Land surface temperature and emissivity retrieval from thermal infrared hyperspectral imaging

A new algorithm, optimized land surface temperature and emissivity retrieval (OLSTER), is presented to compensate for atmospheric effects and retrieve land surface temperature (LST) and emissivity from airborne thermal infrared hyperspectral data. The OLSTER algorithm is designed to retrieve properties of both natural and man-made materials. Multi-directional or multi-temporal observations are not required, and the scenes do not have to be dominated by blackbody features. The OLSTER algorithm consists of a preprocessing step, an iterative search for nearblackbody pixels, and an iterative constrained optimization loop. The preprocessing step provides initial estimates of LST per pixel and the atmospheric parameters of transmittance and upwelling radiance for the entire image. Pixels that are under- or overcompensated by the estimated atmospheric parameters are classified as near-blackbody and lower emissivity pixels, respectively. A constrained optimization of the atmospheric parameters using generalized reduced gradients on the near-blackbody pixels ensures physical results. The downwelling radiance is estimated from the upwelling radiance by applying a look-up table of coefficients based on a polynomial regression of radiative transfer model runs for the same sensor altitude. The LST and emissivity per pixel are retrieved simultaneously using the well established ISSTES algorithm. The OLSTER algorithm retrieves land surface temperatures within about ± 1.0 K, and emissivities within about ± 0.01 based on numerical simulation and validation work comparing results from sensor data with ground truth measurements. The OLSTER algorithm is currently one of only a few algorithms available that have been documented to retrieve accurate land surface temperatures and absolute land surface spectral emissivities from passive airborne hyperspectral LWIR sensor imagery

Landsat Surface Temperature Product: Global Validation and Uncertainty Estimation

Surface temperature is an important Earth system data record that is useful to fields such as change detection, climate research, environmental monitoring, and many smaller scale applications like agriculture. Earth-observing satellites can be used to derive this metric, with the goal that a global product can be established. There are a series of Landsat satellites designed for this purpose, whose data archives provides the longest running source of continuously acquired multispectral imagery. The moderate spatial and temporal resolution, in addition to its well calibrated sensors and data archive make Landsat an unparalleled and attractive choice for many research applications. Through the support of the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS), a Landsat Surface Temperature product (LST) has been developed. Currently, it has been validated for Landsat 5 scenes in North America, and Landsat 7 on a global scale. Transmission and cloud proximity were used to characterize LST error for various conditions, which showed that 30% of the validation data had root mean squared errors (RMSEs) less than 1 K, and 62% had RMSEs less than 2 K. Transmission and cloud proximity were also used to develop a LST uncertainty estimation method, which will allow the user to choose data points that meet their accuracy requirements. For the same dataset, about 20% reported LST uncertainties less than 1 K, and 63% had uncertainties less than 2 K. Enabling global validation and establishing an uncertainty estimation method were crucially important achievements for the LST product, which is now ready to be implemented and scaled so that it is available to the public. This document will describe the LST algorithm in full, and it will also discuss the validation results and uncertainty estimation process

Analysis of the performance of the TES algorithm over urban areas

International audienceThe temperature and emissivity separation (TES) algorithm is used to retrieve the land surface emissivity (LSE) and land surface temperature (LST) values from multispectral thermal infrared sensors. In this paper, we analyze the performance of this methodology over urban areas, which are characterized by a large number of different surface materials, a variability in the lowest layer of the atmospheric profiles, and a 3-D structure. These specificities induce errors in the LSE and LST retrieval, which should be quantified. With this aim, the efficiency of the TES algorithm over urban materials, the atmospheric correction, and the impact of the 3-D architecture of urban scenes are analyzed. The method is based on the use of a 3-D radiative transfer tool, TITAN, for modeling all of the radiative components of the signal registered by a sensor. From the sensor radiance, an atmosphere compensation process is applied, followed by a TES methodology that considers the observed scene to be a flat surface. Finally, the retrieved LSE and LST are compared with the original parameters. Results show the following: First, the TES algorithm used reproduces the LSE (LST) of urban materials within a root-mean-square error (rmse) of 0.017 (0.9 K). Second, 20% of uncertainty in the water vapor content of the total atmosphere introduces an rmse of 0.005 (0.4 K) for the LSE (LST) product. Third, in a standard case, the 3-D structure of an urban canyon leads to an rmse of 0.005 (0.2 K) for the LSE (LST) retrieval of the asphalt at the bottom of the scene

A MODIS/ASTER Airborne Simulator (MASTER) Imagery for Urban Heat Island Research

abstract: Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and other coarse- to medium-resolution remotely sensed imagery to estimate surface temperature. These data are frequently correlated with vegetation, impervious surfaces, and temperature to quantify the drivers of the UHI effect. Because of the coarse- to medium-resolution of the thermal imagery, researchers are unable to correlate these temperature data with the more generally available high-resolution land cover classification, which are derived from high-resolution multispectral imagery. The development of advanced thermal sensors with very high-resolution thermal imagery such as the MODIS/ASTER airborne simulator (MASTER) has investigators quantifying the relationship between detailed land cover and land surface temperature. While this is an obvious next step, the published literature, i.e., the MASTER data, are often used to discriminate burned areas, assess fire severity, and classify urban land cover. Considerably less attention is given to use MASTER data in the UHI research. We demonstrate here that MASTER data in combination with high-resolution multispectral data has made it possible to monitor and model the relationship between temperature and detailed land cover such as building rooftops, residential street pavements, and parcel-based landscaping. Here, we report on data sources to conduct this type of UHI research and endeavor to intrigue researchers and scientists such that high-resolution airborne thermal imagery is used to further explore the UHI effect

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

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 CH_4, H_2S, NH_3, NO_2, and SO_2 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

A MODIS/ASTER airborne simulator (MASTER) imagery for urban heat island research

Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and other coarse- to medium-resolution remotely sensed imagery to estimate surface temperature. These data are frequently correlated with vegetation, impervious surfaces, and temperature to quantify the drivers of the UHI effect. Because of the coarse- to medium-resolution of the thermal imagery, researchers are unable to correlate these temperature data with the more generally available high-resolution land cover classification, which are derived from high-resolution multispectral imagery. The development of advanced thermal sensors with very high-resolution thermal imagery such as the MODIS/ASTER airborne simulator (MASTER) has investigators quantifying the relationship between detailed land cover and land surface temperature. While this is an obvious next step, the published literature, i.e., the MASTER data, are often used to discriminate burned areas, assess fire severity, and classify urban land cover. Considerably less attention is given to use MASTER data in the UHI research. We demonstrate here that MASTER data in combination with high-resolution multispectral data has made it possible to monitor and model the relationship between temperature and detailed land cover such as building rooftops, residential street pavements, and parcel-based landscaping. Here, we report on data sources to conduct this type of UHI research and endeavor to intrigue researchers and scientists such that high-resolution airborne thermal imagery is used to further explore the UHI effect

Atmospheric Compensation for a Landsat Land Surface Temperature Product

The Landsat series of satellites is the longest set of continuously acquired moderate resolution multispectral satellite imagery collected on a single maintained family of instruments. The data are very attractive because the entire archive has been radiometrically calibrated and characterized so that sensor reaching radiance values are well known. However, these values are not easily understood or applied, so this dataset has not been utilized to its fullest potential. This work focuses on atmospheric compensation at each Landsat pixel which will later be used with ASTER derived emissivity data from JPL to perform LST retrievals. We develop a method to automatically generate the effective in band radiative transfer param- eters transmission, upwelled radiance, and downwelled radiance for each pixel. We validate our methodology by comparing our predicted apparent temperatures to ground truth water tempera- tures derived from buoy data at a number of validation sites around the continental United States. Initial validation was performed using Landsat 5. Results show a mean error of -0.267 K and a standard deviation of 0.900 K for 259 cloud free scenes in the validation dataset. Based on the same validation dataset, our current best expectation for a confidence metric for the final product involves categorizing each pixel as cloudy, clouds in the vicinity, or cloud free based on the incorpo- ration of a Landsat cloud product. The mean and standard deviation of the errors associated with each category will be included as a quantitative basis for each category. To support future work, we explore the extension to a global dataset and possible improvements to the atmospheric compensation by more closely exploring the column water vapor contribution to error. Finally, we acknowledge the need for a more formal incorporation of the cloud product, and possibly improvements, in order to finalize the confidence metric for the atmospheric compensation component of the product

Oblique Longwave Infrared Atmospheric Compensation

This research introduces two novel oblique longwave infrared atmospheric compensation techniques for hyperspectral imagery, Oblique In-Scene Atmospheric Compensation (OISAC) and Radiance Detrending (RD). Current atmospheric compensation algorithms have been developed for nadir-viewing geometries which assume that every pixel in the scene is affected by the atmosphere in nearly the same manner. However, this assumption is violated in oblique imaging conditions where the transmission and path radiance vary continuously as a function of object-sensor range, negatively impacting current algorithms in their ability to compensate for the atmosphere. The techniques presented here leverage the changing viewing conditions to improve rather than hinder atmospheric compensation performance. Initial analyses of both synthetic and measured hyperspectral images suggest improved performance in oblique viewing conditions compared to standard techniques. OISAC is an extension of ISAC, a technique that has been used extensively for LWIR AC applications for over 15 years, that has been developed to incorporate the range-dependence of atmospheric transmission and path radiance in identification of the atmospheric state. Similar to ISAC, OISAC requires the existence of near blackbody-like materials over the 11.73 micrometer water band. RD is a newer technique which features unsupervised classification of materials and identifies the atmospheric state which best detrends the observed radiance across all classes of materials, including those of low emissivity

An Examination of Environmental Applications for Uncooled Thermal Infrared Remote Sensing Instruments

Advancements in system design for thermal instruments require assessment of potential environmental applications and appropriate data processing techniques. A novel multi-band thermal imaging system was proposed by DRS Leonardo for the National Aeronautics and Space Administration Earth Science Technology Office Instrument Incubator Program, for which these criteria were assessed. The Multi-Band Uncooled Radiometer Imager (MURI) is a six spectral channel instrument designed to collect images in the thermal infrared, specifically in the range of 7.5 to 12.5 μm. The work detailed in this thesis characterizes the ability of a thermal imager with an uncooled microbolometer focal plane array to provide valuable data for environmental science applications. Here, a pair of studies using simulated data demonstrates the ability of a multispectral instrument such as MURI to detect enhanced levels of atmospheric methane using a novel approach that performs similarly to a state of the art algorithm when applied to MURI data. The novel method is evaluated using a controlled concentration simulated dataset to determine the extent of its detection capabilities and its dependence on atmospheric conditions. The methane investigations reveal the system is capable of detecting a 20 m thick CH4 plume of 10-20 ppm above background levels when column water vapor is low using both the NDMI and matched filter approaches. Additionally, land surface temperature and emissivity retrieval techniques were applied to experimental MURI data recorded during initial test flights to assess their accuracy with MURI data. Utilizing split window and Temperature Emissivity Separation make this examination distinct as this establishes that proven methods can be applied to uncooled multiband imager data. Application of these methods to MURI data demonstrated the system is capable of temperature retrievals with Root Mean Square Errors of less than 1 K to measured reference values and surface emissivity retrievals within 2% of reference database values. The definition and application of the Normalized Differential Methane Index in this thesis demonstrates a novel approach for detection of enhanced plumes of methane utilizing a multispectral system with only a single band allocated to methane absorption features

Learning Set Representations for LWIR In-Scene Atmospheric Compensation

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed in point cloud classification. When applied to collected hyperspectral image data, this method shows comparable performance to Fast Line-of-Sight Atmospheric Analysis of Hypercubes-Infrared (FLAASH-IR), using an auto- mated pixel selection approach. Additionally, inference time is significantly reduced compared to FLAASH-IR with predictions made on average in 0.24 s on a 128 pixel by 5000 pixel data cube using a mobile graphics card. This computational speed-up on a low-power platform results in an autonomous atmospheric compensation method effective for real-time, onboard use, while only requiring a diversity of materials in the scene