This paper reviews the current status of the use of remote sensing for the detection, extraction and monitoring of coastlines. The review takes the US system as an example. However, the issues at hand can be applied to any other part of the world. Visual interpretation of airborne remote sensing data is still widely and popularly used for coastal delineation. However, a variety of remote sensing data and techniques are available to detect, extract and monitor the coastline. The developed techniques have reached a level of maturity such that they are applied in operational settings.

A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, and speed to minimize the strong bidirectional reflectance distribution function (BRDF) effects present at high latitudes and establishing improved data processing workflows for the high-latitude environments. Hyperspectral images were acquired on two clear-sky days in early September, 2018, over three pilot study areas that together represented a wide variety of vegetation and wetland environments. Steps to further minimize BRDF effects and achieve a higher geometric accuracy were added to adapt and improve the Hyspex data processing workflow, developed by the German Aerospace Center (DLR), for high-latitude environments. One-meter spatial resolution hyperspectral images, that included a subset of only 120 selected spectral bands, were used for wetland mapping. A six-category legend was established based on previous U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service (USFWS) information and maps, and three different classification methods—hybrid classification, spectral angle mapper, and maximum likelihood—were used at two selected sites. The best classification performance occurred when using the maximum likelihood classifier with an averaged Kappa index of 0.95; followed by the spectral angle mapper (SAM) classifier with a Kappa index of 0.62; and, lastly, by the hybrid classifier showing lower performance with a Kappa index of 0.51. Recommendations for improvements of future work include the concurrent acquisition of LiDAR or RGB photo-derived digital surface models as well as detailed spectra collection for Alaska wetland cover to improve classification efforts.

Recent Arctic warming has led to changes in the hydrological cycle. Circum-Arctic and circumboreal ecosystems are showing evidence of “greening” and “browning” due to temperature warming leading to shrub encroachment, tree mortality and deciduousness. Increases in latent heat flux from increased evapotranspiration rates associated with deciduous-dominated ecosystems may be significant, because deciduous vegetation has extremely high-water use and water storage capacity compared to coniferous and herbaceous plant species. Thus, the impact of vegetation change in boreal ecosystems on regional surface energy balance is a significant knowledge gap that must be addressed to better understand observed trends in water use/availability and tree mortality. To this end, output from a two-source energy balance model (TSEB) with modifications for high latitude boreal ecosystems was evaluated using flux tower measurements and Terra/Aqua MODIS remote sensing data collected over the two largest boreal forest types in Alaska (birch and black spruce). Data under clear and overcast days and from leaf-out to senescence from 2012 to 2016 were used for validation. Using flux tower observations and local model inputs, modifications to the model formulation for soil heat flux, net radiation partitioning, and canopy transpiration were required for the boreal forest. These improvements resulted in a mean absolute percent difference of around 23% for turbulent daytime fluxes when surface temperature from the flux towers was used, similar to errors reported in other studies conducted in warmer climates. Results when surface temperature from Terra/Aqua MODIS estimates were used as model input suggested that these model improvements are pertinent for regional scale applications. Vegetation indices and LAI time-series from the Terra/Aqua MODIS products were confirmed to be appropriate for energy flux estimation in the boreal forest to describe vegetation properties (LAI and green fraction) when field observations are not available. Model improvements for boreal settings identified in this study will be implemented operationally over North America to map surface energy fluxes at regional scales using long time series of remote sensing estimates as part of NOAA’s GOES Evapotranspiration and Drought Information System.

This letter quantitatively compares two processing strategies for geocoding polarimetric processing results that are available using the interface between the PolSARPro and MapReady software packages. The statistical results show that there is no significant difference between the two approaches. However, closer examination of the products reveals that the segmentation derived from a geocoded coherency matrix is more effective in delineating regional patterns. Alternatively, geocoding the polarimetric segmentation leads to a result that is more sensitive to local variations at a pixel scale. Therefore, the user's application should drive the choice of which processing strategy to follow.

The FAIR Principles—Findable, Accessible, Interoperable, and Reusable—offer a widely accepted framework for improving the sharing and reuse of digital scientific data by both human and machine users. Following these principles is critical for effective scientific data stewardship, broader scientific collaboration, and compliance with federal and agency data policies. This paper, based on the work of NASA’s Open, Free, and FAIR Working Group (O’FAIR WG) under the Earth Science Data Systems Program, presents an overview of how FAIR Principles are being applied within NASA’s Earth science data landscape. It highlights ongoing progress and challenges, identifies FAIR-enabling resources, and offers recommendations and strategic actions to enhance the FAIRness of NASA-funded open and free Earth science data products. The FAIR-enabling resources identified underscore the vital role of NASA’s existing enterprise processes, standards, tools, and infrastructures in supporting FAIR implementation. Our findings show strong performance in making NASA Earth science data findable and accessible. However, further work is needed—especially in enhancing interoperability, so that different systems and tools can better understand and exchange data. This is especially important for enabling machine-driven discovery and analysis. We emphasize the importance of a balanced strategy that combines a centralized, top-down approach—focused on building enterprise-level capabilities and processes—with a decentralized, bottom-up approach driven by discipline-specific needs and community practices. We advocate for coordinated efforts to enhance (meta)data interoperability to facilitate seamless data and information sharing and exchange of Earth science data both within NASA and across other agencies managing Earth science data.