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

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

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

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

Development of Regionally Focused Algorithm for AIRS Temperature and Humidity Retrievals Using a Moving-Window Technique

학위논문 (박사)-- 서울대학교 대학원 자연과학대학 지구환경과학부, 2017. 8. 손병주.Regionally focused algorithm for Atmospheric Infrared Sounder (AIRS) temperature and humidity retrievals was developed. We first employed regression model with a moving window technique. This is done by relating the AIRS measurements to temperature and humidity profiles with consideration of regionally and seasonally changing local climatology. Regression coefficients were obtained from four-year (2006-2009) of ECMWF interim data over East Asia and simulated AIRS radiances. Result showing a notable improvement of mean biases, compared to the regression retrieval which does not consider local features, suggests that the moving-window technique can produce better regression retrievals by including the local climatology in the regression model. For further improvement of the regression retrieval, one dimensional variational (1DVAR) physical model was also included in our algorithm. Error covariance matrix for the moving-window regression was obtained by using pre-developed regression retrieval and its error covariance. To assess the performance of 1DVAR using the mowing-window regression as a priori, error statistics of the physical retrievals from clear-sky AIRS measurements during four months of observation (March, June, September, and December of 2010) were comparedthe results obtained using new a priori information were compared with those using a priori information from a global set of training data which are classified into six classes of infrared (IR) window channel brightness temperature. This comparison demonstrated that the physical retrieval from the moving-window regression shows better result in terms of the root mean square error (RMSE) improvement. For temperature, RMSE improvements of 0.1 – 0.2 K and 0.25 – 0.5 K were achieved over the 150 – 300 hPa and 900 – 1000 hPa layers, respectively. For water vapor given as relative humidity, the RMSE was reduced by 1.5 – 3.5% above the 300 hPa level and by 0.5 – 1% within the 700 – 950 hPa layer. As most of improvements due to use of the moving-window technique were shown in situations in which the relationship between measured radiances and atmospheric state is not clear, we investigated a possible use of surface data for further improving AIRS temperature and humidity retrievals over the boundary layer. Surface data were statistically and physically used for our AIRS retrieval algorithm. Results showing reduced RMSEs at both the surface level and the boundary layer, suggest that the use of surface data can help better resolve vertical structure of temperature and moisture near the surface layer by alleviating the influences of incomplete channel weighting function near the surface on the retrieval. In conclusion, developing regionally focused algorithm, the inclusion of climate features in the AIRS retrieval algorithm can result in better temperature and humidity retrievals. Further improvement was also demonstrated by adding surface station data to the channel radiances as pseudo channels. Since the hyperspectral sounder is available on the geostationary platform, the development of regionally focused algorithm could enhance its applicability to enhance our ability to monitor and forecast severe weather.1. Introduction 1 2. Review of previous satellite-based temperature and humidity soundings 7 3. Infrared hyperspectral measurements 18 4. Development of regionally focused regression model 24 4.1. Construction of training data 24 4.2. Moving-window regression model 32 4.3. Detecting clear-sky FOVS from MODIS measurements 35 4.4. Error analysis 37 4.4.1. Validation by using independent simulation dataset 37 4.4.2. Case study 50 4.4.3. Comparison retrievals from real observation with reanalysis data 55 5. Impact of a priori information improvement on accuracy of 1DVAR 62 5.1. 1DVAR model 62 5.1.1. Background error covariance 63 5.1.2. Averaging kernel 68 5.1.3. Residual analysis for convergence criteria and quality control 68 5.2. Error analysis 74 5.2.1. Validation by using independent simulation dataset 74 5.2.2. Case study 83 5.2.3. Comparison retrievals from real observation with reanalysis data 87 6. Synergetic use of AWS data for AIRS T/q retrievals 94 6.1. Impact of AWS data on AIRS T/q soundings: Statistical perspective 98 6.1.1. Pseudo-AWS data for training 98 6.1.2. Retrieval sensitivity related to error of AWS data 101 6.1.3. Change of regression coefficient due to use of AWS data 108 6.1.4. Application 113 6.2. Impact of AWS data on AIRS T/q soundings: Physical perspective 115 6.2.1. 1DVAR with AWS observation 115 6.2.2. Result 117 7. Summary and discussion 120 References 125 국문초록 135Docto

The Assimilation of Hyperspectral Satellite Radiances in Global Numerical Weather Prediction

Hyperspectral infrared radiance data present opportunities for significant improvements in data assimilation and Numerical Weather Prediction (NWP). The increase in spectral resolution available from the Atmospheric Infrared Sounder (AIRS) sensor, for example, will make it possible to improve the accuracy of temperature and moisture fields. Improved accuracy of the NWP analyses and forecasts should result. In this thesis we incorporate these hyperspectral data, using new assimilation methods, into the National Centers for Environmental Prediction's (NCEP) operational Global Data Assimilation System/Global Forecast System (GDAS/GFS) and investigate their impact on the weather analysis and forecasts. The spatial and spectral resolution of AIRS data used by NWP centers was initially based on theoretical calculations. Synthetic data were used to determine channel selection and spatial density for real time data assimilation. Several problems were previously not fully addressed. These areas include: cloud contamination, surface related issues, dust, and temperature inversions. In this study, several improvements were made to the methods used for assimilation. Spatial resolution was increased to examine every field of view, instead of one in nine or eighteen fields of view. Improved selection criteria were developed to find the best profile for assimilation from a larger sample. New cloud and inversion tests were used to help identify the best profiles to be assimilated in the analysis. The spectral resolution was also increased from 152 to 251 channels. The channels added were mainly near the surface, in the water vapor absorption band, and in the shortwave region. The GFS was run at or near operational resolution and contained all observations available to the operational system. For each experiment the operational version of the GFS was used during that time. The use of full spatial and enhanced spectral resolution data resulted in the first demonstration of significant impact of the AIRS data in both the Northern and Southern Hemisphere. Experiments were performed to show the contribution to the improvements in global weather forecasts from the increase in spatial and spectral resolution. Both spatial and spectral resolution increases were shown to make significant contributions to forecast skill. New methods were also developed to check for clouds, inversions and for estimating surface emissivity. Overall, an improved methodology for assimilating hyperspectral AIRS data was achieved

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.

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