1. CIMSS Seminar December 8, 2006 A global infrared land surface emissivity database Suzanne Wetzel Seemann, Eva Borbas, Robert Knuteson, Elisabeth Weisz, Jun Li, and Hung Lung Huang

2. CIMSS Seminar December 8, 2006 Applications requiring a global land surface emissivity Our goal is to produce a methodology for integrating the best information on land cover and surface emission on a high spatial (5km), high spectral (1 wavenumber), and high temporal (daily) that can be updated to support real-time operations and research for future instruments, including: GOES-R Proxy data set generation GOES-R Surface characterization for TOA radiance calculations. 3. GOES-R/NPOESS Training set (IR surface emissivity) for ABI retrieval 4. GOES-R/NPOESS background field required for 1-D var data assimilation Synthetic (statistical) retrievals of atmospheric temperature, moisture, and ozone from MODIS MOD07 and UW IMAPP AIRS radiances Selected Other Current Applications/Users: Assimilation of radiances over land (JCSDA) Climate Monitoring SAF (EUMETSAT) Cloud and Ozone retrieval from SEVIRI (EUMETSAT) AIRS Retrieval of Dust Optical Depths (UMBC/ASL) IASI-Metop Cal/Val (CNES, France) Retrieval of hot spot data from AATSR (ESA/ESRIN) Energy balance from ASTER over glacier (Univ of Milan) AIRS trace gas retrieval for pollution monitoring (Stellenbosch University, South-Africa) Education (Seoul National Univ.; NTA, Konstantin)

3. CIMSS Seminar December 8, 2006 LEFT : Profiles of mean absolute differences in retrieved temperature (K,left two) and mixing ratio (g/kg, right two) calculated using the IMAPP AIRS retrieval algorithm. Mean absolute differences in retrievals with emissivity = 1.0 minus those = 0.95 (dashed), and emissivity = 1.0 minus BF emissivity (solid) is shown for 1402 globally-distributed land and coastline sites (left), for the 242 profiles with IGBP ecosystem barren/desert land classification (right). Sensitivity of calculated BT to land surface emissivity Difference between BT calculated using the prototype-CRTM model with emis = 1.0 minus emis = 0.95 for 3 Aqua MODIS bands. Each of the 8583 points represents a forward model calculation for one land SeeBor profile, and the colors correspond to the land surface type (IGBP ecosystem category) Band 29 (8.6  m) Band 31 (11  m) Band 33 (13.4  m) Is emissivity important? In what spectral regions?

4. CIMSS Seminar December 8, 2006 Average differences for 8583 land SeeBor profiles of BT for Aqua MODIS IR bands 25, and 27-36 (left) and all 2378 Aqua AIRS channels (right). Each symbol (MODIS) and dot (AIRS) represents the average BT difference over all profiles for one channel. BT calculated with Emis = 1.0 minus that calculated with Emis = 0.95 BT calculated with Emis = 1.0 minus that calculated with Baseline Fit (BF) Emissivity

5. CIMSS Seminar December 8, 2006 Sensitivity of retrieved atmospheric products to land surface emissivity: TPW TPW % difference retrieved using a training data set with two different surface emissivities. MOD07 retrievals used Terra radiances for 314 clear sky cases at the ARM SGP site between April 2001 and August 2005. emis = 1.0 minus emis = 0.95 emis = 1.0 minus BF emis

6. CIMSS Seminar December 8, 2006

7. CIMSS Seminar December 8, 2006 Sensitivity of retrieved atmospheric products to land surface emissivity: T, q profiles Profiles retrieved using the IMAPP AIRS algorithm (Elisabeth Weisz). Temperature K Land and coastlines (1402 profiles) Barren/desert (242 profiles) Mixing Ratio g/kg Land and coastlines (1402 profiles) Barren/desert (242 profiles) Mean absolute differences between collocated radiosondes and retrievals with: emis = 1.0 minus emis = 0.95 (dashed) emis = 1.0 minus BF emis (solid)

8. CIMSS Seminar December 8, 2006 Mean (solid) +/- 1 stdev (dashed) for emissivity assigned to the NOAA-88 training profiles in ATOVS and early MODIS retrieval algorithms In the past, constant value or pseudo-random emissivity spectra have been assigned to the training data for retrieval of atmospheric temperature and moisture Emissivity in regression retrievals of atmospheric properties The MODIS MOD07 synthetic regression retrieval algorithm uses 11 IR channels to retrieve atmospheric profiles of temperature and moisture, total precipitable water vapor (TPW), total ozone, lifted index, surface skin temperature. The algorithm uses clear-sky radiances measured by MODIS over land and ocean for both day and night. To compute the synthetic radiances from the profile training dataset to train the regression, surface emissivity values must be assigned to each profile.

9. CIMSS Seminar December 8, 2006 For Comparison: Laboratory measurements of selected materials from UCSB (compiled by Dr. Zhengming Wan, MODIS Land Team):

10. CIMSS Seminar December 8, 2006 MODIS IR Channels 2022232527282930313233343536 wavelength (  m) 3.83.94.04.56.77.38.69.7111213.313.613.914.2 Channels in MOD07 XXXXXXXXXXX Channels with Emissivity in MOD11 XXXXXX One option for emissivity is the MODIS MOD/MYD11 operational land surface emissivity product but it is not at high enough spectral resolution

11. CIMSS Seminar December 8, 2006 To fill in the spectral gaps in MYD11 emissivity data, high spectral resolution laboratory measurements from the MODIS/USCB and ASTER emissivity libraries are used: High spectral resolution (wavenumber resolution between 2-4cm -1 ), Not necessarily true representations of a global ecosystem as seen from space. The key to deriving a global emissivity database lies in the combination of the high spectral measurements made in the laboratory and moderate spectral resolution satellite observations of actual ecosystems. There are a number of ways to combine the two. One approach, termed the “baseline fit” method is introduced here. Another effort is underway to generate a high spectral resolution emissivity dataset that uses principal component analysis to combine the laboratory data with MOD/MYD11 observations (Eva Borbas).

12. CIMSS Seminar December 8, 2006 Baseline Fit Methodology for Deriving Land Surface Emissivity We use selected laboratory measurements of emissivity to derive a baseline conceptual model of emissivity and MODIS MYD11 measurements to adjust the emissivity. First, we selected 10 inflection points, or hinge points that are important in characterizing the *shape* of a spectrum Then, we developed a set of fitting rules to adjust the emissivity at these wavelengths based on the observed MOD/MYD11 values. The rules were developed based on careful inspection of and testing with 321 high spectral resolution laboratory-measured emissivity spectra.

13. CIMSS Seminar December 8, 2006

14. CIMSS Seminar December 8, 2006 Spectra typically slope up more steeply from 4.3 to 5  m, then less steeply from 5 to 7.6  m. In the 5-7  m region, the spectra typically slopes more steeply from 5-5.8  m, then levels off. Due to a lack of information from MOD11 in the 5-8  m region, one value must be held constant in some cases. A value of 0.976 was used for the 7.6  m emissivity based on an average over the laboratory spectra. Many, but not all, spectra have a broad reduction in emissivity centered around 8.6  m. If MOD11 emissivity at 8.6  m is greater than 0.97, these cases typically have relatively flat emissivity spectra, often with all emissivities higher than 0.97. The emissivity beyond 12  m (the last wavelength for which MOD11 data is available) is assumed to have a constant slope for all spectra equal to a rise of 0.01 over 3.5 microns. This is based on inspection of the laboratory data. Conceptual model of a land surface emissivity spectrum that was used to build the baseline fitting rules

15. CIMSS Seminar December 8, 2006 Results of applying the baseline fit procedure to MYD11 emissivity at 4 locations. The baseline fit spectra are shown by the solid, dotted, dashed, and dash-dot lines and the 6 input MYD11 emissivity values as the symbols.

16. CIMSS Seminar December 8, 2006 Lab emis (black solid lines) was sampled at only the MYD11 wavelengths (vertical dotted lines) and input to the baseline fitting procedure. The result is the baseline fit emissivity (blue dashed). Sliced Santa Barbara Sandstone Tropical Soil, Zimbabwe, Africa Granodiorite Page, Arizona Laurel Leaf Altered Volcanic Tuff Evaluation of the Baseline Fitting Procedure

17. CIMSS Seminar December 8, 2006 Average differences between 321 laboratory spectra and emissivity derived by the baseline fit method (blue). Differences are also shown for a constant emissivity of 1.0 (black) and those derived by linear interpolation between MYD11 wavelengths (red).

18. CIMSS Seminar December 8, 2006 Impact of BF Emissivity on MODIS and AIRS Retrieved Temperature and Moisture TPW (mm) at the ARM SGP site from Terra MODIS MOD07 (red), GOES-8 and -12 (blue), and radiosonde (black), with the ground-based ARM SGP MWR for 313 clear sky cases from 4/2001 to 8/2005. MOD07 Statistics bias = -0.04 mm rms = 2.49 mm n = 313 MOD07 Statistics bias = 1.9 mm rms = 3.76 mm n = 313

19. CIMSS Seminar December 8, 2006 LEFT : Profiles of RMSE (thick lines) and bias (thin lines) between the temperature (oK, top) and moisture (g/kg, bottom) profiles measured by radiosonde and that calculated using the UW-Madison IMAPP AIRS retrieval algorithm with BF emissivity (solid line), and emissivity = 1.0 (dashed line) assigned to the training data profiles. Statistics were averaged over 242 profiles with barren/desert land IGBP ecosystem category. Right-hand panels of each pair show the difference in RMSE. A negative RMSE difference indicates the retrievals made with the BF emissivity compare better to the radiosondes than those made with an emissivity of 1.0.

20. CIMSS Seminar December 8, 2006 Sahara Desert: Terra MODIS ascending (nighttime) passes on 1 August 2005 MOD07 TPW with emis = 0.95MOD07 TPW with Baseline Fit emis NCEP-GDAS TPW analysis

21. CIMSS Seminar December 8, 2006 MODIS MOD07 TPW for the 5 minute Terra granule beginning at 21:40 UTC on August 1, 2005. A closer look at one of the Sahara desert granules Emis = 0.95 Emis = 1.0BF Emis

22. CIMSS Seminar December 8, 2006 Some examples from the database…

23. CIMSS Seminar December 8, 2006 3.7  m4.3  m5  m 8.3  m10.8  m14.3  m Global BF emissivity for 6 wavelengths, August 2002

24. CIMSS Seminar December 8, 2006 4.3  m8.3  m10.8  m BF Emissivity in the Sahara Desert region for August 2003

25. CIMSS Seminar December 8, 2006 FebruaryMay Land Surface Emissivity Time Series August November Monitoring seasonal land changes in a region: 4.3  m BF land surface emissivity

26. CIMSS Seminar December 8, 2006 Monitoring changes over time at 10 point locations: 8.3  m BF land surface emissivity

27. CIMSS Seminar December 8, 2006 Future Work and Limitations The baseline fit emissivity database is tied to the accuracy of MYD11. Future work will apply a similar methodology to operational emissivity products from other platforms such as AIRS for comparison. Monthly temporal resolution is not be sufficient for some applications. MYD11 L3 daily or 8-day global emissivity fields can be used to create BF emissivity with higher temporal resolution. Spectral information between the inflection points is an approximation and will not be sufficient for some applications. Work is ongoing on a new version using a principal component analysis combining the MODIS MOD11 emissivity with laboratory measurements of emissivity. The dataset is based on a regression relationship between the observed MOD11 emissivity data and the principal components of selected laboratory spectra (Eva Borbas). Work to compare the baseline fit database with ground-based measurements is planned. Work to comparison of this dataset with emissivity derived from other sources (AIRS, SEVIRI) is ongoing (Leslie Moy). Dataset available at http://cimss.ssec.wisc.edu/iremis Contact Suzanne.Seemann@ssec.wisc.edu Draft of paper submitted to JAMC September 2006 available upon request