Land surface temperature (LST) is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, stray light artifacts were observed in Landsat-8 TIRS data, mostly affecting Band 11, currently making the split-window technique impractical for retrieving surface temperature without requiring atmospheric data. In this study, a single-channel methodology to retrieve surface temperature from Landsat TM and ETM+ was improved to retrieve LST from Landsat-8 TIRS Band 10 using near-surface air temperature (T_a) and integrated atmospheric column water vapor (w) as input data. This improved methodology was parameterized and successfully evaluated with simulated data from a global and robust radiosonde database and validated with in situ data from four flux tower sites under different types of vegetation and snow cover in 44 Landsat-8 scenes. Evaluation results using simulated data showed that the inclusion of T_a together with w within a single-channel scheme improves LST retrieval, yielding lower errors and less bias than models based only on w. The new proposed LST retrieval model, developed with both w and T_a, yielded overall errors on the order of 1 K and a bias of −0.5 K validated against in situ data, providing a better performance than other models parameterized using w and T_a or only w models that yielded higher error and bias.

Climate change coupled with an intensifying wildfire regime is becoming an important driver of permafrost loss and ecosystem change in the northern boreal forest. There is a growing need to understand the effects of fire on the spatial distribution of permafrost and its associated ecological consequences. We focus on the effects of fire a decade after disturbance in a rocky upland landscape in the interior Alaskan boreal forest. Our main objectives were to (1) map near-surface permafrost distribution and drainage classes and (2) analyze the controls over landscape-scale patterns of post-fire permafrost degradation. Relationships among remote sensing variables and field-based data on soil properties (temperature, moisture, organic layer thickness) and vegetation (plant community composition) were analyzed using correlation, regression, and ordination analyses. The remote sensing data we considered included spectral indices from optical datasets (Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI)), the principal components of a time series of radar backscatter (Advanced Land Observing Satellite—Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR)), and topographic variables from a Light Detection and Ranging (LiDAR)-derived digital elevation model (DEM). We found strong empirical relationships between the normalized difference infrared index (NDII) and post-fire vegetation, soil moisture, and soil temperature, enabling us to indirectly map permafrost status and drainage class using regression-based models. The thickness of the insulating surface organic layer after fire, a measure of burn severity, was an important control over the extent of permafrost degradation. According to our classifications, 90% of the area considered to have experienced high severity burn (using the difference normalized burn ratio (dNBR)) lacked permafrost after fire. Permafrost thaw, in turn, likely increased drainage and resulted in drier surface soils. Burn severity also influenced plant community composition, which was tightly linked to soil temperature and moisture. Overall, interactions between burn severity, topography, and vegetation appear to control the distribution of near-surface permafrost and associated drainage conditions after disturbance.

The Colville is an Arctic river in the Alaska North Slope. The residents of Nuiqsut rely heavily on the Colville for their subsistence needs. Increased erosion has been reported on the Colville, especially along bluffs, which shaped the goals of this study: to use remote sensing techniques to map and quantify erosion rates and the volume of land loss at selected bluff sites along the main channel of the Colville, and to assess the suitability of automated methods of regional erosion monitoring. We used orthomosaics from high resolution aerial photos acquired in 1955 and 1979/1982, as well as high resolution WorldView-2 images from 2015 to quantify long-term erosion rates and the cubic volume of erosion. We found that, at the selected sites, erosion rates averaged 1 to 3.5 m per year. The erosion rate remained the same at one site and increased from 1955 to 2015 at two of the four sites. We estimated the volume of land loss to be in the magnitude of 166,000 m³ to 2.5 million m³ at our largest site. We also found that estimates of erosion were comparable for manual hand-digitized and automated methods, suggesting our automated method was effective and can be extended to monitor erosion at other sites along river systems that are bordered by bluffs.

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.

Alaska’s Arctic and boreal regions, largely dominated by tundra and boreal forest, are witnessing unprecedented changes in response to climate warming. However, the intensity of feedbacks between the hydrosphere and vegetation changes are not yet well quantified in Arctic regions. This lends considerable uncertainty to the prediction of how much, how fast, and where Arctic and boreal hydrology and ecology will change. With a very sparse network of observations (meteorological, flux towers, etc.) in the Alaskan Arctic and boreal regions, remote sensing is the only technology capable of providing the necessary quantitative measurements of land–atmosphere exchanges of water and energy at regional scales in an economically feasible way. Over the last decades, the University of Alaska Fairbanks (UAF) has become the research hub for high-latitude research. UAF’s newly-established Hyperspectral Imaging Laboratory (HyLab) currently provides multiplatform data acquisition, processing, and analysis capabilities spanning microscale laboratory measurements to macroscale analysis of satellite imagery. The specific emphasis is on acquiring and processing satellite and airborne thermal imagery, one of the most important sources of input data in models for the derivation of surface energy fluxes. In this work, we present a synergistic modeling framework that combines multiplatform remote sensing data and calibration/validation (CAL/VAL) activities for the retrieval of land surface temperature (LST). The LST Arctic Dataset will contribute to ecological modeling efforts to help unravel seasonal and spatio-temporal variability in land surface processes and vegetation biophysical properties in Alaska’s Arctic and boreal regions. This dataset will be expanded to other Alaskan Arctic regions, and is expected to have more than 500 images spanning from 1984 to 2012.