1. Algorithms 1. Algorithms Nine LST algorithms (Yu et al., 2008) were analyzed for the land surface temperature retrieval from GOES-R ABI sensor data. Each of the algorithms consists of two parts: the basic split window algorithm and path length correction (the last term in each algorithm). The basic split window algorithms are adapted or adopted from those published literatures, while the path correction term is added for additional atmospheric absorption correction due to path length various. Simulation Procedure 2. Simulation Procedure The following simulation procedure was designed to generate the algorithm coefficients and to test the algorithm performance: Tool: MODTRAN 4.2, NOAA 88 atmospheric profiles Loops: 60 daytime profiles, 66 nighttime profiles View zenith: 0, 10, 20, 30, 40, 50,60 degrees Atmospheric profiles Algorithm coeffs TOA spectral radiances MODIS Sensor RSR functions Sensor BTs MODTRAN simulation BT Calculation Regression Of LST algorithms Algorithm Comparisons Input setting STD Error Of Algorithms start end # 1) T 11 and T 12 represent TOA brightness temperatures of ABI channels 14 and 15, respectively; 2)      and     ), where   and   are the spectral emissivities of land surface at ABI channels 14 and 15, respectively; 3)  is the satellite view zenith angle. Sobrino et al., 1993. 9 Uliveri et al., 1992. 8 Sobrino et al., 1994. 7 Uliveri & Cannizzaro, 1985. 6 Price, 1984. 5 Vodal, 1991. 4 Coll et al. 1997. 3 Prata & Platt, 1995; Modified by Caselles et al. 1997. 2 Wan & Dozier, 1996; Becker & Li, 1990. 1 Reference Formula # No Results 3. Results Statistical Plots (histogram samples for daytime, dry Atmosphere cases) 0.890.310.650.359 0.920.330.700.358 0.920.330.700.357 0.950.450.750.466 0.940.470.720.475 0.920.320.700.354 0.920.330.700.353 0.960.470.750.472 0.920.320.700.351 MoistDryMoistDry NighttimeDaytime No Regression STD Error ( K) References Berk, A., G. P. Anderson, P. K. Acharya, J. H. Chetwynd, M. L. Hoke, L. S. Bernstein, E.P. Shettle, M.W. Matthew and S.M. Alder-Golden, MODTRAN4 Version 2 Vehicles Directorate, Hanscom AFB, MA 01731-3010, April 2000. Wan, Z. and J. Dozier, “A generalized split-window algorithm for retrieving land surface temperature from space”, IEEE Trans. Geosc. Remote Sens., 34, 892- 905, 1996. Becker, F. and Z.-L. Li, “Toward a local split window method over landsurface”, Int. J. Remote Sensing, vol. 11, no. 3, pp. 369–393, 1990. Prata, A. J. and C.M.R. Platt, “Land surface temperature measurements from the AVHRR”, proc. of the 5th AVHRR Data users conference, June25-28, Tromso, Norway, EUM P09,443-438, 1991. Caselles, V., C. Coll and E. Valor, “Land surface temperature determination in the whole Hapex Sahell area from AVHRR data”, Int. J. remote Sens. 18, 5, 1009-1027, 1997. Coll, C., E. Valor, T. Schmugge, V. Caselles, “A procedure for estimating the land surface emissivity difference in the AVHRR channels 4 and 5”, Remote Sensing Application to the Valencian Area, Spain, 1997. Vidal, A., “Atmospheric and emissivity correction of land surface temperature measured from satellite using ground measurements or satellite data”, Int. J. Remote Snes., 12, 2449-2460, 1991. Price, J. C., “Land surface temperature measurements from the split window channels on the NOAA 7 Advanced Very High Resolution Radiometer”, J. Geophys. Res., 89, 7231-7237, 1984. Ulivieri, C. and G. Cannizzaro, “Land surface temperature retrievals from satellite measurements”, Acta Astronautica, 12, 997–985, 1985. Sobrino, J. A., Z.L. Li, M.Ph. Stoll, F. Becker, “Improvements in the split-window technique for land surface temperature determination”, IEEE Trans. Geosc. Remote Sens., 32, 2, 243-253, 1994. Ulivieri, C., M.M. Castronouvo, R. Francioni, A. Cardillo, “A SW algorithm for estimating land surface temperature from satellites”, Adv. Spce res., 14, 3, 59-65, 1992. Sobrino, J. A., Z.L. Li, M.Ph. Stoll, F. Becker, “Determination of the surface temperature from ATSR data”, Proceedings of 25th International Symposium on Remote Sensing of Environment held in Graz, Austria, on 4th-8th April, 1993 (Ann Arbor, ERIM), pp II-19-II-109, 1993. Snyder, W. C., Z. Wan, and Y. Z. Feng, “Classification-based emissivity for land surface temperature measurement from space”, Int. J. Remote Sensing, vol. 19, no. 14, pp. 2753-2774, 1998. Yu, Y, J. Privette, A. Pinheiro, “Evaluation of split window land surface temperature algorithms for generating climate data records”, IEEE Trans. Geosc. Remote Sens., Jan. 2008, in press. Summary 6. Summary Split window LST algorithms were analyzed for GOES-R Mission LST EDR production. SUFRAD ground measurements were used for GOES-R LST algorithm evaluation Algorithms 2 and 6 are recommended for their less sensitivity to emissivity uncertainty. Algorithm coefficients are stratified for daytime and nighttime, dry and moist atmospheric conditions. Recommended algorithms will meet the GOES-R mission requirement (< 2.4 K). Applying Split Window Technique for Land Surface Temperature Measurement from GOES-R Advanced Baseline Imager Yunyue Yu 1, Dan Tarpley 2, M.K. Rama Varma Raja 3, Hui Xu 3, Konstantin Vinnikov 4 1 NOAA/NESDIS Center for Satellite Applications and Research, email: yunyue.yu@noaa.gov 2 Short & Associates, email: Dan.Tarpley@noaa.gov, 3 I.M. Systems Group, Inc., email: rama.mundakkara@noaa.gov, hui.xu@noaa.govDan.Tarpley@noaa.govrama.mundakkara@noaa.gov, hui.xu@noaa.gov 4 University of Maryland, email: kostya@atmos.umd.edu kostya@atmos.umd.edu Sensitivity Analyses 4. Sensitivity Analyses Sensitivity to emissivity Land surface emissivity may be obtain from surface type classifications or from estimations of satellite measurements. Uncertainty in the emissivity information may introduce error in the LST retrieval. The GOES-R LST algorithm should be less sensitive to the emissivity, yet accuracy improved with the emissivity information. (figure: top/right--sample plots for algorithm 2). Sensitivity to View Angle For certain column water vapor (WV), different satellite view angle may result significant absorption difference. Accuracy of the LST retrieval algorithm may be considerably different in different satellite view angles. (figure: middle/right-- sample plots for algorithm 2 ) Sensitivity to Atmospheric Absorption In our algorithm development, coefficients of each algorithm are calculated separately for the dry and moist atmospheric conditions. In practice, WV information is usually provided by satellite measurements and/or by radiosonde measurement. Using such data, two possible errors may occur: 1) the WV value may be miss- measured, 2) due to the spatial resolution difference (usually the WV data resolution is significantly lower than the LST measurement), dry-moist mixed atmospheric conditions may occur in a single WV informed area (which usually contains several LST measurement pixels). Therefore, it is possible that coefficients of the LST algorithm for dry atmosphere being applied for moist atmosphere condition, and vise verse (figure: bottom/right-- sample plots for algorithm 2 ) Virtual Surface Types 78 virtual surface types were constructed using prescribed unique surface emissivity values determined from Snyder et al.’ (1998) surface classification work. (figure: top/right) Atmospheric Profiles 126 atmospheric profiles were used, which were collected from NOAA88 radiosonde and TOVS data, representing a variety of atmospheric conditions and latitude coverage (60 0 S to 70 0 N). The figure shows water vapor-surface air temperature distributions of the daytime (60) profiles. Dry (moist) atmosphere is defined if the water vapor is less (more) than 2.0 g. (figure:bottom/right) Open Shrub Land36.63N, -116.02 W Desert Rock, NV6 Crop Land40.13N, -105. 24W Boulder, CO5 Grass Land48.31N, -105.10W Fort Peck, MT4 Evergreen Needle Leaf Forest 34.25N, -89.87W Goodwin Creek, MS3 Crop Land40.05N, -88.37W Bondeville, IL2 Mixed Forest40.72N, -77.93W Pennsylvania State University, PA 1 Surface Type # LAT, LONGSite LocationSite No. Location and surface types of the six SURFRAD sites. #: UMD land surface type Evaluation Using Ground Measurements 5. Evaluation Using Ground Measurements LSTs Derived from GOES-8 and -10 GOES-8 (and -10) Imager has similar thermal infrared channels and view geometry to the GOES-R Imager. The derived LST algorithm has been applied to the GOES-8 and -10 data and then compared to the ground LST estimations. LSTs Ground Measurements The ground LSTs were estimated over six SUFRAD sites, every three minutes, for the year 2001. Month Site 1Site 2Site 3Site 4Site 5Site 6 DayNightDayNightDayNightDayNightDayNightDayNight 116334669761545712484157113245 21745928368678139359596135 30033927094771252358145141 46684284263892564446711274 54069213110713490645143158190 626393754378327324964235189 7183456314814224834250226 8163335691247106 3964188195 946837011084102697697123226257 105677661011562133967287596152 11591188414847112329411017685147 12255435996114838133731245872 Number of satellite and SURFRAD match-up measurements. Scatter plot comparison of GOES-8 LST and SUFRAD LST of all the match-up data. Better statistical results of the LST differences are observed (not shown here) after removing residual noises using seasonal and annual signals.