Simultaneous retrieval of atmospheric profiles, land-surface temperature, and surface emissivity from Moderate-Resolution Imaging Spectroradiometer thermal infrared data: extension of a two-step physical algorithm
Xia L. Ma, Zhengming Wan, Christopher C. Moeller, W. Paul Menzel, and Liam E. Gumley
Xia L. Ma,
Zhengming Wan,
Christopher C. Moeller,
W. Paul Menzel,
and Liam E. Gumley
When this research was performed, X. L. Ma and Z. Wan were with the Institute for Computational Earth System Science, University of California at Santa Barbara, Santa Barbara, California 93106.
X. L. Ma is now with the Department of Earth System Science, University of California at Irvine, Irvine, California 92697.
X. L. Ma is also with, as is C. C. Moeller and L. E. Gumley, the Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin at Madison, Madison, Wisconsin 53706.
W. P. Menzel is with the Office of Research and Applications, National Oceanic and Atmospheric Administration/National Environmental Satellite Data, and Information Service, 1225 West Dayton Street, Madison, Wisconsin 53706.
Xia L. Ma, Zhengming Wan, Christopher C. Moeller, W. Paul Menzel, and Liam E. Gumley, "Simultaneous retrieval of atmospheric profiles, land-surface temperature, and surface emissivity from Moderate-Resolution Imaging Spectroradiometer thermal infrared data: extension of a two-step physical algorithm," Appl. Opt. 41, 909-924 (2002)
An extension to the two-step physical retrieval algorithm was developed. Combined clear-sky multitemporal and multispectral observations were used to retrieve the atmospheric temperature-humidity profile, land-surface temperature, and surface emissivities in the midwave (3–5 µm) and long-wave (8–14.5 µm) regions. The extended algorithm was tested with both simulated and real data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator. A sensitivity study and error analysis demonstrate that retrieval performance is improved by the extended algorithm. The extended algorithm is relatively insensitive to the uncertainties simulated for the real observations. The extended algorithm was also applied to real MODIS daytime and nighttime observations and showed that it is capable of retrieving medium-scale atmospheric temperature water vapor and retrieving surface temperature emissivity with retrieval accuracy similar to that achieved by the Geostationary Operational Environmental Satellite (GOES) but at a spatial resolution higher than that of GOES.
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Noise-equivalent delta temperature (NEdT) estimated with 17 September 2000 data over Lake Michigan. The NEdT estimates are inflated by natural variability, causing many to exceed the specifications. A recent study26 shows that the NEdT specifications are achieved in most bands.
Note: A, atmospheric studies; L, land studies; O, ocean studies; F, fire.
Table 2
Spectral Characteristics of the MAS TIR Bands in Its 1999 Configuration
Ta, atmospheric layer mean temperature; Ts, surface temperature; TPW, total precipitable water vapor; Mw ∊, surface emissivity in the midwave region; Lw ∊, surface emissivity in the long-wave region.
Table 4
Extended Algorithm Retrieval rms of the Simulated Independent Data Set for Daytime-Nighttime 418 Cases, with No Noise Added
Ta, atmospheric layer mean temperature; Ts, surface temperature; TPW, total precipitable water vapor; Mw ∊, surface emissivity in the midwave region; Lw ∊, surface emissivity in the long-wave region.
Table 5
Extended Algorithm Retrieval rms of the Simulated Global Independent Data Set for Daytime-Nighttime 440 Cases, Versus Daytime or Nighttime Data Set Alone
Ta, Atmospheric layer mean temperature; Ts, surface temperature; TPW, total precipitable water vapor; ∊30, …, ∊46, surface emissivities.
Daytime data set but the surface-reflected solar beam contribution in the midwave region was removed.
Table 6
Retrieval rms of the Simulated Global Independent Data Set for Daytime-Nighttime 440 Cases with Guess SBF, No Noise Added and Constant Emissivity Versus True SBF, Noise Added and Changing Emissivity
Physical Retrieval (guess, SBF, no noise added, varied emissivity)
Daytime
1 (50–200) Ta (K)
8.11
0.88
0.84
0.85
0.87
0.87
2 (200–400) Ta (K)
6.05
1.99
1.78
1.78
1.80
1.78
3 (400–600) Ta (K)
10.92
1.96
1.55
1.56
1.60
1.58
4 (600–800) Ta (K)
12.10
1.88
1.41
1.40
1.41
1.47
5 (800–1000) Ta (K)
13.89
2.66
2.43
2.43
2.45
2.45
Ts (K)
18.80
0.76
0.49
0.49
0.52
0.55
TPW (cm)
1.04
0.36
0.25
0.26
0.27
0.25
Nighttime
1 (50–200) Tleft (K)
8.11
0.88
0.82
0.82
0.89
0.99
2 (200–400) Tleft (K)
6.05
1.99
1.75
1.75
1.80
1.76
3 (400–600) Ta (K)
10.92
1.96
1.51
1.51
1.54
1.54
4 (600–800) Ta (K)
12.09
1.86
1.33
1.33
1.37
1.46
5 (800–1000) Ta (K)
13.90
2.66
2.40
2.40
2.44
2.46
Ts (K)
18.03
0.73
0.41
0.41
0.45
0.48
TPW (cm)
0.96
0.31
0.23
0.23
0.24
0.25
Emissivities
∊30
0.059
0.022
0.011
0.011
0.012
0.011
∊31
0.064
0.025
0.010
0.009
0.011
0.011
∊32
0.062
0.024
0.009
0.007
0.009
0.009
∊33
0.062
0.025
0.008
0.007
0.009
0.009
∊34
0.060
0.027
0.009
0.008
0.009
0.009
∊42
0.043
0.016
0.013
0.010
0.012
0.015
∊44
0.026
0.012
0.008
0.008
0.010
0.010
∊45
0.022
0.011
0.008
0.008
0.009
0.010
∊46
0.016
0.010
0.008
0.008
0.008
0.009
Ta, atmospheric layer mean temperature; SBF, solar BDRF factor; Ts, surface temperature; TPW, total precipitable water vapor; ∊30, …, ∊46, surface emissivities.
Table 7
MAS Band Brightness Temperatures in the Window Regions at Two Sounding Pixels, One over Lake Mendota and Another Over Nearby Land (20:15 UTC, 18 March 1999)
Noise-equivalent delta temperature (NEdT) estimated with 17 September 2000 data over Lake Michigan. The NEdT estimates are inflated by natural variability, causing many to exceed the specifications. A recent study26 shows that the NEdT specifications are achieved in most bands.
Note: A, atmospheric studies; L, land studies; O, ocean studies; F, fire.
Table 2
Spectral Characteristics of the MAS TIR Bands in Its 1999 Configuration
Ta, atmospheric layer mean temperature; Ts, surface temperature; TPW, total precipitable water vapor; Mw ∊, surface emissivity in the midwave region; Lw ∊, surface emissivity in the long-wave region.
Table 4
Extended Algorithm Retrieval rms of the Simulated Independent Data Set for Daytime-Nighttime 418 Cases, with No Noise Added
Ta, atmospheric layer mean temperature; Ts, surface temperature; TPW, total precipitable water vapor; Mw ∊, surface emissivity in the midwave region; Lw ∊, surface emissivity in the long-wave region.
Table 5
Extended Algorithm Retrieval rms of the Simulated Global Independent Data Set for Daytime-Nighttime 440 Cases, Versus Daytime or Nighttime Data Set Alone
Ta, Atmospheric layer mean temperature; Ts, surface temperature; TPW, total precipitable water vapor; ∊30, …, ∊46, surface emissivities.
Daytime data set but the surface-reflected solar beam contribution in the midwave region was removed.
Table 6
Retrieval rms of the Simulated Global Independent Data Set for Daytime-Nighttime 440 Cases with Guess SBF, No Noise Added and Constant Emissivity Versus True SBF, Noise Added and Changing Emissivity
Physical Retrieval (guess, SBF, no noise added, varied emissivity)
Daytime
1 (50–200) Ta (K)
8.11
0.88
0.84
0.85
0.87
0.87
2 (200–400) Ta (K)
6.05
1.99
1.78
1.78
1.80
1.78
3 (400–600) Ta (K)
10.92
1.96
1.55
1.56
1.60
1.58
4 (600–800) Ta (K)
12.10
1.88
1.41
1.40
1.41
1.47
5 (800–1000) Ta (K)
13.89
2.66
2.43
2.43
2.45
2.45
Ts (K)
18.80
0.76
0.49
0.49
0.52
0.55
TPW (cm)
1.04
0.36
0.25
0.26
0.27
0.25
Nighttime
1 (50–200) Tleft (K)
8.11
0.88
0.82
0.82
0.89
0.99
2 (200–400) Tleft (K)
6.05
1.99
1.75
1.75
1.80
1.76
3 (400–600) Ta (K)
10.92
1.96
1.51
1.51
1.54
1.54
4 (600–800) Ta (K)
12.09
1.86
1.33
1.33
1.37
1.46
5 (800–1000) Ta (K)
13.90
2.66
2.40
2.40
2.44
2.46
Ts (K)
18.03
0.73
0.41
0.41
0.45
0.48
TPW (cm)
0.96
0.31
0.23
0.23
0.24
0.25
Emissivities
∊30
0.059
0.022
0.011
0.011
0.012
0.011
∊31
0.064
0.025
0.010
0.009
0.011
0.011
∊32
0.062
0.024
0.009
0.007
0.009
0.009
∊33
0.062
0.025
0.008
0.007
0.009
0.009
∊34
0.060
0.027
0.009
0.008
0.009
0.009
∊42
0.043
0.016
0.013
0.010
0.012
0.015
∊44
0.026
0.012
0.008
0.008
0.010
0.010
∊45
0.022
0.011
0.008
0.008
0.009
0.010
∊46
0.016
0.010
0.008
0.008
0.008
0.009
Ta, atmospheric layer mean temperature; SBF, solar BDRF factor; Ts, surface temperature; TPW, total precipitable water vapor; ∊30, …, ∊46, surface emissivities.
Table 7
MAS Band Brightness Temperatures in the Window Regions at Two Sounding Pixels, One over Lake Mendota and Another Over Nearby Land (20:15 UTC, 18 March 1999)
Add to BibSonomy