Land Surface Temperature and Emissivity (LST&E) products for MODIS and VIIRS Continuity Glynn Hulley, Nabin Malakar, Tanvir Islam Simon Hook, Pierre Guillevic.

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Land Surface Temperature and Emissivity (LST&E) products for MODIS and VIIRS Continuity Glynn Hulley, Nabin Malakar, Tanvir Islam Simon Hook, Pierre Guillevic Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA (c) 2015 California Institute of Technology. Government sponsorship acknowledged. MODAPS: Virginia Kalb, Sadashiva Devadiga, Teng-Kui Lim, Robert Wolfe, Kurt Hoffman, Jerry Shiles National Aeronautics and Space Administration MODIS/VIIRS Science Team Meeting, Silver Spring, MD, 19 – 22 May, 2015

Outline 1.MOD21 LST&E Product 2.MOD21 LST&E Updates 3.New NASA VIIRS LST&E Product 4.MODIS-VIIRS Continuity

Core Products StatusSpatialFormatsAlgorithmSDS MOD11 (C4, 5, 6*) Collection 6 in processing 1-kmL2 Swath, L2G 2X Daily Generalized Split Window (GSW) Wan et al. 1996, LST MOD11B1 (C4, 4.1, 5) ?5-km (C4*) 6-km (C5) Sinusoidal 2X Daily Day/Night Algorithm Wan and Li, LST - Emissivity bands 20-23, 29, 31, 32 VIIRS VLST (IDPS) Mx8*750 mL2 Swath, L2G 2X Daily Single Split-Window Yu et al LST Current MODIS/VIIRS LST&E Products New Products StatusSpatialFormatsAlgorithmSDS MODIS-TES (MOD21 C6) Final testing, released with Collection 6 (Tier-2) 1-kmL2 Swath, L2G 2X Daily L3G Monthly Temperature Emissivity Separation (TES) - LST - Emissivity bands 29, 31, 32 VIIRS-TESUnder production at JPL (Algorithm delivery First quarter 2016) 750 mL2 Swath, L2G 2X Daily L3G Monthly Temperature Emissivity Separation (TES) - LST - Emissivity bands 14, 15, 16 New MODIS/VIIRS LST&E Products (JPL)

LST/Emissivity Error Dependency Overestimation of emissivity leads to underestimation of LST and vice versa. Split-window (11-12 micron) fixes emissivity based on land cover classification (IGBP) TES physically retrieves emissivity and temperature (minimum 3 bands) MOD11 emissivity too high (2%) MOD21 (TES) 0.5% too high In Situ LST Great Sands National Park

Generated using prototype MOD21 algorithm at MODAPS MOD21 LST

Generated using prototype MOD21 algorithm at MODAPS MOD21 Band 29 Emissivity

MOD21 C6 LST&E Uncertainty estimates ROSES 2009: Earth System Data Records Uncertainty Analysis LSTEmissivity  LST  Emissivity

Outline 1.MOD21 LST&E Product 2.MOD21 LST&E Updates 3.New NASA VIIRS LST&E Product 4.MODIS-VIIRS Continuity

ParameterMOD21 (JPL v2)MOD21 (JPL v5) (C6) Radiative Transfer Model MODTRAN (MOD07 at 25 km) RTTOV (MOD07 at 5 km) Water Vapor Scaling (WVS) coefficients V2V5 (day/night and view angle dependent) TES algorithmOne calibration for all surfaces Two calibrations for Graybody and Bare surface types MOD21 Updates and Refinements

Problem Case: Very humid/warm conditions Summertime monsoonal conditions Precipitable Water Vapor – MYD07

MODTRAN Atmospheric Correction Degraded MOD07 C6 Resolution (25 km) Temperatures overestimated in very humid conditions!

RTTOV Atmospheric Correction Full MOD07 C6 Resolution (5 km) Improved with RTTOV Implementation

Redwood Texas Grassland LST&E Validation Sites (Stage 1)

Land Surface Temperature RMSE (K) MOD21(v2) total = 1.21 K MOD21(v5) total = 1.02 K ?

Land Surface Temperature RMSE (K) MOD11 cold bias over Barren sites (3-4 K) Comparable accuracy over water, vegetation (1 K)

Outline 1.MOD21 LST&E Product 2.MOD21 LST&E Updates 3.New NASA VIIRS LST&E Product 4.MODIS-VIIRS Continuity

NASA VIIRS/MODIS Products LST&E Product Characteristics MOD21 (C6)VIIRS-TES Algorithm Temperature Emissivity Separation (TES) Bands used 29 (8.55 µm) 31 (11 µm) 32 (12 µm) 14 (8.55 µm) 15 (10.76 µm) 16 (12 µm) Radiative Transfer Model RTTOV Atmospheric Profiles (T, RH) MOD07 C6MERRA, NUCAPS? ECMWF?, NCEP? Algorithm Software C++/Matlab Data Product Types L2, L2G Daily (1 km) L3 8-day, (1 km) L3 Monthly (0.05  ) L2, L2G Daily (750 m) L3 8-day, (1 km) L3 Monthly (0.05  ) Science Data Products - LST - Emissivity (bands 29, 31, 32) - LST - Emissivity (bands 14, 15, 16) Primary difference

Prototype VIIRS-TES LST&E Retrieval: Sahel-Sahara test granule

VIIRS M14 (8.55 micron) Emissivity First use of VIIRS M14 other than cloud mask, RGB’s? Past Studies have shown 8.55 micron emissivity useful for: Land Degradation (desertification) monitoring e.g. French et al. 2008, Hulley et al Soil Moisture relationships e.g. Mira et al. 2007, Hulley et al. 2009, Masiello et al Land cover, land use change e.g. French et al. 2008, French et al. 2012

VIIRS Emissivity Validation (2014 data)

SiteNWP ModelBias (K)RMSE (K) Lake Tahoe (2014) ECMWF MERRA NCEP VIIRS LST Validation

Outline 1.MOD21 LST&E Product 2.MOD21 LST&E Updates 3.New NASA VIIRS LST&E Product 4.MODIS-VIIRS LST&E Continuity?

MODIS/VIIRS Split-window Continuity (current) Egypt Large discrepancy mostly due to inconsistent emissivity correction

MODIS/VIIRS TES Continuity (planned) Egypt Very good agreement. Gives confidence in future continuity of LST time series

Future Goal(s): 1.Reduce total number of standard LST products for VIIRS/MODIS (currently 3 different MODIS LST!). 2.Generate Unified products for MODIS and VIIRS standard LST products using uncertainty analysis approach. 3.Evaluate MOD/MYD11 C6 LST Unified MODIS LST: Merge MOD11 and MOD21 products (Aqua and Terra) MEaSURES Project Objective (2016) Unified VIIRS LST: Merge VLST (IDPS) and VIIRS-TES products** ** Contingent upon characterization of VLST Uncertainty ROSES VIIRS Projective Objective (2017)

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California

MODIS LST Products DimensionsSpatial Resolution Temporal Resolution AlgorithmOutput Products MOD lines 1354 pixels/line 1 km at nadirSwath 2x daily Split-Window- LST MOD11B1200 rows 200 columns ~5 km (C4) ~6 km (C5) Sinusoidal 2x daily Day/Night- LST - Emissivity (bands 20-23, 29, 31,32) MOD21 (Collection 6) 2030 lines 1354 pixels/line 1 km at nadirSwath 2x daily 8-day Temperature Emissivity Separation (TES) - LST - Emissivity (bands 29, 31, 32) Why MOD21? Consistent LST accuracy across all surfaces Higher spatial resolution dynamic emissivity (1-km) **Current plan is to merge MOD21/MOD11 using Uncertainty Analysis (MEaSUREs) MODIS LST&E Heritage VIIRS LST&E (Hulley)

Well characterized uncertainties! MOD21 Science Data Sets

MOD21 has well defined Quality Control (QC) parameters based on TES algorithm outputs MOD21 QC

JPL LST&E Validation Sites 30

MERRA Global Water Vapor (2m) 06hr UTC12hr UTC Time Interpolated at 11hr VIIRS UTC

Final Transmittance Field Native Grid – 11hr UTC Geolocated to VIIRS – 11hr UTC Triangulation-based Interpolation

MODIS LST Validation: Great Sands, Colorado ** Radiance-based LST validation using lab-measured sand samples collected at dune site

MODIS Emissivity Validation: Great Sands, Colorado MOD11 classification set too high resulting in cold LST bias (>2-5 K)

MOD21/MOD11 LST Validation summary: Graybody surfaces (forest, snow/ice, grassland) MOD21 and MOD11 have similar accuracy over graybody surfaces (<1 K)

SitesObs MOD11MOD21 MOD11MOD21 Bias (K)RMSE (K) Algodones, CA Great Sands, CO Kelso, CA Killpecker, WY Little Sahara, UT White Sands, NM MOD11 C5 cold bias of up to ~5 K over bare sites (due to overestimated classification emissivity) MOD21/MOD11 LST Validation summary: Bare surfaces (pseudo-invariant sand sites)

Future Work and Summary MOD21 PGE in final stages of testing and development in preparation for Collection 6 Reprocessing of MODIS Terra/Aqua to begin May/June Development and optimization of MOD21 algorithm will continue under NASA TERAQ award from MOD21 LST&E products are physically retrieved with TES algorithm resulting in similar accuracy (<1.5 K) over all land cover types and a dynamic spectral emissivity product for detection and monitoring of landscape changes A unified MOD21/MOD11 LST product is in production for a NASA MEaSUREs project at JPL

Theoretical Basis: Planck Function As the temperature increases the peak in the Planck function shifts to shorter and shorter wavelengths Thermal Infrared (TIR)

To solve the under-determined temperature-emissivity problem: N spectral measurements (N radiances) with N + 1 unknowns (N emissivity, 1 Temperature) 1.Split window approach Requires 2 bands Prescribed spectral emissivity Regression coefficients should represent all configurations (atmospheric water content, view angle, surface T air, …) 2.Temperature-Emissivity Separation (TES) – ASTER approach Multispectral (minimum 3 bands) Requires atmospheric profiles for full atmospheric correction with Radiative Transfer Model Based on Emissivity model (Calibration Curve) Temperature/Emissivity retrieval algorithms 40 TES Calibration Curve

ASTER

42 Spectral Emissivity Emissivity: ratio of the spectral radiance of a material to that of a blackbody at the same temperature: = Spectral Radiance

VIIRS –TES Processing Steps Test Code ImplementationEvaluation Read L1B and Cloud Mask Data (Fill radiances – bowtie) NWP atmospheric data (read, geolocate, interpolate) Run RTTOV Radiative Transfer Test NWP Accuracy (ECMWF, MERRA, NCEP) In progress Implement Water Vapor Scaling (WVS) Model  Temperature Emissivity Separation (TES) Validation In progress