LST CDR Algorithm Trade-Off Analysis

Transcription

1 DUE GLOBTEMPERATURE PROJECT Organisation: ULeic

2 Page: ii Signatures Name Organisation Signature Written by Darren Ghent ULeic Reviewed by Darren Ghent ULeic Jerome Bruniquel ULeic Approved and authorised by John Remedios ULeic Accepted and authorized for public release by Simon Pinnock ESA

3 Page: iii Distribution Version People and/or organisation Publicly available on website 1.0 ESA No Change log Version Comments 1.0 First version

4 Page: iv Table of Content 0. EXECUTIVE SUMMARY INTRODUCTION Applicable documents Reference documents Glossary CURRENT BEST PRACTICE: LST ALGORITHMS LST Retrieval Theory Algorithm Approaches Single-channel Algorithms Split-window Algorithms Dual Angle Algorithms Optimal Estimation Techniques DESCRIPTION OF CANDIDATE ALGORITHMS ULeic algorithm for (A)AATSR: UL3_algm Generalised Split Window (GSW) algorithm for MODIS: GSW_algm Generalised Split Window (GSW) algorithm for SEVIRI: SEV_algm Offline SEN4LST Split-Window algorithm: S4S_algm ALGORITHM INTERCOMPARISON Radiative Transfer Model Profile Datasets Total Column Water Vapour Emissivity Skin temperature Construction of a Benchmark Database GT_BDB Profiles Data Description of Profiles Simulation of Brightness Temperatures Data Description of GT_BDB Sensitivity Analysis PRESENTATION OF RESULTS Benchmark Database

5 Page: v Sensitivity Analysis RECOMMENDATIONS APPENDICES Appendix A Sensitivity Plots Appendix B Sensitivity Analysis - Response Parameters

6 Page: vi List of Figures Figure 1: Urban locations highlighted in red together with randomly sampled profile locations marked as crosses for the two potential sampling scenarios: any pixel designated as urban (top); 5% urban domination (bottom) Figure 2: Latitudinally banded water vapour profiles for urban land cover from left to right: 50S-30S, 30S-10S, 10S-10N, 10N-30N, 30N-50N; for each test scenario: any pixel designated as urban (top); 5% urban domination (bottom) Figure 3: Total column water vapour (TCWV) extraction for urban land cover for scenario-b (5% biome dominance): original randomly selected profiles (black), profiles extracted from temperature and pressure selection (green), median of random selected profiles (pink), median of profiles extracted from temperature and pressure selection (red) Figure 4: Normalised distributions of δh 2 O(ppmv)/δmb for randomly selected profiles according to the methodology of scenario-b (black) and the conditionally-extracted profiles (red) within five latitude bands: from left to right [-50,-30 ), [-30,-10 ), [-10,10 ], (10,30 ]. Comparisons are made for the upper troposphere (~113mb) (top row), the mid-troposphere (~535mb) (middle row), and the lowermost troposphere (~821mb) (bottom row) Figure 5: Distribution of the difference between the ECMWF ERA-Interim skin temperature and 2m air temperature for all profiles selected in the model fitting of the candidate algorithms Figure 6: Winter reference atmosphere profiles (January) for six latitudinal bands which have been interpolated onto the RTTOV (v10) 51 level pressure grid. Differing colours denote the different gases while symbols represent the latitude bands to which they belong Figure 7: Examples of the high resolution ASTER emissivity spectra used for the Benchmark Data set for BARE_SOIL and MANMADE surface types. The emissivity data within the Benchmark files are convolved from these spectra Figure 8: Water vapour (top-left), pressure (top-right), ozone (bottom-left) and temperature (bottomright) profiles for the tropical (green), mid-latitude (black), polar south (red) and polar north (blue) reference atmospheres used in the sensitivity study Figure 9: Sensitivity to changes in varying surface 11μm emissivities for each LST retrieval algorithm with a nadir (0 ) viewing angle: UL3_algm (top-left); GSW_algm (top-right); SEV_algm (bottom-left); and S4S_algm (bottom-right). Tropical (red), Mid-latitude (green), Polar South (light blue) and Polar North (purple) refer to the reference atmosphere used in each case Figure 10: Sensitivity to changes in varying surface 12μm emissivities for each LST retrieval algorithm with a nadir (0 ) viewing angle: UL3_algm (top-left); GSW_algm (top-right); SEV_algm (bottom-left); and S4S_algm (bottom-right). Tropical (red), Mid-latitude (green), Polar South (light blue) and Polar North (purple) refer to the reference atmosphere used in each case Figure 11: Sensitivity to changes in varying atmospheric water vapour for each LST retrieval algorithm with a nadir (0 ) viewing angle: UL3_algm (top-middle); GSW_algm (top-right); SEV_algm (bottom-left);

7 Page: vii and S4S_algm (bottom-right). Tropical (red), Mid-latitude (green), Polar South (light blue) and Polar North (purple) refer to the reference atmosphere used in each case Figure 12: Sensitivity to changes in varying skin temperature for each LST retrieval algorithm with a nadir (0 ) viewing angle: UL3_algm (top-middle); GSW_algm (top-right); SEV_algm (bottom-left); and S4S_algm (bottom-right). Tropical (red), Mid-latitude (green), Polar South (light blue) and Polar North (purple) refer to the reference atmosphere used in each case Figure 13: Sensitivity to changes in varying 11μm surface emissivity for each LST retrieval algorithm with a near edge-of-swath (20 ) viewing angle Figure 14: Sensitivity to changes in varying 12μm surface emissivity for each LST retrieval algorithm with a near edge-of-swath (20 ) viewing angle Figure 15: Sensitivity to changes in varying water vapour for each LST retrieval algorithm with a near edge-of-swath (20 ) viewing angle Figure 16: Sensitivity to changes in varying skin temperature for each LST retrieval algorithm with a near edge-of-swath (20 ) viewing angle

8 Page: viii List of Tables Table 1: List of applicable documents Table 2: List of reference documents Table 3: File format specification for the GT_BPDB datafiles in netcdf Table 4: File format specification for the GT_BDB datafiles in netcdf Table 5: AATSR 11 and 12μm channel emissivities derived from data provided by the Aster Spectral Library (1999) for green grass, and bare soil. Column N denotes the number of samples used in the mean and standard deviation (Std. Dev) Table 6: Near-surface (2m) air temperatures and skin temperatures corresponding to each reference atmosphere Table 7: Model fitting errors from the generation of the retrieval coefficients for the four candidate algorithms Table 8: Overall statistics from the GT_BDB for LST retrievals from simulations of TOA BTs at nadir (0 ) viewing angle and near edge-of-swath (20 ) viewing angle. The expected bias for the UL3_algm due to emissivity differences is also given in brackets Table 9: Statistics by season from the GT_BDB for LST retrievals from simulations of TOA BTs at nadir (0 ) viewing angle and near edge-of-swath (20 ) viewing angle. The expected bias for the UL3_algm due to emissivity differences is also given in brackets Table 10: Statistics by surface category from the GT_BDB for LST retrievals from simulations of TOA BTs at nadir (0 ) viewing angle and near edge-of-swath (20 ) viewing angle. The expected bias for the UL3_algm due to emissivity differences is also given in brackets Table 11: Statistics by latitudinal atmosphere climatology from the GT_BDB for LST retrievals from simulations of TOA BTs at nadir (0 ) viewing angle and near edge-of-swath (20 ) viewing angle. The expected bias for the UL3_algm due to emissivity differences is also given in brackets Table 12: Response parameters for the nadir (0 ) viewing angle, where the equation y=a+bx has been fitted to the results for changing skin temperature (LST), mean surface emissivity for 11 and 12μm. The equation y=a+bx+cx 2 has been fitted for water vapour. In each case, the parameter A is the bias in the algorithm for zero deviation in the test parameter. Skin temperature response is per 1 K change; emissivity response is per change; water vapour response is per 5% change at each height level. 56 Table 13: Response parameters for the near edge-of-swath (20 ) viewing angle, where the equation y=a+bx has been fitted to the results for changing skin temperature (LST), mean surface emissivity for 11 and 12μm. The equation y=a+bx+cx 2 has been fitted for water vapour. In each case, the parameter A is the bias in the algorithm for zero deviation in the test parameter. Skin temperature response is per 1 K change; emissivity response is per change; water vapour response is per 5% change at each height level

9 Page: 1 0. Executive Summary The ESA DUE GlobTemperature Land Surface Temperature (LST) Climate Data Record (CDR) Algorithm Trade-off Analysis provides the basis from which a proposed CDR for LST can be constructed. It provides justification of the trade-off in the algorithm selection based on the Advanced Along-Track Scanning Radiometer (AATSR) with the requirement to be applicable also for the other ATSR instruments, the upcoming Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel-3; and other instruments such as the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectrometer (MODIS). In this trade-off analysis a number of algorithms have been assessed as potential candidates for the development of the (A)ATSR Climate Data Record: 1. ULeic enhanced ATSR / SLSTR algorithm (UL3_algm) 2. Generalised Split-Window algorithm for MODIS (GSW_algm) 3. Generalised Split-Window algorithm for SEVIRI (SEV_algm) 4. SEN4LST Split-Window algorithm (S4S_algm) The standard ESA ATSR LST algorithm (ESA_algm) was not considered a potential candidate algorithm since studies [RD-1, RD-2] have illustrated the significant improvement in accuracy and precision of the UL3_algm compared to the ESA_algm. Nevertheless, the ESA_V3 LST Product has still been utilised for comparison purposes. The key points from the LST CDR Algorithm Trade-off Analysis are summarised thus: Candidate algorithms were by design considered only where they could be applied to multiple single sensors without the need for modification. The algorithm intercomparison comprised of the following methodology: i. A single radiative transfer model (RTTOV-10.2 has been being utilised for simulating the radiances) utilising consistent profile datasets for coefficient derivation: a. ECMWF ERA-Interim profiles b. CIMSS emissivities c. Uniform random distribution for spatial and temporal profile selection ii. Construction of GlobTemperature Benchmark Database (GT_BDB): a. A set of reference climatological atmospheric profiles for RTTOV gases and temperature categorised by season and latitudinal bands, based on v4 MIPAS reference atmospheres with high vertical resolution profiles (1 km) interpolated onto RTTOV 51-level pressure array

10 Page: 2 b. ASTER spectral library used to create convolved emissivities using instrument spectral response functions c. Simulated brightness temperatures iii. Sensitivity analysis to quantify the understanding of the variation in bias of each algorithm to changes in atmospheric or surface states, and to provide an assessment on the sources of underlying bias in the candidate LST retrievals. From the sensitivity analysis there is no clear optimum solution in terms of variations in bias with respect to perturbations in parameters with different algorithms showing the smallest variation in bias to different parameters. With regards to the underlying biases when no perturbations are applied the lowest values in general are found for the GSW_algm; although all four candidate algorithms show low underlying biases. From the assessment of the GT_BDB, all four candidate algorithm perform well with low median bias, low median absolute deviations and low RMSEs. Of these algorithms overall the performance of GSW_algm and SEV_algm is encouraging for developing a prototype CDR; although the classification-based approach of the UL3_algm candidate algorithm is also able to retrieve LST with a bias and dispersion as low, if not lower in many cases, than the algorithms which deal with emissivity in an explicit way and is also suitable for a prototype CDR. For narrow swath instruments, such as the ATSRs, similar results are found across the swath, with the inference being that zenith viewing angle banded coefficients do not provide a quantifiable improvement on a single set of coefficients. It is clear that no one algorithm outperforms all the others for every scenario. For instance, while each may exhibit a characteristic which is less desirable, refinements identified here are expected to address these. The trade-off here is to determine the most suitable algorithms on a global basis across different atmospheric and surface regimes. As such, the development of an error model is recommended to assess the component uncertainties in retrieved LST, as part of the iterative process to developing a climate quality data set for LST. Given the evidence, the recommendations can be summarised thus: to select all three of GSW_algm, SEV_algm and UL3_algm for an initial processing of a prototype CDR to build the capability to rapidly switch / modify algorithms in the CDR processing chain to assess the uncertainty in emissivity used in the retrieval of LST A final conclusion of this assessment is that the evidence suggests that refined versions of any of the three recommended algorithms would be suitable for a CDR for LST from ATSR.

11 Page: 3 1. Introduction The ESA DUE GlobTemperature Land Surface Temperature (LST) Climate Data Record (CDR) Algorithm Trade-off Analysis provides the basis from which a proposed CDR for LST can be constructed. A key objective of GlobTemperature is to develop the first long term satellite LST data set of climate quality built from multiple satellite instruments. We can define a climate data record is as a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change [RD-3]. An intercalibrated long time series of single-sensor data sets applying a consistent retrieval algorithm and a careful characterisation of uncertainties provides the means for assessment of climate quality. This document will provide justification of the trade-off in the algorithm selection based on all three instruments from the Along-Track Scanning Radiometer series: ATSR-1, ATSR-2, and the Advanced Along-Track Scanning Radiometer (AATSR); the upcoming Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel-3; and other applicable instruments such as the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectrometer (MODIS). The objectives can briefly be defined as: Comparison of existing algorithms for LST retrieval from (A)ATSR instrument series Determine and justify which algorithm can best provide the required characteristics of an LST climate record The document firstly provides a background on LST retrieval theory with an introduction to the current best practices in LST algorithm development (Section 2). The proposed candidate algorithms are then presented (Section 3) taking into account the restrictions enforced by the instrument requirements. The algorithm intercomparison is then detailed and is sub-divided into a description of the radiative transfer model employed in the analysis (Section 4.1), the selection of the profile data sets (Section 4.2), the construction of a Benchmark Database for independent assessment of the LST retrievals (Section 4.3), and a sensitivity analysis for assessing how the algorithm reacts to changes in the atmospheric or surface conditions (Section 4.4) The results of the trade-off analysis are presented in Section 5; and finally, the document concludes with a set of recommendations for GlobTemperature on the application of the most appropriate candidate algorithm for developing a CDR for (A)ATSR. The methodology presented here in this trade-off analysis is adaptable to other algorithm assessments within the framework of GlobTemperature, such as for the merged LST product development and the prototype Sentinel-3 LST development. Furthermore, the development of a CDR for LST from the ATSR series of instruments would provide evidence that LST should be reviewed by the Global Climate Observing System (GCOS) for classification as an Essential Climate Variable (ECV).

12 Page: Applicable documents Table 1: List of applicable documents Reference Number Document Reference [AD-1] GlobTemperature Technical Specification GlobTemp-WP1-DEL Reference documents Table 2: List of reference documents Reference Number [RD-1] [RD-2] [RD-3] [RD-4] [RD-5] [RD-6] [RD-7] [RD-8] Reference Ghent, D., Land Surface Temperature Validation and Algorithm Verification (Report to European Space Agency). 2012(UL-NILU-ESA-LST-VAV). Ghent, D., et al., Advancing the AATSR land surface temperature retrieval with higher resolution auxiliary datasets: Part B validation. in preparation. Committee on Climate Data Records from NOAA Operational Satellites, Climate Data Records from Environmental Satellites: Interim Report. 2004: National Research Council. Prata, F., Land Surface Temperature Measurement from Space: AATSR Algorithm Theoretical Basis Document Remedios, J., Sentinel-3 Optical Products and Algorithm Definition: Land Surface Temperature Sobrino, J.A. and N. Raissouni, Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. International Journal of Remote Sensing, (2): p Prata, A.J., Land-surface temperatures derived from the Advanced Very High- Resolution Radiometer and the Along-Track Scanning Radiometer.1. Theory. Journal of Geophysical Research-Atmospheres, (D9): p Wan, Z. and J. Dozier, A generalized split-window algorithm for retrieving land surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, : p

13 Page: 5 Reference Number [RD-9] [RD-10] [RD-11] [RD-12] [RD-13] [RD-14] [RD-15] [RD-16] Reference Jiménez-Muñoz, J.C., et al., Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. IEEE Transactions on Geoscience and Remote Sensing, (1): p Jimenez-Munoz, J.C. and J.A. Sobrino, A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research-Atmospheres, (D22). Freitas, S.C., et al., Land surface temperature from multiple geostationary satellites. International Journal of Remote Sensing, (9-10): p Baldridge, A.M., et al., The ASTER spectral library version 2.0. Remote Sensing of Environment, (4): p Seemann, S.W., et al., Development of a global infrared land surface emissivity database for application to clear sky sounding retrievals from multispectral satellite radiance measurements. Journal of Applied Meteorology and Climatology, (1): p Wan, Z.M. and Z.L. Li, A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Transactions on Geoscience and Remote Sensing, (4): p Hulley, G., S. Veraverbeke, and S. Hook, Thermal-based techniques for land cover change detection using a new dynamic MODIS multispectral emissivity product (MOD21). Remote Sensing of Environment, (0): p Sobrino, J.A., et al., Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data. International Journal of Remote Sensing, (11): p [RD-17] SEN4LST Teams, SEN4LST Validation Report (SEN4LST_DL4) [RD-18] [RD-19] [RD-20] Rodgers, C., Inverse Methods for Atmospheric Sounding: Theory and Practise, ed. W.S. Publishing. 2000, Singapore. Collard, A., E. Pavelin, and P. Weston, NWP-SAF Met Office 1D-Var Product Specification, s.l. EUMETSAT, Editor Saunders, R. and B. Conway, NWP-SAF 1D-Var Overview, U. EUMETSAT, Editor 2004.

14 Page: 6 Reference Number [RD-21] [RD-22] [RD-23] [RD-24] [RD-25] [RD-26] [RD-27] [RD-28] [RD-29] [RD-30] [RD-31] Reference Ghent, D., et al., Advancing the AATSR land surface temperature retrieval with higher resolution auxiliary datasets: Part A product specification. in preparation. Coll, C., et al., Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote Sensing of Environment, (3): p Noyes, E.J., Technical Assistance for the Validation of AATSR Land Surface Temperature Products, Final Report - February 2006 (Report to European Space Agency). 2006(ESA Contract Number: 19054/05/NL/FF). Dorman, J.L. and P.J. Sellers, A global climatology of albedo, roughness length and stomatal-resistance for atmospheric general-circulation models as represented by the Simple Biosphere model (SiB). Journal of Applied Meteorology, (9): p Randel, D.L., et al., A new global water vapor dataset. Bulletin of the American Meteorological Society, (6): p Arino, O., et al., GlobCover: ESA service for Global land cover from MERIS. Igarss: 2007 Ieee International Geoscience and Remote Sensing Symposium, Vols 1-12: Sensing and Understanding Our Planet. 2007, New York: Ieee Baret, F., et al., GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, : p Dee, D.P., et al., The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, (656): p Soria, G. and J.A. Sobrino, ENVISAT/AATSR derived land surface temperature over a heterogeneous region. Remote Sensing of Environment, (4): p Trigo, I.F., et al., An assessment of remotely sensed land surface temperature. Journal of Geophysical Research-Atmospheres, (D17). Garcia-Haro, F.J. and T.K. Sommer, Simultaneous land surface temperatureemissivity retrieval in the infrared split window. International Journal of Remote Sensing, : p

15 Page: 7 Reference Number [RD-32] [RD-33] [RD-34] [RD-35] [RD-36] [RD-37] [RD-38] Reference STSE, SEN4LST Project, E.S.S.L.p. Galve, J.A., et al., An atmospheric radiosounding database for generating land surface temperature algorithms. IEEE Transactions on Geoscience and Remote Sensing, (5): p Sobrino, J.A., et al., Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, (2): p Embury, O., C.J. Merchant, and M.J. Filipiak, A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: Basis in radiative transfer. Remote Sensing of Environment, : p Merchant, C.J., et al., Toward the elimination of bias in satellite retrievals of sea surface temperature 1. Theory, modeling and interalgorithm comparison. Journal of Geophysical Research-Oceans, (C10): p Zavody, A.M., C.T. Mutlow, and D.T. Llewellynjones, A radiative-transfer model for sea-surface temperature retrieval for the Along-Track Scanning Radiometer. Journal of Geophysical Research-Oceans, (C1): p Hocking, J., et al., RTTOV v10 Users Guide. EUMETSAT Satellite Application Facility on NumericalWeather Prediction. NWOSAF-MO-UD-023. Version [RD-39] Saunders, R., et al., RTTOV-10 Science And Validation Report [RD-40] [RD-41] [RD-42] Clough, S.A., et al., Atmospheric radiative transfer modeling: a summary of the AER codes. Journal of Quantitative Spectroscopy & Radiative Transfer, (2): p Merchant, C.J. and P. Le Borgne, Retrieval of sea surface temperature from space, based on modeling of infrared radiative transfer: Capabilities and limitations. Journal of Atmospheric and Oceanic Technology, (11): p Embury, O., C.J. Merchant, and G.K. Corlett, A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: Initial validation, accounting for skin and diurnal variability effects. Remote Sensing of Environment, : p

16 Page: 8 Reference Number [RD-43] [RD-44] [RD-45] [RD-46] [RD-47] Reference Randall, D.A., et al., Cilmate Models and Their Evaluation, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, S. Solomon, et al., Editors. 2007, Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA. Sherwood, S.C., et al., TROPOSPHERIC WATER VAPOR, CONVECTION, AND CLIMATE. Reviews of Geophysics, Koenig, M. and E. de Coning, The MSG Global Instability Indices Product and Its Use as a Nowcasting Tool. Weather and Forecasting, (1): p Remedios, J.J., et al., MIPAS reference atmospheres and comparisons to V4.61/V4.62 MIPAS level 2 geophysical data sets. Atmospheric Chemistry and Physics Discussions, : p Remedios, J.J., MIPAS equatorial atmospheric data set designed for use with Oxford RFM ( Glossary ADF Auxiliary Data File (A)ATSR (Advanced) Along Track Scanning Radiometer ALB ATSR LST Biome version-2 ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER-GED ASTER Global Emissivity Database AVHRR Advanced Very High Resolution Radiometer BT Brightness Temperature CDR Climate Data Record CF Climate and Forecast Conventions CIMSS Cooperative Institute for Meteorological Satellite Studies DUE Data user Element ECMWF European Centre for Medium Wave Forecasting ECV Essential Climate Variable EO Earth Observation ESA European Space Agency EUMETSAT European Organisation for the Exploitation of Meteorological Satellites FV Fractional Vegetation

17 Page: 9 GCOS Global Climate Observing System GEO Geostationary Earth Orbit GMES Global Monitoring for Environment and Security GOES Geostationary Operational Environmental Satellites GSW Generalised Split Window GT_BDB GlobTemperature Benchmark Database GT_BPDB GlobTemperature Benchmark Profile Database IASI Infrared Atmospheric Sounding Interferometer ILSTE-WG International Land Surface Temperature & Emissivity Working Group IPCC Intergovernmental Panel on Climate Change IST Ice Surface Temperature JPL Jet Propulsion Laboratory LEO Low Earth Orbit LSA-SAF Land Surface Analysis Satellite Application Facility LSE Land Surface Emissivity LST Land Surface Temperature MERIS Medium Resolution Imaging Spectrometer MIR Mid-Infrared MODIS Moderate Resolution Imaging Spectrometer MSG Meteosat Second Generation MTSAT Multi-functional Transport Satellites NASA National Aeronautics and Space Administration NDVI Normalised Difference Vegetation Index NetCDF Network Common Data Format NOAA National Oceanic and Atmospheric Administration NRT Near Real Time NVAP NASA Water Vapour Project OE Optimal Estimation PW Precipitable Water RTTOV Radiative Transfer Model for TOVS satellite SEN4LST Synergistic Use of the Sentinel Missions for Estimating and Monitoring Land Surface Temperature SEVIRI Spinning Enhanced Visible and Infrared Imager SLSTR Sea and Land Surface Temperature Radiometer SST Sea Surface Temperature SW Split Window SZA Satellite Zenith Angle TCWV Total Column Water Vapour

18 Page: 10 TES Temperature and Emissivity Separation TIR Thermal Infrared TISI Temperature Independent Spectral Indices TOA Top-Of-Atmosphere UTC Coordinated Universal Time

19 Page: Current Best Practice: LST Algorithms 2.1. LST Retrieval Theory In the thermal infrared (TIR) region of the spectrum absorption and emission effects mainly due to the presence of water vapour are responsible for attenuation of the surface signal as observed by a satellite radiometer. As such, instruments on Earth Observation satellites designed for retrieval of surface temperature use spectral windows where these effects are minimised and the surface emission signal is higher. Although both the 3.5 to 4.2 μm window and the 10.5 to 12.5 μm window are options, the former is subject to an non-insignificant contribution from the solar signal, and therefore the latter is most commonly used for LST retrieval purposes. This spectral range is referred to in the literature as the split-window region. Even in this region of high transmission, correcting for atmospheric attenuation is still a necessity for accurate LST retrievals. The starting point for any LST algorithm is a theoretical consideration of the thermal radiative transfer equation for monochromatic radiation emitted and reflected from a surface that is assumed homogenous, and received by a spaceborne radiometer; whereby the homogeneous area is defined by the angular field-of-view of the radiometer [RD-4]. The mathematical description of the radiative transfer problem can briefly be detailed from [RD-4] and [RD-5] as follows where the radiance received at the satellite-borne radiometer can be expressed as: ( ) F { (s) surface (s) atmos (s)} d [ ] n s (s s ) sky d [ ( )] ( )d Where is the radiance at the radiometer, is the surface leaving radiance, is the radiance from the atmosphere, is the atmospheric transmittance, is wavenumber, is height, p is pressure, is the filter response function of the radiometer, s is a unit vector defining the view direction, s is a unit vector defining the sun s direction, is the surface temperature, is the surface emissivity, is the Planck function, is the surface reflectance, and is the downwelling sky radiance. f the surface is in thermodynamic equilibrium with the atmosphere, then according to Kirchhoff s law: n s (s) d n s { n s (s s )d } d We assume that the surface is Lambertian. Then and are independent of direction:

20 Page: 12 The flux density of sky radiation is: d d where is the satellite zenith view angle, and is the satellite azimuth view angle. [ ] { } This leads to the definition of surface temperature as sensed by a space-borne infrared radiometer: { ( ) } This definition, which is only strictly true for monochromatic radiation, has the attribute that is directly measurable from space, is valid at any scale, and for a homogeneous surface it is equivalent to the thermodynamic temperature. For sufficiently narrow channels ( 1 µm width) with relatively smooth filter response functions, the variation of the Planck function with wavenumber is small. Thus an integration of the various quantities (,,, etc.) over the filter function is appropriate. Accurate LST retrieval requires algorithms which correct for both atmospheric and emissivity effects. The radiative transfer equation and the direct inversion procedure to extract highlight the principle challenges in LST retrieval in the TIR: i) the ground-level radiance still includes an atmospheric contribution due to the reflection term; ii) the coupling between surface temperature and surface emissivity, which is an ill-posed problem, whereby n sensor bands will generate n equations but n + 1 unknowns. Retrieval of LST requires an a priori knowledge of surface emissivity. Several techniques may be employed to decouple surface emissivity and surface temperature. The most widely used are: the Day/Night method, which require acquisitions twice per day and assumes that day/night emissivity differences are negligible; the Temperature Independent Spectral Indices (TISI) method, which provides relative emissivity values; the Temperature and Emissivity Separation (TES) algorithm, which requires multispectral thermal infrared data (TIR); and methods based on retrieval from vegetation indices, such as the NDVI THresholds Method (NDVI THM ) [RD-6]. The application of a split-window approach to LST retrieval follows early work [RD-7, RD-8] which show it is possible to formulate the surface leaving radiance in terms of a linear combination of radiances reaching the satellite sensor in two adjacent channels.

21 Page: Algorithm Approaches Single-channel Algorithms It is possible to retrieve LST from the brightness at-sensor temperature T i with only one thermal channel. In order to obtain the atmospheric temperature and the transmissivity of the atmosphere at the view angle in the specific spectral band a well-known atmospheric profile of temperature and humidity from meteorological radiosondes is required of the observed area. To counter this restriction of the dependence on atmospheric profiles single-channel algorithms have been developed which only requires the input of atmospheric water vapour content for example [RD-9, RD-10]. As part of Copernicus Global Land Initial Operations Service a mono-channel method that corrects the TOA brightness temperature of a single channel, T TIR1, for atmospheric attenuation and surface emissivity has been used to retrieve LST from the Geostationary Operational Environmental Satellites (GOES) for daytime conditions, and the Multi-functional Transport Satellites (MTSAT) for both day and night. LST is estimated as a linear function of T TIR1 and T MIR (night) or of T TIR1 (day), using regression coefficients estimated for different classes of water vapour in the atmosphere, view angle and land cover types [RD-11] Split-window Algorithms The split-window (SW) method utilizes radiances reaching the sensor in two adjacent channels whose band centres are close in wavelength (most commonly two different spectral bands within the µm spectral region). The method provides an estimate of the surface temperature from two brightness temperature measurements and assumes that the linearity of the relationship results from linearization of the Planck function, and linearity of the variation of atmospheric transmittance with column water vapour amount. The assumption of a linear relation between the surface leaving radiance and the two split-window radiances means the problem is reduced to one of multiple linear regression. The regression coefficients have physical meaning and physical constraints can be utilised to ensure their validity. For retrieval of LST, emissivity can vary significantly with surface cover and type; and the surface and atmosphere must be treated as a coupled system. There are two approaches to solving the problem of determining LST using the split-window method: 1. To decouple the surface effects (emissivity) from the atmospheric effects (water vapour) 2. To solve the problem without taking explicit account of either emissivity or water vapour, but to allow for their effects simultaneously. Both approaches are exploited currently in the range of operational LST products, and are thus described now in more detail.

22 Page: 14 Explicit Emissivity: Generalised Split Window (GSW) algorithm The first approach poses the challenge of determining an accurate estimate of the emissivity. For instruments with multispectral capability in the TIR, such as MODIS, algorithms which decouple surface emissivity and surface temperature can be exploited. For instruments without multispectral thermal infrared capabilities, such as (A)ATSR and AVHRR, we are primarily reduced to approaches based on vegetation indices or enhancements on this by incorporation of land cover classification. Emissivity retrieval from vegetation indices is based on a simple approach between emissivity and Fractional Vegetation Cover constrained by reference values of vegetation and soil emissivities from laboratory measurement, such as the ASTER spectral library [RD-12]. Land cover information can be also used to categorise these emissivity values by land cover types. The final approach is to utilise a third-party emissivity product, such as the ASTER-GED or the CIMSS dataset [RD-13]. The most common form of the generalized split-window algorithm [RD-8] to retrieve LST of clear-sky pixels from BTs as exploited for both MODIS and SEVIRI can be expressed as: ( ) ( ) For MODIS Level-2 LST this algorithm operates with classification-based emissivities. The retrieval coefficients are determined by interpolation on a set of multi-dimensional look-up tables. In addition, a physics-based day/night algorithm [RD-14] is employed to retrieve both surface spectral emissivity and temperature at 5 km resolution. In contrast, a new MODIS LST product (MOD21) being generated by international collaborators at NASA- JPL applies a physics based approach - the Temperature Emissivity Separation (TES) algorithm, which was originally designed for ASTER LST retrievals, but has been adapted to MODIS data to retrieve LST and land surface emissivity (LSE) in MODIS bands 29, 31 and 32 [RD-15]. One rationale for this development is to improve on the retrieval in semi-arid regions. TES attempts to solve the ill-posed LST problem through the use of empirical methods to predict the minimum emissivity observed from a given spectral contrast. This relationship is referred to as the TES calibration curve, and can be adjusted for any sensor's spectral response function in the TIR. The LSA SAF operational LST algorithm applied to SEVIRI data also applies a formulation based on [RD-8] with the additional constraint that retrieval coefficients are also dependent on water vapour and zenith view angle. The coefficients are calculated using a regressive analysis technique. The channel surface emissivity is calculated from an average of bare-ground and fully vegetated emissivities weighted by the fractional vegetation cover derived from a separate LSA SAF retrieval - and by the fraction of water within the pixel. Implicit Emissivity: (A)ATSR / SLSTR algorithms Both the standard ESA (A)ATSR LST algorithm (ESA_algm) [RD-4] and the enhanced ULeic LST algorithm (UL3_algm) for AATSR (UOL_LST_2P) and SLSTR (SL_2_LST) use a nadir-only split-window approach which utilise the cloud-free top-of-the-atmosphere 11 and 12 µm brightness temperatures and ancillary

23 Page: 15 information to correct for water vapour absorption and spectral emissivity effects. The products are generated using a regression relation and look-up tables that accommodate global and seasonal variations in the main perturbing influences with classes of coefficients for each combination of biomediurnal (day/night) condition. Both the fractional vegetation cover and precipitable water are seasonally dependent whereas the biome is invariant. The nature of the algorithm means that land surface emissivity is implicitly dealt with. For the generation of the retrieval coefficients for each biome diurnal (day/night) combination vertical atmospheric profiles of temperature, ozone, and water vapour, surface and near-surface conditions and the surface emissivities are input, in addition to specifying the spectral response functions of the instrument, into a radiative transfer model in order to simulate TOA BTs. Retrieval coefficients are determined by minimizing the l 2 -norm of the model fitting error (ΔLST) Dual Angle Algorithms Atmospheric effects can be accounted for through the combination of the two single channel views as opposed to two adjacent channels. The rationale being that relative optical properties of the atmospheric absorbents remain constant on the assumption that the atmosphere is locally horizontally stratified and stable during the time it takes the satellite to move the distance between the satellite pixel of the forward view and temporally coincident satellite pixel of the nadir view. This assumption means that the atmospheric transmission and emission are treated as varying only with viewing angle. Indeed, [RD-16] showed that the same difference in atmospheric absorption can be obtained by simultaneous measurements at two different wavelengths, and by measurements at the same wavelength but from different angles. They verified that the atmospheric transmittance of the 12 μm channel at nadir view, and the 11μm channel with a view angle of 53, are equivalent as a function of the total water vapour content at nadir. In the SEN4LST project [RD-17] a dual-angle algorithm was considered with the same mathematical structure as that used for the SEN4LST Split-Window algorithm. Here, observations from the same channel (centred at 11μm) are applied at two different angles nadir and forward (~53 ) rather than from two different channels at nadir. This method of atmospheric correction thus uses the differential absorption between two different angles rather than two different channels: ( ) ( ) ( )( ) ( ) where n and f refer to the nadir and forward views respectively, and c 0 to c 6 are the biangular coefficients obtained from simulations. Dual-angle algorithms though suffer problems because of the angular distribution of temperature, the angular distribution of emissivity and the sensitivity to relative co-alignment errors of the two views; the latter is more significant over land than over ocean. Moreover, in the framework of the GlobTemperature potential Dual-Angle algorithms are only appropriate for the development of the

24 Page: 16 Prototype Sentinel-3 LST product. For the CDR development the requirement for any candidate algorithm to be adaptable to multi-sensors invalidates consideration of a Dual-Angle algorithm Optimal Estimation Techniques Optimal Estimation (OE), sometimes referred to as Inverse Theory, is a technique developed to help solve problems which are either over- or under- constrained and there is some degree of uncertainty in the measurements or formulation. It has proved to be of great use in Earth Observation, specifically in resolving multispectral measurements to give multilevel atmospheric profiles. As an example consider an EO satellite which observes TOA BTs at different wavelengths. The measurements are collated into the single observation vector ( ). The objective is to retrieve a profile ( ), containing different atmospheric and/or surface parameters. If the actual state of the atmosphere and surface was known at the time of observation then: where represents the Forward Model which computes the radiative transfer process based on the input profile ( ), and is the combined forward model and measurement error. The maximum probability solution can be found by minimising the cost function, C, as demonstrated by [RD-18], that is: ( ) ( ) ( ) ( ) ( ( )) ( ( )) where x a is an a priori estimate of the true state with a covariance described by the matrix S a and the observation vector has a covariance matrix described by the matrix S y. [RD-18] essentially describes how the maximum probability solution is calculated based on a compromise between the confidence associated with the predicted state and the confidence associated with the observation. There are number of methods of minimising the cost function, the more common use iterative approaches which require some degree of linearization of the forward model. In comparison to SW techniques, OE methods ensure that more consideration of the true physics is considered. Generally this ensures that the retrieved state is more likely to reproduce the observation vector when used as input in a forward model, hence provides more confidence in the result. However, the cost is whether or not the extra computation required is operationally feasible in the context of a global dataset. 1D-Var is an optimal estimation software solution designed and developed by the NWP-SAF. The NWP- SAF provide three 1D-Var packages: a Met Office version; an ECMWF version; and an SSMIS version. The Met Office 1D-Var package is a flexible stand-alone optimal estimation routine suitable for user adaptation [RD-19]. It was initially designed with the primary purpose of retrieving water vapour profiles using data from the IASI and AIRS instruments in conjunction with the RTTOV forward model. It has been constructed such that it can be easily adapted to use a number of different radiative transfer models and satellite instruments [RD-20].

25 Page: Description of Candidate Algorithms Four candidate algorithms are explored in this algorithm trade-off analysis: 1. ULeic enhanced ATSR / SLSTR algorithm (UL3_algm) 2. Generalised Split-Window algorithm for MODIS (GSW_algm) 3. Generalised Split-Window algorithm for SEVIRI (SEV_algm) 4. SEN4LST Split-Window algorithm (S4S _algm) As noted above the SEN4LST Dual-Angle algorithm is not considered a candidate due to the requirement that the selected CDR algorithm should be adaptable to other satellite sensors such as the single-view instruments AVHRR and MODIS. Make note that ESA_V3 will also be tested for comparison. An optimal estimation technique has also not been considered here, in this case because of its sensitivity to atmospheric profiles of temperature and water vapour with their time dependence possibly introducing unwanted biases in the LST record. Although not a candidate algorithm with regards to the findings of [RD-21] and [RD-2], the standard ESA algorithm (ESA_algm) which follows the same approach to the UL3_algm, albeit with the integration of coarse resolution auxiliary datafiles, is also assessed in this trade-off analysis for comparison purposes ULeic algorithm for (A)AATSR: UL3_algm Both the standard ESA (A)ATSR LST algorithm (ESA_algm) [RD-4] and the enhanced ULeic LST algorithm (UL _algm) for AATSR (UOL_LST_2P) and SLSTR (SL_2_LST) use a nadir-only split-window approach with classes of coefficients for each combination of biome-diurnal (day/night) condition. The full form of the algorithm is presented as follows: ( ( ) ) ( ( ) ) ( ( ) )( ) ( ( )) (( ( ) ) ( ( ) )) where the six retrieval coefficients a s,i, a v,i, b s,i, b v,i, c s,i and c v,i are dependent on the biome (i), fractional vegetation cover (f) - the retrieval coefficients a s,i, b s,i and c s,i relate to bare soil (f = 0) conditions, and a v,i, b v,i and c v,i relate to fully vegetated (f = 1) conditions. The fractional vegetation cover (f) and precipitable water (pw) are seasonally dependent whereas the biome (i) is invariant. The retrieval parameters d and m are empirically determined from validation and control the behaviour of the algorithm for each zenith viewing angle (θ) across the nadir swath. The parameter d resolves increases in atmospheric attenuation as the zenith viewing angle increases due to increased water vapour. The parameter m is supported by previous studies [RD-22, RD-23] which suggest a non-linear dependence term on the BT difference T 11 - T 12 would elicit improvement in the accuracy of the LST retrievals. The rationale here is that the BT

26 Page: 18 difference increases with increasing atmospheric water vapour, since attenuation due to water vapour is greater at 12μm than at 11μm. The nature of the algorithm means that land surface emissivity is implicitly dealt with. For the ESA_algm there are 13 land biome classes and one lake class [RD-24] at a spatial resolution of 0.5. The SiB/ISLSCP fractional vegetation cover product [RD-24] and the precipitable water auxiliary data from the NASA Water Vapour Project (NVAP) climatology [RD-25] also have a spatial resolution of 0.5. These latter two are composed of separate global datasets for each month of the year. Validation of the standard AATSR LST product (ESA_V2 following the 2 nd ATSR re-processing) [RD-22, RD- 23] identified deficiencies in the choice of auxiliary data utilised by the retrieval algorithm, which exploits 1 km BTs from the ATSRs to derive LST whereas the auxiliary data is at the much coarser spatial resolution of 0.5 o ; this restricts the capability to make the necessary corrections for land surface emissivity and atmospheric effects. These scale differences cause artefacts in the LST products which often appear as sharp, straight boundaries aligned with lines of latitude and longitude [RD-21]. In some cases large biases and absent values in the retrieved data were attributed to inaccuracies in the auxiliary data; the conclusion being that the resolution of these auxiliary data are not fine enough for their intended purpose. The UL3_algm incorporates higher resolution auxiliary data files (ADFs) [RD-2, RD-21]. The new biome auxiliary data is a variant of the Globcover classification [RD-26], with the original 1/360 spatial resolution product having been re-gridded to 1/120. In addition, the original Globcover bare soil class has been divided into six separate classes, taking the total number of land and inland water classes to 27. To incorporate these changes the Globcover nomenclature has been replaced, with the new classification system known as the ATSR LST Biome version-2 (ALB-2). In addition to the 27 land and inland water classes an additional biome class (ALB-2 class 0) is included for completeness and represents the ocean and large lakes this corresponds to where the AATSR landsea mask identifies a pixel as sea whereby SST is retrieved rather than LST. Inland and coastal water (ALB-2 class 26) represents the remaining water - classified as land by the land-sea mask. Although the biome classification is invariant - being based on the 2006 Globcover product - on any given orbit every pixel including class 0 is assessed for snow and ice cover. Where snow or ice is identified the pixel is reclassified as permanent ice (ALB-2 class 27) and LST is retrieved by applying the associated coefficients. Fractional vegetation cover is taken now from the Geoland-2 FCOVER dataset, which is available globally at the desired near 1-km resolution of 1/112 every 10-days from 1999 and acquired from a moving temporal window of approximately 30-day composites of observations [RD-27]. The values of FCOVER are computed from leaf area index and other canopy structural variables available in the CYCLOPES (Carbon cycle and Change in Land Observational Products from an Ensemble of Satellites) product using a neural network trained from the 1-D radiative transfer models SAIL and PROSPECT. The 10-day AATSR fractional vegetation cover auxiliary dataset is created from the FCOVER dataset with missing values gap-filled from climatology.

27 Page: 19 Precipitable water is derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis [RD-28]. Each auxiliary data file is derived from 6-hourly monthly climatology corresponding to the 4 synoptic times - 00UTC, 06UTC, 12UTC and 18UTC - covering the 10-year period inclusive.. As such, the retrieval has established an updated set of retrieval coefficients to complement the use of higher resolution auxiliary data in the retrieval of LST from the AATSR instrument. Necessarily, this work has also established the requirement to optimise the parameterisation of the retrieval across the nadir swath. For the generation of the retrieval coefficients for each biome diurnal (day/night) combination vertical atmospheric profiles of temperature, ozone, and water vapour, surface and near-surface conditions and the surface emissivities are input, in addition to specifying the spectral response functions of the instrument, into a radiative transfer model in order to simulate TOA BTs. Retrieval coefficients are determined by minimizing the l 2 -norm of the model fitting error (ΔLST). These algorithms do not take advantage of the dual angle capability of AATSR. The rationale being that the disadvantages, such as surface heterogeneity and non-simultaneity of the two views significant over most land surfaces outweigh any potential benefits [RD-4]. Indeed, over homogeneous surfaces [RD-29] have illustrated that a dual angle algorithm may retrieve LST with more accuracy, but performance degrades as the surface becomes increasingly heterogeneous Generalised Split Window (GSW) algorithm for MODIS: GSW_algm The standard operational MODIS LST products MOD11_L2 (Terra) and MYD11_L2 (Aqua) use the generalized split-window algorithm [RD-8] to retrieve LST of clear-sky pixels from BTs in bands 31 and 32: ( ) ( ) whereby this algorithm operates with classification-based emissivities, whereby the surface emissivity is assumed to be known based on an a priori classification of the Earth surface. Adjustments are made for snow, green vs. senescent vegetation, and TIR BRDF. This approach works well for surfaces with stable emissivities and correct classification assignment, but less well where actual emissivities differ from the assigned values primarily arid and semi-arid surfaces. The retrieval coefficients are determined by interpolation on a set of multi-dimensional look-up tables; these are obtained by linear regression of the simulation data generated by radiative transfer over a broad wide range of surface and atmospheric conditions. The look-up tables incorporate several improvements for the Split-Window LST algorithm such as view-angle dependence, water vapour dependence, and atmospheric lower boundary temperature dependence. The SW approach has high accuracy over graybody surfaces, but a lower accuracy over semi-arid regions. In addition, a physics-based day/night algorithm [RD-14] is employed to retrieve both surface spectral emissivity and temperature at 5 km resolution. It achieves this by utilising seven MODIS MIR and TIR bands (20, 22, 23, 29, 31, 32 and 33) to achieve LST LSE separation; based on the concept that LST

28 Page: 20 changes more rapidly than LSE. By exploiting two independent samples of the same target separated temporally this approach was designed to overcome the ill-posed problem of more unknowns than independent equations in thermal retrieval. It is though reliant on certain assumptions: i) a significant difference in temperature between day and night samples; ii) the surface emissivity remains constant between day and night samples; and iii) emissivity angular anisotropy is negligible Generalised Split Window (GSW) algorithm for SEVIRI: SEV_algm The LSA SAF operational LST algorithm applied to SEVIRI data is a SW algorithm also based on the formulation proposed by [RD-8] for AVHRR and MODIS the exact formulation being ( ) ( ) where and are the TOA-BTs for SEV R channels 10.8μm and 12μm respectively, is the mean land surface emissivity of the two channels, and the emissivity difference between the channels (ε 10.8 ε 12.0 ). The retrieval coefficients:, ( ) and are dependent on water vapour and zenith view angle. The algorithm is dependent on a linearization of the TOA BTs with the surface temperature and the controlling factors are the surface emissivity, atmosphere and satellite view-angle. The coefficients are calculated using a regressive analysis technique, whereby simulated and are obtained from radiative transfer simulations performed over a wide variety of atmospheric profiles. When compared with an independent dataset of radiative transfer simulations the algorithm and it s coefficients were found to be bias free, although random errors tended to increase with increasing water vapour content and at high view-angles [RD-30]. For the operational LST product the water vapour database used is that provided by ECMWF ERA-Interim. The channel surface emissivity is calculated from an average of bare-ground and fully vegetated emissivities weighted by the fractional vegetation cover (FV) and by the fraction of water within the pixel. In a similar fashion to the AATSR algorithm, the emissivity extremes are selected from a look-uptable in accordance to the land cover classification. The FV data used is another product produced by the LSA SAF from SEVIRI/Meteosat and corresponds to a 5-day composite updated daily [RD-31]. The input emissivities, are calculated thus: ( ) ( ) where, and are the previously assigned emissivity values for fully vegetated (FV=1) and bare-ground (FV=0), is the emissivity value for water and is the fraction of land within the pixel.

29 Page: Offline SEN4LST Split-Window algorithm: S4S_algm Although the AATSR instrument does not observe the surface with sufficient TIR channels to separate the LST and LSE explicitly, it is still possible to consider retrieval algorithms with an explicit emissivity term. The rationalisation here is the linear equation for deriving channel emissivity ε λ from fractional vegetation cover (f): ( ) Much work has been carried out in the LST community on developing alternative algorithms for LST from (A)ATSR. These include most notably the candidate algorithms developed for the SEN4LST Project [RD-32] amongst others [RD-33]. The SEN4LST SW candidate algorithm takes the form: ( ) ( ) ( )( ) ( ) where W is the atmospheric water vapour content, is the mean land surface emissivity of the two channels, and the emissivity difference between the channels (ε 11 ε 12 ). These surface emissivities are determined using the NDVI Thresholds Method (NDVI THM ) [RD-6, RD-34], which utilises thresholds values to distinguish between soil pixels (NDVI s ) and fully vegetated pixels (NDVI v ). For mixed pixels an average between soil and vegetation emissivities weighted by the fractional vegetation is applied. The estimates of NDVI are expected to be used in synergy with the brightness temperatures; for instance, in the case of AATSR, from a higher spatial resolution synergistic instrument such as the Medium Resolution Imaging Spectrometer (MERIS).

30 Page: Algorithm Intercomparison This section sets out the methodology for determining the most appropriate candidate algorithm to apply to the AATSR L1b data for constructing a CDR Radiative Transfer Model The starting point for an algorithm to retrieve LST should be consideration of the thermal radiative transfer equation for monochromatic radiation emitted and reflected from an assumed homogeneous surface and received by a space-borne radiometer [RD-4]. The generation of LST retrieval coefficients has in general been to define them by radiative transfer modelling rather than through empirical techniques [RD-4, RD-35, RD-36, RD-37] ensuring independence from in situ measurements - an important criterion for the long-term objective of developing a climate-quality dataset. The chosen model for carrying out forward modelling in the framework of the CDR trade-off analysis is RTTOV-10.2 (Radiative Transfer Model for TOVS satellite), which is a fast model for nadir viewing passive infrared and microwave satellite radiometers, spectrometers and interferometers [RD-38, RD-39]. The rationale being that it is capable of accurately determining the atmospheric transmission, and has the capability of fast processing of sufficient numbers of profiles to satisfactorily characterise the range of potential atmospheric states experienced globally. The RTTOV infrared coefficients are determined for an infrared spectral range of 3-20μm governed by the line-by-line dataset LBLRTMv11.1 [RD-40]. It includes both variable gases (water vapour, carbon dioxide, carbon monoxide, methane, nitrous oxide and ozone) and mixed gases in the transmittance calculation. The largest absorber affecting the two split-window channels is water vapour. The effects of the remaining five variable gases on the simulated 11μm and 12μm BTs are negligible, with only carbon dioxide and ozone exhibiting an effect of more than 0.01 K in comparison to water vapour only atmospheres [RD-35]. Utilising the consistent set of profile data (Section 4.2), surface emissivities (Section 4.2.2) and instrument spectral response functions for AATSR, LST retrieval coefficients are generated from regression by minimizing the l 2 -norm of the model fitting error. This consistent set of simulated TOA BTs from radiative transfer modelling is used in the coefficient generation for all four candidate algorithms (Section 3). Care must be taken here of the introduction of bias through the radiative transfer process itself. The upshot would be a global mean bias in the retrieved surface temperature [RD-41]. Two sources of bias are: i) those directly attributable to the radiative transfer model itself this includes inaccuracy in both the instrument response or calibration definition, and the transmittance calculations; and ii) those associated with a distribution of input profiles that is unrepresentative. Section 4.2 describes the approach to minimise the latter source of bias. Previous investigations have been carried out for AATSR channels [RD-41, RD-42] to assess the accuracy and precision of the RTTOV forward model by means of a comparison between simulated and observed TOA radiances. This was done by comparison with respect to the full line-by-line Reference Forward Model ( for a selection of known atmospheric and surface states; bias was shown to be less than 0.1 K. Here no bias correction for this is made; the rationale being that

31 Page: 23 systematic bias due to the forward model is negligible in comparison to potential bias from unrepresentative profiles which is minimised. Furthermore, the requirement for a representative selection of profiles removes the possibility of exploiting a full line-by-line forward model to generate LST retrieval coefficients due to the significantly increased processing costs. Currently aerosol effects are not included in the simulations since the current best-practice in LST retrieval algorithm does not incorporate any potential correction for these impacts; for the next phase of the CDR development such considerations will be addressed Profile Datasets To simulate TOA BTs using RTTOV-10.2 a profile is defined as consisting of the surface state (the skin temperature, surface emissivity and surface pressure), the near-surface state (2m air temperature and humidity) and the atmospheric column (temperature, pressure, humidity, and trace gases). ECMWF ERA-Interim [RD-28] uses 60-level atmospheric model data on a T255 resolution grid providing analysis and forecast data four times each day. To generate the necessary profile data for our simulations ERA- Interim daily and invariant fields are used. To ensure a representative set of profiles over the whole terrestrial surface of the Earth a uniform random sampling distribution is used. Using the new classification system known as the ATSR LST Biome version-2 (ALB-2) as described in Section 3.1.1, clear-sky profile data for each biome class are selected whereby the global distribution of these land cover classes at 1/120 resolution is re-gridded onto the T255 ERA-Interim resolution grid. This is done by taking the most dominant biome at 1/120 grid as the assigned biome for T255. A few biomes though cover less than 0.1% of the surface of the Earth when regridded. In these cases an alternative approach is taken to ensure a sufficiently large geographical sample, whereby classification is based on 5% dominance criteria; the justification for this is detailed in Section Total Column Water Vapour Atmospheric water vapour is an abundant (yet short-lived) greenhouse gas with the largest concentrations observed within the Earth s troposphere. Globally, water vapour column amounts will change with season over different latitudinal bands; however, land mass and surface characteristics plus the amount of ocean surface mean that variability is not uniform throughout the tropics, mid-latitudes and polar regions, but is more regionally specific [RD-43, RD-44]. To train LST coefficients humidity profiles must characterise the less-variable and radiatively-stable stratosphere (>80 mb), the drier concentrations around the tropopause (~100 mb), and the large variation in concentrations in the mid- and lower troposphere (400mb to 800mb). To ensure the spatial sampling across the different surface types of the Earth is sufficient to encompass the range of atmospheres experienced the sampling of water vapour profiles over a single biome (urban) is investigated. The objective of the random sampling strategy is to sample as many atmospheres as possible characteristic of the given biome while concurrently ensuring the coefficients are tuned to the

32 Page: 24 most representative. In the case of the urban biome, two scenarios A and B are investigated for each 20 latitudinal band. Where any land pixel within the ERA-Interim grid cell is classified as urban (scenario-a) a greater range of the land surface (Figure 1) is available to be sampled; this means that almost any profile over Europe for example could potentially be selected resulting possibly in nonrepresentative atmospheres. Where the selection criteria are more stringent (scenario-b) less of the land surface is available to be sampled (Figure 1); however, a sufficient latitudinal spread of locations is still available for selection. Figure 1: Urban locations highlighted in red together with randomly sampled profile locations marked as crosses for the two potential sampling scenarios: any pixel designated as urban (top); 5% urban domination (bottom). The primary difference by sampling water vapour profiles where any land pixel within the ERA-Interim grid cell is classified as urban (scenario-a) is that the profiles cover a broader range, and particularly include many drier profiles in the 30N-50N latitudinal band (Figure 2). The variation of concentrations observed in these middle latitudes are much more representative for scenario-b than they are for

33 Page: 25 scenario-a as a result of the absence of extremely dry concentrations. Other principal differences include the tropical latitude band (10S-10N) being narrower in scenario-b, although the impact on the median profile (red line) is small when compared to scenario-a. For scenario-b, given the concentration variation observed, the maximum mixing ratios reach ~5000ppmv near the surface and decrease to ~100ppmv at 250mb for all latitude bands indicating that the variability observed accounts for the maximum amount of water the atmosphere can hold. The variability exhibited by these sampled profiles is realistically capturing the nature of the tropical atmosphere - as indicated by the lower (and sharper) minimum concentrations that are elevated (in terms of altitude) - in comparison to other bands, due to the higher and colder tropopause in this region. Figure 2: Latitudinally banded water vapour profiles for urban land cover from left to right: 50S-30S, 30S-10S, 10S-10N, 10N-30N, 30N-50N; for each test scenario: any pixel designated as urban (top); 5% urban domination (bottom). To further investigate the validity of scenario-b water vapour profiles are conditionally-extracted based on the variability of the geophysical parameters of atmospheric temperature and pressure (accompanying fields in the ECMWF ERA-Interim dataset). The extraction is supported by two facts: first, that the changes in atmospheric temperature contribute to the quantity of moisture available in the atmosphere; and second, by considering particular pressure levels key regions of the atmosphere can be well represented. In this assessment, the near surface, and the lower, mid and upper troposphere are of key importance allowing characterisation of water vapour within the convective and weathered zone of the atmosphere, as well as the cooler and drier upper tropospheric region before the more statically stable stratosphere is reached. Factors such as horizontal transport of moisture are not considered here. The assessment can be summarised as follows: first, a randomly distributed set of profiles is selected independent of the dataset selected for the LST coefficient fitting. Second, the atmospheric temperature

34 Page: 26 gradients are extracted from these profiles - for each temperature and pressure pair the gradient of temperature is calculated for consecutive pressure levels (ΔT/ΔP) over the 1000mb to 50mb range. The positions of the minimum, maximum and median atmospheric temperature values are extracted over the pressure levels from the surface to 50mb with the corresponding water vapour extracted. The rationale for analysing the minimum and maximum temperatures at each level is that some of the temperature changes at particular atmospheric levels may be too extreme and therefore bias the extracted water vapour to extreme conditions. To test the variance of randomly selected profiles for scenario-b, a comparison is made between these and the conditionally-extracted sets of profiles within five latitude bands [-50,-30 ), [-30,-10 ), [- 10,10 ], (10,30 ], and (30,50 ] (Figure 3). Furthermore, δh 2 O(ppmv)/δmb is calculated at each latitude band and pressure level. The conditionally-extracted water vapour profiles represent a similar distribution to the randomly selected profiles for scenario-b. The normalised distributions for each of the five latitude bands all show high comparability with correlations (Figure 4) greater than 0.76; for latitude bands outside [-10,10 ] the correlations are all greater than The range of variation captured for both the randomly selected set of profiles (scenario-b) and the conditionally-extracted profiles is thus consistent; the implication being that scenario-b is sufficiently capturing the variability in atmospheric water vapour. Figure 3: Total column water vapour (TCWV) extraction for urban land cover for scenario-b (5% biome dominance): original randomly selected profiles (black), profiles extracted from temperature and pressure selection (green), median of random selected profiles (pink), median of profiles extracted from temperature and pressure selection (red).

35 Page: 27 Figure 4: Normalised distributions of δh 2 O(ppmv)/δmb for randomly selected profiles according to the methodology of scenario-b (black) and the conditionally-extracted profiles (red) within five latitude bands: from left to right [-50,-30 ), [-30,-10 ), [-10,10 ], (10,30 ]. Comparisons are made for the upper troposphere (~113mb) (top row), the mid-troposphere (~535mb) (middle row), and the lower-most troposphere (~821mb) (bottom row) Emissivity In addition to the profile data from ECMWF ERA-Interim surface emissivity values are also required for both 11μm and 12μm channels to simulate TOA brightness temperatures. To ensure representative global coverage an established database of emissivities is exploited; the most well-established being the CIMSS global database of land surface emissivity [RD-13]. Previous studies have shown these emissivities when applied to SEVIRI to generate accurate simulations of radiances over land [RD-45]. This dataset delivers emissivity at a spatial resolution of 0.05 at ten hinge-points between 3.6μm and 14.3μm (including at 10.8μm and 12.1μm) which incorporate the shape of the higher resolution emissivity spectra. It uses the emissivity field available from the MOD11 product and applies a baseline fit method to fill in the spectral gaps between the MOD11 emissivities at infrared wavelengths [RD-13]. For the generation of coefficients for GSW_algm, SEV_algm and S4S_algm a simple selection from the CIMSS database of the emissivity corresponding in space and time to given profile from ERA-Interim is performed. For UL3_algm, coefficients are instead required for theoretical states of f = 0 case and f = 1 covering all 27 land biomes, with f being the fractional vegetation cover. Thus, a linear regression is carried out to fit the equivalent CIMSS emissivities for both the 11 μm and 12 μm split-window channels

36 Page: 28 to the fractional vegetation data derived from the Geoland-2 FCOVER dataset [RD-27] to determine equivalent emissivity estimates for bare soil and fully vegetated states for each biome. As argued in Section the rationalisation being the linear equation for deriving channel emissivity ε λ from fractional vegetation cover (f): ( ) This equation forms the basis of the Simplified NDVI THM (SNDVI THM ) method of deriving emissivity [RD- 34]. The linear regression fit is performed using monthly averages over a 24-month window coving 2007 and 2008for from the fractional vegetation cover data and monthly CIMSS data covering the same temporal window. This involves the re-gridding of the monthly fractional vegetation data (originally at 1/112 ), and the re-gridding of the ALB-2 biome data (originally at 1/120 resolution) onto a 1/20 grid equivalent to the spatial resolution of the CIMSS data. Since the linear regression fits are carried out per biome and per channel the emissivity estimates for f = 0 and f = 1 are determined from the corresponding line of best fit Skin temperature Skin temperature is also extracted from the ECMWF ERA-Interim daily fields. The relationship between the skin temperature and the corresponding 2m air temperature from ERA-Interim for all profiles selected in the model fitting of the candidate algorithms is shown in Figure 5. Figure 5: Distribution of the difference between the ECMWF ERA-Interim skin temperature and 2m air temperature for all profiles selected in the model fitting of the candidate algorithms.

37 Page: Construction of a Benchmark Database The GlobTemperature Benchmark Database (GT_BDB) is a standardised reference to facilitate the study of surface skin temperature measurement sensitivity between a range of infrared radiometers and the various LST algorithms. The primary objective of this benchmark data set is to provide a traceable and reliable set of stable reference atmospheric and surface states for forward model simulations for assessment of these algorithms under the same theoretical conditions. The GT_BDB encapsulates two distinct phases: i) the generation of a complete set of profiles for forward model simulations culminating in the GlobTemperature Benchmark Profile Database (GT_BPDB); and ii) the simulation of TOA brightness temperatures using a radiative transfer model (in this case RTTOV- 10.2). Sections to 0 refer to the construction of the first phase, and Sections and detail the final GT_BDB product from the second phase. An important feature of the GT_BDB with respect to the generation of coefficients described in Section 4.2 is that the profile data and surface emissivity input data are completely independent of the forward modelling for the LST algorithm development GT_BDB Profiles Reference Atmospheres The climatological reference profiles included in the GT_BDB are taken from version 4 of the MIPAS reference atmospheres [RD-46]. This data set contains profiles of temperature, pressure and atmospheric trace gases on 120 levels at 1 km vertical intervals from the surface to the top of atmosphere (TOA). This climatological database includes concentrations of 36 atmospheric species (N 2, O 2, C 2 H 2, C 2 H 6, CO 2,O 3, H 2 O, CH 4, N 2 O, CFC-11, CFC-12, CFC-13, CFC-14, CFC-21, HCFC-22, CFC-113, CFC- 114, CFC-115, CH 3 Cl, CCl 4, HCN, NH 3, SF 6, HNO 3, HNO 4, NO, NO 2, SO 2,CO, HOCl, ClO, H 2 O 2, N 2 O 5, OCS, ClONO 2, COF 2.) and is designed to capture both seasonal and latitudinal variations. To this extent climatological profiles are available on four seasons and six latitude bands: South pole (-90 to -65 ) abbreviated as sp South mid-latitudes (-65 to -20 ) abbreviated as smidl South tropics (-20 to 0 ) abbreviated as strops North tropics (0 to 20 ) abbreviated as ntrops North mid-latitudes (20 to 65 ) abbreviated as nmidl North pole (65 to 90 ) abbreviated as np The four seasons are designated by the middle month of the conventional seasons (DJF, MAM, JJA, SON): January (winter); April (spring); July (summer); and October (autumn). The default profiles included within the Benchmark dataset are: CH 4, CO, CO 2, H 2 O, N 2 O, O 3 and temperature, all of which

38 Page: 30 are interpolated onto the relevant pressure grid for each latitudinal band (Figure 6 shows an example for winter). Figure 6: Winter reference atmosphere profiles (January) for six latitudinal bands which have been interpolated onto the RTTOV (v10) 51 level pressure grid. Differing colours denote the different gases while symbols represent the latitude bands to which they belong. From these profiles two further quantities are calculated and stored, total column water vapour (TCWV) and surface skin temperature (Tskin). The TCWV values are calculated directly from the water vapour profiles by integrating the total specific humidity (q) within an atmospheric column and multiplying through by the reciprocal of the gravitational constant (g): The units for TCWV are given in kgm -2. The original reference atmospheres do not include surface temperatures therefore values for Tskin are calculated by multiplying the surface air temperatures (at 0 km) by in order to create a slight thermal contrast such that the difference in surface and nearsurface temperature is near-zero to be consistent with a stable atmospheric scenario (Figure 5 shows the mean difference between surface and near-surface temperature to be near-zero). Emissivity In addition to the atmospheric description, the other attributes that are contained within the files are emissivity measurements for different materials for a series of prescribed biome/surface type. Version 2.0 of the ASTER spectral library ( is used to provide emissivity needed for the