Potential of MSG for surface temperature and emissivity estimation: Considerations for real-time applications

Meteosat Second Generation (MSG) will provide data with an unprecedented combination of spatial, temporal, and spectral resolutions from geostationary orbit for Africa, and most of Europe and the Atlantic ocean. This article focuses on the potential of MSG's Spinning Enhanced Visible and Infrared Imager (SEVIRI) for Land Surface Temperature (LST) and emissivity estimation. SEVIRI's advantages over the Meteosat Visible and Infrared Imager (MVIRI) and the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) are outlined. On the basis of SEVIRI's spectral and temporal resolutions the Thermal Infrared Spectral Indices (TISI) day/night method is selected for estimating emissivity. The concept of using the Sun as an active source in the 3-4 w m window is summarized and SEVIRI-specific coefficients required by the TISI day/night method are supplied. The sensitivity of the method to atmospheric conditions and to surface emissivity is analysed using simulated radiances for standard atmospheres and channel emissivities derived from spectral laboratory measurements of different surface types. In order to obtain a fast and accurate procedure for the estimation of channel emissivities and LST, the combination of the TISI day/night method with a neural network (NN) for calculating atmospheric variables is proposed.     

Crseharacterization of land surface thermal structure from NOAA-AVHRR data over a northern ecosystem

This paper focuses on the estimation and analysis of surface thermal parameters (emissivities and surface temperatures) in a nordic environment (Québec, Canada). The land cover in this region varies from boreal forest in the south to tundra in the north. The thermal parameters are estimated from two variants of a new model that combines the radiances of the short wave infrared (SWIR) spectral band [advanced very high resolution radiometer (AVHRR) channel 3: 3.55–3.93 μm] and the thermal bands (AVHRR channel 4: 10.5–11.5 μm; and AVHRR channel 5: 11.5–12.5 μm). The study, carried out for images acquired on different dates, reveals that, in most situations, the two approaches allow the separation of emissivities and surface temperatures. Analysis of the variations of the estimated emissivities in relation to surface patterns shows that they are slightly variable in spectral bands 4, and 5, with values generally greater than 0.95. Variations are more important in the SWIR channel, where values less than 0.90 appear, especially in urban areas. In general, surface emissivities increase with the density of the vegetation cover. Moreover, for densely vegetated areas, SWIR surface reflectivities, which can be derived from emissivities, appear to be well correlated with the reflectivities of the AVHRR visible channel. As with emissivities, variations of the estimated land surface temperatures (LST) in relation to vegetation density, characterized by the normalized difference vegetation index (NDVI), were considered. The relations between the two parameters (LST and NDVI) show essentially two opposite directions of linear variations (positive and negative correlations). In the light of the main results obtained, the synergistic use of the different spectral regions (visible, near, mid-, and thermal infrareds) could be very useful in the parameterization of boreal ecosystems.     

Temperature and emissivity separation from ASTER data for low spectral contrast surfaces

The performance of Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) thermal infrared (TIR) data product algorithms was evaluated for low spectral contrast surfaces (such as vegetation and water) in a test site close to Valencia, Spain. Concurrent ground measurements of surface temperature, emissivity, and atmospheric radiosonde profiles were collected at the test site, which is a thermally homogeneous area of rice crops with nearly full vegetation cover in summer. Using the ground data and the local radiosonde profiles, at-sensor radiances were simulated for the ASTER TIR channels and compared with L1B data (calibrated at-sensor radiances) showing discrepancies up to 3% in radiance for channel 10 at 8.3 μm (equivalently, 2.5 °C in temperature or 7% in emissivity), whereas channel 13 (10.7 μm) yielded a closer agreement (maximum difference of 0.5% in radiance or 0.4 °C in temperature). We also tested the ASTER standard products of land surface temperature (LST) and spectral emissivity generated with the Temperature-Emissivity Separation (TES) algorithm with standard atmospheric correction from both global data assimilation system profiles and climatology profiles. These products showed anomalous emissivity spectra with lower emissivity values and larger spectral contrast (or maximum-minimum emissivity difference, MMD) than expected, and as a result, overestimated LSTs. In this work, a scene-based procedure is proposed to obtain more accurate MMD estimates for low spectral contrast materials (vegetation and water) and therefore a better retrieval of LST and emissivity with the TES algorithm. The method uses various gray-bodies or near gray-bodies with known emissivities and assumes that the calibration and atmospheric correction performed with local radiosonde data are accurate for channel 13. Taking the channel 13 temperature (atmospherically and emissivity corrected) as the true LST, the radiances for the other channels were simulated and used to derive linear relationships between ASTER digital numbers and at-ground radiances for each channel. The TES algorithm was applied to the adjusted radiances and the resulting products showed a closer agreement with the ground measurements (differences lower than 1% in channel 13 emissivities and within ± 0.3 °C in temperature for rice and sea pixels).     

Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends

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 article 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 methods reviewed 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.     

Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI

This work addressed the retrieval of Land Surface Emissivity (LSE) from combined mid-infrared and thermal infrared data of Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard the geostationary satellite—Meteosat Second Generation (MSG). To correct for the atmospheric effects in satellite measurements, a new atmospheric correction scheme was developed for both Middle Infra-Red (MIR) and Thermal Infra-Red (TIR) channels. For the MIR channel, because it is less sensitive to the change of water vapor content, the clear-sky and time-nearest European Centre for Median-range Weather Forecast (ECMWF) atmospheric data were used for the images where no atmospheric data are available. For TIR channels, a modified model of Diurnal Temperature Cycle (DTC) used by Göttsche and Olesen [Göttsche, F. M., and Olesen, F. S. (2001). Modeling of diurnal cycles of brightness temperature extracted from METEOSAT data. Remote Sensing of Environment, 76, 337-348.] and Schädlich et al. [Schädlich, S., Göttsche, F. M., and Olesen, F. S. (2001). Influence of land surface 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.] was adopted. The separation of Land Surface Temperature (LST) and LSE is based on the concept of the Temperature Independent Spectral Indices (TISI) [Becker, F., and Li, Z. L. (1990a). Temperature independent spectral indices in thermal infrared bands. Remote Sensing of Environment, 32, 17-33.] constructed with one channel in MIR and one channel in TIR. The results of two different combinations (combination of channels 4 and 9 and of channels 4 and 10) and two successive days at six specific locations over North Africa show that the retrievals are consistent. The range of emissivity in MSG-SEVIRI channel 4 goes from 0.5 for bare areas to 0.96 for densely vegetated areas, whereas the emissivities in MSG-SEVIRI channels 9 and 10 are usually from 0.9 to 0.95 for bare areas and from 0.95 to 1.0 for vegetated areas. For densely vegetated areas, the emissivities in MSG-SEVIRI channel 9 are larger than the ones in channel 10, whereas the opposite is observed over bare areas. The rms differences between two combinations over the whole studied region are 0.017 for emissivity in channel 4, 0.008 for emissivity in channel 9 and 0.007 for emissivity in channel 10.     

Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by Day/Night LST algorithm

Surface emissivity estimation is a significant factor for the land surface temperature estimation from remotely sensed data. For fully vegetated surfaces, the emissivity estimation is performed in a simple manner since the emissivity is relatively uniform. However, for arid land with sparse vegetation, the estimation is more complicated since the emissivity of the exposed soil and rock is highly variable. In this study, mean and difference emissivity for bands 31 and 32 of MODIS sensor have been derived based on NDVI values. First, the NDVI thresholds have been determined to separate bare soil, partially vegetated soil and fully vegetated land. Then regression relations have been derived to estimate mean and difference emissivity of the bare soil samples and partially vegetated surfaces. A constant emissivity is also used for fully vegetated area. Along with the correlations, standard deviations of the regression relations have been examined for a set of representative soil types. Standard deviations smaller than 0.003 in mean emissivity and smaller than 0.004 in difference emissivity are resulted in regression linear relations. Evaluation of the NDVI derived regression relations has been performed using the results of MODIS Day/Night Land Surface Temperature (LST) algorithm on a pair of MODIS images. Using around 45,500 pixels with different soil and land cover types, emissivity of each pixel in bands 31 and 32 have been estimated. The calculated emissivities have been compared with emissivities calculated by MODIS Day/Night LST algorithm. Biases and standard deviations of NDVI-based relations show relatively high agreement for mean and difference emissivity relations with Day/Night method results. It may be concluded that the proposed algorithm can be used as a rather simple alternative to complex emissivity estimation algorithms.     

The impact of spectral emissivity on the measurement of land surface temperature from a satellite

Abstract

The split-window method is successfully used to infer sea surface temperature from satellite radiances, principally because sea surface temperature is not very different from the air temperature near the surface and because the emissivity of the sea is constant over large areas and is not very different from one in the spectral channels of interest. This is not true for land surfaces and the split-window method has to be re-examined for such a case. This is the aim of this paper. In order to relate land surface temperature to the two brightness temperatures measured from space in the two channels of interest (namely, AVHRR 4 and AVHRR 5), several formulae are derived and their accuracies are discussed. Assuming that the emissivities ε₁ and ε₂ in the two channels considered, and therefore their average $ are unity, it is shown that the error ΔT generated on the land surface temperature by correcting atmospheric effects using the split-window method in most situations studied is of the order of

$

This error may be quite significant, except for the sea surface where it is shown to be negligible. In order to infer land surface temperature from space, it is therefore necessary to know the surface spectral emissivity to good accuracy. Possible methods to determine it are then proposed and discussed.     

Classification-based emissivity for land surface temperature measurement from space

Classification-based global emissivity is needed for the National Aeronautics and Space Administration Earth Observing System Moderate Resolution Imaging Spectrometer (NASA EOS/MODIS) satellite instrument land surface temperature (LST) algorithm. It is also useful for Landsat, the Advanced Very High Resolution Radiometer (AVHRR) and other thermal infrared instruments and studies. For our approach, a pixel is classified as one of fourteen 'emissivity classes' based on the conventional land cover classification and dynamic and seasonal factors, such as snow cover and vegetation index. The emissivity models we present provide a range of values for each emissivity class by combining various spectral component measurements with structural factors. Emissivity statistics are reported for the EOS/MODIS channels 31 and 32, which are the channels that will be used in the LST split-window algorithm.     

Progress in the remote sensing of land surface temperature and ground emissivity using NOAA-AVHRR data

The extensive requirement of landsurface temperature (LST) for environmental studies and management activities of the Earth's resources has made the remote sensing of LST an important academic topic during the last two decades. Many studies have been devoted to establishing the methodology for the retrieval of LST from channels 4 and 5 of Advanced Very High Resolution Radiometer (AVHRR) data. Various split-window algorithms have been reviewed and compared in the literature to understand their differences. Different algorithms differ in both their forms and the calculation of their coefficients. The most popular form of split-window algorithm is T _s=T ₄+A(T ₄-T ₅)+B , where T _s is land surface temperature, T ₄ and T ₅ are brightness temperatures of AVHRR channels 4 and 5, A and B are coefficients in relation to atmospheric effects, viewing angle and ground emissivity. For the actual determination of the coefficients, no matter the complexity of their calculation formulae in various algorithms, only two ways are practically applicable, due tothe unavailability of many required data on atmospheric conditions and ground emissivities in situ satellite pass. Ground data measurements can be used to calibrate the brightness temperature obtained by remote sensing into the actual LST through regression analysis on a sample representing the studied region. The other way is standard atmospheric profile simulationusing computer software such as LOWTRAN7. Ground emissivity has a considerable effect on the accuracy of retrieving LST from remote sensing data. Generally, it is rational to assume an emissivity of 0.96 for most ground surfaces. However, the difference of ground emissivity between channels 4 and 5 also has a significant impact on the accuracy of LST retrieval. By combining the data of AVHRR channels 3, 4 and 5, the difference can be directly calculated from remote sensing data. Therefore, much more study is required on how to accurately determine the coefficients of split-window algorithms in the application of remote sensing to examine LST change and distribution in the real world.     

Improved land surface emissivities over agricultural areas using ASTER NDVI

Land surface emissivity retrieval over agricultural regions is important for energy balance estimations, land cover assessment and other related environmental studies. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) produces images of sufficient spatial resolution (from 15 m to 90 m) to be of use in agricultural studies, in which fields of crops are too small to be well-resolved by low resolution sensors. The ASTER project generates land surface emissivity images as a Standard Product (AST05) using the Temperature/Emissivity Separation (TES) algorithm. However, the TES algorithm is prone to scaling errors in estimating emissivities for surfaces with low spectral contrast if the atmospheric correction is inaccurate. This paper shows a comparison between the land surface emissivity estimated with the TES algorithm and from a simple approach using the Normalized Difference Vegetation Index (NDVI) for five ASTER images (28 June 2000, 15 August 2000, 31 August 2000, 28 April 2001 and 02 August 2001) of the agricultural area of Barrax (Albacete, Spain). The results indicate that differences are < 1% for ASTER band 13 (10.7 μm) and < 1.5% for band 14 (11.3 μm), but > 2% for bands 10 (8.3 μm), 11 (8.6 μm) and 12 (9.1 μm). The emissivities for the five ASTER bands were tested against in situ measurements carried out with the CIMEL CE 312-2 field radiometer, the NDVI method giving root mean square errors (RMSE) < 0.005 over vegetated areas and RMSE < 0.015 over bare soil, and the TES algorithm giving RMSE ∼ 0.01 for vegetated areas but RMSE > 0.03 over bare soil. The errors and inconsistencies for ASTER bands 13 and 14 are within those anticipated for TES, but the greater errors for bands 10-12 suggest the presence of problems related to atmospheric compensation and model assumptions about soil spectra. The NDVI method uses visible/near-infrared data co-acquired with the thermal images to estimate vegetation cover and, hence, provides an independent constraint on emissivity. The success of this approach suggests that it may be useful for daytime images of agricultural or other heavily vegetated areas, in which the TES algorithm has occasional failures.     

Vegetation spatial variability and its effect on vegetation indices

Abstract

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification programme. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12·7 per cent in determining these areas. NDVI values less than 0·3 represented fractional vegetated areas of 5 per cent or less, while a value of 0·7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0·89 and 0·95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.     

Obtaining global land-surface broadband emissivity from MODIS collection 5 spectral albedos using a dynamic learning neural network

Surface broadband emissivity (BBE) is a key parameter for estimating surface radiation budget, but it is treated crudely in land-surface models because of a lack of global-scale observational BBE data. In this study, the non-linear relationship between the BBE that is calculated from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity product and the seven Moderate Resolution Imaging Spectroradiometer (MODIS) narrowband albedos was established individually for bare soils, transition areas, and vegetated areas using a dynamic learning neural network (DLNN). The trained DLNN was tested using a vast array of independent samples, and the results are robust with a bias and root-mean square error (RMSE) of –1e^?4 and 0.012 for bare soils, 2e^?4 and 0.012 for transition areas, and 7e^?4 and 0.010 for vegetated areas. Two independent field-measured emissivity data sets that were measured over sand dunes were used to validate the DLNN. With respect to the BBE that was calculated from the field-measured emissivities, the bias was 0.019. Ultimately, we introduced the strategy of generating a global land-surface BBE product and presented an example of a global BBE map.     

Temperature and emissivity separation from multispectral thermal infrared observations

Knowledge of the surface emissivity is important for determining the radiation balance at the land surface. For heavily vegetated surfaces, there is little problem since the emissivity is relatively uniform and close to one. For arid lands with sparse vegetation, the problem is more difficult because the emissivity of the exposed soils and rocks is highly variable. With multispectral thermal infrared (TIR) observations, it is possible to estimate the spectral emissivity variation for these surfaces. We present data from the TIMS (Thermal Infrared Multispectral Scanner) instrument, which has six channels in the 8- to 12-μm region. TIMS is a prototype of the TIR portion of the ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) instrument on NASA's Terra (EOS-AM1) platform launched in December 1999. The Temperature Emissivity Separation (TES) algorithm, developed for use with ASTER data, is used to extract the temperature and six emissivities from the six channels of TIMS data. The algorithm makes use of the empirical relation between the range of observed emissivities and their minimum value. This approach was applied to the TIMS data acquired over the USDA/ARS Jornada Experimental Range in New Mexico. The Jornada site is typical of a desert grassland where the main vegetation components are grass (black grama) and shrubs (primarily mesquite) in the degraded grassland. The data presented here are from flights at a range of altitudes from 800 to 5000 m, yielding a pixel resolution from 3 to 12 m. The resulting spectral emissivities are in qualitative agreement with laboratory measurements of the emissivity for the quartz rich soils of the site. The derived surface temperatures agree with ground measurements within the standard deviations of both sets of observations. The results for the 10.8- and 11.7-μm channels show limited variation of the emissivity values over the mesquite and grass sites indicating that split window approaches may be possible for conditions like these.     

Accuracy assessment of land surface temperature retrievals from MSG2-SEVIRI data

The accuracy of the Land Surface Temperature (LST) product generated operationally by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF) from the data registered by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary METEOSAT Second Generation 2 (MSG2, Meteosat 9) satellite was assessed on two test sites in Eastern Spain: a homogeneous, fully vegetated rice field and a high-plain, homogeneous area of shrubland. The LSA SAF LSTs were compared with ground LST measurements in the conventional temperature-based (T-based) method. We also validated the LSA SAF LST product by using an alternative radiance-based (R-based) method, with ground LSTs calculated from MSG-SEVIRI channel 9 brightness temperatures (at 10.8 μm) through radiative transfer simulations using atmospheric temperature and water vapor profiles together with surface emissivity data. Two lakes were also used for validation with the R-based method. Although the LSA SAF LST algorithm works mostly within the uncertainty expectation of ± 2 K, both validation methods showed significant biases for the LSA SAF LST product, up to 1.5 K in some cases. These biases, with the LSA SAF LST product overestimating reference values, were also observed in previous studies. Nevertheless, the present work points out that the biases are related to the land surface emissivities used in the operational generation of the product. The use of more appropriate emissivity values for the test sites in the LSA SAF LST algorithm led to better results by decreasing the biases by 0.7 K for the shrubland validation site. Furthermore, we proposed and checked an alternative algorithm: a quadratic split-window equation, based on a physical split-window model that has been widely proved for other sensors, with angular-dependent coefficients suitable for the MSG coverage area. The T-based validation results for this algorithm showed LST uncertainties (robust root-mean-squared-errors) from 0.2 K to 0.5 K lower than for the LSA SAF LST algorithm after the emissivity replacement. Nevertheless, the proposed algorithm accuracies were significantly better than those obtained for the current LSA SAF LST product, with an average accuracy difference of 0.6 K.     

Ground-based method for measuring thermal infrared effective emissivities: Implications and perspectives on the measurement of land surface temperature from satellite data

Thermal infrared emissivity is an important parameter for surface characterization and for determining surface temperature. The field-based measurements for ground and vegetation emissivities in 8-14 w m waveband were performed with an emissivity box. A theoretical analysis was carried out using the box and a correcting factor has been determined. The average value for thermal band emissivity of the exposed bare soil was found to be around 0.909; the average value measured for most of the varieties of vegetation present were in the range of 0.980-0.985. A theoretical model is used for obtaining effective emissivity in the 8-14 w m region from Advanced Very High Resolution Radiometer (AVHRR) data considering the proportion of vegetation cover in a pixel and the field-measured emissivity values. The error of the methodology is found to be within 1.5%. Narrow band emissivities for AVHRR channels 4 and 5 have been derived from the emissivity values in the 8-14 w m waveband. The surface temperature has been derived from AVHRR data using a split-window algorithm as a function of emissivities derived in narrow bands. The split-window algorithm accounted for absorption effects of the atmosphere by incorporating the water vapour concentration measured in the campaign. A good agreement was obtained between the satellite-derived surface temperature and the in situ observations. The result suggest that the methodology allows us to derive land surface temperature with an accuracy better than 1.5° C provided the surface emissivity is known. The paper describes the field-based emissivity measurement and approach for deriving surface temperature over land surface.     

Definition of component effective emissivity for heterogeneous and non-isothermal surfaces and its approximate calculation

Measurement of land surface temperature faces many problems. For example, each pixel over land surface is likely to be heterogeneous and non-isothermal with both vegetation canopy and background soil, and the three-dimensional structure of canopy often makes the canopy radiation angular dependent. It is difficult to define an overall land surface temperature and emissivity at pixel scale for heterogeneous and non-isothermal surfaces. After recalling several definitions of effective emissivities, component effective emissivity is defined in this paper under the conditions of local thermal equilibrium and a constant material emissivity for surface temperature variation in the normal Earth environment. Component effective emissivities make it possible to retrieve the component temperature based on multi-directional measurements. The sum of component effective emissivities is equal to the overall effective emissivity, which can be used to inverse pixel-averaged effective temperature. Taking the continuous plant/soil system as an example, the Monte Carlo method is used to simulate the effective emissivities, and an analytical expression equation (AEE) of the effective emissivities including direct-line emission and single scattering contribution is developed. Monte Carlo simulated results show that the sum of direct-line emission and single scattering effective emissivity is close to overall effective emissivity when soil and leaf are set to 0.94 and 0.98 respectively. Then component and overall effective emissivities calculated by Monte Carlo method and AEE are compared, and their differences are analysed for different soil and leaf emissivities. It is shown that when soil and leaf emissivities are set respectively to 0.94 and 0.98, the differences are less than 0.006 within a 50° view zenith angle. When soil or leaf emissivity is set to 0.9, the difference reaches 0.025 or 0.016, which is large enough to introduce a 1?K error in land surface temperature inversion when this effective emissivity is used. The paper finally proposes that the linear relationship of difference with soil and leaf emissivity can be used to compensate the errors.     

An improved NDVI-based threshold method for estimating land surface emissivity using MODIS satellite data

This work estimated the land surface emissivities (LSEs) for MODIS thermal infrared channels 29 (8.4–8.7 μm), 31 (10.78–11.28 μm), and 32 (11.77–12.27 μm) using an improved normalized difference vegetation index (NDVI)-based threshold method. The channel LSEs are expressed as functions of atmospherically corrected reflectance from the MODIS visible and near-infrared channels with wavelengths ranging from 0.4 to 2.2 μm for bare soil. To retain the angular information, the vegetation LSEs were explicitly expressed in the NDVI function. The results exhibited a root mean square error (RMSE) among the estimated LSEs using the improved method, and those calculated using spectral data from Johns Hopkins University (JHU) are below 0.01 for channels 31 and 32. The MODIS land surface temperature/emissivity (LST/E) products, MOD11_L2 with LSE derived via the classification-based method with 1 km resolution and MOD11C1 with LSE retrieved via the day/night LST retrieval method at 0.05° resolution, were used to validate the proposed method. The resultant variances and entropies for the LSEs estimated using the proposed method were larger than those extracted from MOD11_L2, which indicates that the proposed method better described the spectral variation for different land covers. In addition, comparing the estimated LSEs to those from MOD11C1 yielded RMSEs of approximately 0.02 for the three channels; however, more than 70% of pixels exhibited LSE differences within 0.01 for channels 31 and 32, which indicates that the proposed method feasibly depicts LSE variation for different land covers.     

Evaluation and improvement of the MODIS land surface temperature/emissivity products using ground‐based measurements at a semi‐desert site on the western Tibetan Plateau

Current MODerate‐resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST, surface skin temperature)/emissivity products are evaluated and improvements are investigated. The ground‐based measurements of LST at Gaize (32.30° N, 84.06° E, 4420 m) on the western Tibetan Plateau from January 2001 to December 2002 agree well (mean and standard deviation of differences of 0.27 K and 0.84 K) with the 1‐km Version 004 (V4) Terra MODIS LST product (MOD11A1) generated by the split‐window algorithm. Spectral emissivities measured from surface soil samples collected at and around the Gaize site are in close agreement with the landcover‐based emissivities in bands 31 and 32 used by the split‐window algorithm. The LSTs in the V4 MODIS LST/emissivity products (MYD11B1 for Aqua and MOD11B1 for Terra) from the day/night LST algorithm are higher by 1–1.7 K (standard deviation around 0.6 K) in comparisons to the 5‐km grid aggregated values of the LSTs in the 1‐km products, which is consistent with the results of a comparison of emissivities. On average, the emissivity in MYD11B1 (MOD11B1) is 0.0107 (0.0167) less than the ground‐based measurements, which is equivalent to a 0.64 K (1.25 K) overestimation of LST around the average value of 285 K. Knowledge obtained from the evaluation of MODIS LST/emissivity retrievals provides useful information for the improvement of the MODIS LST day/night algorithm. Improved performance of the refined (V5) day/night algorithm was demonstrated with the Terra MODIS data in May–June 2004.     

Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites

Land surface temperature (LST) and emissivity are key parameters in estimating the land surface radiation budget, a major controlling factor of global climate and environmental change. In this study, Terra Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 LST and emissivity products are evaluated using long-term ground-based longwave radiation observations collected at six Surface Radiation Budget Network (SURFRAD) sites from 2000 to 2007. LSTs at a spatial resolution of 90 m from 197 ASTER images during 2000-2007 are directly compared to ground observations at the six SURFRAD sites. For nighttime data, ASTER LST has an average bias of 0.1 °C and the average bias is 0.3 °C during daytime. Aqua MODIS LST at 1 km resolution during nighttime retrieved from a split-window algorithm is evaluated from 2002 to 2007. MODIS LST has an average bias of − 0.2 °C. LST heterogeneity (defined as the Standard Deviation, STD, of ASTER LSTs in 1 × 1 km² region, 11 × 11 pixel in total) and instrument calibration error of pyrgeometer are key factors impacting the ASTER and MODIS LST evaluation using ground-based radiation measurements. The heterogeneity of nighttime ASTER LST is 1.2 °C, which accounts for 71% of the STD of the comparison, while the heterogeneity of the daytime LST is 2.4 °C, which accounts for 60% of the STD. Collection 5 broadband emissivity is 0.01 larger than that of MODIS Collection 4 products and ASTER emissivity. It is essential to filter out the abnormal low values of ASTER daily emissivity data in summer time before its application.     

Temporal analysis of normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters to detect changes in the Iberian land cover between 1981 and 2001

In past decades, the Iberian Peninsula has been shown to have suffered vegetation changes such as desertification and reforestation. Normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters, estimated from data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series, are particularly adapted to assess these changes. This work presents an application of the yearly land-cover dynamics (YLCD) methodology to analyse the behaviour of the vegetation, which consists of a combined multitemporal study of the NDVI and LST parameters on a yearly basis. Throughout the 1981–2001 period, trend analysis of the YLCD parameters emphasizes the areas that have endured the greatest changes in their vegetation. This result is corroborated by results from previous studies.