-21, 2010
NIRST Land and Sea Surface Temperature Algorithms
ĂŒâŻATBD â Algorithm Theoretical Basis Document
ĂŒâŻADD â Architectural Design Document
ĂŒâŻRBD â Product Requirements Baseline Document
ĂŒâŻVPD â Validation Plan Document
And, for every one of these four products we generate the following documents:
Generation of Documentation:
ĂâŻPMP â Project Management Plan
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
4 of 28 July 19-21, 2010
NIRST Land and Sea Surface Temperature Algorithms
Summary of Processing Levels:
âą L0A: No calibration or correction applied
âą L1A: Relative Radiometric Calibration + Inter Band Co-registration
âą L1B1: Relative Radiometric Calibration + Absolute Radiometric Calibration + Co-registration
âą L1B2: Relative Radiometric Calibration + Absolute Radiometric Calibration + Geometric Correction (Map Projection â includes inter band co-registration).
âąâŻ L2: Geophysical Parameter
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
5 of 28 July 19-21, 2010
NIRST Land and Sea Surface Temperature Algorithms
âą L1A is generated from L0 (+ Relat. Radiom. Calib. + Co-regist.) âą L1B1 is generated from L1A (+ Absol. Radiom. Calib.) âą L1B2 is generated from L0 (+ Relat. Radiom. Calib. + Absol.
Radiom. Calib. + Geom. Calib. (Map Projection).
âą L2 is generated from L1B1 (+ Retrieval Algorithms)
Product generation sequence:
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6 of 28 July 19-21, 2010
âąâŻ Dominant wavelength of the emitted energy from Earthâs surface is at about 9.7 ”m.
âąâŻ 3 to 5 ”m region is especially useful for monitoring hot targets such as forest fires
and geothermal activities
âąâŻ Thermal Satellite remote sensing typically utilizes the 3-5”m and 8-14”m
wavelength ranges (atmospheric windows), excluding the 9.2-10.2 ”m
range due to strong Ozone absorption
PHYSICAL BACKGROUND
âąâŻThermal infrared region runs from 3 to 14 ”m
âąâŻThis region is affected by atmospheric
absorption
(Sabins - Remote Sensing: Principles and Interpretation, 1987)
NIRST Land and Sea Surface Temperature Algorithms
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
7 of 28 July 19-21, 2010
Observable area (Field of Regard)
Sensor swath
NIRST Bands and Geometry âąâŻ 3 Bands:
ĂŒâŻB1: 3.4 â 4.2 ”m
ĂŒâŻB2: 10.4 â 11.3 ”m
ĂŒâŻB3: 11.4 â 12.3 ”m
âąâŻ Swath: 180 Km at nadir
âąâŻ Spatial resolution:
ĂŒâŻ350 m across track
ĂŒâŻ410 m along track
âąâŻ Boresight pointing: +/-30° from nadir
âąâŻ About 1000 km observable area across track
NIRST Land and Sea Surface Temperature Algorithms
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
8 of 28 July 19-21, 2010
Fire Mapping and
Fire Radiative Power
NIRST Land and Sea Surface Temperature Algorithms
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
9 of 28 July 19-21, 2010
Fire Mapping and Fire Radiative Power
vâŻMODIS (Aqua) sensor â April 17, 2008 Nighttime acquisition vâŻMODIS (Aqua) sensor â April 17, 2008 Nighttime acquisition
vâŻMODIS (Terra) sensor â April 15, 2008 Nighttime acquisition
Examples of fires detected by MODIS algorithm in the Delta of Parana River - 2008
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
10 of 28 July 19-21, 2010
Fire Mapping and Fire Radiative Power
MODIS data â 4”m band BIRD data â 4”m band
(From Wooster et al., 2003)
NIRST pixel size is similar to BIRD pixel size
Example of MODIS resolution compared to BIRD resolution, which is similar to NIRST
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11 of 28 July 19-21, 2010
FIRE Detection Algorithm (based on MODIS algorithm, Giglio 2003)
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
12 of 28 July 19-21, 2010
Fire Mapping and Fire Radiative Power
ContinuedâŠ
Flow chart of the Fire Detection Algorithm:
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
13 of 28 July 19-21, 2010
Fire Mapping and Fire Radiative Power
ContinuedâŠ
Flow chart of the Fire Detection Algorithm:
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14 of 28 July 19-21, 2010
Fire Mapping and Fire Radiative Power
âąâŻ FRE, for wildfires should be proportional to the total mass of fuel biomass combusted.
vâŻFire Radiative Power (FRP)
âąâŻFRP is the amount of radiant heat energy liberated per unit time âąâŻFRP is related to the rate at which fuel is being consumed.
âąâŻMeasuring FRP and integrating it over the lifetime of the fire provides an estimate of the total Fire Radiative Energy (FRE),
Fires burn extensive areas in different vegetated landscapes across the globe, and constitute a major disturbance to land-based ecosystems.
By voraciously consuming biomass, releasing intense heat energy, and emitting thick plumes of smoke into the atmosphere, such large-scale fires exert several adverse effects on life, property, the environment, weather, and climate both directly and indirectly.
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
15 of 28 July 19-21, 2010
Fire Mapping and Fire Radiative Power
where, T4 à 4-”m brightness temperature of the fire pixel
à mean 4-”m brightness temperature of the non-fire background
Apix Ă total area (in km2) of the pixel in which the fire was detected,
taking into account its deformation due to projections and scan angles.
ĂŒâŻ The resulting value of the FRP is expressed in MW.
pixsensor ATTkmMWKFRP ))(1034.4(84
84
2819 âĂ= âââÎș
For each fire pixel detected, FRP is estimated using the empirical relationship of Kaufman et al. (1998),
4T
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
16 of 28 July 19-21, 2010
Sea Surface Temperature
NIRST Land and Sea Surface Temperature Algorithms
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
17 of 28 July 19-21, 2010
Sea Surface Temperature
512 pixels
182 km
1140 pixels
400 km
What a NIRST (almost 1 minute long) nadiral scan over the Malvinas-ÂâBrazil eddies would be
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
18 of 28 July 19-21, 2010
Sea Surface Temperature
NIRST SST Algorithm will be based on the current algorithm that is used to calculate the AVHRR SST product, a non-linear split window equation: SST = α + ÎČBT11+ Îł(BT11 â BT12 )Tref + ÎŽ[sec(Óš) - 1](BT11 â BT12) Where, âąâŻ Tref is an estimated SST
âąâŻ BT11 and BT12 are the brightness temperatures in bands 11 and 12 ”m
âąâŻ α , ÎČ, Îł and ÎŽ are coefficients that should be computed locally (By regressing equations with in-situ data, plus either MODTRAN simulated BT11 and BT12, or NIRST data once in orbit) and updated periodically.
âąâŻ Óš is the viewing angle
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6th Aquarius/SAC-D Science Meeting Seattle. NIRST L2 Algorithms M.Kalemkarian
19 of 28 July 19-21, 2010
NIRST Land and Sea Surface Temperature Algorithms
Land Surface Temperature
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20 of 28 July 19-21, 2010
Land Surface Temperature
Example of ASTER LST compared to MODIS LST for the same area of study
(From Liu et al., 2009)
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21 of 28 July 19-21, 2010
Land Surface Temperature
CTTBBBTTAAATS +âÎ
+â
+++Î
+â
+=2
)1(2
)1( 12112321
12112321 Δ
ΔΔΔ
ΔΔ
ΔΔ
)( 1211 ΔΔΔ â=Î
are the difference and mean of surface emissivities in bands 11 and 12 ”m, which will be obtained with the MODIS 250m NDVI product, T11 and T12 are the brightness temperatures in bands 11 and 12 ”m, A1, A2, A3, B1, B2 and B3 are coefficients that will be obtained by regressing equations with in situ data and MODTRAN simulated brightness temperatures.
2)( 1211 ΔΔ
Δ+
=
Where:
LST Algorithm
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22 of 28 July 19-21, 2010
NIRST Land and Sea Surface Temperature Algorithms
Volcanic Activity Monitoring
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23 of 28 July 19-21, 2010
Volcanic Activity Monitoring
ĂŒâŻVolcanic ash detection In standard visible and infrared satellite imagery volcanic ash clouds can resemble water-bearing clouds. However, the radiative absorption properties of the silicate in the volcanic ash are different to those of water in the infrared wavelength range 11-12 microns. An image showing the brightness temperature difference between channels at 11 and 12 microns (BT11 - BT12) can be used to distinguish volcanic ash from water-bearing clouds.
In general: âąâŻ BT11 - BT12 > 0 for water-bearing clouds âąâŻ BT11 - BT12 < 0 for volcanic ash clouds
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24 of 28 July 19-21, 2010
Volcanic Activity Monitoring
B 4 ”m - B 12 ”m
B 4 ”m + B 12 ”m ( Watt / m2 str ”m ) NTI =
ĂŒâŻ Hot Spot Detection - Normalized Thermal Index Product (Wright et al., 2003)
ĂŒâŻ Hot Spot Detection Product - Average Zones. (Vicari et al., 2007)
Works with a difference image: BTD ( 3.9 â 11 ) ”m,
and compares values between the zones containing the presumable hot spot and its neighbouring areas.
NTI > Threshold
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25 of 28 July 19-21, 2010
NIRST simulated data Simulations shall be made by using the atmospheric radiative transfer model MODTRAN to simulate the TOA radiance with the appropriate thermal infrared channel response function of the NIRST sensor on board Aquarius/SAC-D.
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VALIDATION
âąâŻ Validation is a critical step in the quality control of products derived from Earth observation.
âąâŻ The main aim of a validation program is to assess whether the measurement processes are being achieved within the science requirements through validation of the final product.
âąâŻ Science Validation of NIRST derived products consists of independent determination of this products and comparison with the ones derived from NIRST processor.
âąâŻ It may involve comparison of products with similar products derived by conventional means (in situ direct measurements of parameters such as wind, temperature, humidity, etc.). It is important that those products have themselves been validated and their accuracy and uncertainty established.
NIRST Land and Sea Surface Temperature Algorithms
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27 of 28 July 19-21, 2010
NIRST Land and Sea Surface Temperature Algorithms
Fulfill Science requirements
Initial AssesmentLong term validation (detailed assesment)
Product level > 2A
collaboration with other government agencies, universities: field
measurements and product evaluation
CONAE: Algorithm verification and processor modifications
In situ measurements_ product parameter
_ ancilliary data
_ Instrumentation_ Buoys
Cross Validation
Airborne
In situ measurements_ product parameter
_ ancilliary data
_ Instrumentation_ Buoys
Cross Validation
Airborne
Data delivery
Science Validation(Products: Fire maping,
surface temperature)
Validation outline
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28 of 28 July 19-21, 2010
Thank you!
NIRST Land and Sea Surface Temperature Algorithms
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<2K @ 300K 1.5K @ 300K 2.5K @ 400K Temp. accuracy
11.4 â 12.3 ”m 10.4 â 11.3 ”m 3.4 â 4.2 ”m Band Limits
Detectable size of fire event
NEÎT
Temperature
Central wavelength 11.85 ”m 10.85 ”m 3.8 ”m
250K 400K Min.
200m2 @ 1000K
<0.4K @ 300K <0.8K @ 300K <1.5K @ 400K
500K 1000K Max.
LWIR3 (Band 3) LWIR2 (Band 2) MWIR2 (Band 1)
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30 of 28 July 19-21, 2010
VALIDATION
âąâŻ Validation is a critical step in the quality control of products derived from Earth observation.
âąâŻ The main aim of a validation program is to assess whether the measurement processes are being achieved within the science requirements through validation of the final product.
âąâŻ Science Validation of NIRST derived products consists of independent determination of this products and comparison with the ones derived from NIRST processor.
âąâŻ It may involve comparison of products with similar products derived by conventional means (in situ direct measurements of parameters such as wind, temperature, humidity, etc.). It is important that those products have themselves been validated and their accuracy and uncertainty established.
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31 of 28 July 19-21, 2010
During the initial validation phase, the main objective is to make a first assessment of the quality of the NIRST derived data products, in a limited number of sites and seasons.
During the mission as a whole, the aims of the on-going validation program are:
- To make a detailed assessment of the quality of the NIRST data products in an increasing number of sites and seasons. - To monitor the quality of the NIRST data products over the duration of the mission.
It shall be performed periodically to monitor the quality of the NIRST data products over the duration of the mission.
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However data is acquired, an implementation for vicarious validation follows: 1- Site determination. The available ancillary data for the site should be taken into account. The extension of the area shall cover several NIRST pixels 2- Determination of necessary data as input to the process. This includes atmospheric and in-situ data from instrumentation or dependable sources. 3- Temperature retrieval from satellite. This will involve the algorithm, radiative transfer calculations (MODTRAN) with emissivity and atmospheric data as input. 4- Surface temperature determination:
4a- Through in-situ temperature measurements. Difference between bulk and skin temperature should be made. 4b- Through simulation with radiative transfer calculations (MODTRAN) with several temperatures and measured or simulated atmospheric conditions for the site of interest.
5- Derived product and measured/simulated product comparison
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6- Evaluation of differences. This could involve a study of temperature as a function of meteorological conditions to determine whether differences can be attributed to a bad simulation or there is no possible match between both products after varying parameters among reasonable values for the site and season. This would require validation sites with equipment or buoys for time series evaluation, a global communication system could be used for this purpose to access the data. Wind speed, humidity and air temperature are some of the parameters needed to determine the boundary layer properties. 7- Determination of causes of temperature retrieval error. Must differentiate between:
- temperature retrieval - model assumptions - meteorological data
8- Process (or algorithm) correction.
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Validation of active fire products has been recognized to be a difficult task which lacks well-established procedures. Flagging pixels that contain burning as âfireâ, which yields âyes/noâ binary fire maps, is commonly referred to as âactive fire detection.â Independent information on fires can be obtained either by the direct observation of fires by alternative means or by observing fire effects such as atmospheric emissions or land cover change. For example, smoke plumes were used for the accuracy assessment of fire detections from the Advanced Very High Resolution Radiometer (AVHRR).
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Geophysical parameters validation: âąâŻ Field work is needed to measure the geophysical parameters of
interest, in specified locations, simultaneously with NIRST measurements
âąâŻ The campaigns must be designed to collect a statistically significant number of samples
âąâŻ Data for parameters validation could be obtained from DCPs, using the DCS instrument on board SAC-D
âąâŻ The selected algorithms must be used for the retrieval of the specific parameters under evaluation from NIRST measurements
âąâŻ A statistical analysis relating the field measurements and the parameter retrieved values must be carried out to obtain representative figures of the algorithms performance
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36 of 28 July 19-21, 2010
At-sensor radiance simulation workflow with 4 input sections based on standard MODTRAN configurations.
NIRST data Simulation: