Analysis of the atmospheric and emissivity influence on the splitwindow equation for sea surface temperature

In this paper we have analysed the effects of the different atmospheric species (water vapour, fixed gases and aerosols) and the surface emissivity on the split-window method for determining the sea surface temperature. The widely used split-window method is based on the differential absorption of water vapour in the atmospheric window 10.5ndash;12.5 μm. Other atmospheric species with absorption coefficients different to that of water vapour can then have a large influence on the split-window. The effect of gases, such as C0₂, N₂0, CH₄, CO and 0₃, and maritime aerosols is evaluated by comparing the effect of the water vapour alone. To do this we simulated AVHRR measurements in channels 4 and 5 for a set of mid-latitude atmospheres using LOWTRAN 7 code. Our results indicated that the fixed gases have a negative effect on the split-window specially for dry atmospheres; in this case the error in retrieved temperatures was shown to increase by about 70 per cent with respect to that obtained considering water vapour only. The effect of maritime aerosols was parameterised in terms of the surface meteorological range and the path optical thickness was measured at 0.55 μm, which can be obtained from both visible channels of AVHRR. The total effect on the split-window appeared to be a linear function of the path optical thickness. On the other hand, we analysed the impact of sea surface emissivity showing that it is strongly dependent on the observation angle, especially for angles larger than 40°. In addition to this it has been shown that the emissivity effect depends on the atmospheric moisture. However, for angles lower than 40° the atmospherically averaged emissivity effect is close to zero. Finally we have given a correction algorithm accounting for all the studied effects, yielding an error estimated at 0–34 degK. over the simulated mid-latitude data set.     

Retrieval of land and sea surface temperature using NOAA-11 AVHRR· data in north-eastern Brazil

The split-window method is investigated and a simple airborne atmospheric correction model for thermal data is proposed. Analysis shows that the atmospheric transmittance has a quadratic behaviour with the water vapour content in the first few kilometres of a wet atmosphere. The effect of the emissivity is evaluated for the retrieval of surface temperature using data from NOAA-11 AVHRR channels 4 and 5 for two extreme atmospheres. The results indicate that a spectral emissivity variation (Δε) in channels 4 and 5 of ±0.01 is not as important for a wet atmosphere as for a dry atmosphere. The split-window algorithm developed in this work has its parameters dependent on the atmospheric state and the values of these parameters are determined by using radiosonde profiles. Data from eighty-five radiosondes have been used to determine and check the local seasonal equatorial split-window parameters. The results of surface temperature retrieval show that the local seasonal equatorial and daily split-window parameters (given by daily radiosonde profiles) for NOAA-11 AVHRR data exhibit good agreement between their surface temperature results and the results of in situ measurements for two days. Comparisons with in situ measurements show that the maximum difference from retrieving a vegetated surface temperature using AVHRR data is less than 1.0°C in a wet atmosphere. Although the seasonal parameters have demonstrated a good performance when applied for two particular days, it does not indicate that they can be used successfully for other times when the atmospheric state differs from the average seasonal profile determined in this work. Evidence of this fact is shown through the variation of water vapour amount in the eighty-five radiosonde atmospheric profiles that have been analysed. This variation presents a wide range in water vapour from 2.8g cm^-2, to 4.92g cm^-2, which can significantly modify the retrieval of surface temperature using remote infrared sensors. Discussion of this problem is given in this paper.     

The estimation of atmospheric corrections to one-channel (11 μ m) data from the AVHRR; simulation using AVHRR/ 2

It has been established that the sea-surface brightness temperatures Tb₄ in the 11 μ m channel and T_b4in the 12 μ m channel of the Advanced Very High Resolution Radiometer (AVHRR/ 2) are linearly related to a good degree of accuracy, i.e. T_b5= α+ β T_b4 Using AVHRR/ 2 data for various dates and from different parts of the world's oceans, the parameters a and 0 have been determined. The above relation may then be used for simulating T_b5 for those cases for which only Tb4 is available (e.g. for the AVHRR on TIROS-N, NOAA-6, NOAA-8, etc.). The brightness temperature TM and pseudo-brightness temperature T_b5 then enable one to use the split-window technique for estimating atmospherically-corrected sea-surface temperatures (SSTs) from the 11μ m channel data alone. Such an atmospheric correction technique should be a possibility because the 11μ m channel of the AVHRR on the various satellites in question are almost identical

This technique has been used with two split-window algorithms for correcting the data from the 11μ m channel of the AVHRR instrument on the TIROS-N satellite obtained off south-western Portugal. One of the algorithms gives ‘ skin’ temperatures and the other algorithm gives bulk temperatures. The resulting SSTs for twelve dates from 15 June 1979 to 14 June 1980 have been compared with sea-surface (skin) temperatures which were obtained with airborne radiometer data obtained on the same dates.     

反演陆面温度的分裂窗口算法与应用分析

分裂窗口算法是目前由热红外遥感图象数据获取陆面温度最主要的方法。文中对１９９７年８月２２日内蒙古巴丹吉林沙漠地区的ＮＯＡＡ／ＡＶＨＲＲ热红外图像,利用辐射传输模型ＬＯＷＴＲＡＮ７计算大气参数进行大气校正,在采用Ｌｉ＆Ｂｅｃｋｅｒ算法（１９９３）反演出具有一定可信度的地表发射率基础上,选取常见的５种分裂窗口算法分别获取了该地区的地表辐射温度,并以Ｓｏｂｒｉｎｏ１９９１算法结果为标准,进行了算法间比较     

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.     

Validation of temperature-emissivity separation and split-window methods from TIMS data and ground measurements

Land surface temperature retrieved with temperature-emissivity separation (TES) and split-window (SW) algorithms from six-channel Thermal Infrared Multispectral Scanner (TIMS) data in the HAPEX-Sahel experiment agreed with contemporaneous ground temperature measurements to within ±1 °C (TES and SW with channels at 10.8 and 11.7 μm, or SW-56). The SW algorithm used with TIMS channels at 8.4 and 8.7 μm (SW-12) underestimated ground temperatures by 2-5 °C. The TES method required atmospheric correction of at-sensor radiances, which was done with local radiosonde data and MODTRAN 4, and an empirical relationship between the spectral range of emissivity and its minimum value. Emissivity data required for the SW algorithms were obtained using vegetation cover estimates from near-coincident reflective remote sensing data. The temperature underestimation of the SW-12 algorithm could be caused by errors in the emissivity inputs calculated from the vegetation cover. Such errors were due to the high variability of surface emissivity in the 8-9-μm waveband, which was much larger than in the 10-12-μm region. This was checked using TES derived emissivities as inputs of the SW algorithms, and comparing the resulting temperatures with the TES temperatures. In this case, both the SW-56 and SW-12 temperatures agreed with TES within ±1 °C for all sites and scenes.     

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.     

AVHRR split window temperature differences and total precipitable water over land surfaces

Measurements from the thermal infrared split window channels of the AVHRR sensor were investigated for their relationship to the total atmospheric water vapour amount over land surfaces. The difference in brightness temperature between the AVHRR channel 4 and 5 (10·3–11·3μm and 11·4–12·3μm respectively) was found to be a linear function of total precipitable water, for several stations in differing climatic regimes. For each individual location the total precipitable water was estimated with a standard error ranging from 0·22 to 0·48 cm for the complete range of conditions from wet to dry season or summer to winter. For mid-latitude continental locations there is very little influence of atmospheric aerosols on the relationship while for the African Sahel region the effect of large airborne particulates with a silicate component introduces a significant effect at large values of aerosol optical depth due to absorption. The influence of spectral emissivity variation in the split window region was also observed for arid regions where there is a significant quartz component to the soil. It is concluded that for regional retrieval of precipitable water, this technique provides sufficient accuracy for application to correction of near-infrared satellite data such as AVHRR channel 2 (0·71 –0·98 μm), however the site specific relation between T ₄-T ₅ and PW needs to be established with independent PW measurements.     

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.     

Estimation of instantaneous air temperature using remote sensing data

In arid areas, the variation of air temperature can be considerable, so instantaneous air temperature (T_ai) estimation is needed in different environmental researches. In this research, two different remote sensing data are used for estimating T_ai for clear sky days in 2009 in Fars Province, Iran, including atmospheric temperature profile and land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer. The T_ai from a number of surface weather sites is used to judge the best T_ai estimation. Stations’ elevation, latitude, and land cover type are considered to show their effect on T_ai estimation. The estimated T_ai evaluation focuses on daily and seasonal timescales in the daytime and night time separately. Both LST and vertical temperature profile data produced relatively high coefficient of determination values and small root mean square error value for T_ai estimation, especially during the night time. Land cover and elevation vary the error values in T_ai estimation more, when LST data is used. In comparison atmospheric temperature profile indicates a smaller error in T_ai estimation in spring and summer and in urban land cover type, while using LST data presents a better result in fall and winter especially at night time.     

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.     

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.     

地表温度热红外遥感反演的研究现状及其发展趋势

区域性或全球性的地表温度, 只有通过遥感手段才能获得, 在诸多应用中是一个非常重要的参数。地表温度反演是热红外遥感研究的热点和难点之一, 大气校正、温度与比辐射率的分离是必须考虑的两个重要方面。近年来有关的研究非常多, 主要反演方法可分为5 类: 单通道方法、分裂窗(双波段) 方法、多波段温度- 比辐射率分离方法、多角度温度反演方法和多角度与多通道相结合的方法。这些方法都各有利弊, 如何提高反演的精度和模型的适用性是地表温度热红外遥感的未来发展趋势, 理论和实验相结合的多种信息源的综合应用成为必然的要求。     

基于静止气象卫星数据的地表温度遥感估算

基于分裂窗算法和地表温度日周期变化模型,探讨了利用多时相热红外遥感数据反演地表温度的方法。首先,利用分裂窗算法及地表温度日周期变化形式,推导了多时相遥感数据反演地表温度的方法。其次,利用辐射传输模型(MODTRAN),以2006年夏季在禹城观测的3 d地表温度、气温及大气水汽数据做为输入参数、变化观测角及比辐射率,模拟了一日多个时刻与风云二号(F-2D)波谱响应函数一致的亮温数据,基于此,模拟数据库对所提算法进行了检验。最后,利用2010年9月30日FY-2D多时相热红外数据对新疆区域地表温度进行了反演,并与相应时刻的MODIS地表温度产品进行了比较。结果表明：利用模拟遥感数据反演地表温度,模拟值与估算值的相关系数达0.9,均方根误差在1.5 K以内;利用在轨FY-2D热红外数据反演得到的地表温度与MODIS温度产品趋势基本一致,两者的相关性达到了0.5,均方根误差为4.4 K。需要说明的是,此方法仅满足于晴朗无云的条件。     

Surface temperature and water vapour retrieval from MODIS data

This paper gives operational algorithms for retrieving sea (SST), land surface temperature (LST) and total atmospheric water vapour content (W) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. To this end, the MODTRAN 3.5 radiative transfer program was used to predict radiances for MODIS channels 31, 32, 2, 17, 18 and 19. To analyse atmospheric effects, a simulation with a set of radiosonde observations was used to cover the variability of surface temperature and water vapour concentration on a worldwide scale. These simulated data were split into two sets (DB1 and DB2), the first one (DB1) was used to fit the coefficients of the algorithms, while the second one (DB2) was used to test the fitted coefficients. The results show that the algorithms are capable of producing SST and LST with a standard deviation of 0.3 K and 0.7 K if the satellite data are error free. The LST product has been validated with in situ data from a field campaign carried out in the Mississippi (USA), the results show for the LST algorithm proposed a root mean square error lower than 0.5K. Regarding water vapour content, a ratio technique is proposed, which is capable of estimating W from the absorbing channels at 0.905, 0.936, and 0.94,µm, and the atmospheric window channel at 0.865,µm, with a standard deviation (in the comparison with radiosonde observations) of 0.4 g cm^?2.     

Remote sensing analysis of the land surface temperature anomaly in the sand-dune region across the Israel-Egypt border

In the sand-dune region across the Israel-Egypt border, an anomalous phenomenon of thermal variation was observed on remote sensing images: the Israeli side with much more vegetation cover has higher surface temperature than the Egyptian side, where bare sand surface prevails. The study intends to examine the phenomenon using NOAA-AVHRR and Landsat TM data. The focus is to analyse the seasonal and spatial change of land surface temperature (LST) in the border region, to verify it through ground truth measurements and to simulate the average LST change on both sides according to surface composition structure. A split window algorithm containing only two parameters (transmittance and emissivity) has been developed for retrieving LST from NOAA-AVHRR data and a mono-window algorithm is proposed for computing LST from the only one thermal band of Landsat TM data. Application of these algorithms to the available AVHRR and Landsat TM data indicates that the LST anomaly does occur not only in one day but almost all the year. In hot dry summer the Israeli side is usually about 2.5-3.5°C hotter. In wet cool winter the LST difference between the sides is not large but the Israeli side still has higher LST. The Egyptian side may have slightly higher LST when surface temperature is below 20°C, several days after heavy rain, which leads to very wet surface conditions. The sharp LST contrast disappears on night-time images. Ground truth measurements indicate that the LST contrast mainly can be attributed to the surface temperature difference on the two typical surface patterns: biogenic crust and bare sand, which have above 3°C difference in surface temperature during summer. Experiments on soil samples from the field indicate that biogenic crust and sand have emissivity values of about 0.972 and 0.954, respectively, in hot dry conditions that match the environment of the region in summer. Surface composition determination based on three methods indicates that more than 72% of the ground on the Israeli side is covered with biogenic crust and more than 80% on the Egyptian side is bare sand. Actually, the LST anomaly can be understood as the direct result of surface composition difference, especially in biogenic crust and sand cover rate. Simulation with this surface composition difference shows that the Israeli side has steadily higher LST when the temperature of the biogenic crust is more than 1°C higher that of the sand surface, which usually occurs at moderate to high temperature levels (>30°C). When temperature is between 15 and 25°C, such as at about midnight, the two sides will have no obvious LST difference. This result is in agreement with the remote sensing observation. Therefore, it can be concluded that the vegetation cover does not contribute much to the LST contrast in comparison to the effect of the biogenic crust and sand cover.     

A generalized split-window algorithm for land surface temperature estimation from MSG-2/SEVIRI data

This paper aims to determine land surface temperature (LST) using data from a spinning enhanced visible and infrared imager (SEVIRI) on board Meteosat Second Generation 2 (MSG-2) by using the generalized split-window (GSW) algorithm. Coefficients in the GSW algorithm are pre-determined for several overlapping sub-ranges of the LST, land surface emissivity (LSE), and atmospheric water vapour content (WVC) using the data simulated with the atmospheric radiative transfer model MODTRAN 4.0 under various surface and atmospheric conditions for 11 view zenith angles (VZAs) ranging from 0° to 67°. The results show that the root mean square error (RMSE) varies with VZA and atmospheric WVC and that the RMSEs are within 1.0 K for the sub-ranges in which the VZA is less than 30° and the atmospheric WVC is less than 4.25 g cm^?2. A sensitivity analysis of LSE uncertainty, atmospheric WVC uncertainty, and instrumental noise (NEΔT) is also performed, and the results demonstrate that LSE uncertainty can result in a larger LST error than other uncertainties and that the total error for the LST is approximately 1.21 and 1.45 K for dry atmosphere and 0.86 and 2.91 K for wet atmosphere at VZA = 0° and at VZA = 67°, respectively, if the uncertainty in the LSE is 1% and that in the WVC is 20%. The GSW algorithm is then applied to the MSG-2 – SEVIRI data with the LSE determined using the temperature-independent spectral indices method and the WVC either determined using the measurements in two split-window channels or interpolated temporally and spatially using European Centre for Medium Range Weather Forecasting (ECMWF) data. Finally, the SEVIRI LST derived in this paper (SEVIRI LST1) is evaluated through comparisons with the SEVIRI LST provided by the land surface analysis satellite applications facility (LSA SAF) (SEVIRI LST2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11B1 LST product). The results show that more than 80% of the differences between SEVIRI LST1 and SEVIRI LST2 are within 2 K, and approximately 70% of the differences between SEVIRI LST1 and MODIS LST are within 4 K. Furthermore, compared to MODIS LST, for four specific areas with different land surfaces, our GSW algorithm overestimates the LST by up to 1.0 K for vegetated surfaces and by 1.3 K for bare soil.     

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.     

Synergistic use of the two-temperature and split-window methods for land-surface temperature retrieval

A strategy is presented with the aim of achieving an operational accuracy of 2.0 K in land-surface temperature (LST) from METEOSAT Second Generation (MSG)/Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. The proposed method is based on a synergistic usage of the split-window (SW) and the two-temperature method (TTM) and consists in combining the use of a priori land-surface emissivity (LSE) estimates from emissivity maps with LST estimates obtained from SW method with the endeavour of defining narrower and more reliable ranges of admissible solutions before applying TTM. The method was tested for different surface types, according to SEVIRI spatial resolution, and atmospheric conditions occurring within the MSG disc. Performance of the method was best in the case of relatively dry atmospheres (water-vapour content less than 3 g cm^?2), an important feature since in this case SW algorithms provide the worst results because of their sensitivity to uncertainties in surface emissivity. The hybrid method was also applied using real MSG/SEVIRI data and then validated with the Moderate resolution Imaging Spectroradiometer (MODIS)/Terra LST/LSE Monthly Global 0.05° geographic climate modeling grid (CMG) product (MOD11C3) generated by the day/night algorithm. The LST and LSE retrievals from the hybrid-method agree well (bias and root mean square error (RMSE) of??0.2 K and 1.4 K for LST, and around 0.003–0.02 and 0.009–0.02 for LSE) with the MOD11C3 product. These figures are also in conformity with the MOD11C3 performance at a semi-desert where LST (LSE) values is 1–1.7 K (0.017) higher (less) than the ground-based measurements.     

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.