VALIDATING SATELLITE LAND SURFACE TEMPERATURE PRODUCTS FOR GOES-R AND JPSS MISSIONS.pptx

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  • 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)

Transcript

  • 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
  • 2. 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
    LST Climate Data Record
  • 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
  • 5. Approach-- Strategy
    • Using Existing Ground Observation Data
    • 6. Cost consideration
    • 7. Site representativeness and selection: characterization analysis
    • 8. Match-up Dataset Generation
    • 9. Stringent cloud filtering: additional measures
    • 10. Data pair quality control
    • 11. Site-to-pixel Model Development
    • 12. Synthetic pixel analysis using high resolution sensor data
    • 13. Proxy data testing
    • 14. Real satellite data evaluation
    • 15. Validation Methodology
    • 16. Direct comparisons
    • 17. Indirection comparisons
    4
  • 18. Approach
    T(x,y,t)
    T(x0,y0,t0)
    Synthetic pixel analysis using ASTER data— an integrated approach for
    site representativeness analysis and site-to-pixel model development
    • Quantitatively characterize the sub-pixel heterogeneity and decide whether a ground site is adequately representative for the satellite pixel. The sub-pixels may be generated from pixels of a higher-resolution satellite.
    • 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
  • 21. ASTER scene (90m) pixel
    6
    Approach
    A Site Characterization Simulation Model – synthesizing VIIRS pixel using higher-resolution ASTER TIR pixels.
    • Each synthetic pixel has the target ground site enclosed, but the distance between the ground site and the center of synthetic pixel varies, which mimics the possible over-passing VIIRS swaths.
    • 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
  • 26. Data Sets
    8
    Data Acquisition
    • Inventory preparation of clear-sky ASTER scenes passing over 23 ground sites (SURFRAD and CRN) during 2001-2007.
    • 27. Collection of the clear-sky ASTER data sets associated with six SURFRAD sites and two CRN sites.
    • 28. AST_04, AST_05, AST_08 and L1B (in total, about 2000 ASTER swath scenes).
    • 29. Collection of the six SURFRAD and the two CRN ground data.
    • 30. Collection of the two CRN ground data.
    • 31. Collection of MODIS LST product data (MOD11_L2).
    • 32. All the swaths passing over the SURFRAD sites in 2001
    • 33. All the swaths corresponding to the ASTER scenes during 2001-2007
    • 34. Collection of the narrow-band emissivity data sets
    • 35. UW-Madison Baseline Fit Emissivity Database
    • 36. North American ASTER Land Surface Emissivity Database (NAALSED)
    • 37. Collection of NCEP reanalysis TPW datasets
  • Data Sets
    Satellite LST: MODIS LST, ASTER, and GOES LST
    Ground LST: Derived from SURFRAD site measurements
    Candidate Ground Sites and Database
    Dataset Used
    • SURFRAD data
    • 38. ASTER Data
    • 39. Data period: 2001-2007
    ASTER data is courtesy by Shunlin Liang
    Table: Matched ASTER Data
    9
  • 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
  • 43. Processing
    13
    Data Processing/Analysis
    • Clear-sky cases analysis
    • 44. Cloud and clear-sky climatology analysis (for site selection)
    • 45. ASTER Clear-sky swath selection from the ASTER inventory from the Warehouse Inventory Search Tool
    • 46. Ground broadband emissivity regression analysis
    • 47. SURFRAD LST estimation from PIR measurements
    • 48. Spatial and temporal match-up among ground sites, ASTER scenes and MODIS scenes
    • 49. Geolocation mapping of ASTER pixels as the sub-pixels of a MODIS pixel
    • 50. Quality-control and enhanced cloud filtering
    • 51. Processing of ASTER LST QC information
    • 52. Processing of ASTER emissivity QC information
    • 53. Processing MODIS cloud masks
    • 54. Processing of MODIS LST QC information
    • 55. Surface observations
    • 56. Statistical testing
  • Processing
    14
    Data Processing/Analysis (con’t)
    • Site representativeness analysis and site-to-pixel difference characterization
    • 57. Semi-variance analysis
    • 58. Synthetic analysis
    • 59. Site-to-pixel model testing
    • 60. Testing with all the MODIS Terra LST swaths passing over SUFRAD sites in 2001
    • 61. Testing with the MODIS LST swaths corresponding with ASTER scenes during 2001-2007
    • 62. VIIRS LST case studies on NPP land LPEATE platform
    • 63. Development of VIIRS LST algorithm modules for flexible offline testing and algorithm improvement
    • 64. Visualization tools of the analysis and results disolay
  • Results
    Additional cloud filtering is need for obtaining high quality satellite-ground match-up dataset
    Left: ATSER cloud free dataset. Right: possible cloud contamination.
    Cloud
    15
  • 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
  • 70. 21
    7/27/2011
    Results
    Site-to-Pixel Statistical Relationship
  • 71. 22
    Results
    Site-to-Pixel Statistical Relationship – MODIS Proxy
    • The standard deviation of the difference between real MODIS pixel LST and SURFRAD LST are consistent with the standard deviation of the difference between synthetic pixel and SURFRAD.
    • 72. The synthetic pixel LST is generally much warmer (about 1.5K) than SURFRAD LST, while MODIS LST is slightly cooler than SURFRAD LST.
    • 73. Implication: High resolution ASTER data be used for site representativeness analysis, but a further verification of ASTER LST quality seems essential for the parameter estimation of the site-to-pixel model (e.g., A,B,C) and the application of the model in real cases.
  • Results –Summary
    • Synthetic pixel analysis model is created for analyzing ground site temperature heterogeneous feature. ASTER data are used to generate synthetic VIIRS pixel data (LST) and compared to the SURFRAD site data
    • 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
  • 79. Future Plan
    • Analysis over time scales of interest, e.g., seasonal variation.
    • 80. Analysis over sites of different surface types
    • 81. Up/down-scaling models
    • 82. Emissivity uncertainties
    • 83. Test of the scaling model using real satellite data
    24