Retrieval of geophysical parameters from Moderate Resolution Imaging Spectroradiometer thermal infrared data: evaluation of a two-step physical algorithm
Xia L. Ma, Zhengming Wan, Christopher C. Moeller, W. Paul Menzel, Liam E. Gumley, and Yulin Zhang
Xia L. Ma,
Zhengming Wan,
Christopher C. Moeller,
W. Paul Menzel,
Liam E. Gumley,
and Yulin Zhang
X. L. Ma (xma@icess.ucsb.edu), Z. Wan, and Y. Zhang are with the Institute for Computational Earth System Science, University of California, Santa Barbara, California, 93106. USA
X. L. Ma, C. C. Moeller, and L. E. Gumley are with the Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, Madison, Wisconsin, 53706. USA
W. P. Menzel is with the Office of Research and Applications, the National Oceanic and Atmospheric Administration, National Environmental Satellite Data Information Service, Madison, Wisconsin 53706. USA
Xia L. Ma, Zhengming Wan, Christopher C. Moeller, W. Paul Menzel, Liam E. Gumley, and Yulin Zhang, "Retrieval of geophysical parameters from Moderate Resolution Imaging Spectroradiometer thermal infrared data: evaluation of a two-step physical algorithm," Appl. Opt. 39, 3537-3550 (2000)
A two-step physical algorithm that simultaneously retrieves
geophysical parameters from Moderate Resolution Imaging
Spectroradiometer (MODIS) measurements was developed. The
retrieved geophysical parameters include atmospheric
temperature–humidity profile, surface skin temperature, and two
surface emissivities within the shortwave (3–5-µm) and
the longwave (8–14.5-µm) regions. The physical
retrieval is accomplished in two steps: (i) The Tikhonov
regularization method is employed to generate a regularization solution
along with an optimum regularization parameter; (ii) the nonlinear
Newtonian iteration algorithm is carried out with the regularization
solution as a first-guess profile to obtain a final maximum probability
solution for geophysical parameters. The algorithm was tested with
both simulated and real MODIS Airborne Simulator (MAS)
data. Sensitivity studies on simulated MAS data demonstrate that
simultaneous retrievals of land and atmospheric parameters improve the
accuracy of the retrieved geophysical parameters. Finally, analysis
and accuracy of retrievals from real MAS data are discussed.
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NEΔT for bands 30–50 are based on
in-flight measurements over the Gulf of Mexico on 9 April 1996.
A, atmospheric studies; L, land studies; O,
ocean studies.
Ts, surface skin temperature; TPW, total
precipitable water vapor; Sw ∊, surface emissivity in the shortwave
region; Lw ∊, surface emissivity in the longwave region.
Table 4
Retrieval RMS of Independent Dataset Simulated for 418
Profiles, Noise (mean = 0 and std = 0.2 K) Added
Ts, surface skin temperature; TPW, total
precipitable water vapor; Sw ∊, surface emissivity in the shortwave
region; Lw ∊, surface emissivity in the longwave region.
Tables (4)
Table 1
Spectral Characteristics of the MAS Sounding Bands in the
1996 Configuration
NEΔT for bands 30–50 are based on
in-flight measurements over the Gulf of Mexico on 9 April 1996.
A, atmospheric studies; L, land studies; O,
ocean studies.
Ts, surface skin temperature; TPW, total
precipitable water vapor; Sw ∊, surface emissivity in the shortwave
region; Lw ∊, surface emissivity in the longwave region.
Table 4
Retrieval RMS of Independent Dataset Simulated for 418
Profiles, Noise (mean = 0 and std = 0.2 K) Added
Ts, surface skin temperature; TPW, total
precipitable water vapor; Sw ∊, surface emissivity in the shortwave
region; Lw ∊, surface emissivity in the longwave region.