Hyperspectral Thermal Emission Spectrometer Research Articles

Algorithms for Detecting Sub‐Pixel Elevated Temperature Features for the NASA Surface Biology and Geology (SBG) Designated Observable

AbstractOne of the top priorities of the Surface Biology and Geology (SBG) Earth Observing System is the detection and retrieval of elevated temperature features (ETF) usually found in the vicinity of active fires and volcanic activity. We test the ability of currently proposed midwave (MIR: 3–5 μm) and thermal infrared (TIR: 8–12 μm) bands to detect ETF within the 400–1200 K range. Specifically, our investigation aims to compare and contrast the use of the 4 and 4.8 μm MIR bands. We use land surface temperature data obtained by the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) instrument over active fire and lava flows to model at‐sensor SBG radiances in the 3–12 μm range. This is achieved using the Temperature Emissivity Uncertainty Simulator (TEUSim) with the designated/proposed SBG MIR and TIR band characteristics. For ETF detection, we applied the Normalized Thermal Index (NTI) and Enhanced Thermal Index (ETI) to determine a suitable threshold for a wide range of ETF sizes and temperatures. We find that combining an NTI threshold of −0.7 followed by an ETI threshold of 0.02 accurately identifies ETFs at a 97% rate. Sensor noise up to 0.5 K has negligible effects on ETF detection in the 400–1200 K range. The currently proposed SBG MIR and TIR bands are sufficient to detect unsaturated ETFs caused by wildfire and volcanic activities at a ∼3 day revisit and subpixel ETF area of ∼9 m2 (at 500 K) that is unattainable by current satellite TIR instruments.

Geothermal Surface Manifestation Identification Using Airborne Hyperspectral Imagery Case Study: Davis-Schrimpf Geothermal Field, Salton Sea, California

DOI:10.17014/ijog.9.1.119-130Research on surface geothermal evidence has been done extensively using remote sensing techniques. For detailed remote sensing exploration on geothermal areas, UAV and airborne based were preferred over the satellite-based sensor. In this research, anomalies in surface temperature, mineral occurrence, and ammonia emission were studied on a set of airborne hyperspectral imagery from NASA, the Hyperspectral Thermal Emission Spectrometer (HyTES). High-resolution surface temperature and mineral maps were able to identify and describe the mineralogy of the mud pots and gryphons at the Davis-Schrimpf Geothermal Field, Salton Sea, California. From the surface temperature map, the surface temperature of the geothermal features was measured at approximately 314°K (40oC) and higher. The purest pixels from MNF transformation of the first four cleanest bands of emissivity map produce endmembers that include the geothermal indicator minerals (barite, anhydrite, quartz, gypsum). Based on the mineralogy deposits, these manifestations are classified as potassic alteration types from a porphyry system that could be an indication of an active geothermal system. This also explains that the surface features are part of the upper reservoir of the Salton Sea Geothermal Field. On the other hand, ammonia detection that was performed in this research failed to get any clear recognition from the simple image processing. It is concluded that the airborne hyperspectral imagery could be a reliable option for remote sensing geothermal exploration, as it was able to characterize the surface geothermal manifestation with quite good detail using this imagery from a wide area of survey.

Joint VSWIR-TIR retrievals of earth's surface and atmosphere

Remote spectroscopy of terrestrial ecosystems generally falls into one of two categories: UV, Visible and ShortWave InfraRed (VSWIR) methods measuring reflected solar illumination, and Thermal InfraRed (TIR) methods measuring radiation emitted from the Earth's surface. Both interpret the measured radiance with inversion algorithms that estimate surface reflectance, emissivity, surface temperatures, and the atmospheric state. However, the two regimes have traditionally used independent inversion methods. Here we present the first simultaneous inversion of the upwelling radiance spectrum from the UV through the VSWIR and the Thermal Infrared. Maximum A Posteriori estimation determines the surface and atmosphere state that is most consistent with measured radiance across the entire range. We find that the complementary information in the two intervals improves estimates of atmospheric properties, surface emissivity, and surface temperature. Posterior uncertainty is 40% lower in surface temperature and 60% in air temperature. Spurious correlations between atmospheric and surface state are also reduced, with 90% lower correlated uncertainty between total column water vapor and both surface and air temperature. Temperature-emissivity separation also improves, with a 33% reduction in correlated posterior uncertainty between the parameters. We demonstrate the method on coincident data from the Classic Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) operating in the VSWIR range, and the Hyperspectral Thermal Emission Spectrometer (HyTES) in the TIR range, during flights on NASA's high-altitude ER-2 aircraft. Measurements spanning 0.4 to 12 microns can improve estimates of surface and atmospheric properties, significantly advancing studies in terrestrial ecosystems, geology, agriculture, and urban management.

Quantification of Ammonia Emissions With High Spatial Resolution Thermal Infrared Observations From the Hyperspectral Thermal Emission Spectrometer (HyTES) Airborne Instrument

Thermal infrared spectra can provide unique information on atmospheric ammonia enhancements, including those produced by agricultural, industrial, and biomass burning sources. In this work, we develop and test a quantitative ammonia retrieval algorithm using data from the airborne hyperspectral thermal emission spectrometer (HyTES) instrument over diverse targets, including persistent ammonia sources, such as cattle feedlots and a power plant, as well as a small, smoldering wildfire. Although no formal validation was possible due to lack of collocated observations, the HyTES ammonia values over feedlots in the California San Joaquin Valley are shown to be consistent within the estimated retrieval uncertainty of $\sim$ 20%–50% with in situ ammonia values collected over the same facilities during the 2013 DISCOVER-AQ field campaign. HyTES also observed meter-scale thermal hot spots associated with a small wildfire in Northern Arizona, which was determined to coincide with the emission of free ammonia, presumed to originate from the fire. This very localized emission source, not visible from satellite sensors, could be resolved in HyTES imagery. The ammonia enhancement immediately downwind from the smoldering fire, before the free ammonia reacted to form aerosols, was found to be distinguishable from the background with values three to ten times smaller than ammonia enhancements from the power plant and cattle feedlots, respectively. High-resolution detection and quantification of ammonia over nonpersistent sources like wildfires from HyTES shows that it is a powerful tool that can be used to improve regional scale fire emission inventories by accounting for the presence of temporally short ammonia processes.

APPLICATION OF HYPERSPECTRAL THERMAL EMISSION SPECTROMETER (HYTES) DATA FOR HYSPIRI OPTIMAL BAND POSITIONING TO CHARACTERIZE SURFACE MINERALS

Abstract. This study aimed to characterize surface minerals from high dimensional HyTES (Hyperspectral Thermal Emission Spectrometer) data comprised of 256 spectral bands between 7.5 and 12 μm (i.e., TIR domain of the electromagnetic spectrum). The HyTES is across-track imager and can image 512 pixels with spatial resolution varies between 5 to 50 m depending upon aircraft flying height. HyTES is developed to support the HyspIRI (Hyperspectral Infrared Imager) mission by acquiring TIR data at much higher spectral and spatial resolutions in-order to define the optimum band positions for the TIR instrument of HyspIRI. For earth compositional mapping, the HyTES images of Cuprite and Death Valley regions were acquired in summer 2014 and spectral emissivities of fifteen minerals classes were extracted from regions of known mineral compositions and were randomly divided into training and testing sets (each mineral class com-prised of 100 spectra). These extracted emissivity signatures were then used for categorizing minerals and for finding HyspIRI's optimal band positions for earth composition mapping using Genetic Algorithm (GA) coupled with Spectral angle mapper (SAM). The GA-SAM was trained for fifteen mineral classes and the algorithms were run iteratively 40 times. High calibration (> 95 %) and validation (> 90 %) accuracies were achieved with limited numbers (seven) of spectral bands selected by GA-SAM. Knowing the important band Positions will help scientist of HyspIRI group to place spectral bands at regions were accuracies of Earth compositional mapping can be enhanced.

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

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

Plant species' spectral emissivity and temperature using the hyperspectral thermal emission spectrometer (HyTES) sensor

The thermal domain (TIR; 2.5–15 μm) delivers unique measurements of plant characteristics that are not possible in other parts of the electromagnetic spectrum. However, these TIR measurements have largely been restricted to laboratory leaf level or coarse spatial resolutions due to the lack of suitable data from airborne and spaceborne instruments. The airborne Hyperspectral Thermal Emission Spectrometer (HyTES) provides an opportunity to retrieve high spectral resolution emissivity and land surface temperature (LST) that can be exploited for canopy level vegetation research. This study is a high spatial resolution analysis of plant species' emissivity and LST using HyTES imagery acquired in the Huntington Botanical Gardens on 2014 July 5 and 2016 Jan 25. Leaf and canopy emissivity variation was identified among 24 plant species and used to determine leaf to canopy scaling capabilities. HyTES LST patterns among species and dates were quantified and correlated to LiDAR derived tree canopy attributes. At the leaf scale, one third of the species showed distinct spectral separation from other species. However, at the canopy scale most species were not spectrally separable. Random forest classification demonstrates the high level of confusion between species with overall accuracies <40%. LST data, derived from TIR measurements, showed that species exhibited significantly different distributions between dates and species. These distributions were largely explained by canopy structure (e.g. tree height and canopy density) and composition of neighboring pixels (e.g. presence of pavement versus trees). While species do not exhibit unique emissivity signatures at the canopy level, the LST variation among species provides a stronger understanding of LST variability in coarser resolution TIR imagery. This study represents the analysis of vegetation characteristics using the NASA's HyTES TIR sensor, opening the door for future remote sensing vegetation studies that include using the recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission.

Selection of HyspIRI optimal band positions for the earth compositional mapping using HyTES data

The National Aeronautics and Space Administration (NASA) has proposed the launch of a new space-borne sensor called HyspIRI (Hyperspectral and Infrared Imager) which will cover the spectral range from 0.4–12μm. Two instruments will be mounted on HyspIRI platform: 1) a hyperspectral instrument which can sense earth surface between 0.4 and 2.5μm at 10nm intervals and 2) a multispectral infrared sensor will acquire images between 3 and 12μm in eight spectral bands (one in Mid infrared (MIR) and seven in Thermal Infrared (TIR)). The TIR spectral wavebands will be positioned based on their importance in various applications. This study aimed to identify HyspIRI optimal TIR wavebands position for earth compositional mapping. A Genetic Algorithm coupled with the Spectral Angle Mapper (GA-SAM) was used as a spectral bands selector. High dimensional HyTES (Hyperspectral Thermal Emission Spectrometer) emissivity spectra comprised of 202 spectral bands of Cuprite and Death Valley regions were used to select meaningful subsets of bands for earth compositional mapping. The GA-SAM was trained for fifteen mineral classes and the algorithms were run iteratively 50 times. High calibration (>95%) and validation (>90%) accuracies were achieved with a limited number (seven) of spectral bands selected by GA-SAM. The knowledge of important band positions will help the scientists of the HyspIRI group to place spectral bands in regions where accuracies of earth compositional mapping can be enhanced.

Improved ISAC Algorithm to Retrieve Atmospheric Parameters From HyTES Hyperspectral Images Using Gaussian Mixture Model and Error Ellipse

In-scene atmospheric correction (ISAC) is a procedure that accounts for atmospheric effects by direct use of the hyperspectral radiance data without recourse to ancillary meteorological data. This letter aims to improve the accuracy of the ISAC algorithm. In the ISAC method after calculating brightness temperature, the computed radiance and measured radiance at the sensor are plotted on a graph in each band. Then, to estimate atmospheric parameters, the straight line is fit to the upper boundary of the plot. One of the issues in ISAC is to find an optimal upper boundary of data. The main innovation of this letter is the use of Gaussian mixture model (GMM) and error ellipse to find the optimal upper boundary of data and fit the line to it. In the line fitting process, first, a GMM with the optimum class number derived by Akaike information criterion (AIC) is implemented on thermal hyperspectral data and then, the optimal upper boundary is selected for each class and a straight line is fit to it. Finally, the desired parameters are obtained with weighted linear combination of the results from all classes. For quality assessment, the results were compared with atmospheric products of Hyperspectral Thermal Emission Spectrometer sensor and atmospheric parameters that were obtained from traditional ISAC. Root-mean-square errors for atmospheric transmittance obtained from GMM for bands 9.8 and $11.5~\mu \text{m}$ are 0.0008 and 0.0106 and those for upwelling atmospheric radiance are 0.675 and 0.0265, respectively.

Linking seasonal foliar traits to VSWIR-TIR spectroscopy across California ecosystems

Abstract Vegetation traits provide critical information on ecosystem function that can be used to assess the effects of disturbance, land use, and climate change. Recent studies have demonstrated the use of spectroscopy to predict vegetation traits accurately and efficiently. To date, most spectroscopic studies have utilized data from the Visible Short Wave Infrared spectrum (VSWIR) or, occasionally, the Thermal Infrared spectrum (TIR), but not in combination. This study focuses on VSWIR and TIR synergy to evaluate the ability to predict leaf level cellulose, lignin, leaf mass per area (LMA), nitrogen, and water content across seasons. We used fresh leaves from sixteen common California shrub and tree species collected in the 2013 spring, summer, and fall seasons. The 284 samples exhibited a wide range of leaf traits as determined by standard analytical procedures: 4.2–27.3% for cellulose, 2.6–22.5% for lignin, 34.7–388.9 g/m 2 for LMA, 0.45–3.81% for nitrogen, and 20.2–76.9% for water content. For each leaf trait, partial least squares regression (PLSR) models were fit using different portions of the spectrum: VSWIR (0.35–2.5 μm), TIR (2.5–15.4 μm), and Full spectrum (0.35–15.4 μm). We also fit PLSR models using spectra resampled to simulate three airborne sensors: the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; 0.4–2.5 μm), the Hyperspectral Thermal Emission Spectrometer (HyTES; 7.5–12 μm), and the Hyperspectral InfraRed Imager (HyspIRI; 0.4–12 μm). The majority of best performing models used the Full spectrum, demonstrating the value of combining TIR and VSWIR spectra for leaf trait prediction. Sensor-simulated PLSR models created with the entire data set yielded validation R 2 and root mean square error of prediction (RMSEP) values as follows: R 2 = 0.70 and RMSEP = 13.1% for cellulose, R 2 = 0.50 and RMSEP = 17.7% for lignin, R 2 = 0.56 and RMSEP = 18.3% for LMA, R 2 = 0.56 and RMSEP = 18.1% for nitrogen, and R 2 = 0.89 and RMSEP = 5.7% for water content. General models successfully captured the variability among all seasons and leaf forms for cellulose and water content, while the other leaf traits were better modeled with season or leaf form-specific models. This study successfully captured the large seasonal and geographical variation in leaf traits across California's diverse ecosystems, supporting the possibility of using HyspIRI's imagery for global mapping efforts of these traits.

Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region

Methane (CH4) impacts climate as the second strongest anthropogenic greenhouse gas and air quality by influencing tropospheric ozone levels. Space-based observations have identified the Four Corners region in the Southwest United States as an area of large CH4 enhancements. We conducted an airborne campaign in Four Corners during April 2015 with the next-generation Airborne Visible/Infrared Imaging Spectrometer (near-infrared) and Hyperspectral Thermal Emission Spectrometer (thermal infrared) imaging spectrometers to better understand the source of methane by measuring methane plumes at 1- to 3-m spatial resolution. Our analysis detected more than 250 individual methane plumes from fossil fuel harvesting, processing, and distributing infrastructures, spanning an emission range from the detection limit [Formula: see text] 2 kg/h to 5 kg/h through [Formula: see text] 5,000 kg/h. Observed sources include gas processing facilities, storage tanks, pipeline leaks, and well pads, as well as a coal mine venting shaft. Overall, plume enhancements and inferred fluxes follow a lognormal distribution, with the top 10% emitters contributing 49 to 66% to the inferred total point source flux of 0.23 Tg/y to 0.39 Tg/y. With the observed confirmation of a lognormal emission distribution, this airborne observing strategy and its ability to locate previously unknown point sources in real time provides an efficient and effective method to identify and mitigate major emissions contributors over a wide geographic area. With improved instrumentation, this capability scales to spaceborne applications [Thompson DR, et al. (2016) Geophys Res Lett 43(12):6571-6578]. Further illustration of this potential is demonstrated with two detected, confirmed, and repaired pipeline leaks during the campaign.

Characterization of anthropogenic methane plumes with the Hyperspectral Thermal Emission Spectrometer (HyTES): a retrieval method and error analysis

Abstract. We introduce a retrieval algorithm to estimate lower tropospheric methane (CH4) concentrations from the surface to 1 km with uncertainty estimates using Hyperspectral Thermal Emission Spectrometer (HyTES) airborne radiance measurements. After resampling, retrievals have a spatial resolution of 6 × 6 m2. The total error from a single retrieval is approximately 20 %, with the uncertainties determined primarily by noise and spectral interferences from air temperature, surface emissivity, and atmospheric water vapor. We demonstrate retrievals for a HyTES flight line over storage tanks near Kern River Oil Field (KROF), Kern County, California, and find an extended plume structure in the set of observations with elevated methane concentrations (3.0 ± 0.6 to 6.0 ± 1.2 ppm), well above mean concentrations (1.8 ± 0.4 ppm) observed for this scene. With typically a 20 % estimated uncertainty, plume enhancements with more than 1 ppm are distinguishable from the background values with its uncertainty. HyTES retrievals are consistent with simultaneous airborne and ground-based in situ CH4 mole fraction measurements within the reported accuracy of approximately 0.2 ppm (or ∼ 8 %), due to retrieval interferences related to air temperature, emissivity, and H2O.

High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

Abstract. Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1–2 km), and high spatial resolution (∼ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH4, H2S, NH3, NO2, and SO2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions.

Integrated visible and near-infrared, shortwave infrared, and longwave infrared full-range hyperspectral data analysis for geologic mapping

Airborne visible/infrared imaging spectrometer (AVIRIS) and spatially coincident hyperspectral thermal emission spectrometer (HyTES) data were used to map geology and alteration for a site in northern Death Valley, California and Nevada. AVIRIS with 224 bands from 0.4 to 2.5 μ m were converted to reflectance. HyTES data with 256 bands covering 8 to 12 μ m were converted to emissivity. Two approaches were investigated for integration of the datasets for full spectrum analysis. A combined (integrated) bands method utilized 332 spectral bands spanning both datasets. Spectral endmembers were extracted, and the predominant material at each pixel was mapped for the full spectral range using partial unmixing. This approach separated a variety of materials, but it was difficult to directly relate mapping results to surface properties. The second method used visible to near-infrared, shortwave infrared, and longwave infrared data independently to determine and map key endmembers in each spectral range. AVIRIS directly mapped a variety of specific minerals, while HyTES separated and mapped several igneous rock phases. Individual mapping results were then combined using geologically directed logical operators. The full-range results illustrate that integrated analysis provides advantages over use of just one spectral range, leading to improved understanding of the distribution of geologic units and alteration.

Derivation of an urban materials spectral library through emittance and reflectance spectroscopy

Recent advances in thermal infrared remote sensing include the increased availability of airborne hyperspectral imagers (such as the Hyperspectral Thermal Emission Spectrometer, HyTES, or the Telops HyperCam and the Specim aisaOWL), and it is planned that an increased number spectral bands in the long-wave infrared (LWIR) region will soon be measured from space at reasonably high spatial resolution (by imagers such as HyspIRI). Detailed LWIR emissivity spectra are required to best interpret the observations from such systems. This includes the highly heterogeneous urban environment, whose construction materials are not yet particularly well represented in spectral libraries. Here, we present a new online spectral library of urban construction materials including LWIR emissivity spectra of 74 samples of impervious surfaces derived using measurements made by a portable Fourier Transform InfraRed (FTIR) spectrometer. FTIR emissivity measurements need to be carefully made, else they are prone to a series of errors relating to instrumental setup and radiometric calibration, which here relies on external blackbody sources. The performance of the laboratory-based emissivity measurement approach applied here, that in future can also be deployed in the field (e.g. to examine urban materials in situ), is evaluated herein. Our spectral library also contains matching short-wave (VIS–SWIR) reflectance spectra observed for each urban sample. This allows us to examine which characteristic (LWIR and) spectral signatures may in future best allow for the identification and discrimination of the various urban construction materials, that often overlap with respect to their chemical/mineralogical constituents. Hyperspectral or even strongly multi-spectral LWIR information appears especially useful, given that many urban materials are composed of minerals exhibiting notable reststrahlen/absorption effects in this spectral region. The final spectra and interpretations are included in the London Urban Micromet data Archive (LUMA; http://LondonClimate.info/LUMA/SLUM.html).