Integrating visible, near infrared and short wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada

Visible, near infrared (VNIR), and short wave infrared (SWIR) hyperspectral and thermal infrared (TIR) multispectral remote sensing have become potential tool for geological mapping. In this dissertation, a series of studies were carried out to investigate the potential impact of combining VNIR/SWIR hyperspectral and TIR multispectral data for surface geological mapping. First, a series of simulated data sets based on the characteristics of hyperspectral AVIRIS and multispectral TIR MASTER sensors was created from surface reflectance and emissivity library spectra. Five common used classification methods including minimum distance, maximum likelihood, spectral angle mapper (SAM), spectral feature fitting (SFF), and binary encoding were applied to these simulated data sets to test the hypothesis. It was found that most methods applied to the combined data actually obtained improvement in overall accuracy of classification by comparison of the results to the simulated AVIRIS data or TIR MASTER alone. And some minerals and rocks with strong spectral features got a marked increase in classification accuracy. Second, two real data sets such as AVIRIS and MASTER of Cuprite, Nevada were used. Four classification methods were each applied to AVIRIS, MASTER, and a combined set. The results of these classifications confirmed most findings from the simulated data analyses. Most silicate bearing rocks achieved great improvement in classification accuracy with the combined data. SFF applied to the combination of AVIRIS with MASTER TIR data are especially valuable for identification of silicified alteration and quartzite sandstone which exhibit strong distinctive absorption features in the TIR region. SAM showed some advantages over SFF in dealing with multiple broad band TIR data, obtaining higher accuracy in discriminating low albedo volcanic rocks and limestone which do not have strong characteristic absorption features in the TIR region. One of the main objectives of these studies is to develop an automated classification algorithm which is effective for the analysis of VNIR/SWIR hyperspectral and TIR multispectral data. A rule based system was constructed to draw the strengths of disparate wavelength regions and different algorithms for geological mapping

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

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

Estimation of Evapotranspiration and Energy Fluxes Using a Deep-Learning-Based High-Resolution Emissivity Model and the Two-Source Energy Balance Model with sUAS Information

Surface temperature is necessary for the estimation of energy fluxes and evapotranspiration from satellites and airborne data sources. For example, the Two-Source Energy Balance (TSEB) model uses thermal information to quantify canopy and soil temperatures as well as their respective energy balance components. While surface (also called kinematic) temperature is desirable for energy balance analysis, obtaining this temperature is not straightforward due to a lack of spatially estimated narrowband (sensor-specific) and broadband emissivities of vegetation and soil, further complicated by spectral characteristics of the UAV thermal camera. This study presents an effort to spatially model narrowband and broadband emissivities for a microbolometer thermal camera at UAV information resolution (~0.15 m) based on Landsat and NASA HyTES information using a deep learning (DL) model. The DL model is calibrated using equivalent optical Landsat / UAV spectral information to spatially estimate narrowband emissivity values of vegetation and soil in the 7–14- nm range at UAV resolution. The resulting DL narrowband emissivity values were then used to estimate broadband emissivity based on a developed narrowband-broadband emissivity relationship using the MODIS UCSB Emissivity Library database. The narrowband and broadband emissivities were incorporated into the TSEB model to determine their impact on the estimation of instantaneous energy balance components against ground measurements. The proposed effort was applied to information collected by the Utah State University AggieAir small Unmanned Aerial Systems (sUAS) Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) over a vineyard located in Lodi, California. A comparison of resulting energy balance component estimates, with and without the inclusion of high-resolution narrowband and broadband emissivities, against eddy covariance (EC) measurements under different scenarios are presented and discussed

Physics-constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios

Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using collected data. These deep learning-based compensation algorithms resulted in comparable detection performance to established methods while accelerating the image processing chain by 8X

Retrieving Decadal Climate Change from Satellite Radiance Observations-A 100-year CO2 Doubling OSSE Demonstration.

Preparing for climate change depends on the observation and prediction of decadal trends of the environmental variables, which have a direct impact on the sustainability of resources affecting the quality of life on our planet. The NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is proposed to provide climate quality benchmark spectral radiance observations for the purpose of determining the decadal trends of climate variables, and validating and improving the long-range climate model forecasts needed to prepare for the changing climate of the Earth. The CLARREO will serve as an in-orbit, absolute, radiometric standard for the cross-calibration of hyperspectral radiance spectra observed by the international system of polar operational satellite sounding sensors. Here, we demonstrate that the resulting accurately cross-calibrated polar satellite global infrared spectral radiance trends (e.g., from the Metop IASI instrument considered here) can be interpreted in terms of temperature and water vapor profile trends. This demonstration is performed using atmospheric state data generated for a 100-year period from 2000-2099, produced by a numerical climate model prediction that was forced by the doubling of the global average atmospheric CO2 over the 100-year period. The vertical profiles and spatial distribution of temperature decadal trends were successfully diagnosed by applying a linear regression profile retrieval algorithm to the simulated hyperspectral radiance spectra for the 100-year period. These results indicate that it is possible to detect decadal trends in atmospheric climate variables from high accuracy all-sky satellite infrared radiance spectra using the linear regression retrieval technique

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Surface and Buried Landmine Scene Generation and Validation Using the Digital Imaging and Remote Sensing Image Generation Model

Detection and neutralization of surface-laid and buried landmines has been a slow and dangerous endeavor for military forces and humanitarian organizations throughout the world. In an effort to make the process faster and safer, scientists have begun to exploit the ever-evolving passive electro-optical realm, both from a broadband perspective and a multi or hyperspectral perspective. Carried with this exploitation is the development of mine detection algorithms that take advantage of spectral features exhibited by mine targets, only available in a multi or hyperspectral data set. Difficulty in algorithm development arises from a lack of robust data, which is needed to appropriately test the validity of an algorithm’s results. This paper discusses the development of synthetic data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. A synthetic landmine scene has been modeled after data collected at a US Army arid testing site by the University of Hawaii’s Airborne Hyperspectral Imager (AHI). The synthetic data has been created and validated to represent the surrogate minefield thermally, spatially, spectrally, and temporally over the 7.9 to 11.5 micron region using 70 bands of data. Validation of the scene has been accomplished by direct comparison to the AHI truth data using qualitative band to band visual analysis, Rank Order Correlation comparison, Principle Components dimensionality analysis, and an evaluation of the R(x) algorithm’s performance. This paper discusses landmine detection phenomenology, describes the steps taken to build the scene, modeling methods utilized to overcome input parameter limitations, and compares the synthetic scene to truth data