UMD Theses and Dissertations
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Item EXPLORING AND ASSESSING LAND-BASED CLIMATE SOLUTIONS USING EARTH OBSERVATIONS, EARTH SYSTEM MODELS, AND INTEGRATED ASSESSMENT MODELS(2024) Gao, Xueyuan; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Anthropogenic greenhouse gas (GHG) emissions have led the global mean temperature to increase by approximately 1.1 °C since the industrial revolution, resulting in mass ice sheet melt, sea level rise, and an increase in extreme climate events, and exposing natural and human systems to uncertainties and the risks of unsustainable development. Meeting the Paris Agreement’s climate goal of keeping temperature increases well below 2 °C — even 1.5 °C — will require removing CO2 from the atmosphere beyond reducing GHG emissions. Therefore, carbon dioxide removal and the sustainable management of global carbon cycles are one of the most urgent society needs and will become the major focus of climate action worldwide. However, research on carbon dioxide removal remains in an early stage with large knowledge gaps. The global potential and scalability, full climate consequences, and potential side effects of currently suggested carbon sequestration options — afforestation and reforestation, bioenergy with carbon capture and storage (BECCS), direct air carbon capture — are uncertain. Moreover, although about 120 national governments have a net-zero emission target, few have actionable plans for developing carbon dioxide removal.This dissertation examines two major categories of land-based carbon removal and sequestration methods: nature-based solutions that rely on the natural carbon uptake of the land ecosystem, and technology-based solutions, especially BECCS. These two options were investigated using four studies with satellite and in-situ observations, Earth system models (climate models), and integrated assessment models (policy models). Study 1 provides evidence that land ecosystem is an important carbon sink, Study 2 assesses the carbon sequestration potential of forest sustainable management via numerical experiments, Study 3 monitors recent tropical landscape restoration efforts, and Study 4 extends to BECCS and explores the impacts of future climate changes on its efficacy. Overall, this dissertation (1) improved monitoring, reporting, and verification of biomass-based carbon sequestration efforts using Earth observations, (2) improved projections on biomass-based carbon sequestration potential using Earth system models and socio-economic models, and (3) provided guidance on scaling up biomass-based carbon sequestration methods to address the climate crisis.Item NON-GAUSSIAN ENSEMBLE FILTERING AND ADAPTIVE INFLATION FOR SOIL MOISTURE DATA ASSIMILATION(2024) Dibia, Emmanuel; Liang, Xin-Zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The forecast error distribution in modern day land data assimilation systems is typically modeled as a Gaussian. The explicit tracking of only the first two moments can be problematic when trying to assimilate bounded quantities like soil moisture that are more accurately described using more general parameterizations. Given this issue, it is worthwhile to test how performance of land models is affected when the accompanying data assimilation system abides by a relatively more relaxed set of underlying assumptions. To study this problem, we perform experiments using the ensemble Kalman filter (EnKF) and rank histogram filter (RHF) to assimilate surface soil moisture content observations into the NASA Catchment land surface model. The EnKF acts as the traditional (Gaussian) standard of comparison whereas the RHF represents the novel and more general data assimilation method. An additional parameter of our tests is the usage of an adaptive inflation scheme that is only applied to the ensemble prior. This is done in an attempt to mitigate the negative effects of systematic deficiencies not accounted for by either filter. The examinations were carried out at a number of globally-distributed test locations, deliberately coinciding with sites used to validate NASA SMAP soil moisture retrieval products. Initial comparisons of the two filtering approaches in a perfect model context show both filters to provide significant benefits to the soil moisture modeling problem, with the RHF edging out the EnKF as the more performant filter. The relative performance gain of the RHF was most noticeable with respect to bias mitigation metrics and to the surface-level anomaly correlation scores, an interesting result given that neither filter is formulated to explicitly accommodate a systematic bias. When additionally applying adaptive inflation, both filters showed improvement in skill but such improvements were not significant. The use of synthetic observations and lack of a bias correction implementation may have led to exaggerated results. To address this concern, the experiments were performed again but using real observations from SMAP soil moisture retrievals, with in situ validation data proxying as truth. A robust bias correction scheme was used as well to more closely approximate practices used in operational settings. The RHF continues to show better metrics than the EnKF, but no longer in a statistically significant sense. A similar result was noted with respect to inflation usage. The most likely reason for this outcome is the low observation count. The findings obtained from the data assimilation experiments in this dissertation offer insight on how best to focus development efforts in soil moisture modeling and land data assimilation.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 Estimation and Spatiotemporal Analysis of All-sky Land Surface Temperature from Multiple Satellite Data(2023) Jia, Aolin; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The daily surface temperature variability, characterizing the dispersion of day-to-day temperature anomalies, is a fundamental aspect of the climate. It can be represented by the temperature standard deviation in a week. Studies reveal that daily temperature variability is a critical determinant of societal and natural outcomes, such as public health, crop yield, economic growth, etc. Although the overall warming trend is now well established in the scientific community, previous studies have shown little consensus about changes in daily temperature variability over the globe in recent decades; this is due to limited simulation accuracy and in-situ measurement distribution. Therefore, it is urgently needed to generate a reliable, global, long-term, observation-derived, daily temperature dataset in order to analyze variability changes and potential driving factors. The Advanced Very High-Resolution Radiometer (AVHRR) data provide an exceptional chance to record long-term land surface temperature (LST) over the entire globe. However, the AVHRR LST suffers from two restrictions: cloud contamination and orbital drift. Accordingly, we develop a surface energy balance (SEB)-based algorithm to recover the LST under clouds, and a two-step method to correct the artificial spurious temperature variation due to orbital drift. In the SEB method, 1) the hypothetical LST of missing pixels is first reconstructed by assimilating dispersed clear-sky retrievals into a continuous LST time-evolving model built by reanalysis data, and 2) the reconstructed LST is then corrected by superposing the cloud effect, estimated by satellite radiation products based on SEB theory. The two-step correction includes 1) calibrating the systematic bias of diurnal temperature cycles (DTCs) simulated from reanalysis data using satellite product climatology, 2) correcting the calibrated DTCs in detail by historical AVHRR LSTs during the years 1981-2021, and averaging the corrected DTCs to get daily mean LSTs. Global, 5-km, all-sky, daily mean LSTs from 1982 to 2021 are produced for the daily variability analysis. In order to mitigate the impact of orbital drift, the SEB method is examined by MODIS and VIIRS LST products. Ground validation suggests that the cloudy-sky VIIRS LST exhibits a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, and R2 of 0.94, comparable to the accuracy of clear-sky LST and the MODIS results. Thus, the algorithm is sensor independent and also works for AVHRR data. To obtain satellite-derived DTC climatology for calibrating simulated DTCs, an optimization module is created to extend the feasibility of the SEB method at night. By collecting clear-sky LSTs from geostationary satellite sensors and two MODIS sensors, global, hourly, 5 km, all-sky LSTs from 2011 to 2021 are produced. The overall RMSE of the hourly LSTs is 3.38 K, with a bias of −0.53 K based on 197 global sites. Finally, after integrating the SEB method and two-step correction method, the target AVHRR LST is recovered with an RMSE of 1.97 K over the globe and few biases. Spatiotemporal analysis of the AVHRR LST suggests that the globally averaged daily LST variability does not have a significant trend from 1982 to 2021 under the global warming background, whereas it showed diverse variation both regionally and seasonally. A significant decrease/increase is detected at high/low latitudes, which matches previous simulation conclusions. However, contrary to the simulation, it reveals significant variability increases in the mid-latitudes, such as the western US, the Mediterranean Basin, and northern China. Historical auxiliary observations indicate that the variability decrease at high-latitudes is driven by downward longwave (DLW) radiation. Arctic amplification mitigates cold temperature anomalies at high latitudes in winter. The enhanced atmospheric convection in the tropics causes the increasing variability of cloud cover and downward shortwave radiation (DSR), and the LST variability has also increased. Climate internal variability, DLW, and DSR all show considerable impact at mid-latitudes. This study proposed innovative cloud-sky LST estimation and orbital drift correction methods. The first global, all-sky, 5-km, daily mean LST product (1982 - 2021) was generated, which shows great potential for long-term energy budget and hydrological cycling analysis. Furthermore, the study fills the knowledge gap about the unknown daily temperature variability trend over the globe and provides an attribution based on historical observations, which will assist the community in understanding the mechanism of high-frequency temperature change, improving model prediction, and coordinating resources for extreme weather adaptation.Item High Resolution Remote Sensing Observations of Summer Sea Ice(2022) Buckley, Ellen Margaret; Farrell, Sinéad L; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)During the Arctic summer melt season, the sea ice transitions from a consolidated ice pack with a highly reflective snow-covered surface to a disintegrating unconsolidated pack with melt ponds spotting the ice surface. The albedo of the Arctic decreases by up to 50%, resulting in increased absorption of solar radiation, triggering the positive sea ice albedo feedback that further enhances melting. Summer melt processes occur at a small scale and are required for melt pond parameterization in models and quantifying albedo change. Arctic-wide observations of melt features were however not available until recently. In this work we develop original techniques for the analysis of high-resolution remote sensing observations of summer sea ice. By applying novel algorithms to data acquired from airborne and satellite sensors onboard IceBridge, Sentinel-2, WorldView and ICESat-2, we derive a set of parameters that describe melt conditions on Arctic sea ice in summer. We present a new, pixel-based classification scheme to identify melt features in high-resolution summer imagery. We apply the classification algorithm to IceBridge Digital Mapping System data and find a greater melt pond fraction (25%) on sea ice in the Beaufort and Chukchi Seas, a region consisting of predominantly first year ice, compared to the Central Arctic, where the melt pond fraction is 14% on predominantly multiyear ice. Expanding the study to observations acquired by the Sentinel-2 Multispectral Instrument, we track the variability in melt pond fraction and sea ice concentration with time, focusing on the anomalously warm summer of 2020. So as to obtain a three-dimensional view of the evolution of summer melt we also exploit ICESat-2 surface elevation measurements. We develop and apply the Melt Pond Algorithm to track ponds in ICESat-2 photon cloud data and derive their depth. Pond depth measurements in conjunction with melt pond fraction and sea ice concentration provide insights into the regional patterns and temporal evolution of melt on summer sea ice. We found mean melt pond fraction increased rapidly in the beginning of the melt season, peaking at 16% on 24 June 2020, while median pond depths increased steadily from 0.4 m at the beginning of the melt season, to peaking at 0.97 m on 16 July, even as melt pond fraction had begun to decrease. Our findings may be used to improve parameterization of melt processes in models, quantify freshwater storage, and study the partitioning of under ice light.Item QUANTIFYING EFFECTS OF SEASONAL INUNDATION ON METHANE FLUXES FROM FORESTED FRESHWATER WETLANDS(2021) Hondula, Kelly Lynn; Palmer, Margaret A; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Developing effective strategies for reducing methane and other greenhouse gas emissions requires a quantitative understanding of their global sources and sinks. Decomposition of organic matter in wet soils is one of the largest sources of methane to the atmosphere, but it is a highly variable process that remains difficult to quantify because we lack a predictive understanding of how environmental factors control methane emissions in wetlands. Hydrology is one of the most important factors controlling methane production wetlands along with temperature and vegetation, however it is unclear how to relate aspects of a wetland’s hydrologic regime to the timing, magnitude, and spatial extent of its methane emissions. Furthermore, discrepancies between the magnitude of global methane emissions calculated using different techniques indicate that current methods for measuring the extent and dynamics of wetland areas in global models may not adequately represent processes controlling methane cycling in wetlands and other small water bodies. I studied the role of seasonal hydrologic variability on methane emissions from forested mineral soil wetlands to inform modeling techniques at different scales. In Chapter 1, I show the importance of inundation extent and duration as major drivers of wetland methane emissions, that methane fluxes have a non-linear relationship with water level, and that methane fluxes are higher when water levels are falling rather than rising. In Chapter 2, I demonstrate a new technique for calculating methane emissions using high resolution satellite data to quantify wetland inundation time series, and some limits of current technology for modeling surface water dynamics in forested wetlands. Chapter 3 presents and applies a modeling framework for quantifying hydrologic fluxes of methane in the context of common forms of wetland restoration In combination, these studies establish how and why quantifying the hydrologic regime of seasonally inundated forested wetlands enables a more accurate estimation of methane emissions at multiple scales, that water level drawdown associated with the natural hydrologic regime of forested wetlands considerably reduces methane producing areas, and that improved methods for detecting and modeling surface water dynamics in low relief landscapes will improve our ability to quantify methane emissions.Item Satellite Remote Sensing of Smoke Particle Optical Properties, Their Evolution and Controlling Factors(2021) Junghenn, Katherine Teresa; Li, Zhanqing; Kahn, Ralph A.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The optical and chemical properties of biomass burning (BB) smoke particles greatly affect the impact wildfires have on climate and air quality. Previous work has demonstrated some links between smoke properties and factors such as fuel type and meteorology. However, the factors controlling BB particle speciation at emission are not adequately understood, nor are those driving particle aging during atmospheric transport. As such, modeling wildfire smoke impacts on climate and air quality remains challenging. The potential to provide robust, statistical characterizations of BB particles based on ecosystem and ambient conditions with remote sensing data is investigated here. Space-based Multi-angle Imaging Spectrometer (MISR) observations, combined with the MISR Research Aerosol (RA) algorithm and the MISR Interactive Explorer (MINX) tool, are used to retrieve smoke plume aerosol optical depth (AOD), and to provide constraints on plume vertical extent, smoke age, and particle size, shape, light-absorption, and absorption spectral dependence. These capabilities are evaluated using near-coincident in situ data from two aircraft field campaigns. Results indicate that the satellite retrievals successfully map particle-type distributions, and that the observed trends in retrieved particle size and light-absorption can be reliably attributed to aging processes such as gravitational settling, oxidation, secondary particle formation, and condensational growth. The remote-sensing methods are then applied to numerous wildfire plumes in Canada and Alaska that are not constrained by field observations. For these plumes, satellite measurements of fire radiative power and land cover characteristics are also collected, as well as short-term meteorological data and drought index. We find statistically significant differences in the retrieved smoke properties based on land cover type, with fires in forests producing the tallest and thickest plumes containing the largest, brightest particles, and fires in savannas and grasslands exhibiting the opposite. Additionally, the inferred dominant aging mechanisms and the timescales over which they occur vary between land types. This work demonstrates the potential of remote sensing to constrain BB particle properties and the mechanisms governing their evolution, over entire ecosystems. It also begins to realize this potential, as a means of improving regional and global climate and air quality modeling in a rapidly changing world.Item APPLICATION OF RECLAIMED WASTEWATER FOR AGRICULTURAL IRRIGATION: DEVELOPING A DECISION SUPPORT TOOL USING SPATIAL MULTI-CRITERIA DECISION ANALYSIS(2020) Paul, Manashi; Negahban-Azar, Masoud; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Intensified climate variability, depleting groundwater, and escalating water demand create severe stress on high-quality freshwater sources used for agricultural irrigation. These challenges necessitate the exploration of alternative water sources such as reclaimed water to reduce the pressure on freshwater sources. To do so, it is key to investigate the spatial pattern of areas that are more suitable for water reuse to determine the potential of reclaimed wastewater use for irrigation. This study provides a systematic decision-analysis framework for the decision-makers using an integrated process-based hydrologic model for sustainable agricultural water management. The outcomes of this study provide evidence of the feasibility of reclaimed wastewater use in the agricultural sector. The two objectives of this study were to: 1) identify the locations that are most suitable for the reclaimed wastewater use in agriculture (hotspots); and 2) develop the watershed-scale models to assess the agricultural water budget and crop production using different water conservation scenarios including reclaimed wastewater use. To achieve the first objective, a decision-making framework was developed by using the Geographic Information System and Multi-Criteria Decision Analysis (GIS-MCDA). This framework was then tested in the Southwest (California), and the Mid-Atlantic (Maryland) regions. Based on WWTPs’ proximity, sufficient water availability, and appropriate treatment process of the treated wastewater, the “Most Suitable” and “Moderately Suitable” agricultural areas were found to be approximately 145.5 km2, and 276 km2 for California and, 26.4 km2 and 798.8 km2 for Maryland, respectively. These results were then used to develop the hydrologic models to examine water conservation and water reuse scenarios under real-world conditions, using the Soil and Water Assessment Tool (SWAT). In California, the combination of auto irrigation (AI) and regulated deficit irrigation (RDI) resulted in higher WP for both almond and grape (> 0.50 kg/m3). Results also suggested that the wastewater reuse in almond and grape irrigation could reduce groundwater consumption more than 74% and 90% under RDI and AI scenarios, respectively. For Maryland, model simulations suggested that the green water productivity (only rainfall) can be improved up to 0.713 kg/m3 for corn and 0.37 kg/m3 for soybean under the reclaimed wastewater use scenario.Item Interdisciplinary Geospatial Assessment of Malaria Exposure in Ann Township, Myanmar(2020) Hall, Amanda Hoffman; Loboda, Tatiana V; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Despite considerable progress toward malaria elimination in Myanmar, challenges remain owing to the persistence of complex focal transmission reservoirs. Nearly all remaining infections are clinically silent, rendering them invisible to routine monitoring. Moreover, limited knowledge of population distributions and human activity on the landscape in remote regions of Myanmar hinders the development of targeted malaria elimination approaches, as advocated by the World Health Organization (WHO). This is especially true for Ann Township, a remote region of Myanmar with a high malaria burden, where a comprehensive understanding of local exposure, which includes the characterization of environmental settings and land use activities, is crucial to developing successful malaria elimination strategies. In this dissertation, I present an interdisciplinary approach that combines satellite earth observations with two separate on-the-ground surveys to assess human exposure to malaria at multiple scales. First, I mapped rural settlements using a fusion of Landsat imagery and multi-temporal auxiliary data sensitive to human activity patterns with a classification accuracy of 93.1%. A satellite data-based map of land cover and land use was then used to assess landscape-scale malaria exposure as a function of environmental settings for a subset of ten villages where a malaria prevalence survey was carried out. While multiple significant associations were discovered, the relationship found between malaria exposure and satellite-measured village forest cover was the most significant. Finally, a separate detailed survey that explored a variety of land use activities, including their frequency and duration along with testing for clinical or subclinical malaria, was used to identify and quantify factors promoting an individual’s likelihood of malaria infection regardless of the environmental settings. This analysis established strong associations between malaria and individual land use activities that bring respondents into direct contact with forested areas. These results highlight that the current Myanmar malaria elimination strategies, which focus on prevention from within the home (i.e., bednets and indoor spraying), are no longer sufficient to remove remaining malaria reservoirs in the country. A paradigm shift in malaria elimination strategies towards targeted interventions that can disrupt malaria transmission in the settings where the exposure occurs are critical to achieving country-wide malaria elimination.Item An evaluation of methods for measuring phytoplankton and ecosystem status in the Chukchi Sea(2020) Neeley, Aimee Renee; Harris, Lora A; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation represents a three-pronged approach for evaluating ecosystem-level changes in the Chukchi Sea: 1) evaluation of uncertainties in field measurements of absorption 2) direct measurements of phytoplankton taxonomy and the community’s interaction with the environment and 3) apply existing and new remote sensing tools to measure ecosystem-level changes over large spatio-temporal scales. The first and final chapters provide context for the dissertation and conclusions. The second chapter quantifies the magnitude of uncertainty within multiple methods for measuring particle absorption. The light field exiting the surface ocean is measured by satellite instruments as ocean color and is impacted by water column absorption. Biogeochemically-relevant products, such as phytoplankton and particle absorption are derived from the light field using algorithms. Therefore, accurate measurements of absorption are critical to algorithm development and validation. I employed a multi-method approach to estimate the precision of measuring optical density of particles on a filter pad using two common spectrophotometric methods, and assessed the uncertainty of the computational techniques for estimating ap. The uncertainty ranged from 7.48%-119%. Values of ap at 555 nm and 670 nm exhibited the highest values of uncertainty. Poor performance of modeled ap compared to measured ap suggests the uncertainties are propagated into bio-optical algorithms. The third chapter investigates the consequences of earlier seasonal sea ice retreat and a longer sea-ice-free season on phytoplankton community composition. The timing of sea ice retreat, light availability and sea surface stratification largely control the phytoplankton community composition in the Chukchi Sea. This region is experiencing a significant warming trend, decrease in sea ice cover, and a documented decline in annual sea ice persistence and thickness over the past several decades. I applied multivariate statistical techniques to elucidate the mechanisms that relate environmental variables to phytoplankton community composition in the Chukchi Sea using data collected during a single field campaign in the summer of 2011. Three phytoplankton groups emerged that were correlated with sea ice, sea surface temperature, nutrients, salinity and light. The fourth chapter evaluates a new remote sensing tool for its utility to trace trends in ocean color over the summer months, 2003-2018, in the Chukchi Sea. The apparent visible wavelength reduces an ocean color spectrum to one number that represents the apparent color of the water. Median trend analysis of apparent visible wavelength and Chlorophyll a indicated that an ecosystem-level change in phytoplankton and nonalgal particles has occurred, correlated with the loss of sea ice.