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This paper will report on the characterization of spatially separated four-color QWIP FPA and LWIR QWIP camera. Optimization of operating parameters for each color and the best optimized operating parameters for all four-color operating simultaneously will be discussed.
This work demonstrates methods of mapping high-spatial-resolution direct normal irradiance (DNI) data from satellites, Total Sky Imagers (TSIs), and analogous data sources onto a heliostat field for characterizing the spatial and temporal variation of the incident flux on a central receiver tower during cloud transient events. The mapping methods are incorporated into an optical software module that interfaces with CoPylot–SolarPILOT’s python API– to provide computationally efficient optical simulation of the heliostat field and the solar power tower. Eventually, this optical model will be incorporated into optimization models whereby a plant operator can understand the effects of cloud transient events on overall power production and receiver lifetime due to creep-fatigue damage and therefore make better informed decisions about receiver shutdown events. By more accurately modelling the effects of cloud events on receiver flux maps, this work may determine the magnitude and frequency of thermal cycling on receiver tubes and panels using actual or realistic cloud shapes instead of averaged DNI values–which may undercount the total cycle number. This work may also prevent unnecessary plant shutdowns due to overly precautionary control strategies and characterize the relative impact of various cloud types on receiver life. We plan to eventually integrate this methodology into the System Advisor Model (SAM) to improve performance model accuracy during periods of cloudiness. In this paper, we demonstrate generating DNI maps and mapping them to a solar field in CoPylot using 10 m resolution data from publicly available Sentinel-2 satellite data over the Crescent Dunes plant.
Fiber-optic Distributed Temperature Sensing, when combined with the Single-probe Heat-pulse technique can measure soil moisture (θ) across spatial scales. The key limitation of this system is in obtaining the relationship between soil thermal conductivity (λ) and θ for a specific field. Using the Department of Energy Atmospheric Radiation Measurement (ARM) site, this study tested a new methodology to account for the spatial variability in the λ-θ relationship using a Gaussian processes model. The resulting accurate θ measurements (RMSE = 0.03 m 3 m –3 ) were used to characterize the spatial variability of θ across scales and to develop an empirical equation that can correct for the changes in the θ spatial variability observed at different spatial resolutions. In addition, the number of required samples to accurately characterize θ and its variability over scales ranging from 5 m and 350 m were estimated. Finally, these findings provide key information to scale soil moisture from centimeters to hundreds of meters for process understanding.
Here, an empirical study assessing the suitability of laser speckle patterns for use in experimental mechanics research conducted at hypersonic conditions is presented. Speckle patterns generated via coherent optical scattering from a gold-coated graphite surface inserted into a Mach 10 air stream were collected and analyzed using Computer-Aided Speckle Interferometry using two methodologies: a typical spanwise approach involving increasing temporal span between image pairs, and a sequential approach that utilizes a combination of temporal re-referencing and spatial resampling. Results were characterized for spatial-temporal stability and variance. Our observations demonstrate that optical methods relying on stable and properly formed speckle patterns can be used in a hypersonic regime at various Reynolds numbers and common camera frame rates, but the technique is sensitive to the conditions of the airflow and the processing methodology chosen.
Background: The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging. Results: Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions. Conclusions: We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.
The aim of this research is to provide a detailed characterization of spatial patterns and temporal trends in the regional and local dust source areas within the desert of the Alashan Prefecture (Inner Mongolia, China). This problem was approached through multi-scale remote sensing analysis of vegetation changes. The primary requirements for this regional analysis are high spatial and spectral resolution data, accurate spectral calibration and good temporal resolution with a suitable temporal baseline. Landsat analysis and field validation along with the low spatial resolution classifications from MODIS and AVHRR are combined to provide a reliable characterization of the different potential dust-producing sources. The representation of intra-annual and inter-annual Normalized Difference Vegetation Index (NDVI) trend to assess land cover discrimination for mapping potential dust source using MODIS and AVHRR at larger scale is enhanced by Landsat Spectral Mixing Analysis (SMA). The combined methodology is to determine the extent to which Landsat can distinguish important soils types in order to better understand how soil reflectance behaves at seasonal and inter-annual timescales. As a final result mapping soil surface properties using SMA is representative of responses of different land and soil cover previously identified by NDVI trend. The results could be used in dust emission models even if they are not reflecting aggregate formation, soil stability or particle coatings showing to be critical for accurately represent dust source over different regional and local emitting areas.
We present experimental, analytical, and numerical methods developed for reconstruction (deconvolution) of one-dimensional (1D) surface slope profiles over the spatial frequency range where the raw data are significantly perturbed due to the limited resolution of the measurement instrument. We characterize the spatial resolution properties of a profiler with the instrument's transfer function (ITF). To precisely measure the ITF, we apply a recently developed method utilizing test surfaces with 1D linear chirped height profiles of constant slope amplitude. Based on the results of the ITF calibration, we determine parameters of an analytical model for the ITF that is used in the original reconstruction software. Here, we treat surface slope metrology data obtained with the Optical Surface Measuring System (OSMS), using as a sensor an electronic autocollimator (AC) ELCOMAT-3000. The spatial resolution of the OSMS is limited by the size of the AC light-beam-collimating aperture. For the purposes of this investigation, the OSMS is equipped with a circular aperture with a diameter of 2.5 mm. This is a typical arrangement of most AC-based slope profilers developed for surface slope metrology of state-of-the-art x-ray mirrors. Using the example of surface slope metrology of two state-of-the-art elliptically shaped x-ray focusing mirrors, we demonstrate that the developed data reconstruction procedure allows us to significantly improve the accuracy of surface slope metrology with the OSMS over the spatial wavelength range from ~1.6 mm to 7 mm. Thus, the amplitude of the quasi-periodic error characteristic of the deterministic polishing process used appears to be higher by a factor of~2 than is apparent from the rough metrology data. Underestimation of the surface slope errors in this spatial wavelength range can lead to serious errors in the expected performance of x-ray mirrors in synchrotron beamlines, especially at modern light sources utilizing coherent x rays, where the perturbations can lead to increased speckle-like intensity variation.
On February 18th 2021, the Perseverance rover landed in Jezero crater, Mars. This site was chosen because orbiter data analysis provides evidence that the crater hosted a stream-fed lake during the Martian Noachian period. The Octavia Butler landing site is located ~1.9 km east of the remnants of a river delta. Deltaic and lacustrine sediments can preserve biosignatures, making Jezero crater a prime target for Mars sample return science. One of the seven instruments on Perseverance’s science payload is SHERLOC –Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals. SHERLOC combines fluorescence and Raman spectroscopy with microscopic imaging to analyze surface material to better understand the history of the aqueous environments recorded in the rocks of Jezero crater and to search for potential biosignatures. SHERLOC imaging obtains high spatial resolution images of geological targets to identify grain-scale structure and texture. SHERLOC spectroscopy enables high-sensitivity detection, characterization, and spatially resolved correlation of trace organic materials. Native fluorescence emissions from aromatic organic species allow for detection and classification of aromatic organic molecules, whereas Raman scattered photons from molecules allow identification of functional groups of organics, chemicals, and minerals. In the first 300 sols, SHERLOC has analyzed 3 natural surfaces, and 5 abraded rock patches created during the Crater Floor Campaign within Jezero crater. SHERLOC has been able to identify phosphates, amorphous/microcrystalline silicate (AMS), olivine, sulfates, and carbonates in abraded patches in the green zone campaign within Jezero Crater Máaz and Séítah formations. Within these detections we have begun to tell the story of what this crater was like when it was full of liquid water over 3 billion years ago. In each of these samples we have identified fluorescence features that are likely aromatic organics native to the rock interiors. We have identified multiple unique fluorescence signatures, within each of the abraded patches. The organic signatures have either been widely distributed over an extended area, which is probably due to planetary wide dust, or have spatially resolved locations that are collocated with different mineral signatures. Note: Additional information available on attachment.
This review explores the current state of the art in spatially resolved characterization of mixed-halide perovskite solar cells. As the size of perovskite cells and modules continues to grow, quantification of the spatial distribution of key cell parameters will become increasingly valuable in predicting ultimate cell-level performance and tracking process homogeneity. Here, both high resolution microscopic approaches using scanning techniques and camera-based methods for full-area cell and/or module analysis are discussed. The value of this local data in predicting performance losses at the cell level is particularly emphasized. Measurable physical parameters sensitive to losses of voltage, current, fill factor, and efficiency are discussed together with selected experimental results. It is demonstrated that a combination of spatially resolved cell parameter mapping/imaging can be used to quantitatively discriminate various loss contributions at high resolution. The impact and control of inhomogeneities become particularly important when upscaling from small devices to large formats compatible with industrial mass production.
An increasing number of spatial multiomic workflows have been recently developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a novel multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. Further, this workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a polyethylene naphthalate-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within to the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 µm x 200 µm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.
The Arctic is experiencing some of the most rapid climate change on Earth, with strong impacts on tundra ecosystems that are characterized by high land-surface and vegetation heterogeneity. Previous studies have explored this complexity using satellite remote sensing, however these typically coarse spatial resolution data have generally missed sub-pixel heterogeneity, leaving critical gaps in our understanding of tundra vegetation dynamics from the community to landscape scales. To address these gaps, we collected very high-resolution (1-5 cm) optical, structural, and thermal data at three low-Arctic tundra sites on the Seward Peninsula, Alaska, using a multi-sensor unoccupied aerial system (UAS). We examined the application of these data to studying tundra vegetation dynamics, by quantifying (1) canopy height and thermoregulation (leaf-air temperature) of representative plant functional types (PFTs), (2) fine-scale patterns of vegetation composition across landscapes, and (3) impacts of fine-scale vegetation composition on landscape-scale variation of canopy height and thermoregulation. Our results show that deciduous tall shrubs (those that can potentially grow >2 m) had a strong cooling effect, with canopy temperatures significantly lower than local air temperatures and other PFTs. Increased cover of tall shrubs also had the potential to reduce the cover of low-stature PFTs across the landscape, potentially associated with their closed canopy (i.e., increased light competition) and strong thermoregulation. To understand the connections between fine-scale vegetation composition and large-scale ecosystem processes, we produced a random forest model which showed that fine-scale PFT composition accounted for 86.8% and 74.2% of the landscape-scale variation in canopy height and thermoregulation, respectively. These findings highlight the importance of spatially detailed characterization of tundra PFTs to improve our ecological understanding and model representation of tundra vegetation, also transcend our study to show the need for continued collection of similar datasets to better understand the impacts of surface heterogeneity on the mapping and modeling of tundra ecosystem dynamics, as well as assist with conservation management and biodiversity monitoring strategies.
NASA’s ICESat-2 has been providing sea ice freeboard measurements across the polar regions since October 2018. In spite of the outstanding spatial resolution and precision of ICESat-2, the spatial sparsity of the data can be a critical issue for sea ice monitoring. This study employs a geostatistical approach (i.e., ordinary kriging) to characterize the spatial autocorrelation of the ICESat-2 freeboard measurements (ATL10) to estimate weekly freeboard variations in 2019 for the entire Ross Sea area, including where ICESat-2 tracks are not directly available. Three variogram models (exponential, Gaussian, and spherical) are compared in this study. According to the cross-validation results, the kriging-estimated freeboards show correlation coefficients of 0.56–0.57, root mean square error (RMSE) of ~0.12 m, and mean absolute error (MAE) of ~0.07 m with the actual ATL10 freeboard measurements. In addition, the estimated errors of the kriging interpolation are low in autumn and high in winter to spring, and low in southern regions and high in northern regions of the Ross Sea. The effective ranges of the variograms are 5–10 km and the results from the three variogram models do not show significant differences with each other. The southwest (SW) sector of the Ross Sea shows low and consistent freeboard over the entire year because of the frequent opening of wide polynya areas generating new ice in this sector. However, the southeast (SE) sector shows large variations in freeboard, which demonstrates the advection of thick multiyear ice from the Amundsen Sea into the Ross Sea. Thus, this kriging-based interpolation of ICESat-2 freeboard can be used in the future to estimate accurate sea ice production over the Ross Sea by incorporating other remote sensing data.
The Atmospheric Infrared Sounder (AIRS) measures the infrared spectrum in 2378 channels between 3.7 and 15.4 microns with a very high spectral resolution of approximately 1200. AIRS footprints are approximately 1.1 by 0.6 degrees. Because AIRS is a grating spectrometer, each channel has a unique spatial response. Image rotation due to the scan mirror causes these spatial responses to rotate. In effect, each channel has 90 spatial responses, one for each scene footprint in the scan line. Although the spatial response for most channels is symmetric and nearly uniform, some channels have significantly asymmetric response. This paper reviews and describes the prelaunch measurements that characterized the spatial response functions. Next, it describes the conversion of the ground-based results into footprint-specific response functions valid in flight. Then we describe the postlaunch validation of the measurements, including centroid location, axes orientations, and a check on the full two-dimensional response functions. This latter check involves comparison of AIRS data with that of the Moderate Resolution Imaging Spectrometer (MODIS), which flies on the same platform as AIRS. An important result is that AIRS/MODIS brightness temperature comparisons are significantly improved (scatter reduced) when the AIRS spatial response is explicitly taken into account. Finally, a status report is given on attempts to fully verify the prelaunch measurements by deriving the AIRS spatial response from flight data alone.
The lateral flux of dissolved organic carbon (DOC) from soils to inland waters and ultimately to the ocean represents a fundamental component of the global carbon cycle. To estimate the DOC flux, we developed an empirical terrestrial-aquatic DOC fluxes model (TAF-DOC). TAF-DOC incorporates various environmental factors (e.g., meteorology, sulfur, and nitrogen deposition) that to-date have not been comprehensively considered or well-represented in existing modeling frameworks. TAF-DOC was applied to estimate spatial-temporal patterns of DOC flux and potential fates across the conterminous United States during the 1985 to 2018 time period. Our results suggest that TAF-DOC successfully characterized spatial-temporal of DOC flux. As expected, the interannual pattern of DOC flux was strongly regulated by precipitation, but the long-term trend was significantly influenced by the rate of atmospheric wet sulfur deposition. From 1985 to 2018, TAF-DOC estimated DOC loading from terrestrial to aquatic ecosystems in the conterminous United States to be 33.5 ± 2.2 TgC per year, which was roughly 0.39–0.49% of total soil organic carbon stock estimates. The dominant fate of terrestrially-derived DOC was delivery to the coastal ocean in riverine export (41%), with another 21% buried in sediment and the remaining 12.8 ± 0.4 TgC per year (38%) returned to the atmosphere through outgassing from inland waters. Finally, assuming the quantities of DOC sediment burial and export to the ocean as an annual sink of terrestrially-derived carbon, budget inventories and models that do not account for DOC flux in the conterminous United States will underestimate the net annual carbon sink by as much as 5.5–6.4%.
This research established a systematic method to generate various pore-size distributions (PSDs) and studied the effect of PSDs on the shock compaction response of granular materials using two-dimensional mesoscale simulations under identical porosity. Simulations utilized various PSDs for three particle shapes (circle, ellipse, and square). Contacting particle configurations using three PSDs, characterized by spatially uniform distributed pores to heterogeneous distributed pores, and non-contacting particle configurations under a single case of PSD were tested. The PSD of generated particle sets was characterized using coordination number, mean diameter, and bimodality coefficient as statistical metrics. Mesoscale simulations showed that regardless of the conditions of pore distributions, shock compaction of granular materials consistently demonstrates a precursor, shock compaction front, and end. However, the shock compaction velocity of contacting particles was dependent on the PSDs despite the constant initial porosity. The compaction velocity was faster in particle configurations with relatively uniform pore distributions than in heterogeneous pore distributions, which our study demonstrated can be attributed to particle rearrangement during compaction. Circular-shaped particles had high sensitivity in shock compaction response to the various PSDs. Furthermore, a contacting particle configuration tended to propagate the shock compaction wave relatively faster than particles that were in a non-contact configuration. This study established the relative importance of considering PSD as a metric over the coordination number in studies of the shock compaction response of granular materials. Further, insights are provided on the evolving shock substructure to characterize the shock compaction response of granular materials.
With the deployment of Earth Observing System (EOS) satellites that provide daily, global imagery, there is increasing interest in defining the limitations of the data and derived products due to its coarse spatial resolution. Much of the detail, i.e. small fragments and notches in boundaries, is lost with coarse resolution imagery such as the EOS MODerate-Resolution Imaging Spectroradiometer (MODIS) data. Higher spatial resolution data such as the EOS Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER), Landsat and airborne sensor imagery provide more detailed information but are less frequently available. There are, however, both theoretical and analytical evidence that burn scars and other fragmented types of land covers form self-similar or self-affine patterns, that is, patterns that look similar when viewed at widely differing spatial scales. Therefore small features of the patterns should be predictable, at least in a statistical sense, with knowledge about the large features. Recent developments in fractal modeling for characterizing the spatial distribution of undiscovered petroleum deposits are thus applicable to generating simulations of finer resolution satellite image products. We will present example EOS products, analysis to investigate self-similarity, and simulation results.
Airborne measurements of flux densities of carbon dioxide CO2, sensible heat, and latent heat (H2O) obtained over the First ISLSCP Field Experiment (FIFE) site during three intensive field campaigns in 1987 and one in 1989 are examined to characterize the spatial and temporal variations of CO2 and energy transfer processes. These data were collected by the National Research Council Twin Otter using low-level flight patterns, all flown at constant pressure altitude during relatively clear days. The spatial variations are larger in 1989 than in 1987 and a higher correlation is observed between the fluxes and the surface features. The temporal patterns are easier to characterize with the relatively homogeneous situation of 1987. Functional relationships obtained between fluxes of CO2 and latent heat, CO2 fluxes and greenness index, latent heat fluxes and greenness index, and between sensible heat fluxes and surface air temperature differences are presented for one day in 1987 and one in 1989 as an example of the kind of information that can be obtained from grid flights at constant pressure altitude.
Climate change is increasing the frequency and intensity of extreme precipitation events, raising the risk of urban flood disasters. This study uses a crowd-sourced municipal call database to characterize the spatial distribution of flood risk in Detroit, MI. Call data including dates and addresses were obtained from the City of Detroit Department of Public Works for 2021. Calls were mapped and aggregated to census tract counts and merged with neighborhood-level data. Associations of predictors with flood calls were tested using spatial regression models. Flooding calls were located throughout the city but were concentrated in specific areas. Multivariate models of census tract level call counts indicated that increased poverty and Black, immigrant, and older residents were positively associated with flood calls, while increased elevation was associated with protective effects. Longer distances from waste water interceptors were associated with higher risk for calls. Crowd-sourced flood hotline call data can be used for effective spatial flood risk assessment. Though flooding occurs throughout the city of Detroit, infrastructural, neighborhood, and household factors influence flooding extent. Limitations included the self-reported nature of calls. Future modeling efforts might include input from local stakeholders to improve spatial risk assessment.