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1. Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges

   https://doi.org/10.1029/2021JG006697  

   Nelson, Peter R.; Maguire, Andrew J.; Pierrat, Zoe; Orcutt, Erica L.; Yang, Dedi; Serbin, Shawn; Frost, Gerald V.; Macander, Matthew J.; Magney, Troy S.; Thompson, David R.; et al (February 2022, Journal of Geophysical Research: Biogeosciences)

   Abstract Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including National Aeronautics and Space Administration’s Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low‐stature plants and very fine‐scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long‐term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi‐scale spectroscopy, from lab studies to satellites that enable frequent and continuous long‐term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics. 

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2. Designing Ocean Vision AI: An Investigation of Community Needs for Imaging-based Ocean Conservation

   https://doi.org/10.1145/3544548.3580886  

   Crosby, Alison; Orenstein, Eric Coughlin; Poulton, Susan E; Bell, Katherine L.C.; Woodward, Benjamin; Ruhl, Henry; Katija, Kakani; Forbes, Angus G. (April 2023, Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems)

   Ocean scientists studying diverse organisms and phenomena increasingly rely on imaging devices for their research. These scientists have many tools to collect their data, but few resources for automated analysis. In this paper, we report on discussions with diverse stakeholders to identify community needs and develop a set of functional requirements for the ongoing development of ocean science-specific analysis tools. We conducted 36 in-depth interviews with individuals working in the Blue Economy space, revealing four central issues inhibiting the development of effective imaging analysis monitoring tools for marine science. We also identified twelve user archetypes that will engage with these services. Additionally, we held a workshop with 246 participants from 35 countries centered around FathomNet, a web-based open-source annotated image database for marine research. Findings from these discussions are being used to define the feature set and interface design of Ocean Vision AI, a suite of tools and services to advance observational capabilities of life in the ocean. 

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3. Comparison of Soil Moisture Content Retrieval Models Utilizing Hyperspectral Goniometer Data and Hyperspectral Imagery From an Unmanned Aerial System

   https://doi.org/10.1029/2023JG007381  

   Nur, Nayma Binte; Bachmann, Charles M. (June 2023, Journal of Geophysical Research: Biogeosciences)

   Abstract To understand surface biogeophysical processes, accurately evaluating the geographical and temporal fluctuations of soil moisture is crucial. It is well known that the surface soil moisture content (SMC) affects soil reflectance at all solar spectrum wavelengths. Therefore, future satellite missions, such as the NASA Surface Biology and Geology mission, will be essential for mapping and monitoring global soil moisture changes. Our study compares two widely used moisture retrieval models: the multilayer radiative transfer model of soil reflectance (MARMIT) and the soil water parametric (SWAP)‐Hapke model. We evaluated the SMC retrieval accuracy of these models using unmanned aerial systems (UAS) hyperspectral imagery and goniometer hyperspectral data. Laboratory analysis employed hyperspectral goniometer data of sediment samples from four locations reflecting diverse environments, while field validation used hyperspectral UAS imaging and coordinated ground truth collected in 2018 and 2019 from a barrier island beach at the Virginia Coast Reserve Long‐Term Ecological Research site. The (SWAP)‐Hapke model achieves comparable accuracy to MARMIT using laboratory hyperspectral data but is less accurate when applied to UAS hyperspectral imagery than the MARMIT model. We proposed a modified version of the (SWAP)‐Hapke model, which achieves better results than MARMIT when applied to laboratory spectral measurements; however, MARMIT's performance is still more accurate when applied to UAS imagery. These results are likely due to differences in the models' descriptions of multiply‐scattered light and MARMIT's more detailed description of air‐water interactions. 

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4. Topological Generality and Spectral Dimensionality in the Earth Mineral Dust Source Investigation (EMIT) Using Joint Characterization and the Spectral Mixture Residual

   https://doi.org/10.3390/rs15092295  

   Sousa, Daniel; Small, Christopher (May 2023, Remote Sensing)

   NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) mission seeks to use spaceborne imaging spectroscopy (hyperspectral imaging) to map the mineralogy of arid dust source regions. Here we apply recent developments in Joint Characterization (JC) and the spectral Mixture Residual (MR) to explore the information content of data from this novel mission. Specifically, for a mosaic of 20 spectrally diverse scenes, we find: (1) a generalized three-endmember (Substrate, Vegetation, Dark; SVD) spectral mixture model is capable of capturing the preponderance (99% in three dimensions) of spectral variance with low misfit (99% pixels with <3.7% RMSE); (2) manifold learning (UMAP) is capable of identifying spatially coherent, physically interpretable clustering relationships in the spectral feature space; (3) UMAP yields results that are at least as informative when applied to the MR as when applied to raw reflectance; (4) SVD fraction information usefully contextualizes UMAP clustering relationships, and vice-versa (JC); and (5) when EMIT data are convolved to spectral response functions of multispectral instruments (Sentinel-2, Landsat 8/9, Planet SuperDove), SVD fractions correlate strongly across sensors, but UMAP clustering relationships for the EMIT hyperspectral feature space are far more informative than for simulated multispectral sensors. Implications are discussed for both the utility of EMIT data in the near-term and for the potential of high signal-to-noise (SNR) spaceborne imaging spectroscopy more generally, to transform the future of optical remote sensing in the years and decades to come. 

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5. Bathymetric data from T8 Lake, Toolik Field Station, Alaska (2020)

   https://doi.org/10.18739/A2XS5JJ05  

   McPherson, Rowan (January 2021, NSF Arctic Data Center)

   Toolik Field Station is located in the foothills of the Brooks Range on Alaska's North Slope and is administered by the Institute of Arctic Biology at the University of Alaska Fairbanks (UAF). Toolik Field Station was first established to support an aquatic program designed to obtain base-line data on the North Slope and inland coastal ponds in 1975. Research has expanded over the years to include terrestrial, atmospheric, vertebrate and other taxa, making Toolik home to a long running and diversified body of arctic ecology. The station currently supports a long-standing and rapidly expanding community of scientists and research projects representing individual and collaborative efforts from United States and international institutions. Growth of Toolik-based science, the number of researchers, and the facility from a tented camp to a research station prompted the establishment of the Toolik GIS (Geographical Information Systems) and Remote Sensing (RS) Program as part of the 5 year Cooperative Agreement between the National Science Foundation (NSF) and the Institute of Arctic Biology, University of Alaska Fairbanks in 2001 (NSF Award Number 9981914). The mission of this Program is to facilitate and enhance arctic research, and to increase research and management efficiency, effectiveness and capability. This is accomplished: 1) through Information Technology (IT) and GIS-RS support of administrative and management infrastructure and production of planning tools for land management and permitting, and 2) through direct consultation and GIS, RS, and Global Posititioning System (GPS) support services to scientists. ToolikGIS provides a rich spatial geodatabase, project-specific data development, spatial analyses, consultation and documentation. 

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