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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;

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Presentation on theme: "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;"— Presentation transcript:

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: eaelupus@umd.edu Overview & Goals SST Evaluation Conclusion in situ Validation Synthetic Dataset 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 Earths 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. 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°. 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. 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 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. 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). Literature Cited 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. Sensitivity 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. 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. b. AGU# 1551


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