Land surface temperature and emissivity estimation from passive sensor data: theory and practice; current trends

Review article

Land surface temperature and emissivity estimation from passive sensor data: theory and practice; current trends

Prasanjit Dash¹, Frank-M. Göttsche, Folke-S. Olesen, and Herbert Fischer

Institute for Meteorology and Climate ResearchForschungszentrum Karlsruhe/University of Karlsruhe

Postfach 3640, D-76021 Karlsruhe, Germany

¹Corresponding author: E-Mail: prasanjit.dash@imk.fzk.de; Tel.: +49-7247-82-3822;

Fax: +49-7247-82-4742; WWW: http://www.fzk.de/imk/imk2/isys

International Journal of Remote Sensing, vol. 23, issue 13, pp. 2563-2594

Abstract

   Land Surface Temperature (LST) and emissivity for large areas can only be derived  from surface-leaving radiation measured by satellite sensors. These measurements represent the integrated effect of the surface and are, thus, for many applications, superior to point measurements on the ground, e.g. in Earth’s radiation budget and climate change detection. Over the years, a substantial amount of research was dedicated to the estimation of LST and emissivity from passive sensor data. This paper provides the theoretical basis and gives an overview of the current status of this research. Sensors operating in the visible, infrared, and microwave range onboard various meteorological satellites are considered, e.g. Meteosat-MVIRI, NOAA-AVHRR, ERS-ATSR, Terra-MODIS, Terra-ASTER, and DMSP-SSM/I. Atmospheric effects on measured brightness temperatures are described and atmospheric corrections using Radiative Transfer Models (RTM) are explained. The substitution of RTM with Neural Networks (NN) for faster forward calculations is also discussed. The reviewed methods for LST estimation are the single-channel method, the Split-Window Techniques (SWT), and the multi-angle method, and, for emissivity estimation, the Normalized Emissivity Method (NEM), the Thermal Infrared Spectral Indices (TISI) method, the spectral ratio method, alpha residuals, Normalized Difference Vegetation Index (NDVI)-based methods, classification-based emissivity, and the Temperature Emissivity Separation (TES) algorithm.

1.         Introduction

Surface temperature is an important quantity for many environmental models, e.g. (1) energy and water exchange between atmosphere and surface, (2) numerical weather prediction, (3) global ocean circulation, (4) climatic variability, etc. (Valor and Caselles 1996). Only by remote sensing from satellites are measurements of surface temperature feasible on a regional or global scale. Satellite sensors measure the surface-leaving radiance modified by the atmosphere in different spectral channels; the corresponding brightness temperatures are calculated by reversing Planck’s function. Various algorithms exist to estimate Land Surface Temperature (LST) from brightness temperatures and auxiliary data. LST is sensitive to vegetation and soil moisture; hence it can be used to detect land surface changes, e.g. tendencies towards desertification. Geographical Information Systems allow a combined interpretation of satellite-derived LST and other geo-data, e.g. vegetation cover and soil-type maps (Andersen 1997).

LST is the temperature measured at the Earth's surface and is regarded as its skin temperature. However, the land surface is far from being a skin or a homogenous two-dimensional entity: it is composed of different materials with various geometries both of which complicate LST estimation (Qin and Karnieli 1999). Additionally, the surface is commonly even more inhomogeneous at low-resolution satellite spatial observations. Only for homogeneous surfaces at thermal equilibrium LST can be defined unambiguously. In remote sensing, LST is defined as the `surface radiometric temperature´ corresponding to the instantaneous field-of-view of the sensor (Prata et al. 1995) or, more precisely, as the `ensemble directional radiometric surface temperature´ (Norman and Becker 1995). The term `ensemble´ depicts the bulk contribution of an inhomogeneous pixel. For a given sensor viewing direction, LST depends on the distribution of temperature and emissivity within a pixel and the spectral channel of measurement (Becker and Li 1995). Thermodynamic temperature is measured at surface/atmosphere/thermometer point-of-contact, and is based on the `zeroth law of thermodynamics´, i.e. two systems in equilibrium with a third system, e.g. a thermometer, are also in equilibrium with each other. Here, the surface has to be clearly defined, i.e. it should be isothermal and homogeneous (the sub-systems have the same thermodynamic temperature). Only for homogeneous isothermal surfaces radiometric and thermodynamic temperatures are equivalent; even for a small-scale ensemble of black bodies at different temperatures there is no equivalent black body temperature yielding the same distribution of spectral radiance (Norman and Becker 1995). LST represents the integrated effect of the whole 'ensemble' within a pixel; thus, LST is not confined to homogeneous isothermal surfaces. However, the definition of surface depends on the acquisition device, e.g. its scale/resolution, and should match the scale of the model (Becker and Li 1995). The definition of LST given above is probably the best for Earth's radiation budget and for canopy temperature estimation.

In order to obtain LST from space radiometry, three main effects have to be considered and corrected for: atmospheric, angular, and emissivity effects. The three major effects of the atmosphere are absorption, upward atmospheric emission, and the downward atmospheric irradiance reflected from the surface (Franca and Cracknell 1994). In the 8-12 mm infrared (IR)-window region, aerosol absorption and scattering are negligible and generally ignored (Prata et al 1995); water vapour is principally responsible for atmospheric effects. Other gases, e.g. ionospheric O₃ and CO₂ also influence atmospheric transmission but unlike water vapour, O₃ and CO₂ vary slowly. Furthermore, CO₂ is evenly distributed in the atmosphere, and tropospheric O₃ is of local importance only. Hence, water vapour, which is poorly mixed and varies on short time-scales, is the most relevant gas. Consequently, frequent information about the state of the atmosphere, especially the temperature and water-vapour profiles, are needed. Radiosonde data provide such information and yield results which, at the location of the radiosonde, are more accurate than any other method for the troposphere, but the soundings have to be synchronous and co-located with the satellite measurements. Therefore, information from vertical sounders, e.g. TIROS Operational Vertical Sounder (TOVS) onboard National Oceanic and Atmospheric Administration's (NOAA) polar-orbiters, are better suited for large-scale studies (Reutter et al. 1994, Lakshmi and Susskind 2000). Satellite sensors observe the land surface at different viewing geometries and, therefore, estimated brightness temperatures must be compensated for the zenith angle (Kimes et al. 1980, 1983, Ignatov and Dergileva 1994). LST algorithms must account for this effect in order to provide results that are independent of observation geometry. Generally, four different atmospheric correction methods are practised (Becker and Li 1990a), all of which require a priori emissivity information: the single-channel method, the Split-Window Technique (SWT), the multi-angle method, and combinations of SWT and multi-angle method, e.g. for Along Track Scanning Radiometer (ATSR) onboard European Remote Sensing Satellite (ERS)-1. The emissivity of land surfaces, unlike oceans, can differ significantly from unity and varies with vegetation, surface moisture, and roughness (Nerry et al. 1988, Salisbury and D’ Aria 1992).

Currently, LST cannot be determined from a single satellite system with the high temporal and spatial resolution necessary for many applications. Combining NOAA Advanced Very High Resolution Radiometer (AVHRR) and Meteosat Visible and Infrared Imager (MVIRI) IR data yields measurements with the temporal resolution of Meteosat (~30 min) and a spatial resolution close to that of AVHRR (~2.5 km) (Olesen et al. 1995). The Spinning Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat Second Generation, scheduled for launch in 2002, will provide data with an unprecedented combination of spatial, temporal, and spectral resolution from geostationary orbit (Cihlar et al. 1999). SEVIRI will improve sufficiently upon MVIRI's capabilities to estimate LST as well as emissivity (Dash et al. 2001). Some sensors provide multispectral IR data with an even higher spectral resolution, e.g. the airborne Thermal Infrared Multispectral Scanner (TIMS, 6 channels between 8-12.5 mm) on the National Aeronautics and Space Administration's (NASA) C-130 aircraft (Schmugge et al. 1998), Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER, channels 10-14 between 8.1-11.6 mm) (Gillespie et al. 1996, 1998, 1999) and the Moderate Resolution Imaging Spectrometer (MODIS, 4 `land´ channels between 8.4-13.5 mm) (Wan and Li 1997, Wan 1999), both onboard NASA’s Terra-spacecraft. Passive microwave data from the Special Sensor Microwave/Imager (SSM/I) onboard Defense Meteorological Satellite Program (DMSP) spacecraft is also used for LST determination (Xiang and Smith 1997).   en 0.6 and 1.0 (also sea surface emissivity) and depends on surface conditions, polarization, and frequency. For frequencies from 1 to 100 GHz atmospheric transmissivity t ranges from 0.90 to 0.99. It is more easily determined than emissivity by using line-by-line codes and spectroscopic data, e.g. from the High Resolution Transmittance (HITRAN) database (Rothman et al. 1998; http://www.hitran.com). Hence, e(n) is the most critical parameter for accurate estimates of LST in the microwave region. Assuming that changes in t are small and using a method similar to IR SWT, Givri (1997) derived the following microwave SWT: 

Conclusions

Thermodynamic point measurements are of limited use for LST estimation: on large scales, i.e. continental, only satellite measurements are applicable. Since a global in situ LST database at satellite sensor spatial resolution is unfeasible, LST/LSE algorithms are usually validated with field measurements of major vegetation and soil types. The estimation of LST and LSE from passive sensor data is an important and ongoing field of research. Owing to its complex and underdetermined nature, the problem is not fully solved with the accuracy and generality desired by many researchers. In the near future, global LST maps with an accuracy of ± 1°C and LSE maps with an accuracy of ±0.005 will be available for many surface types, e.g. from Terra measurements.

Starting from the physics of radiative transfer, this article presented the theoretical basis of LST and LSE extraction from radiance measurements. Within the atmospheric windows, the TOA radiance is dominated by surface-leaving radiance. Therefore, LST can be estimated from satellite based IR measurements. However, for accurate LST estimations three atmospheric contributions have to be accounted for: a) attenuation (absorption) of Earth-emitted radiance by the atmosphere, b) upwelling atmospheric radiance, c) reflected downwelling atmospheric irradiance (LSE must be known). The contributions of LST and LSE to at-ground radiance have to be decoupled; therefore, the determination of LST and LSE is closely linked and is reviewed together. A single `best´ method for LST/LSE determination does not exist. This review helps to select the most appropriate method for a given application and the available information.

The single IR channel method for atmospheric correction (Price 1983) is accurate and globally applicable; for satellites with a single IR channel, e.g. Meteosat, it is also the only applicable method. However, accurate information about the state of the atmosphere and LSE for each IR measurement is needed and the computational cost is high. Substituting the RTM with a NN allows fast and accurate LST determination with the single-channel method (Göttsche and Olesen 2001).

The SWT for sea surfaces (McMillin 1975) can be applied to land surfaces, if LSE is known a priori. Once the coefficients are calculated, SWT formulations are fast and easy to use. However, the coefficients are only valid for the datasets used to derive them. François and Ottlé (1996) demonstrated that the classical linear split-window equation is invalid on a larger scale and showed that quadratic formulations of SWT, which account for LSE and water vapour, yield better results. The choice of a SWT formulation depends on the availability of further information: the SWT formulations of Price (1984) and Becker and Li (1990) account only for LSE variations, while the coefficients of Sobrino et al. (1991) and Becker and Li (1995) also depend on the state of the atmosphere. The algorithm of Kerr et al. (1992) classifies surfaces either as bare soil or as vegetated area and combines their contributions to obtain effective LST.

Multi-angle methods, as proposed by Chedin et al. (1982), can be applied if simultaneous measurements of the same area for different viewing angles are available. The method assumes spatial uniformity of the atmospheric column and the observations must have a significant path difference, otherwise the algorithm looses stability (Prata 1993). The directional variation of emissivity also has to be known.

If no a priori knowledge is available, the Physics-based algorithm of Wan and Li (1997), developed specifically for MODIS data, can be used for combined retrievals of LST and LSE. The method needs day/night pairs of observations in at least 7 channels and assumes that LSE is temporally invariant. Unlike the TISI day/night approach (Li and Becker 1993), Wan and Li's method does not rely on assumed BRDF and atmospheric information.

Methods for LSE determination either aim at relative or absolute emissivity: the `relative´ methods retrieve the spectral shape/ratio of emissivities, while the `absolute´ methods depend on critical assumptions (problem of the missing equation). TISI (Becker and Li 1990b) is a robust method for estimating relative emissivity. In order to obtain absolute LSE using the TISI day/night method, the BRDF is needed and LSE is assumed to be temporally invariable. If the LSE of bare ground and vegetation as well as the vegetation structure and distribution is known, NDVI based methods can be used (Valor and Caselles 1996). The classification based emissivity method (Snyder et al. 1998) needs information about cover type and the amount of vegetation. The TES method of Gillespie et al. (1996, 1998) can be applied if atmospheric information and a sufficient number of IR channels are available. However, the accuracy of the downwelling atmospheric irradiance is a critical issue.

Algorithms developed for multispectral data show promising results, e.g. as highlighted by the algorithms developed for sensors onboard Terra, e.g. TES for ASTER data, which combines several methods. Combining IR and microwave data also should prove to be beneficial, especially for the tropics, where cloud-cover is a major limitation for IR-based methods. The AAC (Gu et al. 2000) facilitates atmospheric correction of hyperspectral data from SEBASS without additional information about the state of the atmosphere. However, the extraction of LST and LSE from at-ground radiance has to be performed separately. Despite the increased possibilities offered by multispectral and hyperspectral data, the `classical´ methods are still relevant for long-term (historic) datasets and form a base for the development of new algorithms.

List of abbreviations

Acronym LST NOAA AVHRR NASA DMSP SSM/I ERS ASTER MODIS ATSR TIMS MVIRI SEVIRI TOVS TIROS VAS RTM RTE MODTRAN LOWTRAN HITRAN ECMWF IR TOA SWT LSE NEM TISI NDVI TES BRDF NN

Meaning Land Surface Temperature National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer National Aeronautics and Space Administration Defense Meteorological Satellite Program Special Sensor Microwave/Imager European Remote Sensing Satellites Advanced Spaceborne Thermal Emission Reflectance Radiometer Moderate Resolution Imaging Spectrometer Along Track Scanning Radiometer Thermal Infrared Multispectral Scanner Meteosat Visible and Infrared Imager Spinning Enhanced Visible and Infrared Imager TIROS Operational Vertical Sounder Television and Infrared Observational Satellite VISSR (Visible and Infrared Spin Scan Radiometer) Atmospheric Sounder Radiative Transfer Model Radiative Transfer Equation Moderate Resolution Transmittance (code) Low Resolution Transmittance (code) High Resolution Transmittance (database) European Centre for Medium Range Weather Forecasts Infrared (0.7 – 300 mm) Top Of the Atmosphere Split Window Technique Land Surface Emissivity Normalized Emissivity Method Thermal Infrared Spectral Indices Normalized Difference Vegetation Index Temperature Emissivity Separation Bidirectional Reflectance Distribution Function Neural Networks

List of variables/constants

Variable/ constant l, lm(T) l1, l2 T, TS, TA TB, Tn, Tf, B(l,T) R(l,T) c1=2phc2 c2=hc/k i, r Li(T) S sat t , G, s, V F, G, F´ P p, ps q f x fi e(l), ei t(l), ti a(l),ares ai, ni, Ci a, b a, b, X, Y x, y, l M, N, Q gi W ki a0, a1, a2, Pn b n

Unit mm mm K K Wm-2mm-1sr-1 Wm-2mm-1sr-1 3.7418×10 –16 Wm2 1.4388×10 –2 mK index Wm-2mm-1 Wm-2mm-1sr-1 index (subscript) index (superscript) index (subscript) index [-] HPa deg deg deg [-] [-] [-] g×cm-2 [%] [-] K [-] Hz

Meaning wavelength, wavelength at peak of radiance (Wien’s law) lower and upper limits(wavelength) of a spectral-range temperature, surface temperature, mean atmospheric T brightness temperature, TB in nadir and forward view (ATSR) spectral radiance emitted from a black body spectral radiance emitted from any surface constant in Planck’s function constant in Planck’s function h Planck’s constant (6.626076 x 10 –34Js) c velocity of light (2.99792458 x 108 ms-1) k Boltzmann’s constant (1.380658 x 10-23 JK-1) channel number of a radiometer, reference channel channel downwelling hemispherical irradiance spectral radiance measured by a sensor (TOA) in channel channel atmospheric downwelling irradiance divided by p   channel atmospheric upwelling radiance property of the surface property at the satellite top, ground and side of pixel, and vegetation respectively form factors (4.1.5) proportion pressure, pressure at Earth’s surface zenith angle azimuth scattering angle normalized response function of the sensor in channel i spectral emissivity, channel emissivity atmospheric spectral transmission, channel transmission spectral absorptivity, coefficient in alpha-residual method channel-specific constants (TISI method) SWT coefficients coefficients in NDVI method parameters in local SWT (3.2.3) Local SWT coefficients Parameter depending on channel and atmosphere (3.2.5) Amount of water vapour in atmosphere absorption coefficient of the atmosphere in channel coefficients in multi-angle method parameter in multi-angle technique (from Planck’s function) emissivity ratios in TES method (4.1.7) frequency

Acknowledgements

The MODTRAN-3 code was supplied free of charge by the Air Force Research Laboratory (AFRL), Hanscom AFB, MA. We wish to thank Dr. T. J. Schmugge at the USDA Hydrology Laboratory, Beltsville, for useful information and for providing some relevant references. We are thankful to Dr. F. Nerry at LSIIT, Ilkrich, France, and to Dr. F. Petitcolin, UMD at College Park, NASA GSFC Maryland, for useful discussions. We also wish to thank the reviewers for their comments, which enabled us to substantially improve this manuscript, and Prof. A. P. Cracknell at the University of Dundee, UK, for his help concerning structural aspects of the manuscript.

List of references

ANDERSON, H. S., 1997, Land surface temperature estimation based on NOAA-AVHRR data during the HAPEX-Sahel experiment. Journal of Hydrology, 189, 788-814.

BECKER, F., 1987, The impact of spectral emissivity on the measurement of land surface temperature from a satellite. International Journal of Remote Sensing, 8, 1509-1522.

BECKER, F., and LI, Z-L., 1990a, Towards a local split window method over land surface. International Journal of Remote Sensing, 11, 369-394.

BECKER, F., and LI, Z-L., 1990b, Temperature-Independent spectral indices in thermal infrared bands. Remote Sensing of Environment, 32, 17-33.

BECKER, F., and LI, Z.-L., 1995, Surface temperature and emissivity at different scales: definition, measurement and related problems. Remote Sensing Reviews, 12, 225-253.

CALVET, J.-C., and JULLIEN, J.-P., 1996, Land surface temperature retrieval in dry conditions from infrared and microwave satellite radiometry. Remote Sensing Reviews, 13, 235-255.

CASELLES, V., and SOBRINO, J. A., 1989, Determination of frosts in orange groves from NOAA-9 AVHRR data. Remote Sensing of Environment, 29, 135-146.

CASELLES, V., COLL, C., and VALOR, E., 1997, Land surface emissivity and temperature determination in the whole HAPEX-Sahel area from AVHRR data. International Journal of Remote Sensing, 18, 1009-1027.

CHEDIN, A., SCOTT, N., and BERROIR, A., 1982, A single-channel double viewing method for SST determination from coincident Meteosat and TIROS-N measurements. Journal of Applied Meteorology, 21, 613-618.

CHERUY, F., CHEVALLIER, F., MORCRETTE, J.-J., SCOTT, N.A., and CHEDIN, A., 1996, Une methode utilisant les techniques neuronales pour le calcul rapide de la distribution verticale du bilan radiatif thermique terreste, C.R. Acad. Sci. Paris, t. 322, Serie II b, 665-672.

CIHLAR, J., BELWARD, A., and GOVAERTS, Y., 1999, Meteosat Second Generation opportunities for land surface research and applications. EUMETSAT Scientific Publications.

CLARMANN, T. V., DUDHIA, A., ECHLE, G., FLAUD, J.-M., HARROLD, C., KERRIDGE, B., KOUTOULAKI, K., LINDEN, A., LÓPEZ-PUERTAS, M., LÓPEZ-VALVERDE, M. Á., MARTÍN-TORRES, F. J., REBURN, J., REMEDIOS, J., REDGERS, C. D., SIDDANS, R., WELLS, R. J., and ZARAGOZA, G., 1998, Study on the simulation of atmospheric infrared spectra, Final Report, ESA contract number 12054/96/NL/CN.

COLL, C., CASELLES, V., SOBRINO, J. A., and VALOR, E., 1994, On the atmospheric dependence of the split-window equation for land surface temperature. International Journal of Remote Sensing, 15, 105-122.

COLL, C., SCHMUGGE, T. J., and HOOK, S. J., 1998, Atmospheric effects on the temperature emissivity separation algorithm. Proceedings of SPIE Europto Conference on Remote Sensing of Agriculture, Ecosystems and Hydrology, 22-24 September 1998, (Barcelona, Spain: SPIE), 3499, pp. 405-415.

CZAJKOWSKI, K. P., GOWARD, S. N., and OUAIDRARI, H., 1998, Impact of AVHRR filter functions on surface temperature estimation from the split window approach. International Journal of Remote Sensing, 19, 2007-2012.

DASH, P., GÖTTSCHE, F-M., and OLESEN, F-S., 2001, Potential of MSG for surface temperature and emissivity estimation: considerations for real-time applications. (submitted).

ESCOBAR-MUNOZ, J., CHEDIN, A., CHERUY, F., and SCOTT, N., 1993, Multi-layer neural networks for the retrieval of atmospheric variables from satellite-borne vertical sounding. Comptes Rendus de l’ Academie des Sciences, Serie II (Mecanique, Physique, Chimie Sciences de la Terre et de l’ Univers), 317, 911-918.

FRANCA, G. B., and CRACKNELL, A. P., 1994, Retrieval of land and sea surface temperature using NOAA-11 AVHRR data in north-eastern Brazil. International Journal of Remote Sensing, 15, 1695-1712.

FRANÇOIS, C., and OTTLÉ, C., 1996, Atmospheric corrections in the thermal infrared: global and water vapor dependent split-window algorithms- applications to ATSR and AVHRR data. IEEE Transactions on Geoscience and Remote Sensing, 34, 457-470.

GILLESPIE, A. R., 1985, Lithologic mapping of silicate rocks using TIMS. Proceedings TIMS Data User’s Workshop, (JPL Pub. 86-38, Jet Propulsion Laboratory, Pasadena, CA), pp. 29-44.

GILLESPIE, A. R., ROKUGAWA, S., HOOK, S. J., MATSUNAGA, T., and KAHLE, A. B., 1996/1999, Temperature/emissivity separation algorithm theoretical basis document, Version 2.3/2.4. , NAS5-31372, NASA/GSFC, Greenbelt MD, USA. (http://eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-03/atbd-ast-03.pdf)

GILLESPIE, A. R., ROKUGAWA, S., MATSUNAGA, T., COTHERN, J. S., HOOK, S. J., and KAHLE, A. B., 1998, A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36, 1113-1126.

GIVRI, J. R., 1997, The extension of the split window technique to passive microwave surface temperature assessment. International Journal of Remote Sensing, 18, 335-353.

GÖTTSCHE, F.-M., and OLESEN, F.-S., 2001, Evolution of neural networks for radiative transfer calculations in the terrestrial infrared. Remote Sensing of Environment (accepted).

GU, D., GILLESPIE, A. R., KAHLE, A. B., and PALLUCONI, F. D., 2000, Autonomous Atmospheric Compensation (AAC) of high resolution hyperspectral thermal infrared remote-sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 38, 2557-2569.

GU, D., and GILLESPIE, A. R., 2000, A new approach for temperature and emissivity separation. International Journal of Remote Sensing, 21, 2127-2132.

GUTMAN, G., and IGNATOV, A., 1998, The derivation of green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing, 19, 1533-1543

HAHN, C. J., WARREN, S. G., and LONDON, J., 1995, The effect of moonlight on observation of cloud cover at night, and application to cloud climatology. Journal of Climate, 8, 1429-1446.

HERRERA, F., ROSA, F., GONZÁLEZ, A., and PÉREZ, J. C., 1999, Method based on a radiative transfer model to extract the solar component from NOAA-AVHRR channel-3. International Journal of Remote Sensing, 20, 699-710.

HOLLINGER, J., 1991, DMSP SSM/I calibration/validation, Final Report, parts 1 and 2, Naval Research Laboratory, Washington, D.C.

HOOK, S. J., GABELL, A. R., GREEN, A. A., KEALY, P. S., 1992, A comparison of techniques for extracting emissivity information from thermal infrared data for geologic studies. Remote Sensing of Environment, 42, 123-135.

HUNT, G. R., 1980, Electromagnetic radiation: The communication link in remote sensing. In Remote Sensing Geology, edited by B. S. SIEGEL and A. R. GILLESPIE (New York: John Wiley and Sons), pp. 5-45.

IGNATOV, A. M., and DERGILEVA, I. L., 1994, Angular effect in dual-window AVHRR brightness temperatures over oceans. International Journal of Remote Sensing, 15, 3845-3850.

JONES, A. S., and VONDER HAAR, T. H., 1997, Retrieval of microwave emittance over land using coincident microwave and infrared satellite measurements. Journal of Geophysical Research, 102, 13609-13626.

KAHLE, A. B., MADURA, D. P., and SOHA, J. M., 1980, Middle infrared multispectral aircraft scanner data: analysis for geological applications. Applied Optics, 19, 2279-2290.

KEALY, P. S., and GABELL, A. R., 1990, Estimation of emissivity and temperature using alpha coefficients. Proceedings of second TIMS workshop, (JPL Pub. 90-95, Jet Propulsion Laboratory, Pasadena, CA), pp. 11-15.

KEALY, P. S., and HOOK, S. J., 1993, Separating temperature and emissivity in thermal infrared multispectral scanner data: Implications for recovering land surface temperatures. IEEE Transactions on Geoscience and Remote Sensing, 31, 1155-1164.

KERR, Y. H., LAGOUARADE, J. P., and IMBERNON, J., 1992, Accurate land surface temperature retrieval from AVHRR data with use of an improved split window algorithm. Remote Sensing of Environment, 41, 197-209.

KEY, J. R., AMANO, E., COLLINS, J., and SCHWEIGER, A. J., 1999. FluxNet User's Guide, Technical Report 96-03, Department of Geography, Boston University.

KIDDER, S. Q., and VONDER HAAR, T. H, 1995, Satellite Meteorology: an introduction, 1st edn (London: Academic Press).

KIMES, D. S., IDSO, S. B., PINTER, P. J., REGINATO, R. J., and JACKSON, R. D., 1980, View angle effects in the radiometric measurement of plant canopy temperature. Remote Sensing of Environment, 10, 273-284.

KIMES, D. S., and KIRCHNER, J. A., 1983, Directional radiometric measurements of row-crops temperatures. International Journal of Remote Sensing, 4, 299-311.

KNEIZYS, F. X., SHETTLE, E. P., ABREU, L. W., ANDERSON, G. P., CHETWYND, J. H., GALLERY, W. O., SELBY, J. E. A., and CLOUGH, S. A., 1988, Users guide to LOWTRAN-7, Optical/Infrared technology division, U. S. Air Force Geophysical Laboratory, Hanscom AFB, USA.

KNEIZYS, F. X., ROBERTSON, D. C., ABREU, L. W., ACHARYA, P., ANDERSON, G. P., ROTHMAN, L. S., CHETWYND, J.H., SELBY, J. E. A., SHETTLE, E. P., GALLERY, W. O., BERG, A., CLOUGH, S. A., and BERNSTEIN, L. S. Edited by: ABREU, L. W., and  ANDERSON, G. P., 11. Jan. 1996, The MODTRAN 2/3 Report and LOWTRAN 7 MODEL. Philipps Laboratory, Hanscom, USA.

KUMAR, S., SAHOO, P. K., and SINGH, R. P., 1999, Monitoring of brightness temperature over Indian and adjoining regions using SSM/I data. International Journal of Remote Sensing, 20, 2305-2307.

LAKSHMI, V., and SUSSKIND, J., 2000, Comparison of TOVS-derived land surface variables with ground observations. Journal of Geophysical Research, 105, 2179-2190.

LI, Z-L., and BECKER, F., 1993, Feasibility of land surface temperature and emissivity determination from AVHRR data. Remote Sensing of Environment, 43, 67-85.

LI, Z-L., BECKER, F., STOLL, M. P., and WAN, Z., 1999, Evaluating six methods for extracting relative emissivity spectra from thermal infrared images. Remote Sensing of Environment, 69, 197-214.

MA, X.-L., WAN, Z., MOELLER, C. C., MENZEL, W. P., GUMLEY, L. E:, and ZHANG, Y., 2000, Retrieval of geophysical parameters from Moderate Resolution Imaging Spectroradiometer thermal infrared data: evaluation of a two-step physical algorithm. Applied Optics, 39, 3537-3550.

McMILLIN, L. M., 1975, Estimation of sea surface temperature from two infrared window measurements with different absorption. Journal of Geophysical Research, 80, 5113-5117.

MORIYAMA, M., 2000, The error analysis of the T/E separation relevant to the atmospheric correction error of the remotely sensed TIR data. Advances In Space Research, 25, 1037-1040.

NERRY, F., LABED, J., and STOLL, M. P., 1988, Emissivity signatures in the thermal IR band for remote sensing: calibration procedure and method of measurements. Applied Optics, 27, 758-764.

NERRY, F., PETITCOLIN, F., and STOLL, M. P., 1998, Bidirectional reflectivity in AVHRR channel 3: application to a region in northern Africa. Remote Sensing of Environment, 66, 298-316.

NORMAN, J. M., and BECKER, F., 1995, Terminology in thermal infrared remote sensing of natural surfaces. Agricultural and Forest Meteorology, 77, 153-166.

OLESEN, F.-S., Kind, O., and REUTER, H., 1995, High resolution time series of IR data from a combination of AVHRR and Meteosat. Advances In Space Research, 16, (10)141-(10)146.

OTTLÉ, C., and VIDAL-MADJAR, D., 1992, Estimation of land surface temperature with NOAA9 data. Remote Sensing of Environment, 40, 27-41.

PRATA, A. J., 1993, Land surface temperatures derived from the advanced very high resolution radiometer and the Along-Track Scanning Radiometer 1. Theory. Journal of Geophysical Research, 98, 16689-16702.

PRATA, A. J., CASELLES, V., COLL, C., SOBRINO, J. A., and OTTLÉ, C., 1995, Thermal remote sensing of land surface temperature from satellites: Current status and future prospects. Remote Sensing Reviews, 12, 175-224.

PRICE, J. C., 1983, Estimation of surface temperatures from satellite thermal infrared data- a simple formulation for the atmospheric effect. Remote Sensing of Environment, 13, 353-361.

PRICE, J. C., 1984, Land surface temperature measurements from the split window channels of the NOAA-7 AVHRR. Journal of Geophysical Research, 89, 7231-7237.

PRINGENT, C., ROSSOW, W. B., and MATTHEWS, E., 1997, Microwave land surface emissivities estimated from SSM/I observations. Journal of Geophysical Research, 102, 21867-21890.

QIN, Z., and KARNIELI, A., 1999, Progress in remote sensing of land surface temperature and ground emissivity using NOAA-AVHRR data. International Journal of Remote Sensing, 20, 2367-2393.

REUTTER, H., OLESEN, F. S., and FISCHER, H., 1994. Distribution of the brightness temperature of land surfaces determined from AVHRR data. International Journal  of Remote Sensing, 15, 95-104.

ROTHMAN, L. S., RINSLAND, C. P., GOLDMAN, A., MASSIE, S. T., EDWARDS, D. P., FLAUD, J.-M., PERRIN, A., CAMY-PEYRET, C., DANA, V., MANDIN, J.-Y., SCHROEDER, J., McCANN, A., GAMACHE, R. R., WATTSON, R. B., YOSHINO, K., CHANCE, K. V., JUCKS, K. W., BROWN, L. R., NEMTCHINOV, V., and VARANASI, P., 1998. The HITRAN molecular spectroscopic data base and HAWKS (HITRAN Atmospheric Workstation): 1996 Edition. Journal of Quantitative Spectroscopy and Radiative Transfer, 60, 665-710.

SALISBURY, W., and D’ ARIA, D. M. , 1992, Emissivity of terrestrial materials in the 8-14 mm atmospheric window. Remote Sensing of Environment, 42, 83-106.

SCHÄDLICH, S., GÖTTSCHE, F.-M., and OLESEN, F.-S., 2001, Influence of land parameters and atmosphere on Meteosat brightness temperatures and generation of land surface temperature maps by temporally and spatially interpolating atmospheric correction. Remote Sensing of Environment, 75, 39-46.

SCHMUGGE, T. J., BECKER, F., and LI, Z. L., 1991, Spectral emissivity variations observed in airborne surface temperature measurements. Remote Sensing of Environment, 35, 95-104.

SCHMUGGE, T. J., HOOK, S. J., and COLL, C., 1998, Recovering surface temperature and emissivity from thermal infrared multispectral data. Remote Sensing of Environment, 65, 121-131.

SCHROEDTER, M., OLESEN, F., and FISCHER, H., 2001, Determination of LST distributions from single channel IR measurements: An effective spatial interpolation method for the use of TOVS, ECMWF and radiosonde profiles in the atmospheric correction scheme. International Journal of Remote Sensing (accepted).

SCHWEIGER, A. J., and KEY, J. R., 1997, Estimating surface radiation fluxes in the Arctic from TOVS brightness temperatures. International Journal of Remote Sensing, 18, 955-970.

SCORER, R. S., 1987, Cloud formations seen by satellite, Proceedings of NATO ASI on Remote Sensing Applications in Meteorology and Climatology, 17 August-6 September 1987,(Dundee, Scotland: NATO Series C: Mathematical and Physical Sciences), 201, pp. 1-81.

SCOTT, N. A., and CHÉDIN, A., 1981, A fast line by line method from atmospheric absorption computations: the Automatized Atmospheric Absorption Atlas. Journal of Applied Meteorology, 20, 802-812.

SIEGAL, R., and HOWELL, J. R., 1992, Thermal radiation heat transfer, 3rd edn (New York: Hemisphere).

SIMMER, C., 1999. Contribution of microwave remote sensing from satellites to studies on the Earth energy budget and the hydrological cycle. Advances In Space Research, 24, 897-905.

SLATER, P. N., 1980, Remote sensing, optics and optical systems, (Reading, Massachusetts: Addison-Wesley).

SNYDER, W. C., WAN, Z., ZHANG, Y., and FENG, Y.-Z., 1998, Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing, 19, 2753-2774.

SNYDER, W. C., and WAN, Z., 1998, BRDF models to predict spectral reflectance and emissivity in the thermal infrared. IEEE Transactions on Geoscience and Remote Sensing, 36, 214-225.

SOBRINO, J. A., COOL, C., and CASELLES, V., 1991, Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Remote Sensing of Environment, 38, 19-34.

SOBRINO, J. A., LI, Z.-L., STOLL, M. P., and BECKER, F., 1996, Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data. International Journal of Remote Sensing, 17, 2089-2114.

SOBRINO, J. A., and CUENCA, J., 1999, Angular variation of thermal infrared emissivity for some natural surfaces from experimental measurements. Applied Optics, 38, 3931-3936.

SUSSKIND, J., ROSENFIELD, J., and REUTER, D., 1983, An accurate radiative transfer model for use in the direct physical inversion of HIRS2 and MSU temperature sounding data. Journal of Geophysical Research, 88, 8550-8568.

SUSSKIND, J., ROSENFIELD, J., REUTER, D., and CHAHINE, M. T., 1984, Remote sensing of weather and climate parameters from HIRS2/Msu on TIROS-N. Journal of Geophysical Research, 89, 4677-4697.

VALOR, E., and CASELLES, V., 1996, Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Remote Sensing of Environment, 57, 167-184.

VAN DE GRIEND, A. A., and OWE, M., 1993, On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International Journal of Remote Sensing, 14, 1119-1131.

WAN, Z., and LI, Z.-L., 1997, A physics-based algorithm for land-surface emissivity and temperature from EOS/MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 35, 980-996.

WAN, Z., 1999, MODIS Land-Surface Temperature algorithm theoretical basis document (LST ATBD), Version 3.3, NAS5-31370, NASA/GSFC, Greenbelt MD, USA. (http://modis.gsfc.nasa.gov/MODIS/ATBD/atbd_mod11.pdf)

WATSON, K., 1992, Spectral ratio method for measuring emissivity. Remote Sensing of Environment, 42, 113-116.

WENG, F., and GRODY, N. C., 1998, Physical retrieval of land surface temperature using special sensor microwave imager. Journal of Geophysical Research, 103, 8839-8848.

WENTZ, F. J., CHELLE, C., SMITH, D., and CHELTON, D., 2000, Satellite measurements of sea surface temperature through clouds. Science, 288, 847-850.

XIANG, X., and SMITH, E. A., 1997, Feasibility of simultaneous surface temperature-emissivity retrieval using SSM/I measurements from HAPEX-Sahel. Journal of Hydrology, 188-189, 330-360.

List of table

Table 1: Sensor specific coefficients for the SWT equation

(Source: Czajkowski et al. 1998)

Table 2: Angular variation of emissivity of some natural substance for the nadir and forward-mode (0° and 55°) of ERS-ATSR.

(Source: Sobrino and Cuenca 1999)

SENSOR	A	b
AVHRR-7 AVHRR-9 AVHRR-11 AVHRR-12 AVHRR-14	2.24 2.56 2.40 2.34 2.08	2.67 1.95 2.86 7.86 5.54

Table 1

Substance	Decrease in absolute emissivity (%)
Water  Sand  Clay  Slime  Gravel  Grass	3.3 1.9 0.5 0.9 1.2 0

Table 2

 

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