SARP West 2025 Land Group

Faculty Advisors:

Daniel Sousa, San Diego State University 

Graduate Mentor:

Megan Ward-Baranyay, San Diego State University 

Land Group Introduction

Faculty Advisor Daniel Sousa

Robert Purvis

Fractional cover estimates of the epiphytic macrolichen Ramalina menziesii in oak canopies from simulated mixed spectra and airborne imaging spectroscopy 

Robert Purvis, Western Kentucky University 

Lichens, a symbiotic relationship between a fungus (mycobiont) and green algae or cyanobacterium (photobiont), occur globally with great variability in form and function. On the North American west coast, Ramalina menziesii is a robust lichen with net-like morphology found across three distinct biomes. In the mediterranean climate of coastal California, R. menziesii can survive with thallus water content as low as 13%, making the lichen a powerful medium for wildfire spread. As a late-successional community member, changes in wildfire incidence observed in the region have caused R. menziesii coverage to decline. Despite their importance, there is little research on the detection of lichen with imaging spectroscopy, which would provide a potentially novel piece of information to wildland firefighters. The lichen primarily grows on oaks of the region, with the percentage of top-cover ranging from near zero to tree canopy overgrowth due to the lichens’ pendulous growth form. These characteristics may make R. menziesii a good candidate for airborne imaging spectroscopy. Reflectance spectra were collected with a field spectrometer and contact probe from the Figueroa creek area of Sedgwick Reserve in Santa Barbara County, California. From this collection, a spectral library was built (n=70) to contain three endmember types: Quercus lobata (California Valley Oak) leaf (GV; n=34), Q. lobata bark (NPV; n=8), and R. menziesii, (lichen; n=28). This library was sampled using a stratification method and was split into a simulation library (n=41) and an unmixing library (n=29). Mixed spectroscopic pixels at 5% increments of lichen coverage were simulated (n=1344) with random fractions of GV and NPV coverage. Multiple endmember spectral mixture analysis (MESMA) on the simulated pixels recovered the known lichen fractions at an RMSE of 0.25 and R2 of 0.38, with some overestimation of lichen coverage at high GV fractions. Future work will include evaluating the performance of the model with Airborne Visible and Infrared Imaging Spectroscopy (AVIRIS) imagery over Sedgwick Reserve. 

Kyra Shimbo

Investigating the Influence of Pre-Fire Fuels and Topography on Burn Severity Prediction in the 2024 Lake Fire in Santa Barbara County, California 

Kyra Shimbo, University of Rochester 

Wildfires can pose significant threats to air and water quality, vegetation, soil health, and public safety. The growing severity, frequency, and intensity of wildfires underscore the need to mitigate their impacts on ecosystems and communities. In California, a total of 8,110 wildfires occurred in 2024—burning over 1 million acres of land and destroying more than 1,800 structures. Prospective modeling of potential burn severity in fire-prone areas can help inform decisions on effectively implementing fire management strategies to reduce wildfire hazards. Previous studies have demonstrated that various combinations of pre-fire environmental characteristics, such as fuels and topography, can explain burn severity patterns. However, identifying the dominant drivers of burn severity and accurately predicting it remains challenging across different landscapes. To gain a stronger understanding of burn severity dynamics, we evaluated the influence of pre-fire fuels and topography on predicting post-fire char fractional cover—a proxy for burn severity—for the 2024 Lake Fire in Santa Barbara County, California. We used a random forest regression model to predict post-fire char fractional cover based on pre-fire measurements of fuel structure, fuel moisture, fuel condition, fuel water stress, and topography. Fuel structure was measured with the Land, Vegetation, and Ice Sensor (LVIS), a full-waveform LiDAR. Fuel moisture, fuel condition, and char fractional cover were derived from surface reflectance collected by the Earth Surface Mineral Dust Source Investigation (EMIT). Variables related to fuel water stress were estimated from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). Topographic variables were acquired from the Shuttle Radar Topography Mission (SRTM). Preliminary results indicate that the model explains 28% of the variance in post-fire burn severity for the Lake Fire (R-squared = 0.28), with canopy height, green vegetation fractional cover, and aspect ranking the highest in predictor importance. Future work could focus on model improvement by incorporating additional pre-fire and active fire weather variables into the model. Overall, this model can be applied to monitoring fuel parameters associated with high burn severity that jeopardize ecosystems and water resources. 

Nimay Mahajan 

Evaluating Spectral Mixture Analysis (SMA) Derived Vegetation Fraction for Improved ET Estimates in the Semi-Arid Ecosystems of the Sierra Foothills 

Nimay Mahajan, University of Miami 

Evapotranspiration (ET) plays a critical role in water and energy cycles, particularly in semi-arid ecosystems. For decades, ET models have used spectral indices like the Normalized Difference Vegetation Index (NDVI) to quantify the abundance of green vegetation. However, NDVI has long-recognized limitations in semi-arid environments, including saturation for densely vegetated pixels and sensitivity to soil reflectance in sparsely vegetated areas. We explore the potential for vegetation fraction (VF) derived from spectral mixture analysis (SMA) of imaging spectroscopy data to provide a more accurate alternative to NDVI for modeling ET. Focusing on a region east of Fresno, California, we leverage data from National Ecological Observatory Network (NEON) flux towers (SJER and SOAP) which provide ground-based measurements of Latent Heat Flux (LE). We derive VF from surface reflectance collected by the Earth Surface Mineral Dust Source Investigation (EMIT) and compare it to the Landsat-based NDVI product currently used by NASA’s Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model. Land Surface Temperature (LST) from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is incorporated as the thermal data source for each PT-JPL model run. Both model configurations use the same six environmental variable inputs, differing only in their representation of fractional vegetation cover. Preliminary findings suggest that SMA-derived VF tends to produce more conservative LE estimates than NDVI, especially in areas with sparse or mixed vegetation cover. These VF-based estimates also appear to better align with flux tower observations, indicating that NDVI may be overestimating ET in this region. While both vegetation metrics show broad agreement in spatial structure (r = 0.73), localized LE differences highlight the importance of subpixel vegetation characterization in ET modeling. As orbital imaging spectrometers become more widely deployed, it is clear that improving remote sensing-based ET modeling can help support water monitoring, drought-resilient agriculture, and wildfire hazard assessments. 

Patricia Sibulo

Comparative Analysis of UAVSAR Derived Flooding Extent During Hurricane Florence (2018) to Urban Flood Hazard Models 

Patricia Sibulo, University of San Francisco 

Urban flooding poses major risks to public safety, infrastructure, and city planning. Yet, floods remain difficult to detect, especially during storms, when high precipitation is often accompanied by spatially and temporally persistent cloud cover. Synthetic aperture radar (SAR) sensors, such as airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), emit microwave pulses that can image regardless of cloud cover or time of day and respond sensitively to surface water. This is due to both the high dielectric constant and the flat geometry of standing water. Given sufficient resources, airborne SAR is capable of capturing rapidly evolving flood events that unfold on hourly timescales. We investigated how daily airborne SAR can be applied to improve flood hazard mapping and monitoring in urban areas. This study incorporates airborne quad-polarized L-band UAVSAR data acquired for five days during the 2018 Hurricane Florence in North Carolina and flood hazard models developed by the state. From daily inundation extent maps, we computed the total area flooded in the Northeast Cape Fear River Basin spanning the area between the cities of Wilmington and Goldsboro. Spatial overlap between the total flooded area estimated by UAVSAR and the region’s projected flood hazard zones was quantified. A LiDAR-derived digital terrain model (DTM) with a spatial resolution of 3ft was also used to identify low-lying areas prone to pooling. Preliminary findings suggest that roughly 66% of the SAR-detected flood did not appear within the state’s modeled 100-year flood hazard zone. Future work could compare UAVSAR estimates of total flooded area to estimates derived from lower temporal resolution (6-12 days) spaceborne SAR to improve flood mapping globally. These results support the integration of high-temporal-resolution airborne SAR and satellite SAR in urban flood workflows for hazard assessment and active flood monitoring. The recently launched NASA-ISRO SAR (NISAR) mission, with global coverage up to twice every 12 days, is expected to enhance this fusion approach by providing more frequent spaceborne observations. Integrating SAR and LiDAR may enable more accurate, timely assessments in response to flood disasters. 

Charlotte Perry

Investigating Spaceborne Detection Limits of Geothermally Active Mud Features, Land Surface Temperature, and Surface Mineralogy in the Salton Sea Geothermal Field 

Charlotte Perry, Stonehill College 

Geothermally active mud features, such as mud pots and mud volcanoes, are manifestations of subsurface geothermal activity. Geothermal activity also provides energy resources. In California’s Salton Trough, geothermal power plants produce roughly 340 Megawatts of electric power annually. Detecting and monitoring geothermal surface features is thus valuable, as these features can be key indicators of geothermal resource potential. Here, we investigated the ability of spaceborne multispectral thermal imaging and imaging spectroscopy to detect and monitor these small-scale (sub-decameter) geothermal mud features near the southeastern edge of the Salton Sea. For this investigation, LST data were obtained from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and surface mineralogy estimates were provided by the Earth Surface Mineral Dust Source Investigation (EMIT) L2B Estimated Mineral Identification and Band Depth product. To examine temporal variability, we processed four images per sensor acquired over two seasons from two consecutive years, May and August for 2023 and 2024. We conducted t-tests to determine if consistent differences in mineralogy and/or LST were observable between known mud pots and control areas. Preliminary results did not find a statistically significant relationship (p > 0.05) between the presence of small-scale geothermal mud features, spaceborne-acquired surface mineralogy, and LST. This study has identified key spatial resolution limitations to locating and monitoring small geothermal mud features. Future work is suggested to determine the threshold for spatial resolution relative to the size of geothermal features of interest. Effectively locating and monitoring geothermally active areas has implications for improving energy security, quantifying the abundance of critical minerals, investigating the effect of their emissions, and understanding the potential geologic hazards they pose. 

Brianna Francis

AVIRIS, Altadena, and Asphalt: Assessing the capabilities of airborne imaging spectroscopy in classifying asphalt road condition 

Brianna Francis, University of Georgia 

Ninety-four percent of paved roads in the United States are surfaced with asphalt. Fire accelerates the aging process of asphalt and causes roads to degrade prematurely. This causes moisture pooling, accelerated pothole formation, and produces hazardous conditions for all motorists. Asphalt can have distinct spectral features depending on its condition. Undamaged asphalt typically has an albedo of 0.05 to 0.10 and is characterized by a notable decrease in reflectance near 1700 nm and 2300 nm due to absorption by the hydrocarbon-based asphalt sealant applied to the top of roads during its initial paving. As road surfaces are subjected to physical and chemical weathering, the hydrocarbon-based sealant is eroded away, revealing the mineral-filled aggregate below. Because of this process, the spectra of weathered asphalt is characterized by a reduction in complex hydrocarbon absorption, an increase in albedo, and an increase in mineral absorptions, especially that of iron oxide near 490 nm. Previous research has applied in situ imaging spectroscopy to identify these absorption features in asphalt roads and correlated them with pavement condition. We evaluated the capabilities of airborne imaging spectroscopy in detecting asphalt damage in Altadena, California after the January 2025 Eaton Fire to assess the accuracy of this method for mapping road damage for repair prioritization. AVIRIS-3 (Airborne Visible Infrared Spectrometer 3) surface reflectance data was collected post-fire over Altadena on January 16, 2025, at a spatial resolution of 1.8m. We compared two spectral methods for road damage classification, the VIS2 band difference and Spectral Angle Mapper (SAM). Results show that road conditions can be classified with an accuracy of 76% for SAM and 85% for VIS2 with a 10% margin of error based on 100 validation samples; however, these methods notably exhibited limited effectiveness in mountainous areas and sensitivity to crack sealing. These findings can contribute to near immediate post–fire recovery efforts by supporting detour planning, repair prioritization, and a smoother restoration process. 

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Milan Loiacono

SARP West 2025 Oceans Group

Faculty Advisor:

Henry Houskeper, Woods Hole Oceanographic Institute 

Graduate Mentor:

Camille Pawlak, University of California, Los Angeles 

Oceans Group Introduction

Faculty Advisor Henry Housekeeper

Molly McKellar

Spatiotemporal dynamics of canopy-forming kelp forests in the Russian province of Kamchatka 

Maria (Molly) McKellar, University of Wisconsin, Madison 

Interannual variability in canopy-forming kelps and the environmental conditions in which kelps thrive have not been studied extensively in the Kamchatka region of eastern Russia. Canopy forming kelps promote diverse and productive coastal ecosystems by boosting coastal resilience and supporting ecological communities. To better understand how kelp in the Kamchatka region contributes to these impacts, we must understand the spatiotemporal dynamics and drivers of kelp forests in the region. In this study, we evaluate spatiotemporal patterns in kelp canopy, including characterizing the climatology and assessing medium and long-term trends. We compare patterns in kelp forest dynamics with biological parameters, such as satellite-derived chlorophyll-a time series, as well as climatological indices, such as the Pacific Decadal Oscillation (PDO) and the Northern Pacific Gyre Oscillation (NPGO). New data from Kelpwatch, a global dataset utilizing Landsat satellite imagery, was used to map kelp canopy area from 1999 to present with quarterly resolution. This study is the first spatially resolved analysis of canopy-forming kelps in the Kamchatka region. Kelp area time series were assessed in three sub-regions corresponding to the eastern, western, and southern margins of Kamchatka. We found that the spatial extent of kelp across the entire region is maximal in the third quarter, which encompasses July 1 to September 30 and corresponds to the latter portion of the northern hemisphere growing season. We observed kelp forest patterns to vary spatially, with the southern subregion indicating a positive trend in climatologically adjusted canopy area. Pearson correlation indicated a strong relationship between phytoplankton and kelp dynamics in the southern subregion, perhaps suggesting the importance of nitrate as a regional driver of kelp forest variability. A weak correlation was found between the PDO and NPGO across the entire Kamchatka region and within the eastern and western subregions. While these results support a primary importance of nutrients to kelp population dynamics in the southern region, more work must be done to understand drivers of nutrients variability in Kamchatka. Further investigation of subregional dynamics is warranted given the climatological and mixing differences between the Sea of Okhotsk and the western Pacific Ocean, which each border Kamchatka. Sea surface temperature may also have an impact on kelp forests and should be considered. Understanding regional patterns and trends in Kamchatka would strengthen our understanding of spatiotemporal variability in kelp at global scales and the key associated drivers, including resolving key oceanic and atmospheric processes or modes. The findings supporting positive trends of kelp area in the southern portion of Kamchatka warrants further future research and investigation. 

Grace Woerner

Tropical Storm Effects on Ocean Dynamics Measured Through a Multi-Platform Observing Approach 

Grace Woerner, North Carolina State University 

Elevated low-latitude sea surface temperatures (SSTs) are associated with heightened intensity and frequency of tropical cyclone events. Tropical systems can modify surface marine ecosystems, often to the detriment of coastal communities and fisheries. Characterizing ocean properties before and after storm events can provide insight into storm-driven mixing and corresponding ecosystem responses. However, extreme conditions during tropical storms can impede ocean observing. For example, satellite remote sensing of SST and ocean color during tropical storms is challenged by cloud cover and surface disturbances such as white capping. This study pairs satellite remote sensing observations with in-situ oceanographic data to characterize oceanographic changes in phytoplankton concentrations and SST associated with a tropical cyclone in the western Pacific during March 2024 to April 2025. Chlorophyll-a is a pigment present in phytoplankton and is commonly used as a proxy for estimating phytoplankton abundance. In-situ chlorophyll-a and SST measurements collected by Argo floats were used to validate satellite ocean color observations from the NASA Plankton, Aerosols, Clouds, ocean Ecosystem (PACE) mission and SST from the Multi-scale Ultra-high Resolution (MUR) dataset before and after Typhoon ShanShan, the equivalent of a category four hurricane. The PACE observations indicate agreement with Argo float data, albeit with a slight positive bias and variability in post-storm conditions. MUR SST data also closely matched Argo measurements. It was found that the typhoon passage did not produce a detectable chlorophyll-a anomaly. This finding was further investigated by comparing changes in the mixed layer depth (MLD) and assessing whether the observed storm-induced mixing reached adequate depths to significantly increase surface nitrogen concentrations, prerequisite to inducing a phytoplankton bloom. The findings suggest that while the MLD deepened, deepening was inadequate at regional scales to bring nitrate and other nutrients to the surface. Although Typhoon Shanshan did not generate mixing deeper than the nutricline, more powerful storms or those occurring in waters with shallower nutriclines may more effectively introduce nutrients into surface waters. Limitations such as cloud coverage for satellite observing, plus the sampling frequency, coverage, and sensor availability of Argo float observations, highlight the importance of continued multi-platform observations for ocean environments to advance knowledge of tropical cyclone effects on surface ocean ecosystems. 

Alex Lacayo

Peruvian Coastal Water Temperature Anomalies Correspond to Variability in El Niño Position and Timing 

Alex Lacayo, Columbia University 

The El Niño–Southern Oscillation (ENSO) is a basin-scale oscillation pattern in the tropical Pacific that drives, via teleconnections, atmospheric and oceanic variability at larger scales. El Niño events are ENSO phenomena defined by anomalously warm sea surface temperatures (SSTs) in low-latitude Pacific domains, and the spatial and temporal expression of El Niño events can vary. Recent literature has established distinct differences between the spatial expression of SST anomalies associated with El Niño events. Elevated SST in the Central (often called “Modoki”) and Eastern equatorial Pacific, for example, have been described as so-called El Niño “flavors” and are associated with different responses across global environments. 

This study investigates the relationship between El Niño variability and coastal upwelling within Peru’s Exclusive Economic Zone (EEZ), using satellite-derived SST as a proxy. Coastal upwelling is a vital driver of strongly elevated biological productivity in the Peru EEZ, sustaining one of the globe’s most productive fisheries and the largest anchovy stock worldwide. This analysis evaluates SST anomalies in the Peruvian EEZ as a function of the spatiotemporal dynamics of SST in the tropical Pacific during the onset and evolution of El Niño events spanning the past three decades. The analysis is conducted for two domains in the Peruvian EEZ. The first corresponds to primarily north-south coastline north of Pisco, and the second to the northwest-southeast coastline south of Pisco. Preliminary findings are consistent with Modoki events corresponding to less pronounced warming in Peru during El Niño peaks, along with a lag in post-event upwelling rebound response, compared to Eastern Pacific events. The findings indicate that seasonal timing of El Niño events modify the strength of temperature anomalies in coastal Peru. The subregional comparison suggests that the northern Peruvian EEZ is more impacted by El Niño timing and position variability, likely consistent with its lower latitude and exposure to Kelvin wave propagation. These findings support improved knowledge of how different El Niño expressions influence Peruvian coastal ecosystems, which is critical for assessing ecosystem resilience and informing the management of coastal fisheries. 

Melanie Lin

Utility of SAR in detection of canopy-forming kelp in South Africa 

Melanie Lin, Boston University 

Kelp forests are valuable to coastal cities and towns because they support marine ecosystems, benefit economies, and dampen the effects of waves and erosion. This study aims to understand the extent to which synthetic aperture radar (SAR) can be used to accurately map the distribution of the South African canopy-forming kelp, Ecklonia maxima, or sea bamboo. SAR data was obtained from Sentinel-1, which has a five-day revisit time. SAR observations use radio waves, which penetrate clouds, thereby supporting observations of kelp forest habitat in any cloud condition. Despite the potential to use SAR to increase data availability on cloudy days, there are fewer SAR products for kelp canopy—especially sea bamboo—relative to passive optical remote sensing, which is obstructed by clouds. SAR observations were validated by comparing with manually classified optical imagery obtained using Airborne Visible Infrared Imagining Spectrometer – Next Generation (AVIRIS-NG), which was flown on NASA’s Gulfstream III in 2023 as part of The Biodiversity Survey of the Cape (BioSCape). BioSCape was an integrated field and airborne campaign collaboration between the United States and South Africa to study the biodiversity of the Great Cape Floristic Region (GCFR). More commonly used passive optical remote sensing datasets were also assessed using imagery from Landsat that had been classified using a random forest. This research shows that SAR observations yield distinct values between kelp and ocean, indicating potential to use SAR data to map kelp canopy extent in calm oceanic conditions. SAR observations in the VH (vertically transmitted, horizontally received) polarization indicates a larger distinction between kelp and calm ocean water than data in the VV (vertically transmitted, vertically received) polarization. The sensitivity and responsivity of SAR kelp forest retrievals was dependent on the tidal state during the data acquisition. In VH polarized data, a lower tidal state supports more accurate classifications between kelp and calm ocean water than a high tidal state. Waves, which may contain kelp beneath them, obscure kelp backscatter response in SAR data. This study improves understanding of the utility of SAR for mapping sea bamboo extent, which in turn supports future opportunities to develop better understanding of marine biodiversity and coastal resilience in the GCFR where sea bamboo is the dominant canopy-forming taxa. 

John Lund

Kinetic energy of multiscale oceanic features derived from SWOT altimetry 

John Lund, Adelphi University 

Oceanic eddies are circular movements of water that separate the main flow and facilitate oceanic energy transfer across multiple scales, thereby underlying biophysical interactions and modifying climate and ocean dynamics. Oceanic eddies correspond to dynamics spanning geostrophic to ageostrophic processes, spatial scales spanning 0.1 to 100 km, and temporal scales spanning hours to months. Eddies spanning horizontal spatial scales of 0.1 to 10 km and temporal scales of hours to days, termed submesoscale eddies, are difficult to resolve from legacy satellites due to the finer spatial resolution requirements for observing smaller scale features. Conversely, eddies spanning larger horizontal spatial scales and longer temporal scales, termed mesoscale eddies, are more readily resolved using legacy satellite altimeters. This research utilizes observations from the recently launched Surface Water and Ocean Topography’s (SWOT) Ka-band Radar Interferometer (KaRIn) to resolve submesoscale eddies and quantify associated kinetic energy. We contextualize our SSHA observations using the Data Unification and Altimeter Combination System (DUACS)—a project that merges satellite data to observe coarser mesoscale fields on a global scale—to visualize ocean dynamics around SWOT swaths more clearly. Comparing the kinetic energy associated with SWOT-detected features to that estimated from DUACS data supports improved understanding of the relative importance of the submesoscale in global energy transfer. Results from this investigation demonstrate that SWOT supports characterizations of features at the upper bound of the submesoscale to analyze ocean dynamics and energy cascades at specific moments and locations. Resolving the temporal dynamics of submesoscale features remains challenging due to SWOT’s 21-day revisit cycle, which also limits submesoscale characterizations to isolated swaths, but novel SWOT observations nonetheless support snapshot opportunities to constrain the role of submesoscale processes in global energy transfer. Future directions with SWOT include coupling data with high-resolution numerical models or additional satellite missions such as PACE to map a wider region and investigate key controls on biophysical interactions associated with submesoscale processes. 

Logan Jewell

Machine Learning Classification of Remote Sensing Imagery for Investigating Changes in Natural Oil Seepage 

Logan Jewell, State University of New York, Brockport 

Spatiotemporal variability in oil content of the Santa Barbara Channel (SBC) corresponds to natural hydrocarbon seepage and past anthropogenic spills. The marine geology of the SBC is characterized by a relatively shallow and abundant hydrocarbon reserve beneath faulted anticlines that run parallel to the shore. Natural seepage occurs when pressure in the reserve exceeds hydrostatic, and gaseous bubbles coated in liquid petroleum seep through the sea floor and enter the marine environment. Because gaseous hydrocarbons and oil are both buoyant in seawater, the seepage manifests as oil slicks at the surface of the ocean. Oil has historically been extracted from the reserve by human drilling, potentially alleviating pressure in the reserve, at sites such as Platform Holly, which operated in the SBC from 1966 until production ceased in 2015. Platform Holly is located roughly 3.2 kilometers from the shore and is the only offshore oil platform in California State waters. Since decommissioning, the only mechanism releasing oil in this region of the hydrocarbon reserves is natural seepage. In this study, machine learning via a random forest model is utilized to identify and classify oil slick regions in Sentinel-2 optical images encompassing the decommissioned oil platform Holly and other nearshore waters near Santa Barbara, CA. The random forest model was developed to predict 3 classes, or targets: clear, turbid, and oil-contaminated waters. Sentinel-2 supports a 5-day revisit time, which mitigates cloud obstruction in the region, and 10-meter spatial resolution appropriate for distinguishing small-scale surface features such as slicks. 6 images were manually classified for training, and classification using the random forest supported an additional 27 classified images. A time analysis was conducted using the combined 33 images, which spanned 2019 to present to assess variability in hydrocarbon seepage starting 4 years after decommissioning to present. Preliminary results do not indicate a trend in the area of the natural oil slick from 2019 to 2025. We conducted sensitivity testing by assessing covariance between oil slick area with wind and tidal measurements and found no significant correlation to winds or tides. More frequent imagery spanning a wider temporal range could help to better determine whether oil slick area is changing or stable through time. 

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Milan Loiacono

SARP West 2025 Whole Air Sampling Group

Faculty Advisor:

Donald Blake, University of California, Irvine

Graduate Mentor:

Oluwaseun Moses Akinola, University of Connecticut

Whole Air Sampling Group Introduction

Faculty Advisor Donald Blake

Sarah Kinlaw

Impact of Dairies on Ozone Production in Ontario, CA 

Sarah Kinlaw, College of William & Mary 

In the center of Ontario, California’s urban sprawl sits 5 square miles of livestock farming, including many dairies. Emissions from silage from dairy farms result in significant amounts of ethanol and methanol entering the atmosphere. These volatile organic compounds (VOCs) can participate in the formation of tropospheric ozone through oxidation and photolytic processes. Ozone is known to have negative impacts on humans, agriculture, and the climate. Of concern is that the dairy regions and regions downwind will likely have enhanced levels of ozone. In this study, 19 samples were collected from dairy farms and downwind sites over two days. The extent of enhancement in reactive species was determined by comparing concentrations of speciated VOCs, collected from air samples from the downwind sampling sites, with estimated upwind background concentrations. The “ozone production potential” (OFP) was estimated by multiplying the mixing ratios of VOCs of interest by their respective hydroxyl rate constants, and it was found that methanol and ethanol were the major VOC contributors to OFP. HYSPLIT trajectory modeling was used to determine the dispersion patterns of air masses originating from the dairy farm area and identify potentially impacted downwind communities. This analysis emphasizes the need for more robust air quality and agricultural management with a focus on directing policies to improve air quality at a local and regional scales. 

Ryan Glenn

Examining the Chemical Composition and Evolution of Palisades Fire Gas Emissions 

Ryan Glenn, Dartmouth College 

Wildland-urban-interface (WUI) fires in the US are increasing in frequency and intensity with disproportionately large impacts on air quality and human health. The 2025 Palisades Fire alone destroyed nearly 7,000 structures and displaced more than 30,000 people. Despite their significance, they remain understudied compared to wildland fires, especially in regard to emission composition, evolution, and ozone formation potential. Here we analyze trace gases and volatile organic compounds (VOCs) collected via air canisters during the Palisades Fire and use the Framework for 0-D Atmospheric Modeling (FOAM) box model to simulate their evolution. Gas chromatography-mass spectrometry reveals high daytime VOC concentrations despite the increase of the boundary layer. C1-C4 oxygenates exhibited by far the highest reactivity and concentrations, accompanied by alkanes, alkenes, aromatics, biogenic, and chlorinated compounds indicative of the combustion of anthropogenic materials. Using the sampling data to constrain the FOAM box model, we characterize the regime as primarily VOC-limited and identify acetaldehyde and methanol as key ozone precursors and nitric acid as the primary nitrogen oxide (NOx) sink. These findings suggest that targeted reductions in oxygenates will be most effective in mitigating ozone formation from WUI fire emissions. This study has significant implications for wildfire air quality management and highlights the need for further research comparing WUI and wildland fire emission chemistry. 

Riley Gallen

Temporal and Spatial Analysis of Nitrogen Dioxide (NO₂) in Long Beach: Assessing Its Role in Ozone Formation and Impact on Nearby Communities/Coastal Ecosystems 

Riley Gallen, University of Florida 

Nitrogen dioxide (NO₂), a key precursor to ozone formation, is emitted from various combustion sources including vehicles, cargo ships, and power plants. In Long Beach, California, these sources are concentrated around highways and the busy port, thus raising concerns about localized air pollution and its broader environmental impact. This project investigates NO₂ concentrations over Long Beach using NASA’s B200 and DC-8 aircraft flight data from 2019, 2021, and 2025. Data were analyzed through latitude–longitude mapping and altitude comparisons to assess temporal trends and spatial distribution of NO₂. The 2021 dataset, collected during pandemic-related port congestion, showed elevated NO₂ levels, though seasonal differences required comparison between 2019 and 2025 for consistency. Overall, NO₂ concentrations increased in 2025 relative to 2019. HYSPLIT wind trajectory modeling often carried pollutants inland, particularly toward the communities of Wilmington and West Long Beach, which already experience elevated respiratory health risks due to pollution exposure. Although the scope of this study was not to determine the exact NO₂ sources in Long Beach, the prevailing wind patterns as indicated from the HYSPLIT model suggests the port as a likely source. While inland transport dominated during the selected flight days, wind patterns are unpredictable. This variability suggests that NO2 and its photochemical transformation into ozone could occur over adjacent marine ecosystems such as Bolsa Bay State Marine Conservation Area and Albone Cove State Marine Conservation Area. Collectively, this study highlights the potential impacts of NO₂ exposure on local communities and nearby coastal ecosystems and emphasizes the need for continued monitoring and apportionment of sources of NO2 in urban coastal regions. 

Owen Rader

Quantifying the Impact of Meteorological Variables on Wildland Fire Spread 

Owen Rader, University of Delaware 

Past studies have revealed that wildfire is becoming more extreme due to increasing hydroclimate variability. Using Los Angeles County’s Eaton Fire, a primarily wind-driven fire, as a case study, I simulate the fire under isolated meteorological variables with a focus on quantifying the impacts of wind speed simulations on the fire’s spread. A comprehensive analysis of the Eaton Fire’s spread can indicate how Wildland Urban Interface (WUI), a growing transition zone particularly in Southern California, is vulnerable to enhanced fire activity under different meteorological conditions. This study aims to demonstrate how fuel metrics behave under different wind conditions, thus providing valuable insight into the potential rates of spread and response times to wildfire-encroached WUI areas. To achieve this, LANDFIRE surface/canopy fuel products and topographical products are used as pre-model run fire parametrizations using FLAMMAP’s built-in Landscape file generator, using variable wind speeds while holding other values constant, to output fuel-load metrics. Following this, I utilized ARSITE, a built-in application to FLAMMAP, to simulate several scenarios over time, using real-time ERA5 Reanalysis meteorological data from the wildfire event period, and quantified the impacts of variable wind speeds. These model runs can provide valuable insights into how fires behave under varying meteorological conditions, which can be further quantified through future research to better understand how a shift towards hydroclimate extremes impacts WUI fires. 

Stephen Shaner

Analysis of Bromoform Concentrations and Impact in California 

Stephen Shaner, University of Maryland, Baltimore County 

Bromoform is a haloalkane which is commonly found over the ocean, with major sources being marine organisms such as phytoplankton and macroalgae. This compound has been measured around California during the NASA Student Airborne Research Program flights campaigns since 2010. Within this sampled period, 2014 showed significantly higher bromoform concentrations than any other measured year. In this study, the concentrations of bromoform from 2010–2022 were analyzed and consistently higher than average concentrations were evident over the Los Angeles, Long Beach, and Inland Empire area. The effect on ozone concentrations in the atmosphere caused by the higher concentrations was measured using the Framework for 0D atmospheric modeling (F0AM). It was found that at its peak of 28 ppt, bromoform decreases ozone concentration by 0.14% at the altitude where the sample was taken. However, the potential impact in the stratosphere of Br radicals which come from Bromoform is expected to be higher due to its reaction rates with various molecules commonly found in the stratosphere. 

Maggie Rasic

Shifting Seas and Changing Chemistry: Gaseous Emissions in Upper Newport Bay 

Maggie Rasic, University of California, Los Angeles 

Coastal wetlands are ecologically rich environments that provide critical regulatory services, including carbon storage and nutrient cycling. However, these ecosystems are vulnerable to the impacts of sea level rise, which may alter biogeochemical cycles and enhance the production of trace gases. This study analyzed whole air samples collected across six sites spanning from San Diego Creek to Upper Newport Bay to investigate the spatial and temporal patterns of volatile organic compound (VOC) emissions at the study areas, with a focus on halomethanes and methane. Results showed increasing concentrations of halomethanes (specifically CHBr₃, CH₃Br, and CH₃Cl) as sample sites increase in proximity to the mouth of Newport Bay. Further research could indicate possible relationships between salinity, microbial activity, and halogenated compound production. Additionally, at the site closest to the ocean, a notably elevated concentration of methane was observed, a common byproduct of anaerobic microbial decomposition in wetlands. These findings suggest that sea level rise could intensify the production of both halomethanes and methane in coastal wetlands. Given their roles as potent greenhouse gases and, in the case of halomethanes, as stratospheric ozone-depleting substances, this emphasizes the importance of monitoring trace gas fluxes in dynamic coastal environments. 

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Milan Loiacono

SARP East 2025 Ecohydrology Group

Faculty Advisor:

Dom Ciruzzi, William & Mary

Graduate Mentor:

Sarah Payne, University of California, Santa Barbara

Ecohydrology Group Introduction

Faculty Advisor Dom Ciruzzi and Graduate Mentor Sarah Payne

Ethan Bledsoe

Uncovering Hidden Green to Reveal Water: Can Spectral Unmixing of Vegetation Reduce Evapotranspiration Bias in Semi-Arid Landscapes? 

Ethan Bledsoe, Northwestern University 

Deserts push life to its limits, presenting sparse vegetation and scarce water that challenge traditional methods for accurately capturing evapotranspiration (ET). Current satellite ET estimates often struggle in dryland areas. These estimates typically rely on vegetation indices like the Normalized Difference Vegetation Index (NDVI), which can be distorted by bright desert soils and sparse vegetation. This distortion leads to inaccurate ET estimates, affecting crucial decisions related to drought management and water resource planning. To address this problem, we used a technique called Multiple Endmember Spectral Mixture Analysis (MESMA), which classifies pixels into percentages of green vegetation, soil, and shade based on unique spectral signatures. We created a spectral library using high-resolution (1 m) hyperspectral images collected from the NEON Airborne Observation Platform (AOP) over the Santa Rita Experimental Range (SRER). This library was then applied to imagery at different resolutions—medium-resolution (30 m) Landsat 8 Operational Land Imager (OLI) satellite imagery and lower-resolution (500 m) Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery—to produce more accurate fractional vegetation maps. We integrated these detailed vegetation maps into OpenET’s Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) ET model and compared the results to ground-based ET measurements from the SRER flux tower near Tucson, Arizona. On August 20, 2021, all models underestimated ET compared to flux tower observations. Among them, the standard PT-JPL model produced the closest estimate, while MESMA-based ET values were lower and generally declined further with decreasing spatial resolution. Because our method uses publicly available imagery and a remotely collected spectral library, it can be applied to other desert regions, enhancing our understanding of modeling ET and, in-turn, improving our water management in an increasingly arid world. 

Rylee Chafin 

Examining Changes in Vegetation Moisture Indices and Biodiversity Estimates at the San Clemente Dam Removal Site in California 

Rylee Chafin, University of North Georgia 

With dam removal becoming a more widespread practice, it is important to understand how riparian ecosystems respond to these hydrological changes. Airborne Visible Infrared Imaging Spectrometer (AVIRIS) flights over the San Clemente Dam near Carmel, California provide an ideal opportunity to understand changes in hydrology and biodiversity across an entire watershed, rather than at small vegetation plots. This case study investigates the only large dam in the nation that has had sufficient AVIRIS data to understand these changes. This study processed AVIRIS data from August 2015 and October 2019 and examined how the Normalized Difference Moisture Index (NDMI) changes between the two flights. This data was then compared with two stream gauges located downstream of the dam to better understand the hydrology of this watershed and the effects of dam removal on streamflow. Then, I used the AVIRIS data to create estimated alpha diversity maps using the biodivMapR R package. This study found that NDMI and alpha diversity estimates were correlated in the riparian area. This indicates that moisture and plant biodiversity have changed across the riparian ecosystem, possibly as a result of dam removal, and helps us understand the ecological implications of this practice. Future AVIRIS (or other hyperspectral) flights over other dam removal sites can help expand this research to evaluate the effectiveness of these methods and better establish correlation between dam removal, moisture, and biodiversity. 

Sumaya Tandon

Tracking Tree Emissions from the Sky: Improving Isoprene Estimates with MEGAN 

Sumaya Tandon, Trinity University 

Isoprene, a biogenic volatile organic compound, is emitted from tree species and contributes to the formation of secondary pollutants such as formaldehyde and ozone. With its short atmospheric life span of up to an hour and complex emissions dynamics, it is hard to quantify, and therefore predict, how much of it is in the atmosphere. This study employs the Model of Emissions of Gases and Aerosols from Nature (MEGAN) to estimate isoprene emissions in California and Missouri, two regions with contrasting vegetation types, during the summer of 2013. The predictions were compared to airborne data, specifically whole air sampling, from the flight campaign SEAC4RS to evaluate MEGAN’s accuracy. Specifically, to parameterize MEGAN this study utilizes the North American Data Assimilation System (NDLAS) to compile a list of meteorological and surface variables. Two different models were run, one with consideration of drought stress and one without, to evaluate the impact of water stress on modeled isoprene emissions. The results of this study show MEGAN consistently underpredicted isoprene in both regions with and without water stress consideration. However, including drought stress can potentially improve predictions for areas with very low-emissions suggesting that accounting for water stress may improve MEGAN. With these findings in mind, it’s beneficial to integrate ecohydrological understanding into emissions models. Isoprene emissions from airborne data has rarely been used in the context of studying drought with MEGAN, therefore this work highlights the importance of understanding and refining stress response parameters- a crucial step towards improving predictions of biogenic emissions for future climate scenarios. 

TJ Ochoa Peterson

Understanding the Relationship Between Cloud Type and Evapotranspiration in Shrubland Vegetation 

TJ Ochoa Peterson, Michigan State University 

Evapotranspiration (ET) is a key indicator of ecosystem health, representing water flux from the surface to the atmosphere. High ET values can result, in-part, from water-intensive vegetation while the inverse can indicate insufficient water for evaporation. A persistent challenge in remote sensing ET is cloud contamination. Thermal infrared sensors used to derive remote sensed ET apply cloud masking which removes affected pixels and results in data gaps. Although prior studies have examined the impact of cloud amount on ET, the influence of specific cloud types remains underexplored. This study investigates how distinct cloud types (Cumulus, Altostratus, and Cumulonimbus) affect surface-level ET over shrubland vegetation in Tucson, Arizona, during the North American Monsoon season. Cloud classification was performed using Cloud Optical Depth (COD) and Cloud Top Pressure (CTP) from GOES-18 Level 2 products, following criteria from the International Satellite Cloud Climatology Project (ISCCP) dataset. These classifications were compared against in-situ ET observations from the Santa Rita Experimental Range NEON flux tower. Results indicate that cloud presence generally reduces instantaneous ET relative to preceding clear-sky conditions. Clouds with low altitude and low density (Cumulus, Stratocumulus) generally showed brief reductions in ET. Notable results include ET values observed under high COD and low CTP conditions, characteristic of Cumulonimbus clouds, did not differ significantly from clear-sky conditions. Future research should incorporate cloud-type into ET models to improve accuracy, particularly in regions prone to frequent cloud cover. Further work could deduce cloud-type patterns for the intent of data gap filling models that estimate ET during cloud-contaminated periods, reducing data loss and enhancing understanding of land-atmosphere interactions. 

Rachel Faessler

Comparing Tree Biodiversity in San Jose, California Using Hyperspectral Imagery and Ground Data 

Rachel Faessler, University of Wisconsin-Green Bay 

Street trees provide a myriad of ecosystem services, such as cooling air temperature, improving air quality, reducing runoff, and improving human well-being. Furthermore, having many different species of trees (high biodiversity) is important, as this improves the overall resilience of a forest community to disturbances, which increases reliable access to ecosystem services. Airborne hyperspectral data is often used to measure biodiversity or health of trees in natural forests, but rarely in urban environments. When urban ecosystems are studied, the focus is on interactions with humans or the local effects of vegetation. Uniquely, this study seeks to compare indicators of alpha and beta diversity compiled from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery (collected in 2024) and Dryad ground-based sampling of 264,000 street trees in San Jose, California (collected in 2021). There appears to be more spatial variability in the AVIRIS estimated beta diversity than in alpha diversity. There is a modest correlation between ground and AVIRIS derived measures of alpha diversity, which is a step forward in expanding estimates of tree biodiversity in cities using hyperspectral imagery. Future work could connect spatial variation of biodiversity to city planning measures such as income, residential/business areas, income, or redlining; compare hyperspectral diversity to areas of alpha diversity with consistent sampling; or build on different measures of diversity than species diversity (e.g. isohydricity). 

Katie Wilson

How Does Antecedent Soil Moisture Influence Flooding in the Southeastern U.S.? A Case Study in Athens, Georgia 

Katie Wilson, North Carolina State University 

Floods are the most common and deadly natural disaster in the United States, known for their rapid and widespread impacts. While previous research has examined how antecedent soil moisture (ASM) affects flood severity, relatively little work has focused on the Southeastern United States. This region is especially vulnerable due to high annual rainfall and tropical systems, both of which can lead to flooding. To address this gap, this study investigates the relationship between ASM and streamflow response during rainfall events in Athens, Georgia, located within the South Atlantic-Gulf watershed. Precipitation datasets (1977–2025) were compiled from three NOAA National Centers for Environmental Information weather stations in Athens. Streamflow data was obtained from the USGS Apalachee River near Bostwick, GA (1977–2025). ASM data was gathered from the Soil Climate Analysis Network (SCAN) Watkinsville station (1997–2025) and NASA’s Soil Moisture Active Passive (SMAP) satellite (2015–2025). Precipitation events were binned by total rainfall, with maximum streamflow recorded during the event and ASM taken from the day prior. Events were grouped into four rainfall categories (0–1″, 1–2″, 3–4″, and 5–6″), and streamflow responses were compared between low (0-40%) and high (60-100%) ASM conditions using the Kruskal-Wallis H test. Results showed statistically significant differences (p < 0.05) in streamflow dependent on ASM across all rainfall bins, confirming that wetter antecedent conditions increase runoff and flood potential. Incorporating ASM into flood forecasting models, along with tools like SMAP, can improve early warning systems in the Southeast U.S. and enhance flood preparedness. 

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Milan Loiacono

NASA’s Mars Spacecraft Capture Images of Comet 3I/ATLAS

Two orbiters and a rover captured images of the interstellar object — from the closest location any of the agency’s spacecraft may get — that could reveal new details.

At the start of October, three of NASA’s Mars spacecraft had front row seats to view 3I/ATLAS, only the third interstellar object so far discovered in our solar system. The Mars Reconnaissance Orbiter (MRO) snapped a close-up of the comet, while the MAVEN (Mars Atmosphere and Volatile EvolutioN) orbiter captured ultraviolet images and the Perseverance rover caught a faint glimpse as well.  

Imagery from MRO will allow scientists to better estimate the comet’s size, and MAVEN’s images are unique among all observations this year in determining the chemical makeup of the comet and how much water vapor is released as the Sun warms the comet. These details will help scientists better understand the past, present, and future of this object.

HiRISE 

The comet will be at its closest approach to Earth on Friday, Dec. 19. On Oct. 2, MRO observed 3I/ATLAS from 19 million miles (30 million kilometers) away, with one of the closest views that any NASA spacecraft or Earth-based telescopes are expected to get.  

The orbiter’s team viewed the comet with a camera called HiRISE (the High Resolution Imaging Science Experiment), which normally points at the Martian surface. By rotating, MRO can point HiRISE at celestial objects as well — a technique used in 2014, when HiRISE joined MAVEN in studying another comet, called Siding Spring. 

Captured at a scale of roughly 19 miles (30 kilometers) per pixel, 3I/ATLAS looks like a pixelated white ball on the HiRISE imagery. That ball is a cloud of dust and ice called the coma, which the comet shed as it continued its trajectory past Mars. 

“Observations of interstellar objects are still rare enough that we learn something new on every occasion,” said Shane Byrne, HiRISE principal investigator at the University of Arizona in Tucson. “We’re fortunate that 3I/ATLAS passed this close to Mars.” 

Further study of the HiRISE imagery could help scientists estimate the size of the comet’s nucleus, its central core of ice and dust. More study also may reveal the size and color of particles within its coma. 

“One of MRO’s biggest contributions to NASA’s work on Mars has been watching surface phenomena that only HiRISE can see,” said MRO’s project scientist Leslie Tamppari of NASA’s Jet Propulsion Laboratory in Southern California. “This is one of those occasions where we get to study a passing space object as well.” 

MAVEN  

Over the course of 10 days starting Sept. 27, MAVEN captured 3I/ATLAS in two unique ways with its Imaging Ultraviolet Spectrograph (IUVS) camera. First, IUVS took multiple images of the comet in several wavelengths, much like using various filters on a camera. Then it snapped high-resolution UV images to identify the hydrogen coming from 3I/ATLAS. Studying a combination of these images, scientists can identify a variety of molecules and better understand the comet’s composition.  

“The images MAVEN captured truly are incredible,” said Shannon Curry, MAVEN’s principal investigator and research scientist at the Laboratory for Atmospheric and Space Physics (LASP) at the University of Colorado Boulder. “The detections we are seeing are significant, and we have only scraped the surface of our analysis.” 

The IUVS data also offers an estimated upper limit of the comet’s ratio of deuterium (a heavy isotope of hydrogen) to regular hydrogen, a tracer of the comet’s origin and evolution. When the comet was at its closest to Mars, the team used more sensitive channels of IUVS to map different atoms and molecules in the comet’s coma, such as hydrogen and hydroxyl. Further study of the comet’s chemical makeup could reveal more about its origins and evolution. 

“There was a lot of adrenaline when we saw what we’d captured,” said MAVEN’s deputy principal investigator, Justin Deighan, a LASP scientist and the lead on the mission’s comet 3I/ATLAS observations. “Every measurement we make of this comet helps to open up a new understanding of interstellar objects.” 

Perseverance 

Far below the orbiters, on the Martian surface, NASA’s Perseverance rover also caught sight of 3I/ATLAS. On Oct. 4, the comet appeared as a faint smudge to the rover’s Mastcam-Z camera. The exposure had to be exceptionally long to detect such a faint object. Unlike telescopes that track objects as they move, Mastcam-Z is fixed in place during long exposures. This technique produces star trails that appear as streaks in the sky, though the comet itself is barely perceptible. 

More about MRO, MAVEN, Perseverance 

A division of Caltech in Pasadena, California, JPL manages MRO for NASA’s Science Mission Directorate in Washington as part of NASA’s Mars Exploration Program portfolio. The University of Arizona in Tucson operates MRO’s HiRISE, which was built by BAE Systems in Boulder, Colorado. Lockheed Martin Space in Denver built MRO and supports its operations. 

The MAVEN mission, also part of NASA’s Mars Exploration Program portfolio, is led by the Laboratory for Atmospheric and Space Physics at the University of Colorado Boulder. It’s managed by NASA’s Goddard Space Flight Center in Greenbelt, Maryland. MAVEN was built and operated by Lockheed Martin Space in Littleton, Colorado, with navigation and network support from JPL. 

JPL built and manages operations of the Perseverance rover on behalf of the agency’s Science Mission Directorate as part of NASA’s Mars Exploration Program portfolio. 

To learn more about NASA’s observations of comet 3I/ATLAS, visit: 

https://go.nasa.gov/3I-ATLAS

News Media Contacts

Andrew Good 
Jet Propulsion Laboratory, Pasadena, Calif. 
818-393-2433 
andrew.c.good@jpl.nasa.gov 

Alise Fisher / Molly Wasser 
NASA Headquarters, Washington 
202-617-4977 / 240-419-1732 
alise.m.fisher@nasa.gov / molly.l.wasser@nasa.gov 

2025-128

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