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.     

Improved land surface emissivities over agricultural areas using ASTER NDVI

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

Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990 m and 90 m resolutions, respectively. Secondly, the relationship between the 990 m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990 m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90 m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90 m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90 m data (R² = 0.709 and RMSE = 2.702 K).     

Identification of mineral components in tropical soils using reflectance spectroscopy and advanced spaceborne thermal emission and reflection radiometer (ASTER) data

Soil characteristics provide important support for understanding transformations that occur in environmental systems. Physical characteristics and chemical compositions of soils controlled by pedogenetic processes, climatic changes and land use imply different types of environmental transformations. Reflectance spectroscopy is an alternative soil mapping technique that uses spectral absorption features between visible (VIS) and short-wave infrared (SWIR) wavelengths (0.3-2.5 μm) for determining soil mineralogy. Soil analysis by means of reflectance spectroscopy and orbital optical sensors have provided favorable results in mapping transformation processes in environmental systems, particularly in arid and semiarid climates in extra-tropical terrains. In the case of inter-tropical environments, these methods cannot be readily applied due to local factors such as lack of exposed regolith, high amounts of soil moisture and the presence of dense vegetation. This study uses Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and reflectance spectroscopy data to map mineral components of soils covering a part of the state of São Paulo, Brazil, which could be linked to key aspects of environmental transformations in this tropical area (e.g., climate change, shifts in agriculture fronts, ph, and soil characteristics). We collected forty-two (42) soil samples at a depth of 0-20 cm, considering that this superficial layer corresponds to the highest correlation with soil properties detected by the ASTER sensor. These samples were measured using a FieldSpec FR spectrometer, and the derived spectra were interpreted for mineral composition. Interpretation was supported by X-ray diffraction analysis on the same samples. The spectral signatures were re-sampled to ASTER VNIR (AST1-4: 0.52-0.86 μm) and SWIR (AST5-9: 1.60-2.43 μm) spectral bandwidths and validated by comparing reflectance spectra of field samples with those extracted from atmospherically corrected and calibrated ASTER pixels. The agreement between spectral signatures measured from soil samples and those derived from ASTER imagery pixels proved plausible, with R² correlation values ranging from 0.6493 to 0.7886. This signifies that diagnostic spectral features of key minerals in tropical soils can be mapped at the spectral resolution of 9-band ASTER VNIR through SWIR reflectance. We used these spectral signatures as end-members in hyperspectral routine classifications adapted for use with ASTER data. Results proved possible the identification and remote mapping of minerals such as kaolinite, montmorillonite and gibbsite, as well as the distinction between iron-rich and iron-poor soils.     

Distribution of hydrothermally altered rocks in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER data

The Reko Diq, Pakistan mineralized study area, approximately 10 km in diameter, is underlain by a central zone of hydrothermally altered rocks associated with Cu-Au mineralization. The surrounding country rocks are a variable mixture of unaltered volcanic rocks, fluvial deposits, and eolian quartz sand. Analysis of 15-band Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data of the study area, aided by laboratory spectral reflectance and spectral emittance measurements of field samples, shows that phyllically altered rocks are laterally extensive, and contain localized areas of argillically altered rocks.In the visible through shortwave-infrared (VNIR + SWIR) phyllically altered rocks are characterized by Al-OH absorption in ASTER band 6 because of molecular vibrations in muscovite, whereas argillically altered rocks have an absorption feature in band 5 resulting from alunite. Propylitically altered rocks form a peripheral zone and are present in scattered exposures within the main altered area. Chlorite and muscovite cause distinctive absorption features at 2.33 and 2.20 μm, respectively, although less intense 2.33 μm absorption is also present in image spectra of country rocks.Important complementary lithologic information was derived by analysis of the spectral emittance data in the 5 thermal-infrared (TIR) bands. Silicified rocks were not distinguished in the 9 VNIR + SWIR bands because of the lack of diagnostic spectral absorption features in quartz in this wavelength region. Quartz-bearing surficial deposits, as well as hydrothermally silicified rocks, were mapped in the TIR bands by using a band 13/band 12 ratio image, which is sensitive to the intensity of the quartz reststrahlen feature. Improved distinction between the quartzose surficial deposits and silicified bedrock was achieved by using matched-filter processing with TIR image spectra for reference.     

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).     

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.     

Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data

Information regarding the extent, timing and magnitude of forest disturbance are key inputs required for accurate estimation of the terrestrial carbon balance. Equally important for studying carbon dynamics is the ability to distinguish the cause or type of forest disturbance occurring on the landscape. Wildfire and timber harvesting are common disturbances occurring in boreal forests, with each having differing carbon consequences (i.e., biomass removed, recovery rates). Development of methods to not only map, but distinguish these types of disturbance with satellite data will depend upon an improved understanding of their distinctive spectral properties. In this study, we mapped wildfires and clearcut harvests occurring in a Landsat time series (LTS) acquired in the boreal plains of Saskatchewan, Canada. This highly accurate reference map (kappa = 0.91) depicting the year and cause of historical disturbances was used to determine the spectral and temporal properties needed to accurately classify fire and clearcut disturbances. The results showed that spectral data from the short-wave infrared (SWIR; e.g., Landsat band 5) portion of the electromagnetic spectrum was most effective at separating fires and clearcut harvests possibly due to differences in structure, shadowing, and amounts of exposed soil left behind by the two disturbance types. Although SWIR data acquired 1 year after disturbance enabled the most accurate discrimination of fires and clearcut harvests, good separation (e.g., kappa ≥ 0.80) could still be achieved with Landsat band 5 and other SWIR-based indices 3 to 4 years after disturbance. Conversely, minimal disturbance responses in near infrared-based indices associated with green leaf area (e.g., NDVI) led to unreliably low classification accuracies regardless of time since disturbance. In addition to exploring the spectral and temporal manifestation of forest disturbance types, we also demonstrate how Landsat change maps which attribute cause of disturbance can be used to help elucidate the social, ecological and carbon consequences associated with wildfire and clearcut harvesting in Canadian boreal forests.     

Spectroscopic characterization of oils yielded from Brazilian offshore basins: Potential applications of remote sensing

Spectroscopy is the basis to detect and characterize offshore hydrocarbon (HC) seeps through optical remote sensing. Diagnostic spectral features of HCs are linked to their chemical composition and fundamental molecular vibrations (SWIR-TIR features), as well as overtones and combinations of these vibrations (VNIR-SWIR). These features allow for the characterization of oil, oil on water and emulsified oil. This work shows the results of lab and field spectral measurements of 17 petroleum samples yielded from key, oil-rich sedimentary basins in Brazil. Measurements comprised reflectance data (VNIR- SWIR), Attenuated Total Reflectance (ATR), Directional Hemispherical Reflectance (DHR), and emissivity data (TIR). These spectra were analyzed by multivariate techniques, such as Principal Components Analysis (PCA) and Partial Least-Square analysis (PLS). The experimental results indicate that for the VNIR-SWIR range: (i) spectral features can be recognized for crude oil, emulsified oil and oil on ocean water; (ii) different oil types can be qualitatively distinguished based on these features (i.e. light or heavy), even considering oil on water; (iii) the same applies for oil measurements simulated at the spectral resolution of hyperspectral (357-bands/ProSpecTIR) and multispectral (9-bands/ASTER) sensors. Within TIR wavelengths (3-14 μm), typical HC spectral features can also be resolved and oil types qualitatively discriminated using PCA/PLS, including both full-resolution spectra and spectra resampled to hyperspectral sensor (128-bands/SEBASS). However, despite the fact that oil emissivity is always lower than that of water, such separation seems unfeasible using 8-12 μm TIR features only; emissivity spectra are essentially flat for all samples in this interval. This research demonstrated that oil can be qualitatively distinguished based on both VNIR-SWIR and TIR spectroscopy data, with important implications for remote off-shore oil exploration and classification of oil leakages.     

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.     

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.     

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.     

ASTER spectral analysis of ultramafic lamprophyres (carbonatites and aillikites) within the Batain Nappe,northeastern margin of Oman: a proposal developed for spectral absorption

We developed a scientific proposal on spectral absorption in remote sensing and a new image-processing method that is purely based on multispectral satellite image spectra to map ultramafic lamprophyre and carbonatite occurrences. The proposed method provides a simple, yet efficient, tool that will help exploration geologists. In this proposal, in which the spectral absorption is applicable to all satellite images obtained in visible, reflected infrared, and thermal infrared spectral wavelength regions, we found that the carbonatites appear white in colour on a greyscale or RGB thermal infrared image obtained in the thermal infrared wavelength region (3–15 μm) due to molecular emission of thermal energy by such carbonate content, particularly the wavelength recorded by the sensor and that the variation of absorption in spectral bands of an outcrop is due to the differences in percentage of carbonate content or the spectral, spatial, radiometric, or temporal resolution of satellite data or the occurrences of carbonatites to incident energy. The results were confirmed by studying the spectral absorption characteristics of carbonatites in selected world occurrences including parts of Batain Nappe, Oman; Fuerteventura (Canary Islands), Spain; Mount Homa, Kenya; Ol Doinyo Lengai, Tanzania; Mount Weld region, (Laverton), Australia, and Phalaborwa region, South Africa, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Thematic Mapper (TM) satellite data. A subsequent study of visible near-infrared (VNIR) and shortwave infrared (SWIR) ASTER spectral bands of Early Cretaceous alkaline ultramafic rocks of Batain Nappe, along the northeastern margin of Oman to map for the occurrences of carbonatite and aillikite (ultramafic lamprophyres) dikes and plugs, showed their detection mainly by the diagnostic CO₃ absorption (2.31–2.33 μm) in ASTER SWIR band 8. The results of image interpretations were verified and confirmed in the field and were validated through the study of laboratory analyses. A few more carbonatite dike occurrences were interpreted directly over the greyscale image of ASTER bands and true-colour interpretations of a Google Earth image along this margin. The carbonatites and aillikite occurrences of the area are rich in apatite, iron oxide, phlogopite, and REE-rich minerals and warrant new exploration projects.     

Multi-resolution spectral analysis of wildfire potassium emission signatures using laboratory, airborne and spaceborne remote sensing

Thermal remote sensing studies of actively burning wildfires are usually based on the detection of Planckian energy emissions in the MIR (3-5 μm), LWIR (8-14 μm) and/or SWIR (1.0-2.5 μm) spectral regions. However, vegetation also contains a series of trace elements which present unique narrowband spectral emission lines in the visible and near infrared wavelength range when the biomass is heated to high temperatures during the process of flaming combustion. These spectral lines can be discriminated by detector systems that are less costly than the longer wavelength, actively cooled instruments more typically used in EO-based active fire studies. The main trace element resulting in the appearance of spectral emission lines appears to be potassium (K), with features at 766.5 nm and 769.9 nm. Here we study K-emission line spectral signature in laboratory scale fires using a field spectrometer, at a series of moderately-sized woodland and shrubland fires using airborne imagery from a new compact hyperspectral imager (HYPER-SIM.GA) operating at a relatively fine spectral sampling interval (1.2 nm), and at large open wildfires using the EO-1 satellite's Hyperion sensor. We derive a metric based on band differencing of the spectral signal both close to and outside of the K-line region in order to quantify the magnitude of the K-emission signature, and find that variations in this metric appear to track quite well with the commonly used measures of fire radiometric temperature and fire radiative power (FRP). We find that substantial flaming activity is required to generate a potassium emission signature, but that once present this can be detected using airborne remote sensing even through a substantial smoke layer that apparently obscures fire across the remainder of the VIS spectral range. Being specific to flaming combustion, detection of the K-emission line signature could prove useful in refining estimates of the gases released in open wildfires, since trace gas emission factors can vary substantially between flaming and smouldering stages. Finally, we demonstrate the first identification of the K-emission line signature from space using the EO-1 Hyperion instrument, but find it detectable only in certain instances. We conclude that a finer spectral and spatial resolution than that offered by Hyperion is required for improved detection performance. Nevertheless, our results point to the potential effectiveness of airborne and spaceborne K-emission signature detection as a complement to the more common thermal remote sensing approaches to wildfire detection and analysis. Sensors targeting this application should consider careful placement of the measurement wavelengths around the location of the K-line wavelengths, in part to minimise influences from the nearby oxygen A-band features.     

Detecting land cover change at the Jornada Experimental Range, New Mexico with ASTER emissivities

Multispectral thermal infrared remote sensing of surface emissivities can detect and monitor long term land vegetation cover changes over arid regions. The technique is based on the link between spectral emissivities within the 8.5-9.5 μm interval and density of sparsely covered terrains. The link exists regardless of plant color, which means that it is often possible to distinguish bare soils from senescent and non-green vegetation. This capability is typically not feasible with vegetation indices. The method is demonstrated and verified using ASTER remote sensing observations between 2001 and 2003 over the Jornada Experimental Range, a semi-arid site in southern New Mexico, USA. A compilation of 27 nearly cloud-free, multispectral thermal infrared scenes revealed spatially coherent patterns of spectral emissivities decreasing at rates on the order of 3% per year with R² values of ∼ 0.82. These patterns are interpreted as regions of decreased vegetation densities, a view supported by ground-based leaf area index transect data. The multi-year trend revealed by ASTER's 90-m resolution data are independently confirmed by 1-km data from Terra MODIS. Comparable NDVI images do not detect the long-term spatially coherent changes in vegetation. These results show that multispectral thermal infrared data, used in conjunction with visible and near infrared data, could be particularly valuable for monitoring land cover changes.     

Spectral characterization and ASTER-based lithological mapping of an ophiolite complex: A case study from Neyriz ophiolite, SW Iran

The Neyriz ophiolite occurs along the Zagros suture zone in SW Iran, and is part of a 3000-km obduction belt thrusting over the edge of the Arabian continent during the late Cretaceous. This complex typically consists of altered dunites and peridotites, layered and massive gabbros, sheeted dykes and pillow lavas, and a thick sequence of radiolarites. Reflectance and emittance spectra of Neyriz ophiolite rock samples were measured in the laboratory and their spectra were used as endmembers in a spectral feature fitting (SFF) algorithm. Laboratory spectral reflectance measurements of field samples showed that in the visible through shortwave infrared (VNIR-SWIR) wavelength region the ultramafic and gabbroic rocks are characterized by ferrous-iron and Fe, MgOH spectral features, and the pillow lavas and radiolarites are characterized by spectral features of ferric-iron and AlOH. The laboratory spectral emittance spectra also revealed a wide wavelength range of SiO spectral features for the ophiolite rock units. After continuum removal of the spectra, the SFF classification method was applied to the VNIR + SWIR 9-band stack, and to the 11-band data set of SWIR and TIR data sets of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor, using field spectra as training sets for evaluating the potential of these data sets in discriminating ophiolite rock units. Output results were compared with the geological map of the area and field observations, and were assessed by the use of confusion matrices. The assessment showed, in terms of kappa coefficient, that the SFF classification method with continuum removal applied to the SWIR data achieved excellent results, which were distinctively better than those obtained using VNIR + SWIR data and TIR data alone.     

Identifying plant species using mid-wave infrared (2.5–6 μm) and thermal infrared (8–14 μm) emissivity spectra

Plant species discrimination using remote sensing is generally limited by the similarity of their reflectance spectra in the visible, NIR and SWIR domains. Laboratory measured emissivity spectra in the mid infrared (MIR; 2.5 μm–6 μm) and the thermal infrared (TIR; 8 μm–14 μm) domain of different plant species, however, reveal significant differences. It is anticipated that with the advances in airborne and space borne hyperspectral thermal sensors, differentiation between plant species may improve. The laboratory emissivity spectra of thirteen common broad leaved species, comprising 3024 spectral bands in the MIR and TIR, were analyzed. For each wavelength the differences between the species were tested for significance using the one way analysis of variance (ANOVA) with the post-hoc Tukey HSD test. The emissivity spectra of the analyzed species were found to be statistically different at various wavebands. Subsequently, six spectral bands were selected (based on the histogram of separable pairs of species for each waveband) to quantify the separability between each species pair based on the Jefferies Matusita (JM) distance. Out of 78 combinations, 76 pairs had a significantly different JM distance. This means that careful selection of hyperspectral bands in the MIR and TIR (2.5 μm–14 μm) results in reliable species discrimination.     

Comparison of fire temperature and fractional area modeled from SWIR, MIR, and TIR multispectral and SWIR hyperspectral airborne data

Spectral mixture modeling has previously been used to retrieve fire temperature and fractional area from multiband radiance data containing emitted radiance from fires. While this type of temperature modeling has potential for improving understanding of fire behavior and emissions, modeled temperature and fractional area may depend on the wavelength region used for modeling. Using airborne hyperspectral (Airborne Visible Infrared Imaging Spectrometer; AVIRIS) and multispectral (MODIS/ASTER Airborne Simulator; MASTER) data acquired simultaneously over the 2008 Indians Fire in California, we examined changes in modeled fire temperature and fractional area that occurred when input wavelength regions were varied. Temperature and fractional area modeled from multiple MASTER runs were directly compared. Incompatible spatial resolutions prevented direct comparison of the AVIRIS and MASTER model runs, so total area modeled at each temperature was used to indirectly compare temperature and fractional area retrieved from these two sensors. AVIRIS and MASTER model runs using shortwave infrared (SWIR) bands produced consistent fire temperatures and fractional areas when modeled temperatures exceeded 800 K. Temperatures and fire fractional areas were poorly correlated for temperatures below 800 K and when the SWIR bands were excluded as model inputs. The single temperature blackbody assumption commonly used in mixing model retrieval of fire temperature is potentially useful for modeling higher temperature fires, but is likely not valid for lower temperature smoldering combustion due to mixed radiance from multiple fuel elements combusting at different temperatures. SWIR data contain limited emitted radiance from combustion at lower temperatures, and are thus essential for consistent modeling of fire temperature and fractional area at higher fire temperatures.     

Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER)

We present an automated fire detection algorithm for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor capable of mapping actively burning fires at 30-m spatial resolution. For daytime scenes, our approach uses near infrared and short-wave infrared reflectance imagery. For nighttime scenes a simple short wave infrared radiance threshold is applied. Based on a statistical analysis of 100 ASTER scenes, we established omission and commission error rates for nine different regions. In most regions the probability of detection was between 0.8 and 0.9. Probabilities of false alarm varied between 9 × 10^− 8 (India) and 2 × 10^− 5 (USA/Canada). In most cases, the majority of false fire pixels were linked to clusters of true fire pixels, suggesting that most false fire pixels occur along ambiguous fire boundaries. We next consider fire characterization, and formulate an empirical method for estimating fire radiative power (FRP), a measure of fire intensity, using three ASTER thermal infrared channels. We performed a preliminary evaluation of our retrieval approach using four prescribed fires which were active at the time of the Terra overpass for which limited ground-truth data were collected. Retrieved FRP was accurate to within 20%, with the exception of one fire partially obscured by heavy soot.     

Accuracy of ASTER Level-2 thermal-infrared Standard Products of an agricultural area in Spain

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) collects five-channel thermal-infrared images that are calibrated, corrected for atmospheric effects, and then converted to land surface temperature and emissivity products by the ASTER Temperature/Emissivity Separation (TES) algorithm. TES scales low- and high-contrast surfaces differently, and has been validated over water (low contrast) and rock (high contrast). Performance of TES over agricultural areas, however, has not been evaluated specifically. To address this issue, field measurements of “ground truth” were made over bare soil in addition to green grass, alfalfa and corn, at an agricultural research site in Spain during two coincident campaigns (SPectrA Barrax Campaign, or SPARC, and Exploitation of AnGular effects in Land surface, or EAGLE) during an ASTER overflight. Comparison of the ASTER Standard Products for land surface temperature (AST-08) and emissivity (AST-05) with ground measurements for the crops (corn and barley, plus grass) showed that accuracies of ± 1.5 K and ± 0.01, respectively, were achieved there. However, bare soil was assessed incorrectly by TES as having high emissivity contrast, leading to inaccurate scaling and low apparent emissivities.