Theses and Dissertations from UMD
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Item Russian Winter Cropland Mapping and Impact on Land Use(2024) Abys, Christian Joseph; Skakun, Sergii Dr.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation provides an in-depth analysis of the transformation in Russia’s winter wheat industry over the past two decades, focusing on production growth, land use changes, and advancements in monitoring techniques. The study reveals a substantial 149% increase in wheat production and a 35% rise in farmland area from 2000 to 2020, driven predominantly by winter wheat, which now represents a significant portion of global exports. Despite this growth, there is notable yearly volatility in production, with USDA Foreign Agriculture Service forecasts exhibiting considerable uncertainty, particularly in area estimations which has substantial impacts on the global wheat export market. To address these challenges, the research utilizes long-term MODIS satellite data to analyze cropland expansion and intensification in southwestern Russia, identifying a 29% increase in winter wheat cropland with distinct patterns of expansion and intensification across different latitudes. The study highlights the ongoing capacity for further cropland intensification. Furthermore, this research introduces Sentinel-1 SAR imagery as an effective solution to the issue of cloud coverage, which hampers optical data accuracy. By employing various machine learning models, including multi-layer perceptron, long short-term memory, and random forest, the study demonstrates that Sentinel-1 SAR enhances the accuracy of in-season cropland mapping. The results show that Sentinel-1 SAR data reduces uncertainty in area estimations by two-thirds compared to MODIS data, offering improved monitoring capabilities. Collectively, this research provides valuable insights into Russia’s agricultural dynamics, addresses key uncertainties in forecasting, and proposes advanced methodologies for more accurate and reliable agricultural assessmentsItem The Impact of Marsh Sill Living Shorelines on Coastal Resilience and Stability: Insights from Maryland's Chesapeake Bay and Coastal Bays(2024) Sun, Limin; Nardin, William WN; Palinkas, Cindy CP; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Climate change and coastal urbanization are accelerating the demand for strategies to reduce shoreline erosion and enhance coastal resilience to storms and sea-level rise. Generally adverse ecological impacts of hardened infrastructure (e.g., seawalls, revetments, and dikes) have led to growing interest in alternative solutions. Living shorelines, increasingly recognized as sustainable Natural and Nature-Based Features (NNBFs; or Nature-Based Solutions (NBSs)) for their dual benefits of stabilizing shorelines while preserving or restoring coastal habitats, represent a promising approach to shoreline stabilization. Marsh sill living shorelines (created marshes with adjacent rock sills) have been extensively constructed in the Chesapeake Bay, notably in Maryland. Despite their popularity, significant uncertainties remain regarding their effectiveness and resiliency, especially during high-energy events. This dissertation investigates the dynamics of marsh sill living shorelines in Maryland’s Chesapeake Bay and Coastal Bays, aiming to fill knowledge gaps and inform effective shoreline stabilization strategies. First, the dissertation examines marsh boundary degradation into open water during high-energy conditions, contrasting mechanisms between living shorelines and natural marshes. Field surveys and numerical modeling reveal that while natural marshes experience erosion through undercutting and slumping at the scarp toe, living shorelines degrade primarily through open-water conversion at the marsh boundary behind rock sills. Differences in sediment characteristics and vegetation between the two ecosystems drive variations in marsh boundary stability between them. Next, the study assesses the impacts of rock sill placement on sediment dynamics and shoreline stability, highlighting the role of tidal gaps in enhancing sediment flux to the marsh and increasing vertical accretion during high-energy events. Numerical modeling demonstrates that while continuous sills mitigate erosion at the marsh edge of living shorelines, they diminish sediment deposition on the marsh platform compared to segmented sills with tidal gaps. Finally, the research identifies key factors driving marsh boundary degradation that are needed to assess the stability of marsh sill living shorelines. Analysis of eco-geomorphic features and hydrodynamics across 18 living shoreline sites reveals that metrics such as the Unvegetated/Vegetated Ratio (UVVR) and sediment deposition rate often used to assess the resilience of natural marshes also apply to the created marshes of living shorelines. Multivariate analyses further reveal that the Relative Exposure Index (REI) and Gap/Rock (G/R) ratio are crucial predictors of shoreline stability in marsh sill living shorelines, and thus should be particularly considered in shoreline design. By integrating remote sensing, field observations, and numerical modeling, this dissertation advances the understanding of sediment dynamics and stability in living shorelines and provides actionable insights for effective shoreline design and management to promote coastal resilience.Item INTEGRATION OF SUPERCONDUCTORS INTO WIDE BANDGAP SEMICONDUCTOR ENVIRONMENTS FOR DEPLOYABLE SINGLE PHOTON DETECTORS(2024) Drechsler, Annaliese Grace; Christou, Aristos; Material Science and Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Superconducting nanowire single photon detectors (SNSPDs) are the photon detecting devices of the future. These devices offer exceptional detecting capabilities over a wide range of wavelengths, which will enable next generation systems for optical communications, light detection and ranging, quantum key decryption, and astronomy among others. There are substantial materials, fabrication, and device development challenges that need to be addressed before these devices are ready for large scale deployment in arrays. This dissertation demonstrates novel approach to SNSPD development by monolithically integrating superconducting materials with wide bandgap semiconductor systems to scale these devices. Specifically, this work explores the integration of niobium nitride (NbN) with multi-channel aluminum gallium nitride (AlGaN)/gallium nitride (GaN) superlattice devices to leverage the benefits of materials similarity and lattice matching to provide high quality detector performance in the proposed system. The multichannel superlattice device selected for this work, the superlattice castellated field effect transistor (SLCFET) utilizes a novel δ-doping approach to generate conducting channels. Epitaxial structures were studied between 300K and 4K. This structure exhibits a substantial reduction in epitaxial resistance, determined to be a result of mobility improvement to 4151.5 cm2/Vs through Hall effect analysis. Phonon scattering modelling indicates that the device is limited by polar optical phonon scattering at high temperatures and interface roughness between the channels at cryogenic conditions. Field effect transistors fabricated from this epitaxial structure were tested and shown to exhibit exceptionally high performance at low temperatures, proving feasibility of device integration. A production-scalable NbN deposition process was developed for SNSPD fabrication. Thorough analyses determined the relationship between deposition parameters and the resultant crystallinity, defectivity, and surface morphology. Analysis of ultra-thin films determined that the NbN films grow through a step-flow growth mechanism. This data was used to develop a temperature-dependent empirical model of the kinetics of the surface morphology and growth mechanism evolution based on the Avrami equation. Fabrication processes were developed using these films to pattern SNSPDs with narrow linewidths down to 50 nanometers composing the meander structure for long wavelength performance. Thorough analysis of the impact of electron beam lithography write conditions were conducted to propose ideal fabrication conditions. Methods were proposed and implemented to address defectivity by reducing the impact of elasto-capillary forces on line collapse including chemical surface modification using hexamethyldisilazane and resist thinning using polymethyl methacrylate (PMMA) and ZEP and implementing charge dissipation layers. Additional processes were proposed and implemented to enable integration into the SLCFET fabrication flow. The SLCFET devices and NbN structures were tested and determined to be functional, thus demonstrating the feasibility of integration. An initial integrated device was designed and modelled by combining a SLCFET with NbN SNSPDs, using the RF output as a readout approach. The devices were successfully fabricated using the processes developed within this dissertation. Testing of the devices showed a 30dB signal difference between the normal and detecting states, thus demonstrating the first device of its kind, representing a substantial contribution to the field. This will open the door for full-scale array development using novel on and off chip signal processing approaches proposed in this work.Item DEEP LEARNING APPROACHES FOR ESTIMATING AND FORECASTING SURFACE DOWNWARD SHORTWAVE RADIATION FROM SATELLITE DATA(2024) Li, Ruohan; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Surface downward shortwave radiation (DSR) designates solar radiation with a wavelength from 300 to 4000 nm received at the Earth’s surface. DSR plays a pivotal role in the surface energy and radiation budget, serving as the primary driver for hydrological, ecological, and biogeochemical cycles (Liang et al., 2010, 2019), and the important input for various earth models (Huang et al., 2019; Liang et al., 2010; Stephens et al., 2012). Given the rising demand for renewable energy, as well as accelerated advancements in solar energy technologies on both utility-scale and residential scale, the precision and resolution in estimating and forecasting DSR have become indispensable for planning and administering solar power plants (Gueymard, 2014; Jiang et al., 2019). This dissertation delves into the potential of integrating deep learning with satellite observations to address the deficiencies in current DSR estimation and forecasting methods, aiming to cater to the evolving needs of solar radiation estimation. The research begins by examining current DSR satellite products, emphasizing their limitations, particularly concerning spatial resolution and performance in snowy, cloudy, and high-latitude areas. In such regions, challenges arise from the degradation of radiative transfer models, band saturation, the pronounced effects of 3D cloud dynamics, and temporal resolution constraints (Li et al., 2021). Identifying these gaps, the study introduces the concept of transfer learning to tackle cases where physical methods degrade and limited training data is available. By combining data from physical simulations and ground observations, the proposed models enhance both the accuracy and adaptability of DSR predictions on a global scale. The investigation further reveals the influence of training data volume on model performance, illustrating how transfer learning can ameliorate these effects (Li et al., 2022). Moreover, the dissertation compares the application of DenseNet, Gated Recurrent Unit (GRU), and a hybrid of Convolutional Neural Network (CNN) and GRU (CNNGRU) to geostationary satellite data, achieving precise and timely DSR estimates. These models underscore their prowess in tackling 3D cloud effects and reducing dependency on additional data sources by the spatial and temporal structure of DL (Li et al., 2023b). Finally, the dissertation introduces the SolarFormer, a space-time transformer neural network adept at forecasting solar radiation up to three hours in advance at 15-minute intervals. By harnessing solely geostationary satellite imagery without the need for ground measurements, this model facilitates expansive DSR predictions, which are crucial for optimizing solar energy distribution at both utility and micro scales. This chapter also highlights the Transformer model's potential for extended forecasting due to its computational and memory efficiency.Item INTEGRATED MONITORING OF DISTURBANCE AND FOREST ATTRIBUTES(2024) Lu, Jiaming; Huang, Chengquan; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Forests provide numerous ecosystem services and are shaped by historical disturbance events. The intensity of disturbance significantly influences the post-disturbance forest structure, species composition, and subsequent forest regrowth. Under the influence of anthropogenic activities and climate change, disturbance regime has undergone unprecedent changes and is subsequently affecting a suite of interrelated forest attributes that are critical in understanding forests dynamics. Historical large-scale disturbance intensity information is needed for understanding the change in disturbance regimes and create linkage to forest dynamics, but such dataset was not available. Forest attributes can be estimated from the spectral information of remote sensing imagery; however, inconsistency exists among the developed product, and the usage of the dataset is limited by accuracy. To fill the research gaps, this dissertation aims to develop a framework that integrates the historical disturbance and the inter-relationship between forest attributes to provide more consistent, and likely more accurate forest attribute estimations. Age, a key attribute that can be the determinant of many ecosystem processes and tree/forest stand develop stage, was selected as the prototype attribute to study. The dissertation started by producing the first set of annual forest disturbance intensity map products quantifying thepercentage of basal area removal (PBAR) at the 30-m resolution for the CONUS from 1986 to 2015, by integrating field plot measurements collected by the Forest Inventory and Analysis program with time series Landsat observations. Compared to other published disturbance products, the maps derived through this study can provide the unique thematic (intensity) information on forest disturbances, precise details critical for understanding forest dynamics across CONUS over multiple decades. The dissertation then proceeded to quantify individual tree age. The tree age was estimated for every tree in the FIA database (over 10 million trees) across the US from our modeling approach that had higher accuracies than existing studies. The developed tree age dataset allows better characterization of tree age distribution, which is important for understanding the disturbance history, functioning, and growth vigor of forest ecosystems. With the disturbance intensity and tree age dataset, the dissertation was able to develop an integrated modeling approach for the forest age mapping. The forest age and complexity maps were produced for 2015 and 2005. The combination of the two metrics should provide a more comprehensive characterization of the forest development condition. The maps provide valuable information for knowing forest conditions, estimating forest growth and carbon sequestration potential, understanding the relationship between age and other forest attributes, evaluating forest health, and planning sustainable forest management practices. This modeling framework developed by the dissertation will enhance the ability to retrieve forest attributes in a broader scale so that with the remote sensing observation, we can not only provide spatially explicit forest structure information, but also review forest status over the decades. Furthermore, when combined with the ecosystem models, these estimations will provide a better prediction for future vegetation and climate dynamics.Item Advances in Mapping Forest Biomass and Old-Growth Conditions Using Waveform Lidar(2023) Bruening, Jamis; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne waveform lidar sys- tem that has transformed scientific understanding of the world’s forests through billions of pre- cise measurements of ecosystem structure. Relative to forest processes that operate on decadal to millennial timescales, the four year period during which GEDI collected these measurements is short, and GEDI’s ability to analyze how forest structure changes over time is mostly unproven. However, fusion efforts that integrate GEDI data with forest inventory measurements and ecosys- tem models hold immense potential for discovery. In this dissertation, I explore the limitations and capabilities of GEDI data for inference into structural and successional dynamics within east- ern US forests. First, I used a forest gap model to quantify uncertainty in biomass predictions for individual GEDI waveforms, and discovered a relationship between biomass uncertainty and successional stage. Next, I investigated uncertainties and errors in large-scale GEDI biomass estimates relative to unbiased estimates from the US forest inventory. I developed a novel mod- eling framework based on fusion between GEDI and the US forest inventory data that corrected these errors, and I produced unbiased and precise maps of forest biomass for the continental US. Lastly, I assessed GEDI’s ability to identify and map different types of old-growth forests, and discovered that GEDI can detect some old forests more effectively than others. This research identified key limitations associated with using GEDI to study forest dynamics, and I leveraged these discoveries to develop new ways of using GEDI data for ecological and successional in- ference. These discoveries will inform new uses of GEDI data and its integration with inventory data and ecosystem modeling to better characterize changes within forest ecosystems.Item A SYNTHETIC APERTURE RADAR (SAR)-BASED GENERALIZED APPROACH FOR SUNFLOWER MAPPING AND AREA ESTIMATION(2023) KHAN, MOHAMMAD ABDUL QADIR; Skakun, Sergii; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The effectiveness of remote sensing-based supervised classification models in crop type mapping and area estimation is contingent upon the availability of sufficient and high-quality calibration or training data. The current challenge lies in the absence of field-level crop labels, impeding the advancement of training supervised classification models. To address the needs of operational crop monitoring there is a pressing demand for the development of generalized classification models applicable for various agricultural areas and across different years, even in the absence of calibration data. This dissertation aims to explore the potential of the C-band Sentinel-1 Synthetic Aperture Radar (SAR) capabilities for building generalized crop type models with a specific focus on identifying and monitoring sunflower crop in Eastern Europe. Globally, the sunflower ranks as the fourth most important oilseed crop and stands out as the most profitable and economically significant oilseed crop. It is extensively cultivated for the production of vegetable oil, biodiesel, and animal feed with Ukraine and Russia as the largest producer and exporter in the world. In the first step, this study explores the interaction of Sentinel-1 (S1) SAR signal with sunflower to identify and monitor phenological stages of sunflower. The analysis examines SAR backscattering coefficients and polarizations in Vertical-Horizontal (VH), Vertical-Vertical (VV) and VH/VV ratio, highlighting differences between ascending and descending orbits due to sunflower directional behavior caused by heliotropism. Based on the unique SAR-based signature of sunflower the study introduces a generalized model for sunflower identification and mapping which is applicable across time and space. It was observed that the model based on features acquired from S1-based descending orbits outperforms the one based on ascending orbit because of the sunflower’s directional behavior: user’s accuracy (UA) of 96%, producer’s accuracy (PA) of 97% and F-score of 97% (descending) compared to UA of 90%, PA of 89% and F-score of 90% (ascending). This model was generalized and validated for selected sites in Ukraine, France, Hungary, Russia and USA. When the model is generalized to other years and regions it yields an F-score of > 77% for all cases, with F-score being the highest (>91%) for Mykolaiv region in Ukraine. The generalized approach to map sunflower was applied to assess the impact of the Russian full-scale invasion of Ukraine on national sunflower planted areas. The sunflower planted areas and corresponding changes in 2021 and 2022 were estimated using a sample-based approach for area estimation. Sunflower area was estimated at 7.10±0.45 million hectares (Mha) in 2021 which was further reduced to 6.75±0.45 Mha in 2022 representing a 5% decrease. The findings suggest spatial shifts in sunflower cultivation after the Russian invasion from southern/south-eastern Ukraine under Russian controlled to south-central region under Ukrainian control. The first objective of this dissertation highlights the difference of ascending and descending S1 orbits for sunflower monitoring due to its directional behavior, an aspect not fully researched and documented previously. The implemented generalized approach based on sunflower phenology proves to be an accurate and space-time generalized classifier, reducing time, cost and resources for operational sunflower mapping for large areas. Also, the disparity between sample-based area estimates and SAR-based mapped areas caused due to speckle were substantially reduced emphasizing the role of S1/SAR in global food security monitoring.Item MULTISCALE, MULTITEMPORAL ASSESSMENT OF CHIMPANZEE (Pan troglodytes) HABITAT USING REMOTELY SENSED DATASETS(2023) Jantz, Samuel M; Hansen, Matthew C; Geography/Library & Information Systems; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)All four sub-species of our closest living relative, the chimpanzee, are listed as endangered by the International Union for the Conservation of Nature (IUCN), and their populations continue to decline due to human activities. Effective conservation efforts require information on their population status and distribution. Traditional field surveys are expensive and impractical for covering large areas at regular time intervals, making it difficult to track population trends. Given that chimpanzees occupy a large range (2.3 x 106 km2), new cost-effective methods and data are needed to provide relevant information on population status and trends across large geographic and time scales. The objective of this dissertation is to help fill this gap by leveraging freely available and regularly updated remotely sensed datasets to map and monitor chimpanzee habitat across their range. This research begins by first producing annual forest cover and change maps for the Greater Gombe (GGE) and Greater Mahale ecosystems (GME) in western Tanzania using Landsat phenological metrics and machine learning methods. Canopy cover was predicted at 30-meter resolution and the Cumulative Sums (CuSum) algorithm was applied to the canopy cover time series to detect forest loss and gain events between 2000-2020. An accuracy assessment showed the CuSum algorithm was able to detect forest loss well but had more difficulty detecting gradual forest gain events. A total of 276,000 ha (+/- 27,000 ha) of gross forest loss was detected between 2000 and 2020 in the GGE and GME; however, loss was not spread equally among the two ecosystems. The results show widespread forest loss in the GME, contrasted with net forest cover gain in the GGE. Next, the annual forest cover maps, and additional derived variables, were used to train an ensemble model to predict the relative encounter rate of chimpanzee nest sightings in the GGE and GME. Model output exhibited a strong linear relationship to chimpanzee abundances and population density estimated from a recent ground survey, enabling model output to be linearly transformed into population estimates. The model predicted the two ecosystems harbor just over 3,000 individuals, which agrees with the upper limit of population estimates from ground surveys. Most importantly, the model can be applied to annually updated variables enabling the detection of potential population shifts caused by changes in landscape condition. Model output indicates a possible population reduction in portions of the GME, while the GGE is predicted to have increased its ability to sustain a larger population. Finally, Random Forests regression was used to relate predictor variables, primarily derived from Landsat data to a coarse resolution, range-wide habitat suitability map enabling the prediction of habitat suitability at 30 meter resolution. The model showed good agreement with the calibration data; however, there was considerable variation in predictive capability among the four chimpanzee sub-species. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. The model can detect changes in suitability to support monitoring and conservation planning across the chimpanzee range. Results from this dissertation highlight the promise of integrating continuously updated satellite data into habitat suitability models to detect changes through time and inform conservation efforts for chimpanzees at multiple scales.Item INCORPORATING UNOCCUPIED AIRCRAFT SYSTEMS (UAS) AND EARTH OBSERVING SATELLITES TO ENHANCE ENVIRONMENTAL REMOTE SENSING OF CHESAPEAKE BAY(2023) Windle, Anna; Silsbe, Greg; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Environmental remote sensing is the science of monitoring physical, chemical, and biological characteristics of the Earth through space and time, and from a distance, by measuring how these environments interact with electromagnetic energy, or more simply through changes in color. This dissertation leverages in situ, satellite, and unoccupied aircraft system (UAS, drones) data to enhance the efficacy of environmental remote sensing in Chesapeake Bay. Satellite data consists of distinct contributions of the surface under observation and the intervening atmosphere. Atmospheric correction (AC) processors seek to isolate the surface signal, and while several variants exist, their accuracy varies widely in optically complex coastal waters. Chapter 2 is a statistical evaluation of four common AC variants applied to data collected by the most recent operational ocean color sensor, the Ocean Land Color Instrument (OLCI) onboard Copernicus Sentinel-3A and -3B satellites. Remote sensing reflectance (Rrs), the product of AC processors from which a suite of water quality metrics is then derived, was obtained from each AC variant and matched in space and time with in situ Rrs data collected in the Chesapeake Bay. AC results varied widely, and the most statistically robust was a neural-net based algorithm (Case 2 Regional Coast Color, C2RCC). The resultant shape and magnitude of Rrs (e.g. color) is governed by the type and concentration of optically active constituents (OACs), namely phytoplankton pigments, chromophoric dissolved organic matter, inorganic sediment, and water itself. In coastal waters where OACs are dynamic and vary independently from each other, deriving accurate water quality metrics remains an open challenge. Chapter 3 applies a spectral clustering classification of OLCI Rrs data (2016-2022) and identifies the fifteen most dominant optical water types (OWTs) of Chesapeake Bay. OWTs were matched in space and time with Chesapeake Bay water quality monitoring data, and a statistical evaluation demonstrates how water quality data are constrained within and across OWTs. In contrast to earth-observing satellites, UAS equipped with optical sensors offer on-demand, highly resolved data. Aquatic UAS applications are in their infancy, and the critical removal of light reflected directly off the skin of water has received little attention in the literature. Chapter 4 proposes four different approaches to remove direct surface reflectance from UAS imagery and evaluates each against in situ Rrs data. The most accurate method is a simple empirical model that exploits measurements in the infrared where water strongly absorbs light; applying this model permits high resolution water quality retrievals with only modest uncertainty. Chapter 5 uses UAS imagery to monitor a wetland restoration site in the Chesapeake Bay across seasons and years. A supervised random forest model is developed with UAS data and used to classify species-specific marsh vegetation with 97-99% accuracy. Vegetation classification maps were compared to as-built planting plans to delineate instances of significant marsh migration. Chapter 6 summarizes how the environmental remote sensing methods used in this dissertation can contribute to a better understanding of coastal research, monitoring, and management by addressing challenges, gaps, and potential solutions at various scales.Item REMOTE SENSING OF ATMOSPHERIC TRACE GASES FROM SPACEBORNE UV MEASUREMENTS(2022) Huang, Xinzhou; Yang, Kai; Dickerson, Russell R.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Satellite measurements of atmospheric trace gases provide continuous long-term information for monitoring the atmospheric chemical environment and air quality at local, regional, and global scales. Trace gas retrievals play a critical role in chemical data assimilation, air quality modeling and forecast, and regulatory decision-making. In this dissertation, I present retrievals of three trace gases species (O3, SO2, and NO2) from measurements of Ultraviolet (UV) radiation made from the imaging spectrometers onboard operational satellites, including the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR), the Ozone Mapping and Profiler Suite - Nadir Mapper (OMPS-NM) onboard Suomi-NPP (SNPP), and the OMPS-NM onboard NOAA-20 satellite. The retrievals of the trace gas vertical columns are achieved through the Direct Vertical Column Fitting (DVCF) algorithm, which is designed to maximize the absorption signature from the Earth’s atmosphere in the UV spectral range. This dissertation first demonstrates the theoretical basis and mathematical procedures of the DVCF algorithm used for retrieving total vertical columns of ozone (O3) and sulfur dioxide (SO2) from DSCOVR EPIC. We describe algorithm advances, including an improved O3 profile representation that enables profile adjustments from multiple spectral measurements and the spatial optimal estimation (SOE) scheme that reduces O3 artifacts resulted from EPIC’s band-to-band misregistrations. Furthermore, we present detailed error analyses to quantify retrieval uncertainties from various sources, assess EPIC-observed volcanic plumes, and validate O3 and SO2 retrievals with correlative data. The second part of this dissertation presents a suite of efforts to retrieve the tropospheric and stratospheric NO2 vertical columns from the new NOAA-20 OMPS hyperspectral Ultraviolet-Visible (UV-Vis) instrument, covering retrieval algorithm, Stratosphere-Troposphere Separation (STS) scheme, measurement sensitivity assessment, inter-comparison with the Ozone Monitoring Instrument (OMI), evaluation with ground-based Pandora spectrometers, as well as a case study of drastic NO2 changes during COVID-19 pandemic. The third part of my dissertation focuses on validation and algorithm improvements for the tropospheric NO2 retrievals from SNPP OMPS UV measurements. OMPS column NO2 was validated against coincidence measurements from two ground-based MAX-DOAS spectrometers deployed in eastern China. To achieve higher retrieval accuracy, we developed and implemented a series of algorithm improvements, including an explicit aerosol correction scheme to account for changes in measurement sensitivity caused by aerosol scattering and absorption, the replacement of climatological a priori NO2 profile with more accurate NO2 vertical distribution from high-resolution CMAQ model simulations, and the application of model-derived spatial weighting kernel to account for the effect of heterogeneous subpixel distribution. These improvements yield more accurate OMPS NO2 retrievals in better agreement with MAX-DOAS NO2 measurements. The analysis concluded that explicit aerosol correction and a priori profile adjustment are critical for improving satellite NO2 observations in highly polluted regions and spatial downscaling is helpful in resolving NO2 subpixel variations.