DUE GLOBTEMPERATURE PROJECT Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Date: 25-Aug-16 Organisation: ULeic © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Signatures Name Organisation Emma Dodd ULeic Karen Veal ULeic Darren Ghent ULeic Darren Ghent ULeic Jerome Bruniquel ACRI-ST Approved and authorised by John Remedios ULeic Accepted and authorized for public release by Simon Pinnock ESA Written by Reviewed by Signature © 2016 GlobTemperature Consortium Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: i Ref.: Satellite LST User Handbook GlobT-WP3-DEL-25 Version: 1.0 WP3.4 – DEL-25 Date: 25-Aug-16 Page: ii Distribution Version People and/or organisation Publicly available on website 1.0 ESA and user community Yes Change log Version Comments 1.0 First version © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: iii Table of Contents 0. EXECUTIVE SUMMARY ------------------------------------------------------------------------------------------1 1. INTRODUCTION ---------------------------------------------------------------------------------------------------2 2. DEFINITIONS OF COMMONLY USED TERMS ---------------------------------------------------------------4 2.1. What is Land Surface Temperature? ---------------------------------------------------------------------------------- 4 2.2. LST is not Land Surface Air Temperature or soil temperature. ------------------------------------------------ 4 2.3. What is a brightness temperature? ----------------------------------------------------------------------------------- 5 2.4. What do we mean by LST retrieval? ---------------------------------------------------------------------------------- 5 2.4.1. Split Window (SW) and neural network (NN) algorithms ------------------------------------------------------------------ 5 2.4.2. Single Channel ------------------------------------------------------------------------------------------------------------------------- 6 2.4.3. Temperature and Emissivity Separation (TES) -------------------------------------------------------------------------------- 6 2.4.4. Optimal Estimation (OE) ------------------------------------------------------------------------------------------------------------ 6 2.5. Geostationary or polar orbiting platform? -------------------------------------------------------------------------- 7 2.6. Infrared or Microwave? -------------------------------------------------------------------------------------------------- 9 2.7. Why should I care about all those angles? -------------------------------------------------------------------------- 9 2.8. To which time do the observations correspond (view time)? ------------------------------------------------ 11 2.9. Do I care about the land cover type (biome)? -------------------------------------------------------------------- 12 3. GUIDE TO LST DATASETS ------------------------------------------------------------------------------------- 13 3.1. Product User Guides and Algorithm Theoretical Basis Documents ----------------------------------------- 13 3.2. File formats: how do I get the data out of the file? ------------------------------------------------------------- 13 3.3. Product Levels: L1, L2, L3, L4 ------------------------------------------------------------------------------------------ 13 3.3.1. Other product level terms you may encounter: -----------------------------------------------------------------------------14 3.4. Validation------------------------------------------------------------------------------------------------------------------- 14 3.4.1. Comparison of the LST to in situ data ------------------------------------------------------------------------------------------14 3.4.2. Radiance based validation---------------------------------------------------------------------------------------------------------15 3.4.3. Inter-comparison with other (satellite) LST data ----------------------------------------------------------------------------15 3.4.4. Time series analysis -----------------------------------------------------------------------------------------------------------------15 © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: iv 3.5. Geolocation ---------------------------------------------------------------------------------------------------------------- 15 3.6. Quality flags ---------------------------------------------------------------------------------------------------------------- 16 3.7. Clouds and aerosol ------------------------------------------------------------------------------------------------------- 16 3.8. Why are there gaps in my data? ------------------------------------------------------------------------------------- 17 4. UNCERTAINTY AND ERROR ---------------------------------------------------------------------------------- 18 4.1. Error or uncertainty? ---------------------------------------------------------------------------------------------------- 18 4.2. Types of uncertainty ----------------------------------------------------------------------------------------------------- 18 4.3. How uncertainties are calculated and propagated. ------------------------------------------------------------- 20 5. SPATIAL AND TEMPORAL AVERAGES---------------------------------------------------------------------- 21 5.1. Things to be aware of. -------------------------------------------------------------------------------------------------- 21 5.1.1. Averaging Parameters --------------------------------------------------------------------------------------------------------------21 5.1.2. Averaging methodologies ---------------------------------------------------------------------------------------------------------21 5.1.3. Edges and boundaries --------------------------------------------------------------------------------------------------------------21 5.1.4. Data coverage and sampling uncertainty -------------------------------------------------------------------------------------22 6. MERGED DATASETS -------------------------------------------------------------------------------------------- 23 6.1. What constitutes a merged dataset? ------------------------------------------------------------------------------- 23 6.2. Why merge data?--------------------------------------------------------------------------------------------------------- 23 6.3. Be aware -------------------------------------------------------------------------------------------------------------------- 23 7. HIGH RESOLUTION LST ---------------------------------------------------------------------------------------- 24 7.1. Platforms and sensors--------------------------------------------------------------------------------------------------- 24 7.1.1. Landsat ---------------------------------------------------------------------------------------------------------------------------------24 7.1.2. ASTER -----------------------------------------------------------------------------------------------------------------------------------24 7.2. Applications ---------------------------------------------------------------------------------------------------------------- 24 7.3. Things to be aware of. -------------------------------------------------------------------------------------------------- 24 8. SO HOW DO I DECIDE WHICH DATASET TO USE? ------------------------------------------------------ 26 9. FUTURE LST INSTRUMENTS ---------------------------------------------------------------------------------- 27 9.1. Operational Instruments ----------------------------------------------------------------------------------------------- 27 © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: v 9.1.1. Sentinel 3 Mission -------------------------------------------------------------------------------------------------------------------27 rd 9.1.2. Meteosat 3 Generation ----------------------------------------------------------------------------------------------------------27 9.1.3. MTSAT ----------------------------------------------------------------------------------------------------------------------------------27 9.1.4. The Joint Polar Satellite System (JPSS) -----------------------------------------------------------------------------------------28 9.2. Research Instruments --------------------------------------------------------------------------------------------------- 28 9.2.1. ECOSTRESS -----------------------------------------------------------------------------------------------------------------------------28 9.2.2. HyspIRI ----------------------------------------------------------------------------------------------------------------------------------28 10. APPENDIX A: LST PRODUCTS ------------------------------------------------------------------------------- 30 List of Figures Figure 1: Illustration of solar zenith angle (ϑsn), solar azimuth angle (αsn).satellite zenith angle (ϑst), and satellite azimuth angle (αst), --------------------------------------------------------------------------------------------------10 Figure 2: Example satellite descending orbits (left column) and ascending orbits (right column) over a full day for AATSR as fractions of a day - in local solar time (top row), and in UTC (bottom row). ---------------12 Figure 3: Importance of error sources in climate data on different analysis scales [RD-26]. This concept is applicable to all surface temperature data including LST, IST and SST. --------------------------------------------19 List of Tables Table 1: List of applicable documents --------------------------------------------------------------------------------------- vi Table 2: List of reference documents ---------------------------------------------------------------------------------------- vi Table 3: Characteristics of some LST instruments on-board geostationary satellites [RD-13]. ---------------- 7 Table 4: Local equator crossing times and time to full Earth coverage for some LST instruments on polar orbiting satellites [RD-13]. ------------------------------------------------------------------------------------------------------ 8 © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: vi Applicable documents Applicable documents are available to download from the GlobTemperature website. Table 1: List of applicable documents Reference Number Document Reference AD-1 Definition of a Common Nomenclature for LST GlobT_WP2_DEL-10 AD-2 GlobTemperature Product User Guide GlobTemp_WP3.4_DEL-11 AD-3 GlobTemperature Validation Report GlobTemp_WP4_DEL-12 AD-4 GlobTemperature Intercomparison Report GlobTemp_WP4_DEL-13 Reference documents Table 2: List of reference documents Reference Number Reference [RD-1] Wan, Z., Y. Zhang, Q. Zhang, and Z.-l. Li (2002), Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data, Remote Sensing of Environment, 83(1–2), 163-180, doi: http://dx.doi.org/10.1016/S0034-4257(02)00093-7. [RD-2] Wan, Z., Y. Zhang, Q. Zhang, and Z. L. Li (2004), Quality assessment and validation of the MODIS global land surface temperature, International Journal of Remote Sensing, 25(1), 261-274, doi: 10.1080/0143116031000116417. [RD-3] Yu, Y., J. L. Privette, and A. C. Pinheiro (2008), Evaluation of Split-Window Land Surface Temperature Algorithms for Generating Climate Data Records, IEEE Transactions on Geoscience and Remote Sensing, 46(1), 179-192, doi: 10.1109/TGRS.2007.909097. [RD-4] Norman, J.M. and F. Becker, Terminology in thermal infrared remote sensing of natural surfaces. Agricultural and Forest Meteorology, 1995. 77: p. 153-166. [RD-5] GES DISC, Goddard Earth Sciences Data and Information Services Center http://disc.sci.gsfc.nasa.gov/). [RD-6] Merchant, C.J., et al., The surface temperatures of Earth: steps towards integrated understanding of variability and change. Geosci. Instrum. Method. Data Syst., 2013. 2(2): p. 305-321. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: vii Reference Number Reference [RD-7] Rayner, N. et al., EU Surface Temperature for All Corners of Earth - the EUSTACE project. EGU General Assembly 2015, held 12-17 April, 2015 in Vienna, Austria. id.14475 [RD-8] AMS, American Meteorological Society Glossary of Meteorology [Available online at http://glossary.ametsoc.org/wiki/”term”]. [RD-9] Schaepman-Strub, G., et al., Reflectance quantities in optical remote sensing— definitions and case studies. Remote Sensing of Environment, 2006. 103(1): p. 27-42. [RD-10] Kealy, P. S., and S. J. Hook, Separating temperature and emissivity in thermal infrared multispectral scanner data: implications for recovering land surface temperatures, IEEE Transactions on Geoscience and Remote Sensing, 1993. 31(6), 1155-1164, doi: 10.1109/36.317447. [RD-11] Baldridge, A. M., S. J. Hook, C. I. Grove, and G. Rivera, The ASTER spectral library version 2.0, Remote Sensing of Environment, 2009. 113(4), 711-715, doi: http://dx.doi.org/10.1016/j.rse.2008.11.007. [RD-12] NASA Earth Observatory Catalogue of Earth Orbits http://earthobservatory.nasa.gov/Features/OrbitsCatalog/ [RD-13] WMO OSCAR (Observing Systems Capability Analysis and Review Tool) [Available online at http://www.wmo-sat.info/oscar/instruments]. [RD-14] Kigawa, S.Meteorological Satellite Center Technical Note March 2001, No.39 Overview of MTSAT-1R Imager. [Available online at http://www.data.jma.go.jp/mscweb/technotes/msctechrep39-3.pdf] [RD-15] ESA Sentinel online – Sentinel 3 [Available online at https://sentinel.esa.int/web/sentinel/missions/sentinel-3/satellitedescription/orbit] [RD-16] Draft GCOS definition of LST as an ECV [RD-13] Ma, H., and Q. Liu, The Analysis of the Difference between Infrared Soil Temperature and L Band Effective Soil Temperature, paper presented at MultiPlatform/Multi-Sensor Remote Sensing and Mapping (M2RSM), 2011 International Workshop on, 10-12 Jan. 2011. [RD-18] Vinnikov, K. Y., Y. Yu, M. D. Goldberg, D. Tarpley, P. Romanov, I. Laszlo, and M. Chen, Angular anisotropy of satellite observations of land surface temperature, Geophysical Research Letters, 2012. 39(23), doi: 10.1029/2012GL054059. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: viii Reference Number Reference [RD-19] Zuhlke, M et al., SNAP (Sentinel Application Platform) and the ESA Sentinel 3 Toolbox. Sentinel-3 for Science Workshop, Proceedings of a workshop held 2-5 June, 2015 in Venice, Italy. Edited by L. Ouwehand. ESA SP-734, ISBN 978-929221-298-8., p.21. [RD-20] GHRSST Science Team (2012), The Recommended GHRSST Data Specification (GDS) 2.0,document revision 5, available from the GHRSST International Project Office, 2012, pp 123. [Also available online at https://www.ghrsst.org/documents/q/category/ghrsst-data-processingspecification-gds/] [RD-21] Schneider, P., et al., Land Surface Temperature Validation Protocol (Report to European Space Agency). 2012 (UL-NILU-ESA-LST-LVP). [RD-22] Joint Commitee for Guides in Metrology, Evaluation of Measurement Data Guide to the Expression of Uncertainty in Measurement. 2008. [RD-23] S. Steinke, S. Eikenberg, U. Löhnert, G. Dick, D. Klocke, P. Di Girolamo, and S. Crewell., Assessment of small-scale integrated water vapour variability during HOPE. Atmospheric Chemistry and Physics, 2015. 15, 2675–2692 [RD-24] Veal, K.L., et al., A time series of mean global skin SST anomaly using data from ATSR-2 and AATSR, Remote Sensing of Environment, 2013. 135, 64-76 [RD-25] Przybylak, R., Chapter 2: Atmospheric circulation, in “The Climate of the Arctic”, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2003. Page 21 [RD-26] Merchant, Christopher J. Importance Of Error Sources In Climate Data On Different Analysis Scales. 2015. Accessed 21 Mar. 2016. [Available online at https://figshare.com/articles/Importance_of_error_sources_in_climate_data_on _different_analysis_scales/1483408] [RD-27] Landsat 8 Thermal Infrared Sensor (TIRS) Calibration Notice (August 22, 2013) http://landsat.usgs.gov/calibration_notices.php [RD-28] CEOS Working Group on Calibration and Validation, Land Product Validation Subgroup, http://lpvs.gsfc.nasa.gov/LST_home.html [RD-29] Bulgin, C.E., Sembhi, H., Ghent, D., Remedios, J., & Merchant, C.J. (2014). Cloud Clearing Techniques over Land for Land Surface Temperature Retrieval from the Advanced Along Track Scanning Radiometer. International Journal of Remote Sensing, 35, 3594-3615 © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: ix Glossary (A)ATSR (Advanced) Along Track Scanning Radiometer ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer ATBD Algorithm Theoretical Basis Documents ATMS- Advanced Technology Microwave Sounder AVHRR Advanced Very High Resolution Radiometer BT Brightness Temperature CERES Clouds and the Earth's Radiant System CrIS Cross-track Infrared Sounder DUE- Data user Element ECOSTRESS ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station ETM Enhanced Thematic Mapper ETM+ Enhanced Thematic Mapper Plus FCI Flexible Combined Imager GEO Geostationary Earth Orbit GIS Geographic Information System HyspIRI Hyperspectral Infrared Imager IR InfraRed IST Ice Surface Temperature JPSS Joint Polar Satellite System LEO Low Earth Orbit LSA SAF Land Surface Analysis Satellite Applications Facility LSAT Land Surface Air Temperature LSE Land Surface Emissivity LST Land Surface Temperature MODIS Moderate Resolution Imaging Spectrometer MSG Meteosat Second Generation MTSAT Multifunctional Transport Satellites MW Microwave OCLI Ocean and Land Colour Instrument OE Optimal Estimation OMPS Ozone Mapping and Profiler Suite PUG Product User Guide PHyTIR Prototype HyspIRI Thermal Infrared Radiometer SEVIRI Spinning Enhanced Visible and Infrared Imager SLSTR Sea and Land Surface Temperature Radiometer SNPP Suomi National Polar-orbiting Partnership SSM/I Special Sensor Microwave / Imager © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 SST Sea Surface Temperature SW Split Window TES Temperature and Emissivity Separation TIR Thermal Infrared TIRS Thermal Infra-Red Sensor TM Thematic Mapper VIIRS Visible/Infrared Imager Radiometer Suite © 2016 GlobTemperature Consortium Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: x Ref.: Satellite LST User Handbook GlobT-WP3-DEL-25 Version: 1.0 WP3.4 – DEL-25 Date: 25-Aug-16 Page: 1 0. Executive Summary LST data products are now of some maturity with some data records having global coverage extending back to the mid-1990s with estimated LST precision better than 1.0 K [RD-1,RD-2,RD-3]. Whilst there has been good uptake amongst many users, there is still a perception amongst some users that satellite data products are complex and difficult to use. The LST Handbook was conceived as a source of information, which would enable new and novice users of LST data to understand the essential facts about products, enabling them to make better and easier selections of data for their application. The Handbook essentially supplies information needed to be ‘up-and-running’ quickly whilst ensuring users have sufficient information to avoid inadvertent misuse of the data. The handbook is not meant to be an in-depth reference but will equip the user with sufficient information to aid ready comprehension of more technical documents such as Product User Guides (PUGs) and Algorithm Theoretical Basis Documents (ATBDs). Sources of further information are signposted where useful. The LST Handbook is set out as a series of frequently asked questions and answers and, where helpful, points are illustrated with common examples. There are nine sections and an appendix. Following an introduction (Section 1), the second section deals with some physical nomenclature and explains some common technical terms. Section 3 describes LST products, the information they contain and gives advice on interpreting quality flags. LST uncertainties and guidance on their use are considered in the fourth section. The methods used to produce averaged products and pitfalls to avoid when using them are discussed in Section 5. The sixth section gives a brief overview of products created using data from more than one instrument. Section 7 deals with the special case of high resolution (pixel sizes of less than 100 m) LST. Section 8 discusses the key points to consider when deciding which data to use; and the last section lists the planned future missions that will generate LST data. An appendix contains a list of some currently available products and where to obtain them. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 2 1. Introduction New to LST? Not sure where to start? Then read on. Set out below are the answers to questions often asked by new and novice users of satellite LST data. First a short introduction: LST is a measure of the radiative temperature of the Earth’s surface, equivalent to the kinetic temperature of the skin, and as such is a major determinant of the partition of surface heat flux into latent and sensible fluxes. It is distinct from the Land Surface Air Temperature (LSAT), which is the temperature of the near-surface air usually measured by in situ weather stations. Satellite products provide far greater spatial coverage than in situ measurements, which are particularly sparse in certain geographical regions. The satellites that provide data for LST are in either sun-synchronous polar orbits or geostationary orbits. Polar orbiting satellites provide near global coverage from twice daily to once every few days depending on swath width and cloud clover, whilst geostationary satellites provide near-continent sized regional coverage every 15-60 minutes depending on observation cycling, cloud cover and spatial extent (latitude/longitude disk). Multi-decadal length records are available with existing datasets extending back to the mid-1990s and work is on-going to extend records back to the mid-1980s. LST is increasingly being used in diverse applications, for example: drought monitoring and crop management, hydrological monitoring and water management, evapotranspiration and landatmosphere feedbacks, landcover change, climate modelling and data assimilation, numerical weather prediction, forecasting and reanalysis, permafrost monitoring, and urban temperatures. This handbook provides the basic information to help you choose the best LST product for your application, to obtain the data and, having got it, to easily make best use of the LST data. Advice is also given on using the LST uncertainties and the quality flags that accompany the data. In addition, the information contained within this handbook will help in deciphering the user guide for your chosen product. Want more? Then helpful sources of more detailed information are sign-posted throughout. In this handbook you will find: Commonly encountered technical terms, which are defined and briefly explained. Information on the kinds of LST products that are available and the information they contain which are described briefly with advice given on how to use the various auxiliary data supplied. Explanation of LST uncertainty terms along with guidance on how to use the uncertainties. An account of the spatial and temporal averaging methods used in creating averaged data products is given along with the things you should know before using them. Information about products made by merging data from more than one satellite instrument. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 3 Some future missions that will provide new data products and/or a continuance of current LST data products are listed. An Appendix which gives a list of current LST products (not exhaustive) with data access information; “current” refers to the date of issue of this document. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 4 2. Definitions of Commonly Used Terms 2.1. What is Land Surface Temperature? The Land Surface Temperature (LST) is a measure of how hot or cold the “surface” of the Earth would feel to the touch. It is the mean radiative temperature of all objects comprising the surface, as measured by ground-based, airborne, and spaceborne remote sensing instruments. A more formal definition can be found in [RD-4] or [RD-28]. The constituents of the Earth’s surface (e.g. soil, vegetation, water, snow) vary from location to location. For each constituent, the LST is related to the points of maximum emission of electromagnetic radiation. Bare soil and thick forest canopy represent clear cases. For bare soil and water, the skin depth is the important factor. The skin is defined as a layer of thickness equal to the penetration depth of the electromagnetic radiation [RD-4] and varies with the wavelength of the radiation and the nature of the material. The skin depth for a material is different at different wavelengths and also varies with surface conditions (such as degree of soil wetness, roughness) and view angle. At infrared (IR) wavelengths the soil skin depth is a few microns and at microwave (MW) wavelengths the soil skin depth is a few millimetres. For a dense forest with a closed canopy the skin temperature will be that of the forest canopy and is close to that of the air temperature at the top of the canopy. For more open vegetation the skin temperature will be an aggregation of all surface types within the field of view: soil and/or bare rock, vegetation and, if present, water or snow. The LST determines the amount of energy emitted by the Earth’s surface and is therefore a major factor in determining heat and water fluxes from the Earth’s surface to the atmosphere. 2.2. LST is not Land Surface Air Temperature or soil temperature. LSAT is a measurement of the average kinetic energy of the air near the surface of the Earth. This is usually measured at 2m height at meteorological stations [RD-2, RD-3]. Investigations into the relationship between measurements of near-surface air temperature made by in situ instruments and satellite estimates of surface skin temperature are being performed by, among others, the EUSTACE project [RD-7]. The goal of the EUSTACE project is to produce a reanalysis of near surface air temperature over all surfaces by combining information estimated from satellite instruments and in situ air temperature measurements using statistical in-filling methods. Soil temperature is the sub-surface temperature measured at a given depth [RD-8] usually with a buried thermometer or thermistor. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 5 2.3. What is a brightness temperature? Brightness Temperature (BT) is a measure of the calibrated radiance detected by the satellite from the top of the Earth’s atmosphere, or by a ground-based instrument, expressed as an equivalent blackbody temperature. IR and MW radiances are often, but not always, given as BTs in Level 1 products. Brightness temperatures are converted from the more fundamental term, radiance, which is the radiative energy flux received by a satellite detector per unit solid angle (SI unit, W sr-1 m-2). The spectral radiance is the radiant flux per unit wavelength per solid angle [RD-9] so that the radiance is then the spectral radiance integrated over wavelength. More simply, an instrument measures the number/energy of photons represented as counts on a detector. Raw counts are subsequently converted into calibrated radiances, which are transformed to calibrated BTs. 2.4. What do we mean by LST retrieval? The LST is estimated (retrieved) from radiances measured in different wavelength bands (channels), in the case of a satellite instrument, at the top of the atmosphere. A retrieval method must account for both attenuation of surface emitted radiation by the atmosphere and radiation emitted by the atmosphere towards the instrument. The atmosphere component can be estimated by inputting information on the atmosphere from elsewhere or by fitting statistical relationships to the well-known variation of brightness temperature with variations of water vapour and atmosphere temperature. Even if the atmospheric state is known, the LST problem is under-determined because the surface radiance at a particular wavelength depends on both the surface emissivity at that wavelength and the LST so that there is always one more unknown than there are channel radiances: emissivity for each channel plus the LST. For a more in-depth discussion of the physical principles of LST retrieval see the GlobTemperature technical note “Definition of a Common Nomenclature for LST” [AD-1]. 2.4.1. Split Window (SW) and neural network (NN) algorithms The most common and accurate algorithsm are split-window. Split-window algorithms correct for atmospheric effects using the differential absorption in two (or more) IR bands within the same atmospheric window (band of relatively high atmospheric transmittance). The LST is estimated as a combination of calculated coefficients and observed BTs. The coefficients are derived by regressing BTs, simulated with a radiative transfer model for a realistic range of atmospheric conditions, against the model input skin temperatures. Separate coefficient sets may be used for different ranges of surface and atmospheric conditions. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 6 Neural network algorithms similarly employ multiple channels and are statistical in nature. They have been typically applied in analyses of microwave data. Some retrieval algorithms handle the surface emissivity implicitly; in other algorithms the emissivity is explicit and derived either from land cover classifications or from vegetation indices. Information on vegetation is usually based on products from other satellite instruments. 2.4.2. Single Channel In single-channel algorithms, the LST retrieval can only be resolved if all other parameters are specified. These methods therefore rely entirely on external knowledge of the atmospheric state in addition to external information of the surface emissivity. These are provided by auxiliary input data. As such, single-channel algorithms are much more dependent on these auxiliary input data than split-channel algorithms. A consequence of this is an increase in the uncertainty budget, particularly for more moist atmospheres. Instruments such as GOES, Meteosat and Landsat require single channel retrievals. 2.4.3. Temperature and Emissivity Separation (TES) Ideally algorithms would derive emissivity at the same time as LST to increase the quality of the LST. In the TES algorithm, an empirical relationship is used to predict the minimum emissivity that would be observed from a given spectral contrast, or minimum-maximum difference [RD-10]. The empirical relationship is referred to as the calibration curve and is derived from a subset of laboratory spectra such as the ASTER spectral library [RD-11]. The TES algorithm has been applied to ASTER data and is being used to derive MODIS Collection 6 MOD21 LST data. 2.4.4. Optimal Estimation (OE) Optimal estimation algorithms are more complex but may become more popular in future. Essentially in OE, estimates of the LST and other parameters (surface and atmosphere) are iterated by using a radiative transfer model to calculate brightness temperatures and match them to observed brightness temperatures. The parameters which are retrieved, such as LST, are known as the state vector. Iterative procedures are used to minimise a cost function, essentially balancing the uncertainty in the predicted state with the uncertainty in the observed state. The retrieved solutions are constrained by a priori estimates of their initial values along with uncertainty estimates. Successful OE can produce a better representation of the true physics and the retrieved state will be closer to the observed radiances or BTs (the observation vector). OE also outputs uncertainty information. The computational cost of OE can be greater than for coefficient approaches, and may still be too slow to be feasible for global operational products. OE also requires very good performance in radiative transfer models and in knowledge of the likely atmospheric temperature, water vapour and clearness of the sky. Schemes are beginning to be tested for some instruments including ASTER whose five infrared channels provide the greatest opportunity to co-retrieve LST and emissivity. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 7 2.5. Geostationary or polar orbiting platform? Geostationary earth orbit (GEO): satellites in geostationary orbit circle the Earth above the equator at the same angular speed as the Earth thus maintaining a near fixed view of the Earth’s surface [RD-12]. The advantage of a geostationary orbit is that instruments on this type of platform can observe the same locations on the Earth’s surface near continually. As an example, the SEVIRI instrument scans a wide field of view that includes Africa, South America and the Atlantic Ocean every 15 minutes. The high frequency of observations allows resolution of the diurnal temperature cycle. The high frequency of observations must be weighed against the coarser spatial resolution (around 3-5 km compared with around 1 km for polar orbiters), the regional coverage and the high satellite zenith angles at the edge of the viewed disc. Each location on the Earth’s surface is viewed by a GEO instrument at an almost fixed satellite zenith and satellite azimuth angle throughout the day and year. Table 3: Characteristics of some LST instruments on-board geostationary satellites [RD-13]. Instrument (platform) Nadir longitude coordinate Land masses covered Scan frequency Scan duration, direction SEVIRI (MSG) 0° Africa, Europe, S. America 15 minutes 12 minutes, E-W continuous, S-N stepping GOES-East 75 ° W E. Australia, N. and S. America 30 minutes 26 minutes, E-W continuous, N-S stepping IMAGER (GOESWest) 137 ° W N. and S. America, S. Europe, Africa 30 minutes 26 minutes, E-W continuous, N-S stepping Himawari-7 (MTSAT-2) 145 ° E SE Asia, Australia 1 hour 20-24 minutes, N-S stepping 10 minutes 10 minutes, N-S stepping [RD-14] Himawari-8 140.7 ° E Polar orbit or Low Earth Orbit (LEO): Polar orbiting satellites pass around the Earth, passing over or close to each pole in turn and passing alternately from daytime to night-time. Sun-synchronous orbits are polar orbits where the orbit precesses at the same rate as the Earth revolves around the Sun and have the property that the satellite crosses a given latitude at the same local solar time on each orbit. Most polar orbiting earth observation satellites are in sun-synchronous orbits. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 8 Instruments on polar orbiting satellites obtain coverage of the whole of the Earth’s surface from twice daily to a few days depending on instrument swath width. The precession of a sun-synchronous orbit depends on the orbit being inclined to the true polar orbit (one where the orbital plane is at 90° to the equatorial plane). This inclination can result in instruments with narrow swaths failing to observe the poles. The majority of these instruments have spatial resolutions of approximately 1 km or higher. Each view of a location may occur with a different satellite zenith and azimuth angle than other views of the same location. This can result in varying LST data across the swath even for a homogeneous scene and is important for instruments with high swath such as MODIS and SLSTR. An example of an LST instrument on board a polar-orbiting satellite is SLSTR on Sentinel 3A. Sentinel 3A passes over the equator from North to South in the morning, at 10:00 (descending) local solar time and from South to North in the evening, at 22:00 local solar time. SLSTR obtains full coverage of the Earth (except very close to the poles) everyday [RD-15]. Table 4: Local equator crossing times and time to full Earth coverage for some LST instruments on polar orbiting satellites [RD-13]. Instrument (platform) Descending node Ascending node Time crosses equator from north to south Time crosses equator from south to north ATSR-1 (ERS-1) 10:30 22:30 3 days ATSR-2 (ERS-2) 10:30 22:30 3 days AATSR (Envisat) 10:00 22:00 3 days SLSTR (Sentinel 3A) 10:00 22:00 1 day MODIS (Terra) 10:30 22:30 Twice daily MODIS (Aqua) 01:30 13:30 Twice daily AMSR-E (Aqua) 01:30 13:30 1 day AMSR-2 (GCOM-W1) 01:30 13:30 1 day VIIRS (SNPP) 01:25 13:25 Twice daily SSM/I (DMSP F*) Various early morning, 1 drifting with time Various afternoon, drifting with time Twice daily 1 Full earth coverage in The DMSP satellite orbits are not actively controlled so that the overpass time drifts during the mission. There is a nice plot of the overpass times on the Remote Sensing Systems website. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 9 2.6. Infrared or Microwave? LST data derived from IR measurements usually use two wave bands: one at around 10.8 m and one at around 12 m. However, retrieval algorithms exist that use only one band and more than two bands (with lower accuracy). The bands lie in so-called atmospheric windows – where water vapour absorption is low. Infrared soil penetration is of the order of a few microns and is said represent the surface skin temperature to a depth of around 50 µm [RD-16]. For densely vegetated regions the IR LST is the skin temperature of the vegetation canopy. LSTs cannot be retrieved through cloud from IR radiometers because IR radiation is absorbed by clouds. Aerosol also affects IR wavelengths and will prevent accurate LST retrieval. Aerosol impacted retrievals are flagged in a number of products or have an aerosol product available for the same instrument. The spatial resolution of IR products is high typically a few km for geostationary satellite instruments and 1 km or less for instruments on polar orbiting satellites. MW data products provide all-sky retrievals (i.e. in both clear and cloudy scenes). The spatial resolution of products tends to be closer to 12 km but with decreasing influences from locations up to 30 km away from the footprint centre. The soil penetration depth depends on MW frequency and soil moisture: penetration can be up to tens of centimetres in very dry soils but only a few centimetres in moist soils. MW penetration of ice with low water content may reach around 1m [RD-17]. In consequence, MW LSTs may refer to a skin depth of from a few centimetres to a metre depending on substrate. Microwaves are attenuated by high convective activity and precipitation. In addition, microwave measurements may suffer from radio frequency interference RFI. LST retrievals at IR wavelengths are expected to be more accurate than MW retrievals due to the smaller variation of surface emissivity in the IR, their independence of measurements from other temperature datasets and the stronger dependence of radiance on temperature in the IR region. However, they are more sensitive to the presence of small amounts of cloud. To summarise, microwave instruments provide “all sky” LST. They are not affected by non-precipitating clouds and so have superior data coverage to infrared instruments away from coastlines. However, infrared instruments have higher spatial resolution and in general have lower LST uncertainties. 2.7. Why should I care about all those angles? Satellite and solar, azimuth and zenith angles are provided with satellite data. These angles, defined below, give the user information about the angle from which the satellite views the surface and the relative position of the sun. LST varies with radiometer view angle and local Sun position, by up to 2-4 K [RD-18]. The satellite zenith angle (sometimes referred to as view-angle) increases from the centre to the edge of the swath. Changing the satellite zenith angle changes what the instrument measures, imagine looking at a tree from different angles: from nadir you will see canopy only, from wider angles you will see parts of the trunk. In addition, the emissivities of bare soil and water decrease with increasing satellite zenith angle; this is not the case for vegetation. Angular anisotropy due to emissivity is an issue for daytime and night- © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 10 time LST. During the day incoming solar radiation, inhomogeneity of evaporation and shadowing cause angular dependency of LST. Confidence in the LST is greatest at low satellite and low solar zenith angles. The satellite azimuth angle will give the sign of the satellite zenith angle (zenith angles are sometimes given without a sign). Satellite azimuth angles are usually given as degrees clockwise from north so that directions to the west (east) of north are negative (positive). Thus, for positive satellite azimuth angles the satellite is viewing from the East and the pixel is on the West side of the swath. For negative satellite azimuth angles the reverse is true. Figure 1: Illustration of solar zenith angle (ϑsn), solar azimuth angle (αsn).satellite zenith angle (ϑst), and satellite azimuth angle (αst), Solar zenith angle: The angle between a straight line from a point on the earth's surface to the sun and a line from the same point on the earth's surface that is perpendicular to the earth's surface at that point (zenith). Solar Azimuth angle: The length of the arc of the horizon (in degrees) intercepted between a reference direction (usually North) and the direction of the sun as seen from the observation point (pixel) measured clockwise from the reference direction [RD-4]. Satellite zenith angle: The angle between a straight line from a point on the earth's surface to the satellite and a line from the same point on the earth's surface that is perpendicular to the earth's surface at that point (zenith) Satellite azimuth angle: The length of the arc of the horizon (in degrees) intercepted between a reference direction (usually North) and the direction (view) of the satellite from the observation point (pixel) measured clockwise from the reference direction [RD-4]. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 11 These angles are illustrated in Figure 1. The satellite is observing a point on the edge of the swath under daytime conditions with the sun to the south-east of the observation point (i.e. the pixel of interest on a satellite image). The zenith is perpendicular to the Earth’s surface at the location which is being observed and is one reference direction. The solar and satellite zenith angles are then easily defined. The reference direction for the azimuth angles in this figure is North in direction. Since the sun is to the south-east when viewed from the location of the observed point, the solar azimuth angle is greater than 90 degrees. Similarly the satellite is to the North East/East of the observed pixel and so the satellite azimuth angle is less than 90 degrees (i.e. the satellite azimuth direction is the direction a person is looking if they are standing on the observation point and looking towards the satellite). 2.8. To which time do the observations correspond (view time)? A measurement time is given for each pixel, usually given in UTC. In Level 2 products, the time is sometimes given as a delta t to be added to the nominal time of the file. Be aware that even daily Level 3 products may contain LSTs at different times depending on the time over which the data are collated; averaged or maximum values within the collation time may be used for each gridpoint. Currently, no account is usually taken of viewing angles in this process. Whereas air temperature reanalyses usually present a grid of temperatures at the same UTC time, polarorbiting, sun-synchronous satellite LSTs will contain temperatures with times ‘close’ to a common local solar time. The actual local time will depend on the position of the pixel on the original orbit swath: pixels that are closer to the equator will have observation times closer to the nominal overpass time, the local time of observations will also vary across the swath (because of the variation of local time with longitude and, if the instrument scans across the swath, the different scan time). For geostationary satellites, the pixel observation time will be close to a common UTC time but will also depend on the instrument scan duration and pattern. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 12 Figure 2: Example satellite descending orbits (left column) and ascending orbits (right column) over a full day for AATSR as fractions of a day - in local solar time (top row), and in UTC (bottom row). 2.9. Do I care about the land cover type (biome)? The surface of the Earth is obviously described by a set of discrete land cover types one of which can be assigned to each location on the Earth. Locations do sometimes display dynamic change of biomes through the seasons or quite heterogeneous complexity. Nonetheless, land cover typing is improving rapidly as more visible and SAR sensors come on-stream; LST imagery may also support if at high enough spatial resolution relative to cover type changes. There are two reasons why land cover type or biome is important for LST data sets. First of all, in the real world LST and emissivity both depend on land cover as well as other factors. Hence one would not expect two adjacent pixels with different types to have the same LST. This is important for analysis of LST data. Secondly, LST uncertainties will vary with land cover type due to differing uncertainties in coefficients. There may also be uncertainties or errors in land cover type assignment which will result in corresponding effects in the LST data themselves. The term biome refers to a generic type of vegetation, a biome set being used to constitute a land cover classification for all biologically-controlled points [AD-1]. For some LST retrieval schemes, coefficients may be categorised by land cover types, including biome, and therefore consideration of the biome is relevant in any interpretation of the LST data. While the biome categorises the generic type of land cover, the fraction of a specified area that is covered by green vegetation is also relevant to LST science. This is defined as the Fractional Vegetation Cover (FVC) and is the ratio of the vertically projected area of vegetation on the ground to the total vegetation area [AD-1]. For LST retrievals, this parameter is often used to infer emissivity given emissivities for the fully vegetated or low vegetation states. At the fine scale, in areas of mixed land use, mixed vegetation, or high topography the composition of the land cover affects the amount of sunlit / shadow area within a satellite pixel FOV. This consideration is related to the viewing geometry of the satellite instrument. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 13 3. Guide to LST Datasets 3.1. Product User Guides and Algorithm Theoretical Basis Documents Satellite data products are usually accompanied by a Product User Guide (PUG) [e.g. AD-2] which provides users with information about the product format including the product structure, file naming conventions, metadata definitions and quality control information. For example, the GlobTemperature project maintains one on its website. Details of the algorithms used in creation of the products can be found in the product’s Algorithm Theoretical Basis Document (ATBD). Typically, an ATBD describes the theoretical basis, both the physical theory and the mathematical procedures including assumptions made, for the calculations that are used to convert the radiances received by the instruments to geophysical quantities. 3.2. File formats: how do I get the data out of the file? Most data will come in either (netCDF) or Hierarchical Data Format (HDF). netCDF files commonly have the extension ‘.nc’ and HDF files usually have an extension ‘.hdf’. The file contents of a netCDF file can quickly be viewed on Linux machines using the ncdump utility. Likewise, the linux utility hdp can be used to view the contents of HDF files. Libraries of routines for manipulating netCDF and HDF files exist in the commonly used languages. The ESA Sentinel toolboxes provide tools for visualization and processing of Sentinel data and can also be used to open most netCDF or HDF files [RD-19]. Further information and software downloads can be found on the ESA Science Toolbox Exploitation Platform (STEP) web pages. All GlobTemperature produced data is made available in netCDF. The datafiles adhere to a harmonised format [AD-2] to enable users to easily work with multiple data streams. Some common GIS packages do not have the capacity to work with netCDF or HDF datafiles. The GlobTemperature Data Portal therefore provides the facility to access LST data that is transformed into GIS-compatible GeoTIFF format. 3.3. Product Levels: L1, L2, L3, L4 Satellite products are described by level of processing from raw instrument counts at Level 0 to Level 4 gap-free, global grids of a geophysical variable. Level 1 (L1b): radiometrically calibrated and geometrically corrected radiances or brightness temperatures presented on the orbit swath at native resolution and geolocated to latitude and longitude of centres (and/or corners) of pixels or to tie-points. Should the geolocation information be © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 14 given at tie-points only, then the user is required to perform an interpolation in order to geolocate pixels in between the tie-points. Level 2 (L2): geophysical variables, e.g. LST, derived from L1 data at same resolution as L1 data i.e. not spatially or temporally manipulated. Geolocation information may be included with the L2 product, but not necessarily at full resolution, or in other cases the user must source the geolocation information from a L1 product. Level 3 (L3) : geophysical product that has been temporally or spatially manipulated and in a gridded map projection format e.g. daily LST on a 0.05° longitude by 0.05° latitude global grid. Level 4 (L4): results of analysis of lower level products usually forming a gap-free product. 3.3.1. Other product level terms you may encounter: Level 2 pre-processed (L2P): the original L2 data in native resolution with additional fields (confidence flags, uncertainty information) as determined in some standard, for example the GHRSST Data Specification for Sea surface Temperature[RD-20]. Level 3 uncollated (L3U): L2 data regridded to a spatial grid without combining data from different orbits. Level 3 collated (L3C): L2 data from a single instrument regridded to a space-time grid, data from several orbits may be combined. Level 3 super-collated (L3S): L2 data from multiple instruments combined in a space-time grid See Section 5 and Section 6 below for more on spatio-temporal averaging and merged datasets. 3.4. Validation Validation involves comparison of a test dataset to a reference dataset. Validation results are informative on product quality. The best sources of validation information are literature references in the PUG for example. GlobTemperature will publish validation against in situ data and satellite product intercomparisons on its website. There are four principal methods of validating satellite LSTs, they are outlined below. The first two methods are typically applied to Level 2 LSTs, the second two are chiefly applied to level 3 LSTs. 3.4.1. Comparison of the LST to in situ data The first method utilises in situ radiometer measurements made at various in situ sites around the globe. The in situ and satellite measurements are matched within a temporal and spatial window. Statistical analysis is then performed on satellite minus in situ differences. It must be remembered that © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 15 uncertainties in the in situ data, the satellite data and the matchup process will all contribute to the statistics. 3.4.2. Radiance based validation In the second method, radiances are simulated with a radiative transfer model. The LST input to the model is then perturbed until the radiances match the satellite observed radiances. The LST retrieval error is then the difference between the perturbed model input LST and the LST retrieved from the observed radiances. 3.4.3. Inter-comparison with other (satellite) LST data Inter-comparison with another dataset is done after rigorous quality control and cloud-clearing. Both datasets are regridded to a common grid and temporally matched –using temporal interpolation if necessary. View angle differences are considered. 3.4.4. Time series analysis Time series analysis can be used to reveal any time dependent issues such as a drift in the instrument calibration. Validation results are reported in the literature and product documentation. More information about validation protocols can be found in [RD-21]. 3.5. Geolocation In order for one to meaningfully apply a data point a from satellite instrument it needs to be tied to a location on the Earth's surface. A necessary pre-processing step is therefore the removal of all geometric and sensor distortions to get a true representation of the location of each pixel, dealing with the geometric thermo-elastic deformations of the instrument with respect to the platform. Orthorectification removes terrain displacement. In other words, an image in its original geometry is accurately adjusted so that distortions due to topographic variation are corrected. Thus, geolocation processing aims to compute the ortho-geolocation - the geodetic latitude and the geocentric longitude, both corrected from the real altitude of the Earth's surface. Geolocation processing is handled within the Level-1 processing (Section 3.3). Level-1A processing involves - in addition to radiometric calibration - computing the ortho-geolocation of each instrument pixel without applying it. Level-1B processing applies the ortho-geolocation. LST datasets at medium (1 km) to low resolution can suffer errors in geolocation. It is worth checking on the quality of geolocation of level 1 data sets for each instrument or for the output LST products if produced from instrument channels with different spatial resolutions. Level 1 experts are constantly trying to improve geolocation quality. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 16 3.6. Quality flags Quality flags are used to inform the user as to the reliability of the data. Flags are used to indicate such things as: proximity to cloud (IR); proximity to coastline (MW); out of calibration range; aerosol present; flooded areas etc. Best practice dictates that quality flags should be examined before data is used in an application. Quality flags are often given as bitmasks with each bit of an integer word corresponding to an on/off setting of a different flag. Bits are numbered starting from zero for the bit with the lowest value, with each successive bit corresponding to increasing powers of 2, i.e.: bit 0 is units or 2 0, bit 1 is 21, bit 2 is 22, … bit n is bit 2n. If a bit is set then it has a value of 1, if it is not set then it has a value of zero. Programming the interpretation of bitwords depends on the language used: FORTRAN has the BTEST function: Result = BTEST(bitword,pos), returns TRUE if the bit at position pos in bitword is set For IDL use the bitwise operators AND and NOT: to check if bit n in conf_word is set: Result = (conf_word AND 2^n) EQ 2^n gives result=1 if bit n is set or 0 if not set In Python a function equivalent to the FORTRAN function BTEST can be defined: def btest(bitword, pos): return bool(bitword & (1 << pos)) 3.7. Clouds and aerosol Clouds and atmospheric aerosol absorb and re-emit IR radiation (similarly thick convective and heavily precipitating clouds in the microwave region). Thus, when clouds or aerosol are present, radiation reaching the satellite instrument is not all direct from the ground and the retrieved temperature will not accurately represent the surface temperature. Information about clouds and aerosol will be available either in the LST product itself or as a separate product. Some L2 products will include temperatures retrieved from cloudy or aerosol-affected pixels and rely on the user to mask the data, in other products the temperature fields will be cloud-masked with fill values in cloud affected pixels and a quality flag will indicate the reason for the absence of a valid surface temperature. Whilst the latter may be easier to use for the inexperienced user the former allows the user to choose their own method of masking. In general, L3 products have been cloud cleared as part of the process of spatially and/or temporally regridding onto the output grid. Sometimes cloud information will be given as a clear-sky or cloud probability. In other cases, cloud information may be given as a flag i.e. cloudy/clear under one or more cloud tests. There may be several flags each indicating the result of a different test with one flag giving an overarching cloudy yes/no mask. Note: in some products, the quality of the cloud flagging may differ between daytime and night© 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 17 time data due to different cloud tests being applied (information in visible channels that may be used in daytime tests is not available at night). Recently cloud detection algorithms have improved greatly as Bayesian and Probabilistic schemes have been developed. Analysis of different types of cloud clearing schemes indicate these outperform more traditional threshold based approaches [RD-29]. See section on quality flags for advice on how to interpret bitwords. 3.8. Why are there gaps in my data? As mentioned above, cloud and aerosol will prevent accurate IR LST retrieval and precipitation and RFI will do the same for MW data. There may be gaps in coverage due to the instrument not observing the whole grid in the time frame of the product – a narrow swath sensor will not have complete global coverage in one day so grids of daily average data will have incomplete coverage. There are times when the instrument will not be observing because of maintenance to the satellite and/or instrument anomalies. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 18 4. Uncertainty and Error An estimate of LST derived from satellite observations may be of limited value without an estimate of the uncertainty in that LST value. Uncertainty analysis for LST is becoming more rigorous and sophisticated. The best LST datasets come with uncertainty values. A brief review of current progress is given here. The Joint Committee for Guides in Metrology defines uncertainty as “a parameter associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand that is the value of the particular quantity to be measured” [RD-22]. Essentially, it is the amount of doubt we have surrounding the value of a variable. Uncertainty is not the same as error. 4.1. Error or uncertainty? Error is the result of a measurement minus a true value (often unknown) of the measurand. For satellite derived LSTs the true value of LST cannot be determined and therefore we talk about uncertainty, rather than error, in these measurements. All LST observations will have an associated uncertainty estimate; effects such as atmospheric attenuation and variability of surface emissivities are not known to sufficient accuracy. Another way of thinking about this is that the error is the “wrongness” of the measured value, whereas the uncertainty is the “doubt” given our knowledge of the measured value the effects causing the errors. 4.2. Types of uncertainty The types of uncertainty commonly associated with satellite derived LST can be divided into 3 different components representing the uncertainty from effects whose errors have distinct correlation properties: Random, Locally Correlated and Large Scale Systematic (Figure 3). It should be noted that the 3-component uncertainty model described here is equally applicable at different processing levels and across LST products. Random uncertainties are those that are uncorrelated on all spatial and temporal scales; there is no correlation of error components between pixels. They occur due to factors such as instrumental noise, sub-pixel variability in surface emissivity and, for Level 3 and Level 4 products, sampling uncertainty. Sampling uncertainty results from missing data across a sampling window (in space or time) such as from cloud clearing or missing data. Locally correlated uncertainties are uncertainties that are correlated between pixels at relatively local scales. They occur due to uncertainty in atmospheric conditions, such as water vapour, and surface factors, such as surface emissivity uncertainty (within a biome) and geolocation uncertainty (correlated with biome). Their correlation length scale is dependent on the source of the uncertainty. For example, recent studies suggest that water vapour may only be correlated on scales of a few kilometres and a few minutes [RD-23]. The correlation may also be dependent on the area of interest. For example, the © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 19 temporal synoptic scale can be assumed to be around 3 days for much of the Earth [RD-24] but in the Arctic changes of synoptic processes are about 1.5 times faster [RD-25]. Pixel uncertainties are correlated if the observations are within the correlation length scale of each other and uncorrelated if the spatial and/or temporal separation is larger than correlation length scale. Large Scale Systematic uncertainties are uncertainties that are correlated at larger scales than locally correlated factors, for example monthly temporal scales and global spatial scales. They are due to factors such as sensor calibration and seasonally persistent bias patterns. The idealised illustration in Figure 3 shows how one should think about the errors that might be important for a given application. The error budget for a single pixel from one observation (1 km, instant) might be largely random, with a significant contribution from locally systematic errors and a component of systematic error for a single observation. If one averages the data and extends the time period of analysis to years, then the random errors will decrease through the averaging process but also so will the locally systematic ones. By the far right hand side of the diagram, where data has been averaged to coarser spatial scales and the analyses is over many years then the important errors will be systematic for a single measurement and any additional systematic errors that are present over time/space. Figure 3: Importance of error sources in climate data on different analysis scales [RD-26]. This concept is applicable to all surface temperature data including LST, IST and SST. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 20 4.3. How uncertainties are calculated and propagated. For processing Level 3 and 4 data uncertainties must be propagated correctly from Level 2, depending on whether the uncertainties are correlated or uncorrelated for a given spatio-temporal scale. In addition, for these higher-level products a sampling uncertainty may need to be calculated. If the product is spatially or temporally averaged to a lower resolution than the satellite orbit then sampling uncertainty should be calculated using the LST variance, number of observations and number of missing observations. For more information on how uncertainties are calculated and propagated, see the GlobTemperature UCM3 Uncertainty Breakout Session documents. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 21 5. Spatial and Temporal Averages Averages of satellite derived LSTs are higher-level (Level 3 or 4) products produced by spatially and/or temporally averaging LST data to resolutions lower than those of the satellite orbit. These averages include, for example, monthly LST products and regional average LST products. These products include only the highest quality pixels such as cloud-cleared if using TIR derived temperatures. Such averages may be produced to enable comparison of satellite LSTs with datasets of different resolution (such as from models), enable analysis of region or area-averages, or for studies in which a high-resolution dataset is not required. 5.1. Things to be aware of. Averaging LST products is not necessarily performed using a simple average in space or time. There are various different methodologies and parameters that can be employed when producing LST averages and products will not necessarily use the same methods. This section provides a brief introduction and some examples of factors may need to be considered when using or developing LST averages from available LST products. 5.1.1. Averaging Parameters In the context of LST averages, spatio-temporal averaging parameters should be understood and considered. The choice to use or produce a dataset with a given parameter will affect the eventual user or the results of the application for which it is used. A simple example would be that a multi-day averaged product of LST would not be of high enough resolution for a user investigating the diurnal cycle of LST. Temporal parameters to be considered for the application are the temporal scale of the product (single orbit, daytime or night-time average, multi-day average) and the time standard used (UTC, local time). The spatial grid should also be considered, whether the data is being gridded to a lower spatial resolution or is a re-projection of single orbit files. 5.1.2. Averaging methodologies Another aspect to be aware of with averaged LST data is the averaging methodology. Common averaging methodologies include: Nearest Neighbour Interpolation; Weighted Averages; and Simple Averages. There are both advantages and disadvantages to each method. For example, Nearest Neighbour Interpolation, where the output value is that of the “nearest” neighbouring pixel, is simple and quick to use. However, it can produce “blocky” data if the spatial resolution of the input grid is larger than that of the output grid – for example at the edge of the swath (LEO) or edge of the disk (GEO). 5.1.3. Edges and boundaries Edges and boundaries are another aspect to be attentive to. Output pixels may encompass or cross the boundaries and edges of features such as biomes, land, water, or output grid cells. This means that they © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 22 may also encompass, or cross, correlation length scales for locally and large scale correlated uncertainties. How these factors are addressed in the production of an average may differ between products and may influence the resulting LST data. Furthermore, propagating uncertainties in these situations will need careful consideration to ensure the appropriate method is used (see Section 4.3). 5.1.4. Data coverage and sampling uncertainty For averages of LST, data coverage may or may not be spatially and temporally complete. Data coverage and volume will depend on pixel and grid resolutions, the temporal window sampled and the area of interest. At higher latitudes, there may be multiple Level 2 observations for each average LST that may be temporally averaged or a subset of selected observations selected, usually the “best pixel” method of selection determined using a criterion such as lowest uncertainty or lowest satellite zenith angle. For lower latitudes observations are less frequent. Clear-sky TIR observations will also be less frequent in some areas such as the amazon. Furthermore, satellite swaths may not cover the poles. If data is missing in an LST average, for whatever reason (e.g. cloud, precipitation, missing orbits, or uneven temporal spread of data over an averaging window) a sampling uncertainty should be produced (Section 4.3). © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 23 6. Merged Datasets 6.1. What constitutes a merged dataset? Merged datasets are Level 4 products where data from more than one instrument are combined after the removal of inter-instrument biases. In this instance, we do not include combining data from a sequence of instruments in order to generate a longer climate record. 6.2. Why merge data? Satellite data products have gaps in coverage – the major cause of gaps in the case of IR products is cloud. Merging data from two instruments especially if MW is used can increase spatial coverage. In addition, combining data from different satellites with different overpass times improves temporal resolution – a geostationary instrument that observes near-continuously can be used to remove interinstrument biases between two polar orbiters. 6.3. Be aware There will be biases between different instruments that may not have been completely removed. There will be time differences and view angle differences between pixels in the same product – adjustments/corrections for these may not have been made. Different cloud-clearing algorithms will likely have been used for different instruments. There may be discontinuities due to neighbouring pixels having different view times and/or different view angles. The product will combine different physical measurements if the product contains both IR and MW LST – see Section 1 above. © 2016 GlobTemperature Consortium Ref.: Satellite LST User Handbook GlobT-WP3-DEL-25 Version: 1.0 WP3.4 – DEL-25 Date: 25-Aug-16 Page: 24 7. High Resolution LST Satellite derived LSTs which have a spatial resolution of less than 100m are often referred to as “high resolution” LSTs. They have a higher resolution than other satellite sensor LSTs, for example ATSR which has a spatial resolution of around 1km, and can therefore resolve smaller LST features. 7.1. Platforms and sensors Currently there are two satellite platform series providing high-resolution LSTs - Landsat and ASTER. Planned instruments include ECOSTRESS (Section 9.2.1), while ESA are also researching potential highresolution TIR instruments. 7.1.1. Landsat Recent Landsat platforms (Landsat 5 to Landsat 8) with the Thematic Mapper (TM), Enhanced Thematic Mapper (ETM) and Enhanced Thematic Mapper + (ETM+), and Thermal Infra-Red Sensor (TIRS) instruments on board provide LSTs with 90-120m spatial resolution derived from 1 (TM, ETM and ETM+ instruments) to 2 (TIRS) TIR channels. The two-channel TIR configuration is planned to continue in the next mission (Landset-9) which is expected to launch in the 2020s. 7.1.2. ASTER ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) is flown on board the Terra platform. Both LST and channel emissivities are derived from ASTER through use of the TemperatureEmissivity Separation (TES) algorithm on 5 IR channels. LST is provided at 90m resolution. 7.2. Applications High resolution LSTs are used for applications that require better spatial resolution than available from the majority of TIR sensors. These tend to be locally focused temperature studies, especially if targeting a very heterogeneous landscape or those that are investigating relatively small-scale temperature changes. For example, studies investigating urban heat islands or crop and vegetation monitoring in some European countries, such as the UK. Another key application for high resolution LST is the investigation of evapotranspiration. Plant responses to water and heat stress can be quantified using surface evapotranspiration which can help us understand future biosphere changes with climate. Understanding and monitoring evapotranspiration is also useful for water management and agriculture. 7.3. Things to be aware of. Although high resolution LSTs are useful for various applications, there are some limitations associated with the current observing system. Overall observations are limited by a low repeat cycle. For some © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 25 sensors, you may not have more than one observation in a month for a given location, and less if there is cloud cover. Furthermore, the sensors providing high resolution LSTs may have more specific limitations. The Thematic Mappers on board Landsat 5-7 only measure in 1 TIR channel. An SW algorithm cannot be used to derive LSTs and therefore auxiliary data is required for these sensors. The TIRS on Landsat 8 does have 2 TIR channels, and therefore an SW algorithm could be applied in principle. However, there are stray light issues for the second channel and the first TIR channel is not well calibrated. Large errors are associated with STs derived from this sensor, for example by 2K or more for SST [RD-27]. In addition, Landsat requires auxiliary data on emissivity. ASTER is able to solve for the LST and land surface emissivity simultaneously. However, data availability can be an issue. Data is freely available over the US, but obtaining data for other areas requires contacting the data provider as well as paying for the use of the data. Also not a lot of data is stored from ASTER so data may not be available at the required temporal resolution for a study. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 26 8. So how do I decide which dataset to use? In simple terms, this will depend partly on the type of application you are running. For global applications for instance, only datasets with global coverage are suitable, such as from the polar-orbiting satellites. Merged datasets may also meet these requirements but may be subject to higher uncertainties. For global applications, spatial resolution may be less important than temporal resolution. The reverse may be true for local applications. If a user is interested in long-term LST data covering more than one instrument of a suite of missions, then it is important that he/she use products that have been correctly inter-calibrated between instruments. Each data product should be accompanied by information per pixel to determine whether the LST observation can be used with sufficient confidence. The quality flags (Section 3.6) indicate whether pixels are cloudy or whether the confidence is low. Aerosol information in some cases is also included. Furthermore, all LST data should be accompanied by information on the uncertainty. This should be at a pixel level (Level-2) or grid-cell level (Level-3) with sufficient breakdown to enable a user to propagate these in space and time (Section 4). Any LST product not accompanied by uncertainty information should be treated with caution, since meaningful evaluation otherwise is difficult to assess. Each GlobTemperature product is summarised in [AD-2], and recommendations on best use are provided. Before deciding though on which LST product best fits your needs, one should understand the absolute and relative performance of the data with respect to reference measurements taken in situ and with respect to other satellite LST products. Validation and intercomparison literature provides this information, such the GlobTemperature Validation and Intercomparison Reports [AD-3; AD-4]. In GlobTemperature, the concept of a harmonised format allows the user to easily switch between data products if he/she determines that a different product is more suitable for an application. In addition to the necessary quality information and full uncertainty breakdown provided in each product for optimum application, GlobTemperature products are also accompanied by several auxiliary pieces of information depending on the individual product, such as biome, fractional vegetation, emissivity, and elevation. Together with the Product User Guide [AD-2], and Validation and Intercomparison Reports [AD-3; AD-4] the datafiles themselves and accompanying metadata form a complete package for the user to make an informed decision on the best possible product to use and how to use it. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 27 9. Future LST Instruments 9.1. Operational Instruments 9.1.1. Sentinel 3 Mission The European Space Agency’s Sentinel 3 mission will consist of 4 satellites: Sentinels 3A, 3B, 3C and 3D. Sentinel 3A was launched in February 2016. Sentinel 3B is scheduled for launch in 2017. Further satellites, Sentinel 3C and 3D, have been ordered and 3C is scheduled to launch before 2020. Each Sentinel 3 satellite carries the same payload: the Sea and Land Surface Temperature Radiometer (SLSTR) and the Ocean and Land Colour Instrument (OCLI) and a suite of topography instruments. Global coverage by the SLSTR instrument will be achieved in < 2days with one spacecraft and < 1 day with two spacecraft in constellation (in the same orbit but with a phase separation of 180°). 9.1.1.1. Further Information European Space Agency eoPortal - Copernicus Sentinel-3 Donlon, C., et al. (2012), The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission, Remote Sensing of Environment, 120, 37-57, doi: http://dx.doi.org/10.1016/j.rse.2011.07.024. 9.1.2. Meteosat 3rd Generation The Meteosat 3rd generation payload will include the Flexible Combined Imager (FCI), the successor to SEVIRI. FCI will have more channels and a higher spatial resolution: 2km in the IR channels. 9.1.2.1. Further Information EUMETSAT Meteosat Third Generation 9.1.3. MTSAT Himawari-9 will be launched in 2016 as backup for, and future successor to, Himawari-8 which began operation in July 2015. 9.1.3.1. Further Information Japanese Meteorological Agency - Meteorological Satellites © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 28 9.1.4. The Joint Polar Satellite System (JPSS) The JPSS consists of five operational satellites and one experimental satellite. The first satellite (Suomi NPP) was launched in October 2011. The second satellite is due to be launched in October 2017. Successive launches will provide continuity until late 2030s. The five operational satellites will each carry a payload of the same five instruments. The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument provides global coverage twice daily with a spatial resolution of 750 m. The other instruments are: the Cross-track Infrared Sounder (CrIS); the Ozone Mapping and Profiler Suite (OMPS); the Advanced Technology Microwave Sounder (ATMS); and the Clouds and the Earth's Radiant System (CERES). 9.1.4.1. Further Information The NOAA NESDIS Joint Polar Satellite System Suomi NPP VIIRS data products are available through the Level 1 and Atmosphere Archive and Distribution System (LAADS) . 9.2. Research Instruments 9.2.1. ECOSTRESS The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission aims to measure the temperature of plants and use the information to determine the water requirements of plants and how they respond to water-stress. The instrument is the space-ready Prototype HyspIRI Thermal Infrared Radiometer (PHyTIR) which will be deployed on the International Space Station (ISS). The instrument has a spatial resolution of 38m by 69m. The expected radiometric accuracy is <= 0.5 K with a radiometric precision of <= 0.15 K. 9.2.1.1. Further Information Jet Propulsion Laboratory ECOSTRESS 9.2.2. HyspIRI The Hyperspectral Infrared Imager (HyspIRI) mission is currently in the study phase. The HyspIRI TIR instrument has a spatial resolution of 60m and revisit time of 5 days. © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 29 9.2.2.1. Further Information Hochberg, E. J., D. A. Roberts, P. E. Dennison, and G. C. Hulley (2015), Special issue on the Hyperspectral Infrared Imager (HyspIRI): Emerging science in terrestrial and aquatic ecology, radiation balance and hazards, Remote Sensing of Environment, 167, 1-5, doi: http://dx.doi.org/10.1016/j.rse.2015.06.011. Jet Propulsion Laboratory HyspIRI Mission Study © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 30 10. Appendix A: LST Products Name Processing Level Spatial Resolution Temporal Resolution Dataset Availability and Status 1 km 35 days repeat cycle, ~14 orbits per day of ~108 minute duration GlobTemperature Data Portal (http://data.globtemperature.info). 35 days repeat cycle, ~14 orbits per day of ~108 minute duration GlobTemperature Data Portal (http://data.globtemperature.info). 16 days repeat cycle, 288 granules per day of 5 minute duration GlobTemperature Data Portal (http://data.globtemperature.info). GlobTemperature Products GlobTemperature AATSR LST Product GlobTemperature ATSR-2 LST Product GlobTemperature MODIS LST Product L2/L3 L2/L3 L2 1 km 1 km Archived version Archived version Archived version GlobTemperature SEVIRI LST Product L2 0.05° equalangle Hourly GlobTemperature Data Portal (http://data.globtemperature.info). Archived version GlobTemperature SSM/I LST Product L2 0.25° equalarea Twice daily GlobTemperature Data Portal (http://data.globtemperature.info). Archived version GlobTemperature AMSR-E LST Product L2 12 km Twice daily GlobTemperature Data Portal (http://data.globtemperature.info). Archived version GlobTemperature GOES LST Product L2 0.05° equalangle Hourly GlobTemperature Data Portal (http://data.globtemperature.info). Archived version © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 GlobTemperature MTSAT LST Product L2 0.05° equalangle Hourly Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 31 GlobTemperature Data Portal (http://data.globtemperature.info). Archived version GlobTemperature ATSR LST Climate Data Record (CDR) L3 0.05° equalangle Monthly Day/Night GlobTemperature Data Portal (http://data.globtemperature.info). Archived version GlobTemperature Merged LST Product L4 0.05° equalangle 3-hourly (Merged GEO, GEO+LEO); GlobTemperature Data Portal (http://data.globtemperature.info). 10-day composites (Merged LEO) Archived version Global - single dataset; NASA JPL (https://lpdaac.usgs.gov/dataset_di scovery/community/community_pr oducts_table). Single Sensor Datasets Advanced Spaceborne Thermal Emission and Reflection radiometer Global Emissivity Database (ASTER GED) L3 GOES LST Product L2 100 m; 1 km; 1° x 1° equal-angle N. America – summer and winter Archived version 4 km at nadir Hourly (N. Hemisphere); NOAA (http://www.ospo.noaa.gov/Produc ts/land/glst/) 3-hourly (Full disk) Operational version MODIS land surface temperature and emissivity (LST&E) (MOD11 / MYD11) L2 JPSS VIIRS Environmental Data Record (VIIRS_EDR) L2 1 km 16 days repeat cycle, 288 granules per day of 5 minute duration NASA DAAC (https://lpdaac.usgs.gov/) Operational version 750 m 16 days repeat cycle, 14 orbits per day NOAA (http://www.class.ncdc.noaa.gov) Operational version © 2016 GlobTemperature Consortium Satellite LST User Handbook WP3.4 – DEL-25 Land Surface Analysis Satellite Applications Facility (LSA SAF) MSG/SEVIRI LST (LSA-001) L2 3 km at nadir 15 minutes 0.05° equalangle Hourly Ref.: GlobT-WP3-DEL-25 Version: 1.0 Date: 25-Aug-16 Page: 32 LandSAF (http://landsaf.meteo.pt) Operational version Multiple Sensor Datasets Copernicus Global Land Service GEO (MSG/GOES/MTSA T) LST L3 Copernicus Global Land Service (http://land.copernicus.eu/global/pr oducts/lst) Operational version End of document © 2016 GlobTemperature Consortium
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