VALIDATING SATELLITE LAND SURFACE TEMPERATURE PRODUCTS FOR GOES-R AND JPSS MISSIONS.pptx
1. 1 Validating Satellite Land Surface Temperature Products for GOES-R and JPSS Missions Yunyue Yu, Mitchell Goldberg, Ivan Csiszar NOAA/NESDIS Center for Satellite Applications and Research
4. 3 Motivation-- LST validation issues LST Validation Difficulties In situ data limitation Measurement difficulty Cloud contamination effect particularly the partial or thin cloudy pixels Spatial and temporal variations Spot vs pixel difference Sub-pixel heterogeneity Accurate match-up process (different sampling rates and sampling timing) Others i.e. angle effect Surface heterogeneity is shown in a 4km x 4km Google map (1km x 1km, in the center box) around the Bondville station area
19. For pixel that is relatively homogeneous, analyze statistical relationship of the ground-site sub-pixel with the surrounding sub-pixels: {T(x,y) } ~ T(x0,y0)
20. Establish relationship between the objective pixel and its sub-pixels (i.e., up-scaling model), e.g., Tpixel = T(x,y) + DT (time dependent?)ASTER pixel The site pixel MODIS pixel The Synthetic pixel/sub-pixel model 5
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22. Distance of every synthetic pixel center from the ground site is within the pixel size (~1Km).
23. Different colors are used for the 9 synthetic pixels, and the center of each pixel is marked with a small numbered square of the same corresponding color.
24. The numbers on the squares are the pixel IDs used in the relevant analysis.Colored squares: Ground site synthetic VIIRS pixels
25. Approach 7 Quantification of the difference between the Synthetic Pixel and Ground Measurement, that is, Evaluation: and with the model: Note that: Sub-pixel heterogeneity Systematic bias between ASTER and ground measurements Tsat– satellite pixel LST Tg -- ground site LST Tc -- central sub-pixel LST
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27. Collection of the clear-sky ASTER data sets associated with six SURFRAD sites and two CRN sites.
40. 10 Jul. 2011 Ground Site Broadband Emissivity Regression based on the UW-Madison Baseline Fit Emissivity Database ( Seemann et al., 2008). Data Sets Regression of Broadband emissivity from well-developed narrowband emissivity database: UW-Madison baseline Fit Emissivity Database a=0.2122, b=0.3859,c=0.4029 (Wang, 2004)
41. Processing General Components of Validation Processing Satellite Data Geolocation Match-up Satellite Data Reader Time Match-up Ground Data Reader Ground Data Match-up Datasets Satellite Cloud Mask Satellite LST Calculation/Extraction Ground Data Mask Ground LST Estimation/Extraction Manual Cloud Control Outputs (Plots, Tables, etc.) Direct Comparison Synthetic Analysis and Correction Indirect Comparison Statistical Analysis 11
42. Processing Sample Match-up Flow Chart Time Match-up (< 5 mins) Satellite Data Cloud Mask Geolocation Match-up Spatial Difference Test: BT -- 3X3 pix STDs, Visual -- 0.5 deg SURFRAD Data Manual Tuning Channel BT Difference Test: (Ts, T10mm), (T10mm, T3.9mm) (T10mm, T12mm) Matched Dataset Time Series Smoothness Check (if available): Upwelling, Downwelling Irradiances Additional cloud filter Note: this flow chart is specifically for GOES Imager Similar procedure is/will be applied for the ASTER and MODIS/VIIRS data 12
63. Development of VIIRS LST algorithm modules for flexible offline testing and algorithm improvement
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65. 16 Results Comparison of the temperatures calculated from synthetic pixel average (top-right), center-pixel (bottom-left), and nearest pixel (bottom-right) with the ground site temperature. Note the different colors represent for the 9 different synthetic pixels shown previously. For this particular site the ground site location within the satellite pixel does not have significance impact to the validation process, simply because the land surface thermal emission at Desert Rock is fairly homogeneous. SURFRAD Station: Desert Rock
66. 17 Results Site=Desert Rock, NV Ts - Tc Tc-Ta Ts – Ta Case Mean STD STD` Mean STD Mean -1.81 2.46 0.69 0.04 2.13 -1,78 0 0.60 -0.01 2.26 -1.82 1 0.61 0.08 2.20 -1.74 2 0.92 0.20 1.99 -1.61 3 0.96 0.06 2.03 -1.75 4 0.98 -0.24 2.18 -2.05 5 0.80 -0.34 2.30 -2.15 6 0.65 -0.26 2.40 -2.07 7 0.60 -0.16 2.37 -1.97 8 0.76 -0.07 2.21 -1.88 Average Sample statistical analysis result on the Desert Rock site. Impact of pixel location bias to the ground site The Ta and Ts difference is tested by comparing its spatial structure to the site geographic structure. It shows that such Ta and Ts difference matches the site geographic feature well, which implies that the synthetic pixel temperature calculation is reasonable. Ts: LST of SURFRAD site Ta: average LST over 13x13 ASTER pixels Tc: LST of ASTER pixel nearest to the site
67. Jul. 2011 18 Results Site=Bondville, IL Ts - Tc Tc-Ta Ts – Ta Case Mean STD STD` Mean STD Mean -0.59 2.01 0.92 -0.07 2.04 -0.66 0 1.04 -0.14 2.01 -0.73 1 1.07 -0.05 2.05 -0.64 2 1.27 -0.05 2.17 -0.64 3 1.15 -0.03 2.10 -0.68 4 1.10 -0.09 2.14 -0.60 5 0.97 -0.001 2.12 -0.62 6 0.95 -0.03 2.05 -0.77 7 0.97 -0.18 2.02 -0.77 8 1.05 -0.09 2.08 -0.80 Average Sample statistical analysis result on the Bondville site. Impact of pixel location bias to the ground site
68. Jul. 2011 19 Results Sample statistical analysis result on the Boulder site. Ts - Tc Tc-Ta Ts – Ta Case Mean STD STD` Mean STD Mean -0.77 2.60 0.58 -0.07 2.62 -0.84 0 0.85 -0.38 2.61 -1.15 1 Site=Boulder, CO 0.91 -0.27 2.30 -1.03 2 0.84 -0.14 2.27 -0.91 3 0.61 -0.03 2.54 -0.80 4 0.61 -0.10 2.64 -0.67 5 Impact of pixel location bias to the ground site 0.69 -0.00 2.75 -0.77 6 0.70 -0.10 2.80 -0.87 7 0.70 -0.25 2.70 -1.02 8 0.72 -0.13 2.58 -0.90 Average
69. Results Sample scatter plots show the linear relationship between satellite LST and ground LST. Tsat = A Tg + B + e
72. The synthetic pixel LST is generally much warmer (about 1.5K) than SURFRAD LST, while MODIS LST is slightly cooler than SURFRAD LST.
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74. LST of SURFRAD measurements may be used as good references for VIIRS/ABI LST cal/valif the measurements are of high-quality and the in-situ estimation of LST is accurate enough.
75. Directional variations are small, so small geo-referencing (within 1Km) bias may not be an issue affecting VIIRS/ABI LST cal/val at the above SURFRAD sites.
76. Application of the site-to-pixel model depends on the ASTER LST data quality.
77. Directional variation of the potential sub-pixel heterogeneity is found to be consistent with the physical topographic features, even if it is small.
78. The limited datasets doesn’t allow us to characterize the seasonal variation of heterogeneities, which is more desirable than a simple mean difference. More datasets are expected. And about 1K difference seems unavoidable in practice.23
83. Test of the scaling model using real satellite data24
Editor's Notes
Just listed major LST sources, not include microwave sensors, LEO sensors.GOES generation: SMS – GOES 1 (1975) to 3 1st - GOES 4 to 72nd – GOES 8 to 123rd – GOES 13 to 154th – GOES R, S, T, U
In situ data limitationDifferent spatial scales Different sampling rates and sample timing Sub-pixel cloud contamination Sub-pixel heterogeneity Limited samples of clear case
Issues: 1) different t and t0 resulting different Ts. 2) the ground homogeneity maybe location/time dependent 3) the up-scaling model 4) the uncertainty of up-scaling estimationSolutions: 1) develop or use proper random statistic model for heterogeneity and interpolation analysis 2) develop or use proper up-scaling model to perform point-to-area estimation
Why Tc – Tg is systematic error?
Should use my one (CRN sites are not used in the table, therefore not need to be shown)
![2 Motivation-- LST validation needs LST Products Derived from Different Sensors for Decades POES NOAA AVHRR (TIROS-N to NOAA-19) since 1978 ESA ATSR/AATSR since 1991 EOS MODIS since 1999 MetOp AVHRR since 2006 GOES US GOES/Imagers since 1975 Meteosat (MVIRI since 1995 and then) SEVIRI since 2004 Future LST products ,[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)