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An extension to the two-step physical retrieval algorithm was developed. Combined clear-sky multitemporal and multispectral observations were used to retrieve the atmospheric temperature--humidity profile, land-surface temperature, and surface emissivities in the midwave (3-5 [micro]m) and long-wave (8-14.5 [micro]m) regions. The extended algorithm was tested with both simulated and real data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator. A sensitivity study and error analysis demonstrate that retrieval performance is improved by the extended algorithm. The extended algorithm is relatively insensitive to the uncertainties simulated for the real observations. The extended algorithm was also applied to real MODIS daytime and nighttime observations and showed that it is capable of retrieving medium-scale atmospheric temperature water vapor and retrieving surface temperature emissivity with retrieval accuracy similar to that achieved by the Geostationary Operational Environmental Satellite (GOES) but at a spatial resolution higher than that of GOES. OCIS codes: 280.0280, 300.1030, 300.2140, 350.5610, 130.3060.

We made an experimental vicarious calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) thermal infrared (TIR) channel data acquired in the field campaign near Mono Lake, Calif. On 10 March 1998 to demonstrate the advantage of using high-elevation sites in dry atmospheric conditions for vicarious calibration. With three lake-surface sites and one snow-field site, we estimated the MAS noise-equivalent temperature difference as 0.7-1.0 degrees C for bands 30-32 in the 3.68-4.13 mu m region and 0.1-0.5 degrees C for bands 42, 45, 46, and 48 in the 8-13.5 mu m region. This study shows that the MAS calibration error is within +/- 0.4 degrees C in the split-window channels (at 11 and 12 mu m) and larger in other TIR channels based on the MAS data over Mono Lake and in situ measurement data over the snow-field site.

The first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is planned for launch by NASA in 1998. This instrument will provide a new and improved capability for terrestrial satellite remote sensing aimed at meeting the needs of global change research. The MODIS standard products will provide new and improved tools for moderate resolution land surface monitoring. These higher order data products have been designed to remove the burden of certain common types of data processing from the user community and meet the more general needs of global-to-regional monitoring, modeling, and assessment. The near-daily coverage of moderate resolution data from MODIS, coupled with the planned increase in high-resolution sampling from Landsat 7, will provide a powerful combination of observations. The full potential of MODIS will be realized once a stable and well-calibrated time-series of multispectral data has been established. In this paper the proposed MODIS standard products for land applications are described along with the current plans for data quality assessment and product validation. Index Terms - Earth Observing System-Moderate Resolution Imaging Spectroradiometer (EOS-MODIS), global change, monitoring, satellite remote sensing.

This paper presents modifications to the linear kernel bidirectional reflectance distribution function (BRDF) models from Roujean et al. and from Warmer et al. that extend the spectral range into the thermal infrared (TIR). Our application is to synthesize the TIR optical properties of a scene pixel from laboratory component measurements. The angular reflectance and emissivity are needed to convert the radiance of a pixel as measured from space to land-surface temperature. The kernel models will be applied to develop a look-up table for the MODIS land-surface temperature algorithm to estimate the spectral, angular scene emissivity from land cover classification. A shrub scene and a dense canopy scene illustrate qualitative differences in angular emissivity that would not be evident without the kernel model modifications. We conclude that the modified models provide a simple and efficient way to estimate scene optical properties over a wide spectral range. Index Terms - Bidirectional reflectance, emissivity, thermal infrared.

We have developed a physics-based land-surface temperature (LST) algorithm for simultaneously retrieving surface band-averaged emissivities and temperatures from day/night pairs of MODIS (Moderate Resolution Imaging Spectroradiometer) data in seven thermal infrared bands. The set of 14 nonlinear equations in the algorithm is solved with the statistical regression method and the least-squares fit method. This new LST algorithm was tested with simulated MODIS data for 80 sets of band-averaged emissivities calculated from published spectral data of terrestrial materials in wide ranges of atmospheric and surface temperature conditions. Comprehensive sensitivity and error analysis has been made to evaluate the performance of the new LST algorithm and its dependence on variations in surface emissivity and temperature, upon atmospheric conditions, as well as the noise-equivalent temperature difference (NEAT) and calibration accuracy specifications of the MODIS instrument. In cases with a systematic calibration error of 0.5%, the standard deviations of errors in retrieved surface daytime and nighttime temperatures fall between 0.4-0.5 K over a wide range of surface temperatures for mid-latitude summer conditions. The standard deviations of errors in retrieved emissivities in bands 31 and 32 (in the 10-12.5 [[micro]meter] IR spectral window region) are 0.009, and the maximum error in retrieved LST values falls between 2-3 K.

We propose a generalized split-window method for retrieving land-surface temperature (LST) from AVHRR and MODIS data. Accurate radiative transfer simulations show that the coefficients in the split-window algorithm for LST must vary with the viewing angle, if we are to achieve a LST accuracy of about 1 K for the whole scan swath range ([+ or -]55[degrees] from nadir) and for the ranges of surface temperature and atmospheric conditions over land, which are much wider than those over oceans. We obtain these coefficients from regression analysis of radiative transfer simulations, and we analyze sensitivity and error over wide ranges of surface temperature and emissivity and atmospheric water vapor abundance and temperature. Simulations show that when atmospheric water vapor increases and viewing angle is larger than 45[degrees], it is necessary to optimize the split-window method by separating the ranges of the atmospheric water vapor, lower boundary temperature, and the surface temperature into tractable subranges. The atmospheric lower boundary temperature and (vertical) column water vapor values retrieved from HIRS/2 or MODIS atmospheric sounding channels can be used to determine the range for the optimum coefficients of the split-window method. This new algorithm not only retrieves land-surface temperature more accurately, but is also less sensitive to uncertainty in emissivity and to instrument quantization error.

Land surface temperature algorithms for the moderate resolution imaging spectroradiometer satellite instrument will require the spectral bidirectional reflectance distribution function (BRDF) of natural surfaces in the thermal infrared. We designed the spectral infrared bidirectional reflectance and emissivity instrument to provide such measurements by the use of a Fourier transform infrared spectrometer. A problem we encountered is the unavoidable surface heating caused by the source irradiance. For our system, the effects of the heating can cause a 30% error in the measured BRDF. The error caused by heating is corrected by temporally curve fitting the radiance signal. This curve-fitting technique isolates the radiance caused by reflected irradiance. With this correction, other factors dominate the BRDF error. It is now [approximately]5% and can be improved further. The method is illustrated with measurements of soil BRDF. Key words: Emissivity, reflectance, thermal infrared, spectrometer.

A two-step physical algorithm that simultaneously retrieves geophysical parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements was developed. The retrieved geophysical parameters include atmospheric temperature-humidity profile, surface skin temperature, and two surface emissivities within the shortwave (3-5-[micro]m) and the longwave (8-14.5-[micro]m) regions. The physical retrieval is accomplished in two steps: (i) The Tikhonov regularization method is employed to generate a regularization solution along with an optimum regularization parameter; (ii) the nonlinear Newtonian iteration algorithm is carried out with the regularization solution as a first-guess profile to obtain a final maximum probability solution for geophysical parameters. The algorithm was tested with both simulated and real MODIS Airborne Simulator (MAS) data. Sensitivity studies on simulated MAS data demonstrate that simultaneous retrievals of land and atmospheric parameters improve the accuracy of the retrieved geophysical parameters. Finally, analysis and accuracy of retrievals from real MAS data are discussed. [C] 2000 Optical Society of America OCIS codes: 280.0280, 030.5620.

As an intrinsic property of natural materials, land surface emissivity (LSE) is an important surface parameter and can be derived from the emitted radiance measured from space. Besides radiometric calibration and cloud detection, two main problems need to be resolved to obtain LSE values from space measurements. These problems are often referred to as land surface temperature (LST) and emissivity separation from radiance at ground level and as atmospheric corrections in the literature. To date, many LSE retrieval methods have been proposed with the same goal but different application conditions, advantages, and limitations. The aim of this article is to review these LSE retrieval methods and to provide technical assistance for estimating LSE from space. This article first gives a description of the theoretical basis of LSE measurements and then reviews the published methods. For clarity, we categorize these methods into (1) (semi-)empirical or theoretical methods, (2) multi-channel temperature emissivity separation (TES) methods, and (3) physically based methods (PBMs). This article also discusses the validation methods, which are of importance in verifying the uncertainty and accuracy of retrieved emissivity. Finally, the prospects for further developments are given. [ABSTRACT FROM PUBLISHER]

The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua satellites conducts continuous monitoring of the Earth's land surface and oceans. Recently, a sharp discontinuity (averaging 1.9°C) has been noticed at 60° N in both MODIS daytime and night-time land surface temperature (LST) products. This linear artefact arises because the CO2 high cloud test in the operational code for the generation of the MODIS cloud mask product is used only in the non-polar region (between 60° N and 60° S). The resulting discontinuity clearly has negative implications for any statistical applications of these temperature data. In this technical note we present a new algorithm, which minimizes this discontinuity. The method uses edge detection and elimination based on a mixture of Sobel and non-linear Laplacian filters (edge detection and quantification), cubic splines (edge modelling), and a controllable power function for image restoration. The implementation of this algorithm is demonstrated on an image of average minimum night-time LST between 2001 and 2008. [ABSTRACT FROM PUBLISHER]

This paper discusses the lessons learned from analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) Land-Surface Temperature/Emissivity (LST) products in the current (V4) and previous versions, and presents eight new refinements for V5 product generation executive code (PGE16) and the test results with real Terra and Aqua MODIS data. The major refinements include considering surface elevation when using the MODIS cloudmask product, removal of temporal averaging in the 1 km daily level-3 LST product, removal of cloud-contaminated LSTs in level-3 LST products, and the refinements for the day/night LST algorithm. These refinements significantly improved the spatial coverage of LSTs, especially in highland regions, and the accuracy and stability of the MODIS LST products. Comparisons between V5 LSTs and in-situ values in 47 clear-sky cases (in the LST range from −10 °C to 58 °C and atmospheric column water vapor range from 0.4 to 3.5 cm) indicate that the accuracy of the MODIS LST product is better than 1 K in most cases (39 out of 47) and the root of mean squares of differences is less than 0.7 K for all 47 cases or 0.5 K for all but the 8 cases apparently with heavy aerosol loadings. Emissivities retrieved by the day/night algorithm are well compared to the surface emissivity spectra measured by a sun-shadow method in two field campaigns. The time series of V5 MODIS LST product over two sites (Lake Tahoe in California and Namco lake in Tibet) in 2003 are evaluated, showing that the quantity and quality of MODIS LST products depend on clear-sky conditions because of the inherent limitation of the thermal infrared remote sensing. [Copyright &y& Elsevier]

This paper presents the status of land-surface temperature (LST) standard products retrieved from Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Based on estimates of the channel-dependence error and noise equivalent temperature difference (NEDT) and the calibration accuracy of MODIS thermal–infrared data, the impact of instrument performance on the accuracy of LST is discussed. A double-screen scheme based on the difference between the 5-km LST retrieved by the day/night LST algorithm and the aggregated 1-km LST retrieved by the generalized split-window algorithm, and the difference between daytime and nighttime LSTs, is proposed to remove the LSTs contaminated with cloud effects. The accuracy of daily MODIS LST product at 1-km resolution, which was produced by the generalized split-window algorithm, was validated in 11 clear-sky cases with in situ measurement data collected in field campaigns in 2000 and 2001. The MODIS LST accuracy is better than 1 K in the range from 263 to 300 K over Lake Titicaca in Bolivia, Mono Lake, Bridgeport grassland, and a rice field in Chico, CA, and Walker Lake, Nevada (NV), in the atmospheric column water vapor range from 0.4 to 3.0 cm. It is difficult to validate the daytime LST product over land sites rather than lakes with ground-based measurements alone because of the high spatial variations in the in situ LST measurement data, which was verified by the daytime data of the MODIS Airborne Simulator (MAS) over a grassland in Bridgeport, CA, on October 6, 2000. In six cases over a silt playa in Railroad Valley, NV, the 1-km MODIS LSTs are a few Kelvin degrees lower than the in situ measured LSTs because the surface emissivities inferred from land cover types in the split-window LST method are often overestimated in semi-arid and arid regions. After a correction with the difference between the 5-km LST retrieved by the day/night LST method and the LST aggregated from 1-km LSTs retrieved by the split-window method, the MODIS LSTs agree with in situ measured LSTs within ±1 K in the range 263–322 K for the six cases in Railroad Valley and one case of snowcover in Bridgeport, CA, leading a recommendation for use of the 5-km LST product retrieved by the day/night LST method in bare and sparse vegetated areas. [Copyright &y& Elsevier]