An analysis of GOES and NOAA derived land surface temperatures estimated over a boreal forest

Precise estimates of land surface temperature (LST) are important for land monitoring investigations. This study compares LST values calculated using different satellite platforms (Geostationary Operational Environmental Satellite-Imager and National Oceanographic and Atmospheric Administration-Advanced Very High Resolution Radiometer) and five different split window algorithms. The analysis includes (1) a fitting test with the reference dataset, (2) a comparison of differences between algorithms, and (3) an inter-sensor comparison. Considering the hypothesis of the Temperature/Vegetation Index (TVX) technique, the reference dataset was made with air temperature measured over dense canopy having maximum Normalized Difference Vegetation Index (NDVI). The first and second analyses show that algorithms used by Becker and Li and Ulivieri et al. have smaller estimation errors (less than 2.3 K) than the other algorithms, for example, best-fit linear regression. Although these algorithms show a good agreement in the paired algorithms analysis, the final analysis presents a considerable difference in the root mean square error between Imager and AVHRR (1.7 K for the Ulivieri et al. algorithm and 5.3 K for the Becker and Li algorithm). Finally we considered that the Ulivieri et al. method is more stable for both satellites.     

Derivation of land surface temperatures from MODIS data using the general split‐window technique

Fast Atmospheric Signature Code (FASCODE), a line‐by‐line radiative transfer programme, was used to simulate Moderate Resolution Imaging Spectroradiometer (MODIS) data at wavelengths 11.03 and 12.02 µm to ascertain how accurately the land surface temperature (LST) can be inferred, by the split‐window technique (SWT), for a wide range of atmospheric and terrestrial conditions. The approach starts from the Ulivieri algorithm, originally applied to Advanced Very High Resolution Radiometer (AVHRR) channels 4 and 5. This algorithm proved to be very accurate compared to several others and takes into account the atmospheric effects, in particular the water vapour column (WVC) amount and a non‐unitary surface emissivity. Extended simulations allowed the determination of new coefficients of this algorithm appropriate to MODIS bands 31 and 32, using different atmospheric conditions. The algorithm was also improved by removing some of the hypothesis on which its original expression was based. This led to the addition of a new corrective term that took into account the interdependence between water vapour and non‐unitary emissivity values and their effects on the retrieved surface temperature. The LST products were validated within 1 K with in situ LSTs in 11 cases.     

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.     

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.     

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.     

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.     

Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in pixel-wise LST. Spatial scaling may account for the uncertainty, however, different approaches lead to differences in scaled values. Satellite-retrieved LST may be representative of the pixel-wise LST and useful for scaling analysis, but the limited accuracy of retrieved values adds uncertainty into the scaled values. Based on the Stefan-Boltzmann (S-B) law, this study proposed scaling approaches for LST over flat and relief areas to explore the combined uncertainties in scaling using satellite-retrieved data. To take advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from these two sensors were examined for part of the Loess Plateau in China. 90-m ASTER LST data were scaled up to 1 km using the proposed approaches, and variation in the LST was generally reduced after scaling. Amongst the sources of uncertainties, surface heterogeneity (emissivity) and different scaling approaches resulted in very minor differences, with a maximum difference of 0.2 K for the upscaled LST. Terrain features, taken as an areal weighting factor, had negligible effects on the upscaled value. Limited accuracy of the retrieved LST was the major uncertainty. The overall LST increased 0.6 K on average with correction for terrain-induced angular effect and 0.4 K for both angular and adjacency effects over the study area. Accounting for terrain correction in scaling is necessary for rugged areas. With terrain correction, the upscaled ASTER LST achieved an agreement of − 0.1 ± 1.87 K and a root mean square error (RMSE) of 1.87 K overall with the 1-km MODIS LST rectified by Wan et al.'s approach [Wan, Z., Zhang, Y., Zhang Q., Li, Z.-L. (2002b), Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 83, 163-180]. Refining the rectification approach resulted in a better agreement of − 0.2 ± 1.57 K and a RMSE of 1.58 K.     

基于分组分裂窗算法的MTSAT-1R地表温度反演

以黑河流域上游和中游为研究区,针对MTSAT-1R卫星数据,运用MODTRAN 4.0及晴空状态下的TIGR大气廓线数据,发展了根据地表比辐射率、大气水汽含量、传感器观测角度分组模拟的分裂窗算法,进行地表温度反演。分析了传感器噪声、地表比辐射率和大气水汽含量3个参数对该算法的影响,并结合模拟数据、地面观测数据及MODIS地表温度产品,对反演结果进行分析评价。结果表明:当传感器垂直观测或大气水汽含量小于2.5g/cm2时,反演精度在1K以内;反演结果与地面观测数据对比差异较小,在阿柔站RMSE为3.7 K(日)/1.4 K(夜),在盈科站RMSE为2.4K(日)/2.0K(夜);与MODIS地表温度产品比较,空间分布呈现出一致性。总之,分组分裂窗算法能较好地用于MTSAT-1R卫星数据进行地表温度反演。     

Mapping micro-urban heat islands using NOAA/AVHRR images and CORINE Land Cover: an application to coastal cities of Greece

Land Surface Temperature (LST) is a significant parameter for identifying micro-climatic changes, their spatial distribution and intensities in relation to the urban environment. In this study, LST is estimated using thermal infrared data as acquired by the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the National Oceanic and Atmospheric Administration (NOAA) satellite and by using a split window algorithm that is adjusted to account for the region of Greece. For the assignment of the surface emissivity, a new methodology based on the Coordination of Information on the Environment (CORINE) Land Cover database for Greece is used. The algorithm is applied to a night-time series of NOAA/AVHRR images of Greece in order to produce surface temperature maps of an enhanced spatial resolution of 250?m for the cities of Thessaloniki, Patra, Volos and Iraklion, which are the most significant harbour cities of Greece. Results indicate the presence of urban heat islands (UHIs) in each case study, with highest temperatures detected along the coastal zone of the harbour cities resulting from denser urban fabric and road network as well as intense human activity.     

Separating surface emissivity and temperature using two-channel spectral indices and emissivity composites and comparison with a vegetation fraction method

The temperature-independent thermal infrared spectral indices (TISI) method is employed for the separation of land surface temperature (LST) and emissivity from surface radiances (atmospherically corrected satellite data). The daytime reflected solar irradiance and the surface emission at ∼3.8 μm have comparable magnitudes. Using surface radiances and a combination of day-night 2-channel TISI ratios, the ∼3.8 μm reflectivity is derived. For implementing the TISI method, coefficients for NOAA 9-16 AVHRR channels are obtained. A numerical analysis with simulated surface radiances shows that for most surface types (showing nearly Lambertian behavior) the achievable accuracy is ∼0.005 for emissivity (AVHRR channel-5) and ∼1.5 K for LST. Data from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for calculation of atmospheric attenuation. Comparisons are made over a part of central Europe on two different dates (seasons). Clouds pose a major problem to surface observations; hence, monthly emissivity composites are derived. Additionally, using TISI-based monthly composites of emissivities, a normalized difference vegetation index (NDVI)-based method is tuned to the particular study area and the results are intercompared. Once the coefficients are known, the NDVI method is easily implemented but holds well only for vegetated areas. The error of the NDVI-based emissivities (with respect to the TISI results) ranges between −0.038 and 0.032, but for vegetated areas the peak of the error-histogram is at ∼0.002. The algorithm for retrieving emissivity via TISI was validated with synthetic data. Due to the different spatial scales of satellite and surface measurements and the lack of homogeneous areas, which are representative for low-resolution pixels and ground measurements, ground-validation is a daunting task. However, for operational products ground-truth validation is necessary. Therefore, also an approach to identify suitable validation sites for meteorological satellite products in Europe is described.     

Estimation of the vapour pressure deficit using NOAA-AVHRR data

In this study, the calculation of vapour pressure deficit (VPD) using the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA/AVHRR) satellite data set is shown. Twenty-four NOAA/AVHRR data images were arranged and turned to account for both VPD and land surface temperature (LST), which was necessary to calculate the VPD. The most accurate LST values were obtained from the Ulivieri et al. split-window algorithm with a root mean square error (RMSE) of 2.7 K, whereas the VPD values were retrieved with an RMSE of 6 mb. Furthermore, the VPD value was calculated on an average monthly basis and its correlation coefficient was found to be 0.991, while the RMSE value was calculated to be 2.67 mb. As a result, VPD can be used in studies that examine plants (germination, growth, and harvest), controlling illness outbreak, drought determination, and evapotranspiration.     

Developed split window method to retrieve land surface temperature from HYTES thermal hyperspectral images using genetic algorithm

This article aims to establish a new method to retrieve land surface temperature (LST) from hyperspectral thermal emission spectrometer (HYTES) data with split window (SW) algorithm. First, the optimal bands of HYTES sensor were selected with the genetic algorithm and then were used in the SW algorithm. In the SW algorithm, its coefficients were obtained based on several subranges of atmospheric column water vapours (CWVs) and view zenith angle (VZA) under various land surface conditions, in order to remove the atmospheric effect and improve the retrieval accuracy. Results showed that the root-mean-square error (RMSE) varies for different CWV and VZA, and with the increasing CWV and VZA, the RMSE value also increases. The emissivity, CWV, and VZA were also obtained for pixels. The sensitive analysis of LST retrieval to instrument noise and uncertainty of pixel emissivity and water vapour demonstrated the good performance of the proposed algorithm. Finally, the new algorithm was applied to HYTES sensor data, and the LST was validated using LST product of HYTES sensor obtained by NASA. The results showed that the RMSE of the LST retrieval with the proposed algorithm and the LST product of sensor for data 1 and data 2 is 1.3 K and 1.6 K, respectively.     

Quantitative comparison of the operational NOAA-AVHRR LST product of DLR and the MODIS LST product V005

Land surface temperature (LST) products of two different sensors – the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) – were cross-compared. The analysis was conducted on a daily basis for four different years. Only pixels that stuck to certain homogeneity criteria were chosen. Furthermore, a time criterion defining the maximal time difference between two acquisitions was introduced.

The differences between the two products showed both a diurnal and an annual pattern, with LST of AVHRR being higher than that of MODIS at high surface temperatures and AVHRR LST being lower than MODIS LST at lower temperatures. Additionally, some irregular patterns were identified and attributed to the different algorithm approaches. Mean annual absolute differences were relatively low: only 2.2 K for the daytime and 1.4 K for the night-time scenes, speaking for a general good agreement between the two products. The coefficient of determination (R ²) between the LST of AVHRR and MODIS of both day and night scenes was 0.99.     

基于MODIS 影像数据的劈窗算法研究及其参数确定

劈窗算法是目前由热红外遥感数据获取陆面温度的主要方法。在介绍劈窗算法的一般表现形式的基础上, 我们推导出适合于MOD IS 影像数据的劈窗算法。大气透过率和地表比辐射率是求解地表温度的两个关键参数。由于MOD IS 图像分辨率较低,MOD IS 像元主要由水面、植被和裸土3种地物类型构成, 故可依据这3 种地物的构成比例确定地表比辐射率。从遥感影像上反演大气的水汽含量, 再根据大气水汽含量与大气透过率的关系计算出大气透过率。最后将文中推导的劈窗算法用于江苏省地表温度的反演。反演出来的地表温度图显示出明显的地表温度空间差异、城市热岛效应和不同的地物类型。     

New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products

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.     

Evaluation of MODIS land surface temperature with in-situ snow surface temperature from CREST-SAFE

This article presents the procedure and results of a temperature-based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) product provided by the National Aeronautics and Space Administration Terra and Aqua Earth Observing System satellites using in-situ LST observations recorded at the Cooperative Remote Sensing Science and Technology Center – Snow Analysis and Field Experiment (CREST-SAFE) during the years of 2013 (January–April) and 2014 (February–April). A total of 314 day-and-night clear-sky thermal images, acquired by the Terra and Aqua satellites, were processed and compared to ground-truth data from CREST-SAFE with a frequency of one measurement every 3 min. CREST-SAFE is a synoptic ground station, located in the cold county of Caribou in Maine, USA, with a distinct advantage over most meteorological stations because it provides automated and continuous LST observations via an Apogee Model SI-111 Infrared Radiometer. This article also attempts to answer the question of whether a single pixel (1 km²) or several spatially averaged pixels should be used for satellite LST validation by increasing the MODIS window size to 5 × 5, 9 × 9, and 25 × 25 windows.

Several trends in the MODIS LST data were observed, including the underestimation of daytime values and night-time values. Results indicate that although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation with ground measurements, day values yielded slightly higher accuracy (about 1°C), both suggesting that MODIS LST retrievals are reliable for similar land-cover classes and atmospheric conditions. Increasing the MODIS window size showed an overestimation of in-situ LST and some improvement in the daytime Terra and night-time Aqua biases, with the highest accuracy achieved with the 5 × 5 window. A comparison between MODIS emissivity from bands 31, 32, and in-situ emissivity showed that emissivity errors (relative error = ?0.30%) were insignificant.     

利用MODIS数据反演地表温度的研究

地表温度(LST)是气象、水文、生态等研究中一个重要的参数,目前国内的研究大多使用NOAA/AVHRR数据来获取地表温度,应用MODIS数据获取LST基本上还是空白。MODISLST反演算法精度较高但是计算复杂,在很大程度上限制了其应用。采用简单的统计方法和神经网络方法,得出了内蒙古东北地区的LST计算公式。该公式计算简单而且精度很高,完全能够满足一般的研究需要。     

Estimation of diurnal air temperature using MSG SEVIRI data in West Africa

Spatially distributed air temperature data with high temporal resolution are desired for several modeling applications. By exploiting the thermal split window channels in combination with the red and near infrared channels of the geostationary MSG SEVIRI sensor, multiple daily air temperature estimates can be achieved using the contextual temperature-vegetation index method. Air temperature was estimated for 436 image acquisitions during the 2005 rainy season over West Africa and evaluated against in situ data from a field test site in Dahra, Northern Senegal. The methodology was adjusted using data from the test site resulting in RMSE = 2.55 K, MBE = − 0.30 K and R² = 0.63 for the estimated versus observed air temperatures. A spatial validation of the method using 12 synoptic weather stations from Senegal and Mali within the Senegal River basin resulted in overall values of RMSE = 2.96 K, MBE = − 1.11 K and R² = 0.68. The daytime temperature curve is interpolated using a sine function based on the multiple daily air temperature estimates from the SEVIRI data. These estimates (covering the 8:00-20:00 UCT time window) were in good agreement with observed values with RMSE = 2.99 K, MBE = − 0.70 K and R² = 0.64. The temperature-vegetation index method was applied as a moving window technique to produce distributed maps of air temperature with 15 min intervals and 3 km spatial resolution for application in a distributed hydrological model.     

A practical split‐window algorithm for retrieving land‐surface temperature from MODIS data

This paper presents a practical split‐window algorithm utilized to retrieve land‐surface temperature (LST) from Moderate‐resolution Imaging Spectroradiometer (MODIS) data, which involves two essential parameters (transmittance and emissivity), and a new method to simplify Planck function has been proposed. The method for linearization of Planck function, how to obtain atmosphere transmittance from MODIS near‐infrared (NIR) bands and the method for estimating of emissivity of ground are discussed with details. Sensitivity analysis of the algorithm has been performed for the evaluation of probable LST estimation error due to the possible errors in water content and emissivity. Analysis indicates that the algorithm is not sensitive to these two parameters. Especially, the average LST error is changed between 0.19–1.1°C when the water content error in the simulation standard atmosphere changes between ?80 and 130%. We confirm the conclusion by retrieving LST from MODIS image data through changing retrieval water content error. Two methods have been used to validate the proposed algorithm. Results from validation and comparison using the standard atmospheric simulation and the comparison with the MODIS LST product demonstrate the applicability of the algorithm. Validation with standard atmospheric simulation indicates that this algorithm can achieve the average accuracy of this algorithm is about 0.32°C in LST retrieval for the case without error in both transmittance and emissivity estimations. The accuracy of this algorithm is about 0.37°C and 0.49°C respectively when the transmittance is computed from the simulation water content by exponent fit and linear fit respectively.     

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.