Spectral imaging modalities, including reflectance and X-ray fluorescence, play an important role in conservation science. In reflectance hyperspectral imaging, the data are classified into areas having similar spectra and turned into labeled pigment maps using spectral features and fusing with other information. Direct classification and labeling remain challenging because many paints are intimate pigment mixtures that require a non-linear unmixing model for a robust solution. Neural networks have been successful in modeling non-linear mixtures in remote sensing with large training datasets. For paintings, however, existing spectral databases are small and do not encompass the diversity encountered. Given that painting practices are relatively consistent within schools of artistic practices, we tested the suitability of using reflectance spectra from a subgroup of well-characterized paintings to build a large database to train a one-dimensional (spectral) convolutional neural network. The labeled pigment maps produced were found to be robust within similar styles of paintings.

 Fusion of satellites with models for high space and time resolution land surface temperature needed for many surface-atmosphere studies  Developed an approach that evaluates well across array of towers and aircraft observations from an intensive field experiment  Additional downscaling with airborne hyperspectral imagery further refines identification of hot spots as evaluated with drone observations Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

The split window technique has been used for over thirty years to derive surface temperatures of the Earth with image data collected from spaceborne sensors containing two thermal channels. The latest NASA/USGS Landsat satellites contain the Thermal Infrared Sensor (TIRS) instruments that acquire Earth data in two longwave infrared bands, as opposed to a single band with earlier Landsats. The United States Geological Survey (USGS) will soon begin releasing a surface temperature product for Landsats 4 through 8 based on the single spectral channel methodology. However, progress is being made toward developing and validating a more accurate and less computationally intensive surface temperature product based on the split window method for Landsat 8 and 9 datasets. This work presents the progress made towards developing an operational split window algorithm for TIRS. Specifically, details of how the generalized split window algorithm was tailored for the TIRS sensors are presented, along with geometric considerations that should be addressed to avoid spatial artifacts in the final surface temperature product. Validation studies indicate that the proposed algorithm is accurate to within 2 K when compared to land-based measurements and to within 1 K when compared to water-based measurements, highlighting the improved accuracy that may be achieved over the current single-channel methodology being used to derive surface temperature in the Landsat Collection 2 surface temperature product. Surface temperature products using the split window methodologies described here can be made available upon request for testing purposes.

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Abstract:Image data from space-borne thermal infrared (IR) sensors are used for a variety of applications, however they are often limited by their temporal resolution (i.e., repeat coverage). To potentially increase the temporal availability of thermal image data, a study was performed to determine the extent to which thermal image data can be simulated from available atmospheric and surface data. The work conducted here explored the use of Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) developed by The National Aeronautics and Space Administration (NASA) to predict top-of-atmosphere (TOA) thermal IR radiance globally at time scales finer than available satellite data. For this case study, TOA radiance data was derived for band 31 (10.97 µm) of the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. Two approaches have been followed, namely an atmospheric radiative transfer forward modeling approach and a supervised learning approach. The first approach uses forward modeling to predict TOA radiance from the available surface and atmospheric data. The second approach applied four different supervised learning algorithms to the atmospheric data. The algorithms included a linear least squares regression model, a non-linear support vector regression (SVR) model, a multi-layer perceptron (MLP), and a convolutional neural network (CNN). This research found that the multi-layer perceptron model produced the lowest overall error rates with an root mean square error (RMSE) of 1.36 W/m 2 ·sr·µm when compared to actual Terra/MODIS band 31 image data. These studies found that for radiances above 6 W/m 2 ·sr·µm, the forward modeling approach could predict TOA radiance to within 12 percent, and the best supervised learning approach can predict TOA to within 11 percent.

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To better understand and preserve works of art, knowledge is needed about the pigments used to create the artwork. Various noninvasive techniques have been used previously to create pigment maps, such as combining X-ray fluorescence and hyperspectral imaging data. Unfortunately, most museums have limited funding for the expense of specialized research equipment, such as hyperspectral reflectance imaging systems. However, many museums have hand-held point X-ray fluorescence systems attached to motorized easels for scanning artwork. To assist museums in acquiring data that can produce similar results to that of HSI systems, while minimizing equipment costs, this study designed and modeled a prototype system to demonstrate the expected performance of a low-cost multispectral system that can be attached to existing motorized easels. We show that multispectral systems with a well-chosen set of spectral bands can often produce classification maps with value on par with hyperspectral systems. This study analyzed the potential for capturing data with a point scanning system through predefined filters. By applying the system and noise modeling parameters to HSI data captured from a 14th-Century illumination, the study reveals that the proposed multispectral imaging system is a viable option for this need.

Space borne thermal infrared sensors have been extensively used for environmental research as well as cross-calibration of other thermal sensing systems. Thermal infrared data from satellites such as Landsat and Terra/MODIS have limited temporal resolution (with a repeat cycle of 1 to 2 days for Terra/MODIS, and 16 days for Landsat). Thermal instruments with finer temporal resolution on geostationary satellites have limited utility for cross-calibration due to their large view angles. Reanalysis atmospheric data is available on a global spatial grid at three hour intervals making it a potential alternative to existing satellite image data. This research explores using the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data product to predict top-of-atmosphere (TOA) thermal infrared radiance globally at time scales finer than available satellite data. The MERRA-2 data product provides global atmospheric data every three hours from 1980 to the present. Due to the high temporal resolution of the MERRA-2 data product, opportunities for novel research and applications are presented. While MERRA-2 has been used in renewable energy and hydrological studies, this work seeks to leverage the model to predict TOA thermal radiance. Two approaches i I would like to thank my fellow graduate students for their feedback, cooperation and of course friendship. In addition I would like to express my gratitude to the staff of the Carlson Center for Imaging Science for their support and assistance. I am indebted to the members of my committee, specifically Matthew Montanaro and Aaron Gerace, for their input, patience and support throughout my research. I am grateful to my family: my parents, my sister and my husband for encouraging me to broaden my horizons and jump. I would like to thank NASA Goddard Space Flight Center (NNX13AQ73G) for providing funding for this research. iii Dedication Aan my Ma, wat my altyd ondersteun het, maak nie saak hoe moeilik haar eie lewe was nie.