Characterization of the Earth\u27s surface and atmosphere for multispectral and hyperspectral thermal imagery

The goal of this research was to develop a new approach to solve the inverse problem of thermal remote sensing of the Earth. This problem falls under a large class of inverse problems that are ill-conditioned because there are many more unknowns than observations. The approach is based on a multivariate analysis technique known as Canonical Correlation Analysis (CCA). By collecting two ensembles of observations, it is possible to find the latent dimensionality where the data are maximally correlated. This produces a reduced and orthogonal space where the problem is not ill-conditioned. In this research, CCA was used to extract atmospheric physical parameters such as temperature and water vapor profiles from multispectral and hyperspectral thermal imagery. CCA was also used to infer atmospheric optical properties such as spectral transmission, upwelled radiance, and downwelled radiance. These properties were used to compensate images for atmospheric effects and retrieve surface temperature and emissivity. Results obtained from MODTRAN simulations, the MODerate resolution Imaging Spectrometer (MODIS) Airborne Sensor (MAS), and the MODIS and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (MASTER) airborne sensor show that it is feasible to retrieve land surface temperature and emissivity with 1.0 K and 0.01 accuracies, respectively

New Approaches in Airborne Thermal Image Processing for Landscape Assessment

Letecká termální hyperspektrální data přinášejí řadu informací o teplotě a emisivitě zemského povrchu. Při odhadování těchto parametrů z dálkového snímání tepelného záření je třeba řešit nedourčený systém rovnic. Bylo navrhnuto několik přístupů jak tento problém vyřešit, přičemž nejrozšířenější je algoritmus označovaný jako Temperature and Emissivity Separation (TES). Tato práce má dva hlavní cíle: 1) zlepšení algoritmu TES a 2) jeho implementaci do procesingového řetězce pro zpracování obrazových dat získaných senzorem TASI. Zlepšení algoritmu TES je možné dosáhnout nahrazením používaného modulu normalizování emisivity (tzv. Normalized Emissivity Module) částí, která je založena na vyhlazení spektrálních charakteristik nasnímané radiance. Nový modul je pak označen jako Optimized Smoothing for Temperature Emissivity Separation (OSTES). Algoritmus OSTES je připojen k procesingovému řetězci pro zpracování obrazových dat ze senzoru TASI. Testování na simulovaných datech ukázalo, že použití algoritmu OSTES vede k přesnějším odhadům teploty a emisivity. OSTES byl dále testován na datech získaných ze senzorů ASTER a TASI. V těchto případech však není možné pozorovat výrazné zlepšení z důvodu nedokonalých atmosférických korekcí. Nicméně hodnoty emisivity získané algoritmem OSTES vykazují více homogenní vlastnosti než hodnoty ze standardního produktu senzoru ASTER.Airborne thermal hyperspectral data delivers valuable information about the temperature and emissivity of the Earth's surface. However, attempting to derive temperature and emissivity from remotely sensed thermal radiation results in an underdetermined system of equations. Several approaches have been suggested to overcome this problem, but the most widespread one is called the Temperature and Emissivity Separation (TES) algorithm. This work focuses on two major topics: 1) improving the TES algorithm and 2) implementing it in a processing chain of image data acquired from the TASI sensor. The improvement of the TES algorithm is achieved by replacing the Normalized Emissivity Module with a new module, which is based on smoothing of spectral radiance signatures. The improved TES algorithm is called Optimized Smoothing for Temperature Emissivity Separation (OSTES). The OSTES algorithm is appended to a pre-processing chain of image data acquired from the TASI sensor. The testing of OSTES with simulated data shows that OSTES produces more accurate and precise temperature and emissivity retrievals. OSTES was further applied on ASTER standard products and on TASI image data. In both cases is not possible to observe significant improvement of the OSTES algorithm due to imperfect atmospheric corrections. However, the OSTES emissivitites are smoother than emissivities delivered as ASTER standard product over homogeneous surfaces.

Passively Estimating Index of Refraction for Specular Reflectors Using Polarimetric Hyperspectral Imaging

As off-nadir viewing platforms becoming increasingly prevalent in remote sensing, material classification and ID techniques robust to changing viewing geometries must be developed. Traditionally, either reflectivity or emissivity are used for classification, but these quantities vary with viewing angle. Instead, estimating index of refraction may be advantageous as it is invariant with respect to viewing geometry. This work focuses on estimating index of refraction from LWIR (875-1250 wavenumbers) polarimetric hyperspectral radiance measurements

Kalman filter physical retrieval of surface emissivity and temperature from geostationary infrared radiances

The high temporal resolution of data acquisition by geostationary satellites and their capability to resolve the diurnal cycle allows for the retrieval of a valuable source of information about geophysical parameters. In this paper, we implement a Kalman filter approach to applying tempo-ral constraints on the retrieval of surface emissivity and temperature from radiance measurements made from geostationary platforms. Although we consider a case study in which we apply a strictly temporal constraint alone, the methodology will be presented in its general four-dimensional, i.e., space-time, setting. The case study we consider is the retrieval of emissivity and surface temperature from SEVIRI (Spinning Enhanced Visible and Infrared Imager) observations over a target area encompassing the Iberian Peninsula and northwestern Africa. The retrievals are then compared with in situ data and other similar satellite products. Our findings show that the Kalman filter strategy can simultaneously retrieve surface emissivity and temperature with an accuracy of ± 0.005 and ±0.2 K, respectively

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists

On the Relationship Between Land Surface Infrared Emissivity and Soil Moisture

The relationship between surface infrared (IR) emissivity and soil moisture content has been investigated based on satellite measurements. Surface soil moisture content can be estimated by IR remote sensing, namely using the surface parameters of IR emissivity, temperature, vegetation coverage, and soil texture. It is possible to separate IR emissivity from other parameters affecting surface soil moisture estimation. The main objective of this paper is to examine the correlation between land surface IR emissivity and soil moisture. To this end, we have developed a simple yet effective scheme to estimate volumetric soil moisture (VSM) using IR land surface emissivity retrieved from satellite IR spectral radiance measurements, assuming those other parameters impacting the radiative transfer (e.g., temperature, vegetation coverage, and surface roughness) are known for an acceptable time and space reference location. This scheme is applied to a decade of global IR emissivity data retrieved from MetOp-A infrared atmospheric sounding interferometer measurements. The VSM estimated from these IR emissivity data (denoted as IR-VSM) is used to demonstrate its measurement-to-measurement variations. Representative 0.25-deg spatially-gridded monthly-mean IR-VSM global datasets are then assembled to compare with those routinely provided from satellite microwave (MW) multisensor measurements (denoted as MW-VSM), demonstrating VSM spatial variations as well as seasonal-cycles and interannual variability. Initial positive agreement is shown to exist between IR- and MW-VSM (i.e., R(sup 2) = 0.85). IR land surface emissivity contains surface water content information. So, when IR measurements are used to estimate soil moisture, this correlation produces results that correspond with those customarily achievable from MW measurements. A decade-long monthly-gridded emissivity atlas is used to estimate IR-VSM, to demonstrate its seasonal-cycle and interannual variation, which is spatially coherent and consistent with that from MW measurements, and, moreover, to achieve our objective of investigating the relationship between land surface IR emissivity and soil moisture