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).     

The North American ASTER Land Surface Emissivity Database (NAALSED) Version 2.0

Thermal Infrared (TIR) data are supplied by instruments on several satellite platforms including the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER), which was launched on the Terra satellite in 1999. ASTER has five bands in the TIR and a spatial resolution of 90 m. A mean seasonal, gridded, Land Surface Temperature and Emissivity (LST&E) database has been produced at 100 m spatial resolution using all the ASTER scenes acquired for the months of Jan-Mar (winter) and Jul-Sep (summer) over North America. Version 2.0 of the North American ASTER Land Surface Database (NAALSED) (http://emissivity.jpl.nasa.gov) has now been released and includes two key refinements designed to improve the accuracy of emissivities over water bodies and account for the effects of fractional vegetation cover. The water adjustment replaces ASTER emissivity values over inland water bodies with a measured library emissivity spectrum of distilled water, and then re-calculates the surface temperatures using a split-window algorithm. The accuracy of ASTER emissivities over vegetated surfaces is improved by applying a fractional vegetation cover adjustment (TES_Pv) to the ASTER Temperature Emissivity Separation (TES) calibration curve. Comparisons of NAALSED emissivity spectra with in-situ data measured over a grassland in Northern Texas resulted in a combined absolute difference for all five ASTER bands of 1.0% for the summer emissivity data, and 0.1% for the winter data—a 33-50% improvement over the original TES results.     

Validation of the North American ASTER Land Surface Emissivity Database (NAALSED) version 2.0 using pseudo-invariant sand dune sites

Knowledge of the Land Surface Emissivity (LSE) in the Thermal Infrared (TIR: 8-12 µm) part of the electromagnetic spectrum is essential to derive accurate Land Surface Temperatures (LSTs) from spaceborne TIR measurements. This study focuses on validation of the emissivity product in the North American ASTER Land Surface Emissivity Database (NAALSED) v2.0 — a mean seasonal, gridded emissivity product produced at 100 m spatial resolution using all Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes from 2000 to 2008 over North America (http://emissivity.jpl.nasa.gov). The NAALSED emissivity product was validated over bare surfaces with laboratory measurements of sand samples collected at nine pseudo-invariant sand dune sites located in the western/southwestern USA. The nine sand dune sites cover a broad range of surface emissivities in the TIR. Results show that the absolute mean emissivity difference between NAALSED and the laboratory results for the nine validation sites and all five ASTER TIR bands was 0.016 (1.6%). This emissivity difference is equivalent to approximately a 1 K error in the land surface temperature for a material at 300 K in the TIR.     

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.     

Lithological discrimination of the Phenaimata felsic–mafic complex,Gujarat, India,using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

This study deals with an evaluation of the efficacy of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image for lithological mapping. ASTER level-1B data in the visible near-infrared (VNIR), short wave infrared (SWIR) and thermal infrared (TIR) regions have been processed to generate a lithological map of the study area in and around the Phenaimata igneous complex, in mainland Gujarat, India. ASTER band combinations, band ratio images and spectral angle mapper (SAM) processing techniques were evaluated for mapping various lithologies. The reflectance and emissivity spectra of rock samples collected from the study area were obtained in the laboratory and were used as reference spectra for ASTER image analysis. The original data in the scaled digital number (DN) values were converted to radiance and then to relative reflectance by using a scene-derived correction technique prior to SAM classification. The SAM classification in the VNIR–SWIR region is found to be effective in differentiating felsic and mafic lithologies. The relative band depth (RBD) images were generated from the continuum-removed images of ASTER VNIR–SWIR bands. Four RBD combinations (3, 5, 6 and 8) were used to identify Al-OH (aluminium hydroxide), Fe-OH (iron hydroxide), Mg-OH (magnesium hydroxide) and CO₃ (carbonate) absorption from various lithological components. ASTER TIR spectral emittance data and the laboratory emissivity measurements show the presence of a number of discrete Si-O spectral features that can differentiate mafic and felsic rock types reflecting the lithological diversity around the regions of Phenaimata igneous complex. SAM classification using emittance data failed to distinguish the felsic and mafic lithology due to the wider spectral bandwidth. The felsic class comprises the granitoid composition of rocks. RBD12 and 13 images in the TIR region were used to derive the mafic index (MI) and the silica index (SI). The MI shows the highest value in regions of gabbro–basalt occurrence, while the SI indicates regions of high silica content. The MI is lowest in regions where granophyres occur. The complimentary attributes based on the spectral reflectance and emittance data resulted in the discrimination of silica-rich and silica-poor lithologies.     

Near‐surface air temperature estimation from ASTER data based on neural network algorithm

An algorithm based on the radiance transfer model (MODTRAN4) and a dynamic learning neural network for estimation of near‐surface air temperature from ASTER data are developed in this paper. MODTRAN4 is used to simulate radiance transfer from the ground with different combinations of land surface temperature, near surface air temperature, emissivity and water vapour content. The dynamic learning neural network is used to estimate near surface air temperature. The analysis indicates that near surface air temperature cannot be directly and accurately estimated from thermal remote sensing data. If the land surface temperature and emissivity were made as prior knowledge, the mean and the standard deviation of estimation error are both about 1.0 K. The mean and the standard deviation of estimation error are about 2.0 K and 2.3 K, considering the estimation error of land surface temperature and emissivity. Finally, the comparison of estimation results with ground measurement data at meteorological stations indicates that the RM‐NN can be used to estimate near surface air temperature from ASTER data.     

Residual errors in ASTER temperature and emissivity standard products AST08 and AST05

Land surface temperature and emissivity? are independent variables, and the thermal-infrared spectral radiance measured in remote sensing is dependent on both. Therefore the inverse Planck equation is under-determined, with two unknowns and a single measurement. Practical inversion algorithms designed to calculate temperature and emissivity from the measurements cannot do a perfect job of separation, and recovered temperature and emissivity may co-vary. For ASTER images, validation studies of recovered temperature and emissivity, regarded individually, have shown that they are within the precision and accuracy limits predicted in designing the ASTER TES algorithm used to calculate the standard products AST05 and AST08. Nevertheless, a closer look at emissivity recovered for water targets shows that emissivity appears to vary, incorrectly, as a function of temperature. One cause of this is electronic striping; another is incomplete characterization of atmospheric temperature and humidity profiles used in compensation for atmospheric absorption and path radiance. The linkage varies from band to band, with the greatest emissivity effect of − 0.0003 K^− 1 for ASTER band 12 (9.1 μm) relative to band 13 (10.6 μm). Although this inaccuracy in emissivity is small, it can approach or exceed the inaccuracy prediction of ± 0.015 for the standard product when the entire gamut of terrestrial water and land temperatures is examined. Therefore, spatial filtering and upgrading the atmosphere compensation algorithm to use water-vapor scaling should be considered in making AST05 and AST08.     

ASTER- and field-based observations at Bezymianny Volcano: Focus on the 11 May 2007 pyroclastic flow deposit

Bezymianny (Kamchatka Peninsula, Russia) is an active stratovolcano, characterized by a summit lava dome and overlapping pyroclastic flow (PF) deposits to the southeast. Three explosive eruptions (24 December 2006, 11 May 2007, and 14 October 2007) generated PFs that were dominated by juvenile material and were emplaced primarily due to column collapse. Following this, a gravitational lava flow front collapse event generated block and ash flow on 5 November 2007. Moderate spatial resolution data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument were collected between October 2006 and December 2007 to assist in post-eruption monitoring and interpretation of the volcanological processes that produced the PF deposits. Using multitemporal ASTER thermal infrared (TIR) data, three periods of increased activity were observed that coincided with each eruption and subsequent activity. During a field campaign in August 2007, the May 2007 PF deposit was investigated in detail. Eight ASTER TIR pixels (90 m spatial resolution) were selected from the 30 June 2007 ASTER TIR image, seven of which were accessible in the field. Forward-Looking Infrared (FLIR) image and thermocouple data over these areas were collected to observe thermal heterogeneities with distance along the PF deposit. Although synchronous ASTER data were not possible at the time of fieldwork due to cloud cover, a field survey of blocks versus ash in each pixel was carried out to investigate thermal and textural variation with distance from the vent and to provide preliminary field results. Based on the field-derived temperature data and surface block percentages, the May 2007 PF deposit was more block-rich in the medial portion of the flow surface, but more ash-dominated at the PF terminus region, which promoted more rapid cooling. We present multitemporal ASTER data spanning a 14 month period and highlight ground-based observations acquired within the same period of eruptive and dome-growth activity. These data collectively provide thermal radiative and emissivity information on an actively changing explosive volcanic system and specifically documents changes over recently-emplaced and cooling PF deposits.     

Sub-pixel water temperature estimation from thermal-infrared imagery using vectorized lake features

Water skin temperature derived from thermal infrared satellite data are used in a wide variety of studies. Many of these studies would benefit from frequent, high spatial resolution (100 m pixels) thermal imagery but currently, at any given location, such data are only available every few weeks from spaceborne sensors such as ASTER. Lower spatial resolution (1 km pixels) thermal imagery is available multiple times per day at any given location, from several sensors such as MODIS on board both the AQUA and TERRA satellite platforms. In order to fully exploit lower spatial resolution imagery, a sub-pixel unmixing technique has been developed and tested at Quesnel Lake, British Columbia, Canada. This approach produces accurate, frequent high spatial resolution water skin temperature maps by exploiting a priori knowledge of water boundaries derived from vectorized water features. The pixel water-fraction maps are then input to a gradient descent algorithm to solve the mixed pixel ground leaving radiance equation for sub-pixel water temperature. Ground-leaving radiance is estimated from standard temperature and emissivity data products for pure pixels and a simple regression technique to estimate atmospheric effects. In this test case, MODIS 1 km thermal imagery was used along with 1:50,000 water features to create a high-resolution (100 m) water skin temperature map. This map is compared to a concurrent ASTER temperature image and found to be within 1 °C of the ASTER skin temperature 99% of the time. This is a considerable improvement over the 2.55 °C difference between the original MODIS product and ASTER image due to land temperature contamination. The algorithm is simple, effective, and unlocks a largely untapped resource for limnological and hydrological studies.     

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.     

Detection of sandy soil surfaces using ASTER‐derived reflectance,emissivity and elevation data: potential for the identification of land degradation

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) reflectance and emissivity data were used to discriminate nonphotosynthetic vegetation (NPV) from exposed soils, to produce a topsoil texture image, and to relate sand fraction estimates with elevation data in an agricultural area of central Brazil. The results show that the combination of the shortwave infrared (SWIR) bands 5 and 6 (hydroxyl absorption band) and thermal infrared (TIR) bands 10 and 14 (quartz reststrahlen feature) discriminated dark red clayey soils and bright sandy soils from NPV (crop litter), respectively. The ratio of the bands 10 and 14 was correlated with laboratory measured total sand fraction. When applied to the image and associated with topography, a predominance of sandy soil surfaces at lower elevations and clayey soil surfaces at higher elevations was observed. Areas presenting the largest sand fraction values, identified from ASTER band 10/14 emissivity ratio, were coincident with land degradation processes.     

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.     

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.     

Lithologic mapping in the Oscar II Coast area,Graham Land,Antarctic Peninsula using ASTER data

The results of the first attempt to use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for the purposes of lithologic mapping on the Antarctic Peninsula are presented for an area on the Oscar II Coast, eastern Graham Land. This study included undertaking laboratory reflectance spectroscopy of ～70 rock samples from the study area and spectral lithologic analysis of two ASTER scenes. Spectra of the granitoids, silicic volcanic/volcaniclastic and terrestrial sedimentary rocks in the study area display a limited range of absorption features associated with muscovite, smectite and chlorite that are generally present as the alteration products of regional metamorphism. ASTER data analysis was undertaken using the reflective bands of the Level 1B registered radiance at-sensor data and the standard thermal infrared (TIR) emissivity product (AST05). For both wavelength regions, standard qualitative image processing methods were employed to define image end-members that were used as reference within Matched Filter (MF) processing procedures. The results were interpreted with reference to existing field observations, and photogeologic analysis of the ASTER visible to near-infrared (VNIR)/shortwave infrared (SWIR) data was used to resolve ambiguities in the spectral mapping results. The results have enabled the discrimination of most of the major lithologic groups within the study area as well as delineation of hydrothermal alteration zones of propylitic, and argillic grades associated with the Mesozoic Mapple Formation volcanics. The results have extended the mapped coverage of the Mapple Formation into un-investigated regions further north and validated previously inferred geological observations concerning other rocks throughout the study area. The outcomes will enable important revisions to be made to the existing geological map of the Oscar II Coast and demonstrate that ASTER data offers potential for improving geological mapping coverage across the Antarctic Peninsula.     

Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval

Data from three thermal sensors with different spatial resolution were assessed for urban surface temperature retrieval over the Yokohama City, Japan. The sensors are Thermal Airborne Broadband Imager (TABI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODerate resolution Imaging Spectroradiometer (MODIS). Two algorithms were developed for land surface temperature (LST) retrieval from TABI image and ASTER thermal infrared (TIR) channels 13 and 14. In addition, ASTER LST and MODIS LST products were also collected. All the LST images were assessed by analyzing the relationship between LST and normalized difference vegetation index (NDVI) and by spatial distributions of LST profiles, derived from typical transects over the LST images. In this study, a strong negative relationship between LST and NDVI has been demonstrated although the degree of correlation between NDVI and LST varies slightly among the different LST images. Cross-validation among the LST images retrieved from the three thermal sensors of different spatial resolutions indicates that the LST images retrieved from the 2 channel ASTER data and a single band TABI thermal image using our developed algorithms are reliable. The LST images retrieved from the three sensors should have different potential to urban environmental studies. The MODIS thermal sensor can be used for the synoptic overview of an urban area and for studying urban thermal environment. The ASTER, with its TIR subsystem of 90-m resolution, allows for a more accurate determination of thermal patterns and properties of urban land use/land cover types, and hence, a more accurate determination of the LST. In consideration of the high heterogeneity of urban environment, the TABI thermal image, with a high spatial resolution of 2 m, can be used for rendering and assessing complex urban thermal patterns and detailed distribution of LST at the individual house level more accurately.     

A technique based on non-linear transform and multivariate analysis to merge thermal infrared data and higher-resolution multispectral data

A thermal infrared (TIR) image is a measure of the Earth's surface temperature and TIR emittance; however, its low spatial resolution severely limits its potential applications. Image fusion techniques can be used to fuse a TIR image with higher spatial resolution reflective bands to generate a synthetic TIR image. Because of the weak correlation between TIR and reflective data, such a synthetic image typically contains significant spectral distortion. In this paper, a multivariate analysis technique is used to derive a variable as a linear function of multiple reflective bands and their non-linearly transformed versions, to produce the maximum correlation with the TIR image. The spatial details of the variable are then injected into the TIR image to yield a synthetic image with reduced spectral distortion. In an experiment on Landsat Thematic Mapper (TM) TIR and reflective data, the fusion method proposed in this paper outperforms several existing methods in preserving the spectral characteristics of TIR data.     

一种基于ASTER数据的陆面温度反演方法

陆面温度是地表物体热红外辐射的综合定量形式,是地表热量平衡的结果。陆面温度作为一个重要的基本参数已广泛用于相关模型的计算及生态环境等领域的研究。ASTER数据具有较高的空间分辨率与光谱分辨率,可提供比陆地卫星、NOAA/AVHRR等常见卫星数据更丰富的地表信息,有助于提高陆面温度的反演精度。根据温度/比辐射率分离(TES)的思想,基于ASTER热红外数据的特性,获取了一种反演陆面温度的方法,并以长白山为例进行了试验。结果表明,所用的方法仅依赖ASTER遥感数据便可快速获取地面温度的空间分布特征,对自然地表可取得比较理想的结果,具有较好的应用前景。     

A multi-scale wavelet-based temperature and emissivity separation algorithm for hyperspectral thermal infrared data

ABSTRACT

Land surface temperature and emissivity are essential variables in numerous environmental studies. This article proposes a multi-scale wavelet-based temperature and emissivity separation (MSWTES) algorithm. MSWTES is based on the fact that the high frequencies of ground-leaving radiance and derived emissivity spectra using inaccurate temperature are both closely correlated with the atmospheric downward radiance spectrum. First, surface emissivity can be decomposed by multi-scale wavelet into an optimal level that can be derived from correlation between reconstructed high frequency of ground-leaving radiance and atmospheric downward radiance. Then the ratio of high-frequency energy to low-frequency energy of surface emissivity spectrum is used to measure the degree of atmospheric downward radiance residue in the calculated emissivity spectrum as well as the disparity between the initial surface temperature and the true value. Finally, we can derive the optimal estimate of surface temperature and calculate the surface emissivity spectrum accordingly with this criterion. The MSWTES is first tested by simulation data. When a noise-equivalent spectral error of 2.5 × 10^–9 W cm^?2 sr^?1 cm is considered, the average temperature bias is 0.027 K and the root mean square error (RMSE) of emissivity is less than 0.003, except at the low and high ends of the 750–1250 cm^?1 spectral region. Then, the MSWTES is applied to field measurements. As a whole, the MSWTES achieves an RMSE of 0.01 for emissivity retrieval under most conditions, but its accuracy degrades when sample emissivity is extremely low. Meanwhile, the MSWTES is compared to the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm. The performance of the MSWTES is better than that of the ISSTES, which demonstrates the good performance of the MSWTES.     

Surface-sediment dynamics in a dust source from spaceborne multispectral thermal infrared data

This study utilized spaceborne multispectral thermal infrared (TIR) data to document spatial relationships of surface sediments over time in a modern depositional environment associated with dust emissions, Soda Lake playa, Mojave Desert, United States. The approach employed here involved time-series TIR data acquired from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and a linear spectral mixture analysis. An automated algorithm was applied to derive emissivity image endmembers. Evaluation of the chosen endmembers revealed that they can be categorized into five major spectra classes based on diagnostic absorption features. Each spectrum has been identified in relation to mineral abundance and soil arrangement that are common in playa settings: A, “clayey silt-rich crust”; B, “intermediate-salt crust”; C, “quartz-rich deposit”; D, “salt-rich rough crust”; E, “sulfate-rich crust”. Spectral classes A-B-C-D yielded the lowest RMS errors (0-0.025) over time in the iterative deconvolution algorithm between the measured and modeled spectra. The produced fractional abundance images show high areal concentrations for clayey silt-rich crust, salt-rich rough crust, and quartz-rich deposit, as the first surficial mapping of Soda Lake. Significant changes in the spatial relationships of the major surface sediments in Soda Lake were observed after a flooding event (2005) and a relatively dry period (2006). The approach utilized in this study can be advantageous for continuous monitoring of environments characterized by a small area and a complex surface, which may enable a better understanding of their responses to climate changes and potential for dust emissions.     

Compensation for subpixel roughness effects in thermal infrared images

Emissivity spectra recovered from spectral radiance images may have lowered spectral contrast due to irradiance from nearby surface elements (‘cavity effect’). For analysis based only on photointerpretation or Reststrahlen band identification, it is not always necessary to account for cavity effects, but for full spectral analysis it may be desirable. We present an approach to compensate thermal infrared (TIR) images for cavity radiation. This approach is based on optical estimates of subpixel surface roughness and estimation of cavity contribution for different natural surfaces using a TIR radiosity model. It was tested using tripod-mounted Hyper-Cam (Telops, Inc., Quebec City, Canada) hyperspectral TIR images of natural targets from the Mojave Desert, California, USA, along with centimetre-scale digital elevation models of similar targets measured by ground lidar. For remote subpixel roughness estimation, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) nadir- and aft-looking (27.6°) near-infrared (NIR) brightness ratios, as well as synthetic aperture radar (SAR) images calibrated to roughness root mean square (RMS), were used. The TIR compensation approach is adaptable for different spectral resolutions, including hyperspectral.