1. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Developing GOES and POES Land Surface Temperature Products Yunyue Yu 1 (PRINCIPAL GOVERNMENT INVESTIGATOR), Dan Tarpley 1, Ming Chen 2, Hui Xu 2, Konstantin Vinnikov 3, Donglian Sun 4 1 NOAA/NESDIS/STAR/SMCD, 2 I. M. Systems Group Inc., 3 University of Maryland, 4 George Mason University Requirement: Increase quantity, quality, and accuracy of satellite data that are processed and distributed within targeted time. Science: understand issues in the satellite Land Surface Temperature (LST) derivation; develop LST products for the current and future GOES and POES satellite missions; provide LST validation method and tools. Benefit: weather forecasting model, weather and water monitoring, climate data record, agricultural monitoring, earth surface radiation budget Science Challenges: Surface heterogeneity of temperature and emissivity; lack of quality validation data; sub-pixel temperature and emissivity variation. Next Steps: Developing algorithm for quality surface emissivity; satellite- ground validation dataset acquisition; validation site data characterization, reprocessing for LST climate data record. Transition Path: 1) Developing consistent GOES LST. 2) developing consistent POES LST. 3) Blending GOES and POES LST. 4) Developing LST Climate Data Record. Atmospheric profiles TOA radiances MODTRAN Input (looping) parameters Start Surface Type Configuration Algorithm Coeffs. Sensor Spectral Response Funs Sensor Brightness Temperatures Sensor Brightness Temperature Calculation Regression of LST Algorithms STD Error Of Algorithms Input parameters Filter of Data Distribution Algorithm Selected Accuracy Analysis Sensitivities Analysis tables plots Radiative transfer Simulation Process Regression Process Analyzing Process Procedure of the Simulation Analyses » Radiative Transfer (RT) Process » Regression Computation » Algorithm Accuracy Analysis » Sensitivity Study Simulation analysis of multi-channel regression algorithm Multi-channel regression technique has been developed for over decades for deriving satellite LST products. Simulation analysis using radiative transfer is usually the first step for developing LST algorithm for a satellite mission. Above: left -- LST diurnal and seasonal variation at a SUFRAD ground station (Desert Rock, NV; 36.63 0 N, 116. 02 0 W); right -- The variation difference from satellite and from the ground measurement. Satellite LST: Algorithms applied to GOES-8/10 data Ground LST: Derived from SURFRAD site measurements Duration: Jan 1 – Dec 31, 2001 SURFRAD radiance Temperature :  emit  up  down  emit   Spectral Correction: T=T+dT pir - dT  dT pir =(d   pir /   )(T/4) dT  =(T/4)(d  ) Plots & Tables GOES 8/10 data Cloud filter Match-up and Comparison Statistics Algorithm: LST Calculation Satellite-ground data match up process Validation Efforts Satellite LST can be validated using measurement of ground stations (i.g., US SURFRAD stations), though special treatment must be performed considering the sub-pixel LST variation. Example applications LST diurnal and seasonal variation were observed using GOES Imager data, Above: Ground LST estimation at Cardington station (UK) is compared to satellite (MSG/SEVIRI) LST. Above: Scatter plots of LSTs derived from GOES-8 Imager data vs. LSTs estimated from six SURFRAD stations in year 2001. Data sets are stratified for daytime (red) and night time (blue) atmospheric conditions A field campaign carried out in the zone of Bordeaux (44◦4301.7 N, 0◦4609.8 W), forest zone of Le Bray (right). LSTs derived from two radiometers (Raytek (R) and Everest (Ev) ) installed in a tower of 33-m altitude were compared with the satellite LSTs derived from MSG/SEVIRI data (below). ---- Field data Courtesy by J.A. Sobrino LST Algorithm Tested Using MSG/SEVIRI Data Sample data time: April 15, 2008, 10:45 UTC. The VIIRS LST evaluation: a sample LST image using MODIS data as proxy. Testing LST algorithm for GOES Imager: a sample LST image using GOES-12 Imager data Extract ABI Inputs ABI Radiances ABI Solar-View Geometry ABI Cloud Mask Data Mapping Cloud Filtering Emissivity Day/Night Flagging Land Check QC Control SW LST Algorithm LST Wrap up Coeffs LUT Extract Ancillary Data Dry/Moist Flagging Criteria values ABI Sensor QC flags ABI Goelocation NCEP WVLand/coast Mask LST EDR LST Processing Chain Designed for the GOES-R mission Other Input Non-ABI Ancillary Input ABI Ancillary Input ABI Sensor Input ABI Snow/Ice Mask Real Time Satellite LST Production A straightforward processing procedure is applied to calculate the satellite LST. Corrections for atmospheric absorption may be stratified for different atmospheric conditions such as daytime/nighttime, dry/moist air etc. Cloud filtering must be done before the calculation using infrared channels. A set of ancillary data is usually required for surface type/emissivity and land/coast/snow mask. Simulation Tools, Configuration and Inputs: Atmospheric Profiles NOAA88 Radiosonde dataset Cloud-free profiles (see the plots on right) Daytime 62, Nighttime 66 Latitude range 60° S to 70° N Simulation tool : i.e., MODTRAN 4.2 Iteration Loops Profiles: 62/66 daytime/nighttime View zenith angle: 0 to 70° Ts range: Tair-15 < Ts < Tair+15 Surface types: 78 real and virtual types Spectral range: 10 – 13.5  m  Result: Spectral Radiances Directional effect observed from AVHRR data. Top: AVHRR land surface temperature at Ghanzi ground station; bottom: corresponding view zenith angles of the observation. [Pinheiro et al., Remote Sensing of Environment, 103 (2006)] LST directional effect is shown by the LST difference observed from GOES-10 (west) and GOES-8 (east) along the month (x axis), and local solar time (y axis). Angle-dependency of LST and emissivity revealed from simulation study. Apparent EmissivityApparent LST Scientific Issue of LST Retrieval Major difficulties for producing LST from satellite observation is the land surface heterogeneity, emissivity uncertainty, directional effect, cloud effect, validation difficulty, etc.