Remote Sensing of Environmental Change in Arctic Coastal Aquatic Ecosystems
PI: Dr. Wes Moses -- NRL
We propose to develop a remote sensing capability to monitor ecosystem changes in coastal Arctic waters, in particular, changes in primary production, due to changing riverine fluxes caused by recent warming trends. We will focus on coastal waters around Colville, Kuparuk, and Sagavanirktok rivers - three of the largest rivers in the North Slope of Alaska. Changing riverine fluxes affect light and nutrient availability - two most critical factors affecting primary production - in a complex manner. Understanding how riverine materials transported into the coastal Arctic mix with ocean waters and affect primary production and phytoplankton community structure using a combination of in situ data, remote sensing measurements, and modeling is the overarching objective of this effort.
We will integrate existing data with limited new measurements to characterize ecosystem variability and changes in coastal Arctic waters. Past and ongoing efforts that will be leveraged include results from two NASA funded projects focused on characterizing material transport along Arctic rivers, data from the ongoing Beaufort Lagoon Ecosystem Long Term Ecological Research program, and data from a separate project funded by the Naval Research Laboratory (NRL) to quantify optical properties of coastal Arctic waters in the same study area.
We will use in situ measurements of constituent concentrations and composition, inherent optical properties, in situ radiometry and airborne and spaceborne hyperspectral and multispectral data to retrieve essential biogeochemical quantities from remote sensing data and use them within a modeling framework. Remote sensing assets that will be used include airborne hyperspectral data from the Airborne Visible / Infrared Imaging Spectrometer - Next Generation sensor and MicroSHINE, a custom-made sensor owned by NRL, high-resolution spaceborne data from WorldView-2/3, Landsat-8, and Sentinel- 2 satellites, and ocean color data from MODIS, VIIRS, and OLCI. The high-resolution airborne and spaceborne data will be used to assess spatial scales of mixing in the coastal ocean and investigate sub-pixel variability in current multi-spectral ocean color sensors and future high-spectral and high-spatial resolution sensors such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and Surface Biology and Geology (SBG) missions.
Arctic Deltas and Coastal Margins as Buffers and Transformers of Carbon Along a Rapidly Changing Land-Ocean Continuum
PI: Dr. Maria Tzortziou--CCNY
Warming at twice the global rate is the new 'normal' for the Arctic. Its ice is melting, its permafrost is thawing, its ocean is acidifying. Freshwater and carbon cycles are intensifying, with direct impacts on ecosystems and resources. Changes on the Arctic land and in the Arctic Ocean are expected to continue in the future. Yet, estimates of the changing carbon fluxes and transformations across these inherently linked systems the land, the ocean, and the rivers that connect them are poorly constrained, increasing uncertainties in our understanding and modeling of their impacts on local and larger scale ocean community composition, acidification, and productivity.
Bringing together a diverse team of satellite researchers, experimentalists, and modelers this study will address this high priority research area. Focused on the Yukon River-Bering Sea continuum one of the most productive areas for Alaska fisheries and simultaneously a ''ground zero'' for climate change we will address three key science questions: (i) How do changes in hydrological forcing and terrestrial sources affect the transport and export of particulate and dissolved organic and inorganic carbon along this rapidly changing land-ocean continuum? (ii) What is the relative importance and interplay of physical and biogeochemical processes (flocculation, microbial, photochemistry) in transforming carbon as it moves from the Alaskan terrestrial landscape to the Arctic Ocean? (iii) How will changing environmental conditions and future pressures (e.g., increasing temperatures, shifting river flow, and increasing levels of atmospheric carbon dioxide) affect these processes and their impact on carbon fluxes and cycles under various scenarios?
The proposed effort uniquely integrates new and existing field datasets, process experiments, and satellite observations with a novel ecosystem model to improve quantitative and predictive understanding of the coupled physical-biogeochemical processes that transform organic and inorganic carbon as it moves from the Alaskan terrestrial landscape to the Yukon River, delta, plume and adjacent northern Bering Sea. Incorporating strong collaborations with indigenous Alaskan communities, Alaska Department of Fish and Game, and NOAA Alaska Fisheries Science Center, we will combine existing datasets with process experiments, ongoing oceanographic research surveys, and targeted new measurements encompassing aquatic, soil, sediment and terrestrial vegetation endmembers. Remote sensing and modeling efforts will inform the location and timing of new field measurements. Satellite and field data will improve model parameterizations and enable hindcasting, scenario testing, and scaling of processes in time and space, thus reducing uncertainty in model predictions of carbon cycles in a changing climate.
The proposed study responds to this solicitation's Sub-element 3.1 on: ''Carbon Fluxes between and within Land, Freshwater, and Marine Systems'', specifically addressing sub-topics 3.1.1. The Land-Ocean Continuum, and 3.1.3. Fluxes and Biogeochemistry of Carbon within Oceans. The proposed collection of new hyperspectral datasets and development of improved algorithms in high latitude environments will allow to constrain uncertainties in key carbon parameters and biogeochemical fluxes in the context of preparing for future sensors, including the high spatial resolution Surface Biology and Geology (SBG) Designated Observable and the upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission that is expected to make unprecedented observations of the changing Arctic ocean ecology and biogeochemistry.
Integration of Observations and Models into Machine Learning for Coastal Water Quality
Novel Data Analysis Development
PI: Dr. Stephanie Schollaert Uz -- NASA GSFC
Coastal areas are impacted by population growth, development, aging infrastructure, and extreme weather events causing greater runoff from land. Monitoring water quality is an urgent societal need. A growing fleet of satellites at multiple resolutions provide the ability to monitor large coastal areas using big data analytics and machine learning. Within our AIST18 project, we started working closely with state agencies who manage water resources around the Chesapeake Bay. We propose to build upon these activities to improve the integration of assets to monitor water quality and ecosystem properties and how they change over time and space. Initially we are taking advantage of technologies and data collected in and around the Chesapeake Bay, with a plan to expand to other watersheds.
As the largest estuary in North America, the Chesapeake Bay receives runoff from approximately 100,000 tributaries, carrying sediment, fertilizer, and pollutants from farms, developed communities, urban areas, and forests. These constituents degrade water quality and contribute to its optical complexity. Resource managers tasked with enforcing pollution reduction goals for these point and non-point sources are also challenged by shrinking budgets with which to monitor multiple aspects of the ecosystems while the use of the Bay for recreation, fishing, and aquaculture is increasing. Of particular concern are the increasing number of harmful algal blooms (HABs) and septic tank leaks due to aging infrastructure and rising sea level [Wolny et al., 2020; Mitchell et al., 2021]. State agencies already work closely with NOAA and EPA and are looking to NASA to apply advanced technologies to further improve their natural resource management.
Our AIST18 project demonstrated promising results with multispectral optical, medium spatial resolution satellite data trained using geophysical model variables within a machine learning (ML) architecture by extracting multi-source feature maps. The nearshore environment demands finer spatial resolution than government assets alone can provide, thus we plan to build on this work by utilizing higher spatial resolution data from commercial satellites. Following our demonstration of feasibility using medium resolution satellite imagery from one sensor, we will now derive feature maps from many sensors of varying spatial, spectral, and temporal resolution. These can be effectively merged regardless of initial source resolution at progressively higher (hierarchical) contextual levels by fusing at multiple layers within the ML model. Heterogeneous feature maps can be adaptively scored and weighted, which influences their significance in the resulting predictions. We plan to analyze higher spectral information from in situ inherent optical property observations to determine the minimum set of requirements for remote sensing of water quality, e.g. water clarity, phytoplankton blooms, and the detection of pollutants. In situ observations will facilitate ML training using higher spectral and spatial resolution imagery from commercial satellites at the coast. We are also collaborating with community experts to evaluate the utility of hyperspectral remote sensing for detecting aquatic features not discernable through multispectral imaging, such as phytoplankton community structure and the likelihood of harmful blooms. In situ observations will facilitate ML training using hyperspectral and higher spatial resolution imagery from commercial satellites at the coastal margins and land-water interface. Finally, we aim to eventually integrate upstream assets of land cover classification, elevation, vertical land motion, and hydrology as inputs to the ML architecture, leveraging other projects that characterize the watershed and runoff of sediments and nutrients to coastal water bodies. Adapting our process to an open science framework will facilitate future integration of these data beyond the aquatic community.
Integrating lateral fluxes into CMS ocean carbon estimates
Co-Investigator and CMS Science Team Member
PI: Dr. Cecile Rousseaux -- NASA GSFC
Climate, weather, and land characteristics directly affect the concentration and composition of organic and inorganic matter, including carbon, delivered to the rivers and ultimately to the oceans. Although the uptake of carbon dioxide by phytoplankton at the surface of the ocean and its recycling into dissolved organic carbon and nutrients are routinely represented in models, the lateral transfer of carbon from land to oceans is severely underrepresented or completely missing from current models. This is sorely needed for carbon accounting and particularly critical in the global assessment and estimates of carbon stocks. In this project we improve existing CMS products by adding this transfer and transformation of organic and inorganic matter as well as quantifying the effects of land use and changes on the resulting global ocean carbon flux. An existing terrestrial biosphere model (Ecosystem Demography model, ED) combined with the Land-Use Harmonization (LUH) dataset provide fluxes of carbon and nutrients from land to rivers under varying land use and land cover change scenarios. The River-Estuary model transports and transforms aqueous forms of carbon and nutrients to represent the lateral fluxes of carbon and nutrients from rivers to the NASA Ocean Biogeochemical Model (NOBM) currently used to produce the CMS global carbon fluxes. This project will add critical components and processes to the current CMS-flux products by adding the effects of land use and change on the transfer of carbon to rivers, the transport and transformation of organic and inorganic matter in rivers and the effects these processes have on the global ocean carbon budget. The modeling tools and output developed by this project will directly feed into the global carbon budget and be adopted by stakeholders in the ocean carbon sector, among others, who will provide feedback that will be used to codevelop monitoring tools and mitigation solutions.