1. AlgorithmsAlgorithms

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.

2. Simulation ProcedureSimulation 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 profilesLoops: 60 daytime profiles, 66 nighttime profilesView zenith: 0, 10, 20, 30, 40, 50 ,60 degrees

Atmosphericprofiles

Algorithmcoeffs

TOA spectralradiances

MODIS Sensor RSR functions

Sensor BTs

MODTRANsimulation

BTCalculation

RegressionOf

LST algorithms

AlgorithmComparisons

Inputsetting

STD ErrorOf

Algorithmsstart

end

# 1) T11 and T12 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

ReferenceFormula#No

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3. ResultsResults

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

NighttimeDaytimeNo

Regression STD Error ( K)

ReferencesReferences•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.

6. SummarySummary

• 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 Yu1, Dan Tarpley2, M.K. Rama Varma Raja3, Hui Xu3, Konstantin Vinnikov4

1NOAA/NESDIS Center for Satellite Applications and Research, email: yunyue.yu@noaa.gov 2Short & Associates, email: Dan.Tarpley@noaa.gov, 3I.M. Systems Group, Inc., email: rama.mundakkara@noaa.gov, hui.xu@noaa.gov

4University of Maryland, email: kostya@atmos.umd.edu

4. Sensitivity AnalysesSensitivity 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 AngleFor 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 AbsorptionIn 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 Types78 virtual surface types were constructed 78 virtual surface types were constructed using prescribed unique surface using prescribed unique surface emissivity values determined from emissivity values determined from Snyder Snyder et alet al.’ (1998) surface .’ (1998) surface classification work. (figure: classification work. (figure: top/righttop/right))

• Atmospheric Profiles126 atmospheric profiles were used, which were collected from NOAA88 radiosonde and TOVS data, representing a variety of atmospheric conditions and latitude coverage (600 S to 700 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 WDesert Rock, NV6

Crop Land40.13N, -105. 24WBoulder, CO5

Grass Land48.31N, -105.10WFort Peck, MT4

Evergreen Needle Leaf Forest

34.25N, -89.87WGoodwin Creek, MS3

Crop Land40.05N, -88.37WBondeville, IL2

Mixed Forest40.72N, -77.93WPennsylvania State

University, PA1

Surface Type#LAT, LONGSite LocationSite No.

Location and surface types of the six SURFRAD sites.#: UMD land surface type

5. Evaluation Using Ground MeasurementsEvaluation 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 MeasurementsThe ground LSTs were estimated over six SUFRAD sites, every three minutes, for the

year 2001. Month

Site 1 Site 2 Site 3 Site 4 Site 5 Site 6

Day Night Day Night Day Night Day Night Day Night Day Night1 16 33 46 69 76 154 57 124 84 157 113 2452 17 45 9 28 36 86 78 139 35 95 96 1353 0 0 33 92 70 94 77 125 23 58 145 1414 66 84 28 42 63 89 25 64 44 67 112 745 40 69 21 31 107 134 90 64 51 43 158 1906 26 39 37 54 37 83 27 32 49 64 235 1897 1 8 34 56 31 48 14 22 48 34 250 2268 16 33 35 69 12 47 106 106 39 64 188 1959 46 83 70 110 84 102 69 76 97 123 226 257

10 56 77 66 101 156 213 39 67 28 75 96 15211 59 118 84 148 47 112 32 94 110 176 85 14712 25 54 35 99 61 148 38 133 73 124 58 72

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.