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.     

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.     

Evaluation of land surface temperature and emissivities retrieved from MSG/SEVIRI data with MODIS land surface temperature and emissivity products

Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters in global climate study. This article aims to cross-validate LST/LSE products retrieved from data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first geostationary satellite, Meteosat Second Generation (MSG), with Moderate Resolution Imaging Spectroradiometer (MODIS) LST/LSE version 5 products over the Iberian Peninsula and over Egypt and the Middle East. Besides time matching, coordinate matching is another requirement of the cross-validation. An area-weighted aggregation algorithm was used to aggregate SEVIRI and MODIS LST/LSE products into the same spatial resolution. According to the quality control (QC) criterion and the view angle, the cross-validation was completed under clear-sky conditions and within a view angle difference of less than 5° for the two instruments to prevent land surface anisotropic effects. The results showed that the SEVIRI LST/LSE products are consistent with MODIS LST/LSE products and have the same trend over the two study areas during both the daytime and the night-time. The SEVIRI LST overestimates the temperature by approximately 1.0 K during the night-time and by approximately 2.0 K during the daytime compared to MODIS products over these two study areas. The SEVIRI LSE underestimates by about 0.015 in 11 μm and by about 0.025 in 12 μm over the Iberian Peninsula. However, both LSEs agree and show a difference of less than 0.01 over Egypt and the Middle East.     

Comparison of land surface emissivity and radiometric temperature derived from MODIS and ASTER sensors

This study compared surface emissivity and radiometric temperature retrievals derived from data collected with the MODerate resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) sensors, onboard the NASA's Earth Observation System (EOS)-TERRA satellite. Two study sites were selected: a semi-arid area located in northern Chihuahuan desert, USA, and a Savannah landscape located in central Africa. Atmospheric corrections were performed using the MODTRAN 4 atmospheric radiative transfer code along with atmospheric profiles generated by the National Center for Environmental Predictions (NCEP). Atmospheric radiative properties were derived from MODTRAN 4 calculations according to the sensor swaths, which yielded different strategies from one sensor to the other. The MODIS estimates were then computed using a designed Temperature-Independent Spectral Indices of Emissivity (TISIE) method. The ASTER estimates were derived using the Temperature Emissivity Separation (TES) algorithm. The MODIS and ASTER radiometric temperature retrievals were in good agreement when the atmospheric corrections were similar, with differences lower than 0.9 K. The emissivity estimates were compared for MODIS/ASTER matching bands at 8.5 and 11 μm. It was shown that the retrievals agreed well, with RMSD ranging from 0.005 to 0.015, and biases ranging from −0.01 to 0.005. At 8.5 μm, the ranges of emissivities from both sensors were very similar. At 11 μm, however, the ranges of MODIS values were broader than those of the ASTER estimates. The larger MODIS values were ascribed to the gray body problem of the TES algorithm, whereas the lower MODIS values were not consistent with field references. Finally, we assessed the combined effects of spatial variability and sensor resolution. It was shown that for the study areas we considered, these effects were not critical.     

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.     

Land surface temperature and emissivity retrieval by integrating MODIS data onboard Terra and Aqua satellites

This paper presents an algorithm to retrieve land surface temperature (LST) and emissivity by integrating MODIS (Moderate Resolution Imaging Spectroradiometer) data onboard Terra and Aqua satellites. For a study area, there will be four pairs of day and night observations by MODIS onboard two satellites every day. Solar zenith angle, view zenith angle, and atmospheric water vapour have first been taken as independent variables to analyse their sensitivities to the same infrared channel measurements of MODIS on both Terra and Aqua satellites. Owing to their similar influences on the same MODIS band from Terra and Aqua satellites, four pairs of MODIS data from Terra and Aqua satellites can be thought of as MODIS measurement on a satellite at different viewing angles and viewing time. Comparisons between the retrieved results and in-situ measurements at three test sites (Qinghai Lake, Poyang Lake and Luancheng in China) indicate that the root mean square (rms) error is 0.66 K, except for the sand in Poyang Lake area. The rms error is less than 0.7 K when the retrieved results are compared with Earth Observing System (EOS) MODIS LST data products using the physics-based day/night algorithm. Emissivities retrieved by this algorithm are well compared to EOS MODIS emissivity data products (V5). The proposed algorithm can therefore be regarded as complementary and an extension to the EOS physics-based day/night algorithm.     

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.     

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.     

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.     

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.     

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.     

Use of satellite-derived emissivity to detect coalfire-related surface temperature anomalies in Jharia coalfield,India

Among natural geo-hazards, spontaneous combustion of coal is unique in nature but common in most coal-producing countries. Coalfires can occur in coal seams and stockpiles of coal at ambient temperature in certain conditions, e.g. those concerning coal type, exposed area and moisture content. Once started, coalfires are difficult to extinguish and sometimes cannot be controlled. In addition to burning millions of tonnes of coal, the fires have enormous negative impacts on local and global environments. In the field of coalfire study, remote sensing is used as a powerful tool to detect and monitor coalfires. Nevertheless, most remote-sensing coalfire studies are based on a fixed emissivity (0.95 or 0.96) which is contrary to the real representation of the Earth's surface. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-derived emissivity was used to detect coalfire-related surface anomalies in an Indian coal mining region. Later, the temperature anomalies detected were validated with ground truth data. Additionally, the ASTER-derived emissivity value was used to extract surface temperatures from Landsat Enhanced Thematic Mapper Plus (ETM+) thermal infrared (TIR) data.     

A new approach for temperature and emissivity separation

A Taylor expansion can be used to linearize the Planck function that governs the emission from a material at a given temperature. Such a linearization is more accurate than using Wien's approximation. This approach can be used to improve the accuracy of those logarithms that currently utilize Wien's approximation, such as the alpha residual and alpha emissivity techniques.     

Retrieval of land surface temperature and emissivity from ASTER1B data using a dynamic learning neural network

The accuracy of a radiance transfer model neural network (RM-NN) for separating land surface temperature (LST) and emissivity from AST09 (the Advanced Spaceborne and Thermal Emission and Reflection Radiometer (ASTER) Standard Data Product, surface leaving radiance) is very high, but it is limited by the accuracy of the atmospheric correction. This article uses a neural network and radiance transfer model (MODTRAN4) to directly retrieve the LST and emissivity from ASTER1B data, which overcomes the difficulty of atmospheric correction in previous methods. The retrieval average accuracy of LST is about 1.1 K, and the average accuracy of emissivity in bands 11–14 is under 0.016 for simulated data when the input nodes are a combination of brightness temperature in bands 11–14. The average accuracy of LST is under 0.8 K when the input nodes are a combination of water vapour content and brightness temperature in bands 11–14. Finally, the comparison of retrieval results with ground measurement data indicates that the RM-NN can be used to accurately retrieve LST and emissivity from ASTER1B data.     

Spatial analysis of the homogeneity of the land surface temperature in three Spanish test sites

Calibration and validation (cal/val) are key activities to test the data quality acquired from satellite-based instruments, as well as to report the accuracy of derived products such as the land surface temperature (LST). Calibration of thermal infrared (TIR) data and validation of LST products at low spatial resolution requires the identification of large and homogeneous areas, which is a difficult task. In this work, spatial and temporal homogeneity of LST was analysed over three Spanish regions: the agricultural area of Barrax, Doñana National Park, and Cabo de Gata Natural Park. For this purpose, very high spatial resolution (approximately 3 m) imagery acquired with the Airborne Hyperspectral Scanner (AHS) in the framework of different field campaigns and high–medium spatial resolution (approximately 100 m) imagery acquired with the Landsat-8 (L8) TIR sensor (TIRS) have been used to retrieve homogeneity of high–medium and low spatial resolution sensors, respectively. Different LST retrieval algorithms were applied to AHS and TIRS to compare the LST for a given pixel against the LST of neighbour pixels through the computation of the root mean square error (RMSE). The results obtained from the analysis of LST derived from AHS data over Barrax and Doñana test sites show that part of these regions have an RMSE lower than 1 K, which is consistent with the accuracy of the LST validation (between 0.5 and 1.5 K). The analysis of LST derived from the TIRS shows that some parts of Doñana and Cabo de Gata sites have a mean RMSE of 1 K over the period of a year, with maximal homogeneity in autumn and winter (lower than 1 K) and minimal in spring and summer (around 2 K). These results are lower than the accuracy of the LST validation (approximately 2 K). The results show the usefulness of these three test sites to perform cal/val activities for both low and high spatial resolution sensors. The methodology presented in this study also allows the identification of suitable areas for future cal/val activities.     

复合温度传感器有效发射率影响因素分析

为消除复合温度传感器所形成的在线黑体空腔"不等温性"和"非密闭性"对测量的影响,采用矩形区域近似法进行了不等温有效发射率的计算。在分析传感器几何特性、材料发射率、温度分布和环境温度对有效发射率影响的基础上,给出了传感器结构的优化参数。实验表明:优化后的传感器具有较高灵敏度和准确度。     

Investigating the effects of soil moisture on thermal infrared land surface temperature and emissivity using satellite retrievals and laboratory measurements

This study investigates the effects of soil moisture (SM) on thermal infrared (TIR) land surface emissivity (LSE) using field- and satellite-measurements. Laboratory measurements were used to simulate the effects of rainfall and subsequent surface evaporation on the LSE for two different sand types. The results showed that the LSE returned to the dry equilibrium state within an hour after initial wetting, and during the drying process the SM changes were uncorrelated with changes in LSE. Satellite retrievals of LSE from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) were examined for an anomalous rainfall event over the Namib Desert in Namibia during April, 2006. The results showed that increases in Advanced Microwave Scanning Radiometer (AMSR-E) derived soil moisture and Tropical Rainfall Measuring Mission (TRMM) rainfall estimates corresponded closely with LSE increases of between 0.08-0.3 at 8.6 µm and up to 0.03 at 11 µm for MODIS v4 and AIRS products. This dependence was lost in the more recent MODIS v5 product which artificially removed the correlation due to a stronger coupling with the split-window algorithm, and is lost in any algorithms that force the LSE to a pre-determined constant as in split-window type algorithms like those planned for use with the NPOESS Visible Infrared Imager Radiometer Suite (VIIRS). Good agreement was found between MODIS land surface temperatures (LSTs) derived from the Temperature Emissivity Separation (TES) and day/night v4 algorithm (MOD11B1 v4), while the split-window dependent products (MOD11B1 v5 and MOD11A1) had cooler mean temperatures on the order of 1-2 K over the Namib Desert for the month of April 2006.     

SEBASS hyperspectral thermal infrared data: surface emissivity measurement and mineral mapping

This study focuses on mapping surface minerals using a new hyperspectral thermal infrared (TIR) sensor: the spatially enhanced broadband array spectrograph system (SEBASS). SEBASS measures radiance in 128 contiguous spectral channels in the 7.5- to 13.5-μm region with a ground spatial resolution of 2 m. In September 1999, three SEBASS flight lines were acquired over Virginia City and Steamboat Springs, Nevada. At-sensor data were corrected for atmospheric effects using an empirical method that derives the atmospheric characteristics from the scene itself, rather than relying on a predicted model. The apparent surface radiance data were reduced to surface emissivity using an emissivity normalization technique to remove the effects of temperature. Mineral maps were created with a pixel classification routine based on matching instrument- and laboratory-measured emissivity spectra, similar to methods used for other hyperspectral data sets (e.g. AVIRIS). Linear mixtures of library spectra match SEBASS spectra reasonably well, and silicate and sulfate minerals mapped remotely, agree with the dominant minerals identified with laboratory X-ray powder diffraction and spectroscopic analyses of field samples. Though improvements in instrument calibration, atmospheric correction, and information extraction would improve the ability to map more pixels, these hyperspectral TIR data nevertheless show significant advancement over multispectral thermal imaging by mapping surface materials and lithologic units with subtle spectral differences in mineralogy.     

Study on modeling of multispectral emissivity and optimization algorithm

Target's spectral emissivity changes variously, and how to obtain target's continuous spectral emissivity is a difficult problem to be well solved nowadays. In this letter, an activation-function-tunable neural network is established, and a multistep searching method which can be used to train the model is proposed. The proposed method can effectively calculate the object's continuous spectral emissivity from the multispectral radiation information. It is a universal method, which can be used to realize on-line emissivity demarcation.