Principal Investigator Dr. Eric Vermote Geography Dept., University of Maryland Co. P.I.
Evan A. Ellicott 1, Eric F. Vermote 1, Francois Petitcolin 2, and Simon J. Hook 3 1 Department of...
1
Evan A. Ellicott 1 , Eric F. Vermote 1 , Francois Petitcolin 2 , and Simon J. Hook 3 1 Department of Geography, University of Maryland, USA; 2 ACRI-ST, France; 3 JPL, USA 1 Contact info: [email protected] Overview & Goals Overview & Goals SST Evaluation SST Evaluation Conclusion Conclusion in situ in situ Validation Validation Synthetic Dataset Synthetic Dataset Analysis Analysis 1. Performance of the parametric model correction for atmospheric upward and downward radiances and atmospheric transmittance was assessed against MODTRAN using NCEP atmospheric profiles and sun/sensor geometric data from Terra MODIS granule 2001.047.0800. The parametric model accuracy was tested against the Aqua-MODIS sea- surface temperature product (MYD28-SST) (Figure 3). The split-window approach generally used for retrieving SST is accepted to be fairly accurate over water targets (Eugenio et al., 2005), due in part to stable target emissivity, thus offering a bench-mark to compare against. Results from multiple observations show a good fit (Figure 5). Surface temperature is a key component for understanding energy fluxes between the Earth’s surface and atmosphere, and therefore a critical part of climate prediction, analyzing vegetative stress, and hydrologic modeling. Accurate retrieval of surface temperature from satellite observations requires proper correction of the thermal channels for atmospheric emission and attenuation. Although the split-window approach has offered a relatively accurate method, this empirical approach is subject to bias (Minnett, 2003). Single channel correction reduces uncertainty inherent to the split window method and preserves spectral signatures in thermal infrared, but requires an accurate radiative transfer model and detailed description of the atmospheric profile. Computation time is generally a limitation of radiative transfer models for operational use. We present here a thermal parametric model based upon the MODTRAN radiative transfer code and tuned to MODIS channels. The goal was to assess the accuracy of the parametric model against MODTRAN and then with various sources of surface temperature data. In addition, we investigated several sources of atmospheric profile data – a critical input variable to radiative transfer modeling for accurate atmospheric correction. Fig. 1. Comparison of MODTRAN and the parametric model derived (a) atmospheric upward radiance, (b) atmospheric downward radiance, and (c) atmospheric transmittance using synthetic dataset. N=423. B 31 upw ard radiance (W .sr-1.cm -2.µm -1) y = 0.9889x R 2 = 0.9975 R M S = 7.75e-06 1.E-05 1.E-04 2.E-04 3.E-04 4.E-04 5.E-04 6.E-04 7.E-04 M O D TR AN Param etric M odel B 31 dow nw ard radiance (W .sr-1.cm -2.µm -1) y = 0.9941x R 2 = 0.9974 R M S = 6.18e-06 1.E-05 1.E-04 2.E-04 3.E-04 4.E-04 5.E-04 6.E-04 M O D TR AN Param etric M odel B 31 Transm ittance y= 1.0081x R 2 = 0.9983 R M S = 0.0079 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 M O D TR AN Param etric M odel a. b. c. Fig 3. MODIS SST product (MYD28) for the western United States and Pacific Ocean: 12 August 2003. Cloud cover is distinguished by the dark blue-black. -40°C 40°C 2. With TOA radiance, surface emissivity, and the atmospheric parameters retrieved above we calculated surface radiance [1]. The Plank function is inverted to calculate surface brightness temperature [2]. [1] [2] Bending of the optical path when view angles exceed 60° is accounted for in MODTRAN , but not in the parametric model; therefore we excluded observations above 60°. T L c c T i i i i surf 5 1 2 , 1 log Fig. 2. Surface brightness temperatures comparison between the parametric model and MODTRAN. Emissivity was set to (a) 1.0 and (b) 0.9. N=423 b31:Surface B rightness Tem perature (C ) (Em issivity = 1.0) y = 1.0013x R 2 = 0.9995 RM S = 0.0731 12 14 16 18 20 22 24 26 28 30 32 34 12 14 16 18 20 22 24 26 28 30 32 34 MODTRAN Param etricModel b31:S urface B rightness Tem perature (C ) (Em issivity = 0.9) y = 1.0073x R 2 = 0.9993 R M S = 0.0812 8 10 12 14 16 18 20 22 24 26 28 8 10 12 14 16 18 20 22 24 26 28 M O D TR A N Param etricM odel Param etric M odel atm ospheric corrected tem perature vs.Aqua M O D IS SST (M O D 28) y = 0.9705x R 2 = 0.9967 RM S = 0.1950 6 8 10 12 14 16 18 20 22 24 26 28 30 6 8 10 12 14 16 18 20 22 24 26 28 30 M O D IS SS T (C ) P aram etric M odel (C ) 337-17:25 336-01:50 012-08:45 Fig. 4. Comparison of the parametric model and Aqua-MODIS (MYD28) SST. Near- nadir observations are from 2004 (N = 82 ); blue points correspond with DOY=337 at 17:25 UTC; magenta with DOY=336 at 01:50 UTC; and green with DOY=012 at 08:45 UTC. Ground based temperature measurements using in situ radiometers offers the opportunity to fully validate the accuracy of our parametric model against real temperatures that have little to no influence from atmospheric perturbation. Comparisons were made between both water and land surface targets. Water surface observations were made over Lake Tahoe, Nevada, using radiometers located on 4 permanently moored buoys (see Hook et al., 2003) (Figure 5). Validation with land surface temperature observations was made with data provided by Coll et al. (2005). Measurements were made with tripod mounted radiometers placed over stable, homogeneous sites in eastern Spain (Figure6). Fig. 5. Bathymetric (100m) map showing locations of in situ skin and bulk temperature observations of Lake Tahoe, NV (TR1 – TR4). A raft at each location contains a radiometer mounted 1m above the surface making continuous observations in the 7.8-13.6μm range. Additional temperature sensors at several depths record bulk Fig. 6. Study sites used for measurement of LST. In situ data were recorded along transects within flat, homogeneous plots consisting of cultivated rice fields. Courtesy of César Coll. M O D IS Band 31 Surface Tem perature C om parisons in situ vs. satellite derived y = 1.0011x R 2 = 0.987 RM S = 0.5453 10 12 14 16 18 20 22 24 26 28 30 32 34 10 12 14 16 18 20 22 24 26 28 30 32 34 in situ Tem peratures (C ) Param etric M odel derived tem peratures(C) Lake Tahoe Agriculture Fig. 7. A good agreement between surface temperature derived from the parametric model and in situ measurements. The blue diamonds correspond with Lake Tahoe measurements. Pink squares correspond with rice field LST measurements. The eventual goal is to employ the parametric model as part of an operational atmospheric correction scheme for MODIS. With this in mind we included an analysis to test several sources of atmospheric profile data. We developed a “reference” data set using radiosonde profiles and MODTRAN. The model tested against the reference using MODIS and AIRS profiles (Figure 8). Literature Literature Cited Cited B31: R adiosonde-M O D TR AN vs AIR S-PM y = 0.9997x R 2 = 0.9841 RM S = 0.5348 8 10 12 14 16 18 20 22 24 26 28 8 10 12 14 16 18 20 22 24 26 28 R adiosonde -M O DTRAN Tem p (C ) AIR S -PM Tem p (C ) B31: R adiosonde-M O D TR AN vs M O D 07-PM y = 1.0578x R 2 = 0.9455 RM S = 0.8394 14 16 18 20 22 24 26 28 14 16 18 20 22 24 26 28 Radiosonde -M O D TRAN Tem p (C ) M O D 07 -P M Tem p (C ) Fig.8. MODIS band 31 temperature comparison between the radiosonde-MODTRAN derived “reference” and parametric model derivation using (a) AIRS or (b) MOD07 profiles. Fig. 9. The larger error in the MODIS profile seen in Figure 8 can be attributed to lower spectral resolution as compared with AIRS. Higher columnar water vapor content recorded by MODIS led to an overcorrection and thus bias towards overestimation of temperatures. This graph shows an example of the variation between profile sources. R elative H um idity as a function of A tm ospheric Tem perature 2005141.1125 0 10 20 30 40 50 60 70 80 -65 -60 -55 -50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 Tem p (C ) R ela tive H um idity AIR S R adiosonde NCEP M O D 07 Sensitivity to Sensitivity to Atmospheric Profile Atmospheric Profile Coll, C., Caselles, V., Galve, J. M., Valor, E., Niclos, R.., Sanchez, J. M., & Rivas, R. (2005). Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote Sensing of Environment, 97, 288-300. Eugenio, F., Marcello, J., Hernandez-Guerra, A., & Rovaris, E. (2005). Regional optimization of an atmospheric correction algorithm for the retrieval of sea surface temperature from the Canary Islands-Azores- Gibraltar area using NOAA/AVHRR data. International Journal of Remote Sensing, 26, 1799-1814. Hook, S. J., Prata, F. J., Alley, R. E., Abtahi, A., Richards, R. C., Schladow, S. G., & Palmarsson, S. O. (2003). Retrieval of lake bulk and skin temperatures using Along-Track Scanning Radiometer (ATSR-2) data: A case study using Lake Tahoe, California. Journal of Atmospheric and Oceanic Technology, 20, 534-548. The parametric model offers a method to operationally correct the at-sensor radiance values for atmospheric perturbations. Although computational time was not measured as a part of this analysis, anecdotal comparisons between the parametric model and MODTRAN suggests at least an order of magnitude increase in speed. Consideration of the profile used in any radiative transfer model is paramount to achieving accurate correction for atmospheric effects. This study highlights a variety of potential profile sources and demonstrates a relatively good accuracy, especially when compared with in situ temperature data. b. a. atm atm toa surf L t L L L ) 1 ( ] ) [( a. b. AGU# 1551 AGU# 1551
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Transcript of Evan A. Ellicott 1, Eric F. Vermote 1, Francois Petitcolin 2, and Simon J. Hook 3 1 Department of...
Evan A. Ellicott1, Eric F. Vermote1, Francois Petitcolin2, and Simon J. Hook3
1Department of Geography, University of Maryland, USA; 2ACRI-ST, France; 3JPL, USA1Contact info: [email protected]
Overview & GoalsOverview & Goals
SST EvaluationSST Evaluation
ConclusionConclusion
in situ in situ ValidationValidation
Synthetic Dataset AnalysisSynthetic Dataset Analysis1. Performance of the parametric model correction for atmospheric upward and downward radiances and atmospheric transmittance was assessed against MODTRAN using NCEP atmospheric profiles and sun/sensor geometric data from Terra MODIS granule 2001.047.0800.
1. Performance of the parametric model correction for atmospheric upward and downward radiances and atmospheric transmittance was assessed against MODTRAN using NCEP atmospheric profiles and sun/sensor geometric data from Terra MODIS granule 2001.047.0800.
The parametric model accuracy was tested against the Aqua-MODIS sea-surface temperature product (MYD28-SST) (Figure 3). The split-window approach generally used for retrieving SST is accepted to be fairly accurate over water targets (Eugenio et al., 2005), due in part to stable target emissivity, thus offering a bench-mark to compare against. Results from multiple observations show a good fit (Figure 5).
The parametric model accuracy was tested against the Aqua-MODIS sea-surface temperature product (MYD28-SST) (Figure 3). The split-window approach generally used for retrieving SST is accepted to be fairly accurate over water targets (Eugenio et al., 2005), due in part to stable target emissivity, thus offering a bench-mark to compare against. Results from multiple observations show a good fit (Figure 5).
Surface temperature is a key component for understanding energy fluxes between the Earth’s surface and atmosphere, and therefore a critical part of climate prediction, analyzing vegetative stress, and hydrologic modeling. Accurate retrieval of surface temperature from satellite observations requires proper correction of the thermal channels for atmospheric emission and attenuation. Although the split-window approach has offered a relatively accurate method, this empirical approach is subject to bias (Minnett, 2003). Single channel correction reduces uncertainty inherent to the split window method and preserves spectral signatures in thermal infrared, but requires an accurate radiative transfer model and detailed description of the atmospheric profile. Computation time is generally a limitation of radiative transfer models for operational use.
We present here a thermal parametric model based upon the MODTRAN radiative transfer code and tuned to MODIS channels. The goal was to assess the accuracy of the parametric model against MODTRAN and then with various sources of surface temperature data. In addition, we investigated several sources of atmospheric profile data – a critical input variable to radiative transfer modeling for accurate atmospheric correction.
Surface temperature is a key component for understanding energy fluxes between the Earth’s surface and atmosphere, and therefore a critical part of climate prediction, analyzing vegetative stress, and hydrologic modeling. Accurate retrieval of surface temperature from satellite observations requires proper correction of the thermal channels for atmospheric emission and attenuation. Although the split-window approach has offered a relatively accurate method, this empirical approach is subject to bias (Minnett, 2003). Single channel correction reduces uncertainty inherent to the split window method and preserves spectral signatures in thermal infrared, but requires an accurate radiative transfer model and detailed description of the atmospheric profile. Computation time is generally a limitation of radiative transfer models for operational use.
We present here a thermal parametric model based upon the MODTRAN radiative transfer code and tuned to MODIS channels. The goal was to assess the accuracy of the parametric model against MODTRAN and then with various sources of surface temperature data. In addition, we investigated several sources of atmospheric profile data – a critical input variable to radiative transfer modeling for accurate atmospheric correction.
Fig. 1. Comparison of MODTRAN and the parametric model derived (a) atmospheric upward radiance, (b) atmospheric downward radiance, and (c)
atmospheric transmittance using synthetic dataset. N=423.
Fig. 1. Comparison of MODTRAN and the parametric model derived (a) atmospheric upward radiance, (b) atmospheric downward radiance, and (c)
atmospheric transmittance using synthetic dataset. N=423.
B31 upward radiance (W.sr-1.cm-2.µm-1)
y = 0.9889x
R2 = 0.9975RMS = 7.75e-06
1.E-05
1.E-04
2.E-04
3.E-04
4.E-04
5.E-04
6.E-04
7.E-04
MODTRAN
Pa
ram
etr
ic M
od
el
B31 downward radiance (W.sr-1.cm-2.µm-1)
y = 0.9941x
R2 = 0.9974RMS = 6.18e-06
1.E-05
1.E-04
2.E-04
3.E-04
4.E-04
5.E-04
6.E-04
MODTRAN
Pa
ram
etr
ic M
od
el
B31 Transmittance
y = 1.0081x
R2 = 0.9983RMS = 0.0079
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
MODTRAN
Pa
ram
etr
ic M
od
el
a.a. b.b. c.c.
Fig 3. MODIS SST product (MYD28) for the western United States and Pacific Ocean: 12 August 2003. Cloud cover is distinguished by the dark blue-black.
Fig 3. MODIS SST product (MYD28) for the western United States and Pacific Ocean: 12 August 2003. Cloud cover is distinguished by the dark blue-black.
-40°C 40°C
2. With TOA radiance, surface emissivity, and the atmospheric parameters retrieved above we calculated surface radiance [1]. The Plank function is inverted to calculate surface brightness temperature [2].
[1]
[2]
Bending of the optical path when view angles exceed 60° is accounted for in MODTRAN , but not in the parametric model; therefore we excluded observations above 60°.
2. With TOA radiance, surface emissivity, and the atmospheric parameters retrieved above we calculated surface radiance [1]. The Plank function is inverted to calculate surface brightness temperature [2].
[1]
[2]
Bending of the optical path when view angles exceed 60° is accounted for in MODTRAN , but not in the parametric model; therefore we excluded observations above 60°.
TL
c
cT
ii
i
isurf
51
2,
1log
Fig. 2. Surface brightness temperatures comparison between the parametric model and MODTRAN. Emissivity was set to (a) 1.0 and (b)
0.9. N=423
Fig. 2. Surface brightness temperatures comparison between the parametric model and MODTRAN. Emissivity was set to (a) 1.0 and (b)
0.9. N=423
b31: Surface Brightness Temperature (C) (Emissivity = 1.0)
y = 1.0013x
R2 = 0.9995RMS = 0.0731
12
14
16
18
20
22
24
26
28
30
32
34
12 14 16 18 20 22 24 26 28 30 32 34
MODTRAN
Par
amet
ric M
odel
b31: Surface Brightness Temperature (C) (Emissivity = 0.9)
y = 1.0073x
R2 = 0.9993RMS = 0.0812
8
10
12
14
16
18
20
22
24
26
28
8 10 12 14 16 18 20 22 24 26 28
MODTRAN
Par
amet
ric M
odel
Parametric Model atmospheric corrected temperature vs. Aqua MODIS SST (MOD28)
y = 0.9705x
R2 = 0.9967RMS = 0.1950
6
8
10
12
14
16
18
20
22
24
26
28
30
6 8 10 12 14 16 18 20 22 24 26 28 30
MODIS SST (C)
Par
amet
ric
Mod
el (C
)
337-17:25
336-01:50
012-08:45
Fig. 4. Comparison of the parametric model and Aqua-MODIS (MYD28) SST. Near-nadir observations are from 2004 (N = 82 ); blue points correspond with DOY=337 at 17:25 UTC; magenta with DOY=336 at 01:50 UTC; and green with DOY=012 at 08:45 UTC.
Fig. 4. Comparison of the parametric model and Aqua-MODIS (MYD28) SST. Near-nadir observations are from 2004 (N = 82 ); blue points correspond with DOY=337 at 17:25 UTC; magenta with DOY=336 at 01:50 UTC; and green with DOY=012 at 08:45 UTC.
Ground based temperature measurements using in situ radiometers offers the opportunity to fully validate the accuracy of our parametric model against real temperatures that have little to no influence from atmospheric perturbation. Comparisons were made between both water and land surface targets.
Water surface observations were made over Lake Tahoe, Nevada, using radiometers located on 4 permanently moored buoys (see Hook et al., 2003) (Figure 5).
Validation with land surface temperature observations was made with data provided by Coll et al. (2005). Measurements were made with tripod mounted radiometers placed over stable, homogeneous sites in eastern Spain (Figure6).
Ground based temperature measurements using in situ radiometers offers the opportunity to fully validate the accuracy of our parametric model against real temperatures that have little to no influence from atmospheric perturbation. Comparisons were made between both water and land surface targets.
Water surface observations were made over Lake Tahoe, Nevada, using radiometers located on 4 permanently moored buoys (see Hook et al., 2003) (Figure 5).
Validation with land surface temperature observations was made with data provided by Coll et al. (2005). Measurements were made with tripod mounted radiometers placed over stable, homogeneous sites in eastern Spain (Figure6).
Fig. 5. Bathymetric (100m) map showing locations of in situ skin and bulk temperature observations of Lake Tahoe, NV (TR1 – TR4). A raft at each location contains a radiometer mounted 1m above the surface making continuous observations in the 7.8-13.6μm range. Additional temperature sensors at several depths record bulk temperature.
Fig. 6. Study sites used for measurement of LST. In situ data were recorded along transects within flat, homogeneous plots consisting of cultivated rice fields. Courtesy of César Coll.
MODIS Band 31 Surface Temperature Comparisonsin situ vs. satellite derived
y = 1.0011x
R2 = 0.987RMS = 0.5453
10
12
14
16
18
20
22
24
26
28
30
32
34
10 12 14 16 18 20 22 24 26 28 30 32 34
in situ Temperatures (C)
Par
amet
ric M
odel
der
ived
tem
pera
ture
s (C
)
Lake Tahoe
Agriculture
Fig. 7. A good agreement between surface temperature derived from the parametric model and in situ measurements. The blue diamonds correspond with Lake Tahoe measurements. Pink squares correspond with rice field LST measurements.
The eventual goal is to employ the parametric model as part of an operational atmospheric correction scheme for MODIS. With this in mind we included an analysis to test several sources of atmospheric profile data. We developed a “reference” data set using radiosonde profiles and MODTRAN. The parametric model was then tested against the reference using MODIS and AIRS profiles (Figure 8).
The eventual goal is to employ the parametric model as part of an operational atmospheric correction scheme for MODIS. With this in mind we included an analysis to test several sources of atmospheric profile data. We developed a “reference” data set using radiosonde profiles and MODTRAN. The parametric model was then tested against the reference using MODIS and AIRS profiles (Figure 8).
Literature CitedLiterature Cited
B31: Radiosonde-MODTRAN vs AIRS-PM
y = 0.9997x
R2 = 0.9841RMS = 0.5348
8
10
12
14
16
18
20
22
24
26
28
8 10 12 14 16 18 20 22 24 26 28
Radiosonde - MODTRAN Temp (C)
AIR
S -P
M T
emp
(C)
B31: Radiosonde-MODTRAN vs MOD07-PM
y = 1.0578x
R2 = 0.9455RMS = 0.8394
14
16
18
20
22
24
26
28
14 16 18 20 22 24 26 28
Radiosonde - MODTRAN Temp (C)
MO
D07
-PM
Tem
p (C
)
Fig.8. MODIS band 31 temperature comparison between the radiosonde-MODTRAN derived “reference” and parametric
model derivation using (a) AIRS or (b) MOD07 profiles.
Fig.8. MODIS band 31 temperature comparison between the radiosonde-MODTRAN derived “reference” and parametric
model derivation using (a) AIRS or (b) MOD07 profiles.
Fig. 9. The larger error in the MODIS profile seen in Figure 8 can be attributed to lower spectral resolution as compared with AIRS. Higher columnar water vapor content recorded by MODIS led to an overcorrection and thus bias towards overestimation of temperatures. This graph shows an example of the variation between profile sources.
Fig. 9. The larger error in the MODIS profile seen in Figure 8 can be attributed to lower spectral resolution as compared with AIRS. Higher columnar water vapor content recorded by MODIS led to an overcorrection and thus bias towards overestimation of temperatures. This graph shows an example of the variation between profile sources.
Relative Humidity as a function of Atmospheric Temperature2005141.1125
0
10
20
30
40
50
60
70
80
-65 -60 -55 -50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30
Temp (C)
Re
lative
Hu
mid
ity (
%)
AIRS
Radiosonde
NCEP
MOD07
Sensitivity to Atmospheric ProfileSensitivity to Atmospheric Profile
Coll, C., Caselles, V., Galve, J. M., Valor, E., Niclos, R.., Sanchez, J. M., & Rivas, R. (2005). Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote Sensing of Environment, 97, 288-300.
Eugenio, F., Marcello, J., Hernandez-Guerra, A., & Rovaris, E. (2005). Regional optimization of an atmospheric correction algorithm for the retrieval of sea surface temperature from the Canary Islands-Azores-Gibraltar area using NOAA/AVHRR data. International Journal of Remote Sensing, 26, 1799-1814.
Hook, S. J., Prata, F. J., Alley, R. E., Abtahi, A., Richards, R. C., Schladow, S. G., & Palmarsson, S. O. (2003). Retrieval of lake bulk and skin temperatures using Along-Track Scanning Radiometer (ATSR-2) data: A case study using Lake Tahoe, California. Journal of Atmospheric and Oceanic Technology, 20, 534-548.
Minnett, P. J. (2003). Radiometric measurements of the sea-surface skin temperature: the competing roles of the diurnal thermocline and the cool skin. International Journal of Remote Sensing, 24, 5033-5047.
Coll, C., Caselles, V., Galve, J. M., Valor, E., Niclos, R.., Sanchez, J. M., & Rivas, R. (2005). Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote Sensing of Environment, 97, 288-300.
Eugenio, F., Marcello, J., Hernandez-Guerra, A., & Rovaris, E. (2005). Regional optimization of an atmospheric correction algorithm for the retrieval of sea surface temperature from the Canary Islands-Azores-Gibraltar area using NOAA/AVHRR data. International Journal of Remote Sensing, 26, 1799-1814.
Hook, S. J., Prata, F. J., Alley, R. E., Abtahi, A., Richards, R. C., Schladow, S. G., & Palmarsson, S. O. (2003). Retrieval of lake bulk and skin temperatures using Along-Track Scanning Radiometer (ATSR-2) data: A case study using Lake Tahoe, California. Journal of Atmospheric and Oceanic Technology, 20, 534-548.
Minnett, P. J. (2003). Radiometric measurements of the sea-surface skin temperature: the competing roles of the diurnal thermocline and the cool skin. International Journal of Remote Sensing, 24, 5033-5047.
The parametric model offers a method to operationally correct the at-sensor radiance values for atmospheric perturbations. Although computational time was not measured as a part of this analysis, anecdotal comparisons between the parametric model and MODTRAN suggests at least an order of magnitude increase in speed. Consideration of the profile used in any radiative transfer model is paramount to achieving accurate correction for atmospheric effects. This study highlights a variety of potential profile sources and demonstrates a relatively good accuracy, especially when compared with in situ temperature data.
The parametric model offers a method to operationally correct the at-sensor radiance values for atmospheric perturbations. Although computational time was not measured as a part of this analysis, anecdotal comparisons between the parametric model and MODTRAN suggests at least an order of magnitude increase in speed. Consideration of the profile used in any radiative transfer model is paramount to achieving accurate correction for atmospheric effects. This study highlights a variety of potential profile sources and demonstrates a relatively good accuracy, especially when compared with in situ temperature data.
b.b.a.a.
atmatmtoasurf LtLLL )1(])[(
a.a. b.b.
AGU# 1551AGU# 1551
