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USGS2013

Estimated global nitrogen deposition using NO 2 column density

Global nitrogen deposition has increased over the past 100 years. Monitoring and simulation studies of nitrogen deposition have evaluated nitrogen deposition at both the global and regional scale. With the development of remote-sensing instruments, tropospheric NO 2 column density retrieved from Global Ozone Monitoring Experiment (GOME) and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) sensors now provides us with a new opportunity to understand changes in reactive nitrogen in the atmosphere. The concentration of NO 2 in the atmosphere has a significant effect on atmospheric nitrogen deposition. According to the general nitrogen deposition calculation method, we use the principal component regression method to evaluate global nitrogen deposition based on global NO2 column density and meteorological data. From the accuracy of the simulation, about 70% of the land area of the Earth passed a significance test of regression. In addition, NO2 column density has a significant influence on regression results over 44% of global land. The simulated results show that global average nitrogen deposition was 0.34 g m −2 yr −1 from 1996 to 2009 and is increasing at about 1% per year. Our simulated results show that China, Europe, and the USA are the three hotspots of nitrogen deposition according to previous research findings. In this study, Southern Asia was found to be another hotspot of nitrogen deposition (about 1.58 g m −2 yr −1 and maintaining a high growth rate). As nitrogen deposition increases, the number of regions threatened by high nitrogen deposits is also increasing. With N emissions continuing to increase in the future, areas whose ecosystem is affected by high level nitrogen deposition will increase.

International Journal of Remote Sensing↗

USGS2015

A new method to generate a high-resolution global distribution map of lake chlorophyll

A new method was developed, evaluated, and applied to generate a global dataset of growing-season chlorophyll- a (chl) concentrations in 2011 for freshwater lakes. Chl observations from freshwater lakes are valuable for estimating lake productivity as well as assessing the role that these lakes play in carbon budgets. The standard 4 km NASA OceanColor L3 chlorophyll concentration products generated from MODIS and MERIS sensor data are not sufficiently representative of global chl values because these can only resolve larger lakes, which generally have lower chl concentrations than lakes of smaller surface area. Our new methodology utilizes the 300 m-resolution MERIS full-resolution full-swath (FRS) global dataset as input and does not rely on the land mask used to generate standard NASA products, which masks many lakes that are otherwise resolvable in MERIS imagery. The new method produced chl concentration values for 78,938 and 1,074 lakes in the northern and southern hemispheres, respectively. The mean chl for lakes visible in the MERIS composite was 19.2 ± 19.2, the median was 13.3, and the interquartile range was 3.90–28.6 mg m −3 . The accuracy of the MERIS-derived values was assessed by comparison with temporally near-coincident and globally distributed in situ measurements from the literature ( n = 185, RMSE = 9.39, R 2 = 0.72). This represents the first global-scale dataset of satellite-derived chl estimates for medium to large lakes.

International Journal of Remote Sensing↗

USGS2016

Radar and optical mapping of surge persistence and marsh dieback along the New Jersey Mid-Atlantic coast after Hurricane Sandy

This study combined a radar-based time series of Hurricane Sandy surge and estimated persistence with optical sensor-based marsh condition change to assess potential causal linkages of surge persistence and marsh condition change along the New Jersey Atlantic Ocean coast. Results based on processed TerraSAR-X and COSMO-SkyMed synthetic aperture radar (SAR) images indicated that surge flooding persisted for 12 h past landfall in marshes from Great Bay to Great Egg Harbor Bay and up to 59 h after landfall in many back-barrier lagoon marshes. Marsh condition change (i.e. loss of green marsh vegetation) was assessed from optical satellite images (Satellite Pour l’Observation de la Terre and Moderate Resolution Imaging Spectroradiometer) collected before and after Hurricane Sandy. High change in condition often showed spatial correspondence, with high surge persistence in marsh surrounding the lagoon portion of Great Bay, while in contrast, low change and high persistence spatial correspondence dominated the interior marshes of the Great Bay and Great Egg Harbor Bay estuaries. Salinity measurements suggest that these areas were influenced by freshwater discharges after landfall possibly mitigating damage. Back-barrier marshes outside these regions exhibited mixed correspondences. In some cases, topographic features supporting longer surge persistence suggested that non-correspondence between radar and optical data-based results may be due to differential resilience; however, in many cases, reference information was lacking to determine a reason for non-correspondence.

New Jersey↗

USGS2016

Estimating 40 years of nitrogen deposition in global biomes using the SCIAMACHY NO 2 column

Owing to human activity, global nitrogen (N) cycles have been altered. In the past 100 years, global N deposition has increased. Currently, the monitoring and estimating of N deposition and the evaluation of its effects on global carbon budgets are the focus of many researchers. NO 2 columns retrieved by space-borne sensors provide us with a new way of exploring global N cycles and these have the ability to estimate N deposition. However, the time range limitation of NO 2 columns makes the estimation of long timescale N deposition difficult. In this study we used ground-based NO x emission data to expand the density of NO 2 columns, and 40 years of N deposition (1970–2009) was inverted using the multivariate linear model with expanded NO 2 columns. The dynamic of N deposition was examined in both global and biome scales. The results show that the average N deposition was 0.34 g N m –2 year –1 in the 2000s, which was an increase of 38.4% compared with the 1970s’. The total N deposition in different biomes is unbalanced. N deposition is only 38.0% of the global total in forest biomes; this is made up of 25.9%, 11.3, and 0.7% in tropical, temperate, and boreal forests, respectively. As N-limited biomes, there was little increase of N deposition in boreal forests. However, N deposition has increased by a total of 59.6% in tropical forests and croplands, which are N-rich biomes. Such characteristics may influence the effects on global carbon budgets.

International Journal of Remote Sensing↗

USGS2017

Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.

A remote-sensing-based model to estimate evaporative fraction (EF) – the ratio of latent heat (LE; energy equivalent of evapotranspiration –ET–) to total available energy – from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micrometeorological and flux tower observations or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature ( T s ) normalized difference vegetation index (NDVI) and daily maximum air temperature ( T a ) . The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using T s and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the T s from Landsat relative to the T s from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

International Journal of Remote Sensing↗

USGS2018

Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager

In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance product and field-collected chlorophyll- a (chl- a ) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl- a concentrations using spectral data. We found that using the three band reflectance approach with a combination of OLI spectral bands 1, 3, and 5 produced the most promising results for accurately estimating chl- a concentrations in lakes ( R 2 value of 0.66; root mean square error value of 8.9 µg l −1 ). Using this model, we forecast the spatial and temporal variability of chl- a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.

North Carolina↗

USGS2025

Assessing gap-filled Landsat land surface temperature time-series data using different observational datasets

Landsat Analysis Ready Data (ARD)-based time-series present challenges in monitoring surface urban heat islands (SUHI) due to rapid changes in land surface temperature (LST) compared to cloud-free satellite observations. This research investigates the use of a spatiotemporal gap-filling model as a feasible and cost-effective solution to produce Landsat time-series LST products with both high spatial resolution and temporal frequency. The study identified and filled Landsat ARD thermal times-series data gaps due to missing data, cloud and shadow effects, and data quality. The accuracy of Landsat gap-filled products was assessed using randomly selected clear observations of Landsat and uncertainty products from the gap-filling model and was evaluated using various existing temperature datasets, including climate data from NOAA Global Historical Climate Network station observations, Daily Surface Weather and Climatological Summaries (DAYMET), and LST including MODIS, VIIRS and ECOSTRESS. The result suggests that the gap-filled Landsat LST has significant correlations with existing datasets including field observation and remote sensing data derived from other sensors that have similar monthly and seasonal variation patterns. The uncertainty maps show spatial distributions of uncertainty for gap-filled pixels that have high or low uncertainties. The Landsat gap-filled time-series datasets can be used to measure annual, seasonal, or even monthly landscape thermal conditions, which are useful for SUHI and relevant research, and to perform multi-decade time-series LST change analysis under climate change conditions.

International Journal of Remote Sensing↗

USGS2009

What is a picture worth? A history of remote sensing

Remote sensing is the use of electromagnetic energy to measure the physical properties of distant objects. It includes photography and geophysical surveying as well as newer techniques that use other parts of the electromagnetic spectrum. The history of remote sensing begins with photography. The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors have been designed to operate in virtually all of the electromagnetic spectrum. Today a wide variety of remote sensing instruments are available for use in hydrological studies; satellite data, such as Skylab photographs and Landsat images are particularly suitable for regional problems and studies. Planned future satellites will provide a ground resolution of 10–80 m. Remote sensing is currently used for hydrological applications in most countries of the world. The range of applications includes groundwater exploration determination of physical water quality, snowfield mapping, flood-inundation delineation, and making inventories of irrigated land. The use of remote sensing commonly results in considerable hydrological information at minimal cost. This information can be used to speed-up the development of water resources, to improve management practices, and to monitor environmental problems.

Hydrological Sciences Bulletin↗

USGS2007

A whole image approach using field measurements for transforming EO1 Hyperion hyperspectral data into canopy reflectance spectra

To maximize the spectral distinctiveness (information) of the canopy reflectance, an atmospheric correction strategy was implemented to provide accurate estimates of the intrinsic reflectance from the Earth Observing 1 (EO1) satellite Hyperion sensor signal. In rendering the canopy reflectance, an estimate of optical depth derived from a measurement of downwelling irradiance was used to drive a radiative transfer simulation of atmospheric scattering and attenuation. During the atmospheric model simulation, the input whole-terrain background reflectance estimate was changed to minimize the differences between the model predicted and the observed canopy reflectance spectra at 34 sites. Lacking appropriate spectrally invariant scene targets, inclusion of the field and predicted comparison maximized the model accuracy and, thereby, the detail and precision in the canopy reflectance necessary to detect low percentage occurrences of invasive plants. After accounting for artifacts surrounding prominent absorption features from about 400nm to 1000nm, the atmospheric adjustment strategy correctly explained 99% of the observed canopy reflectance spectra variance. Separately, model simulation explained an average of 88%??9% of the observed variance in the visible and 98% ?? 1% in the near-infrared wavelengths. In the 34 model simulations, maximum differences between the observed and predicted reflectances were typically less than ?? 1% in the visible; however, maximum reflectance differences higher than ?? 1.6% (<??2.3%) at more than a few wavelengths were observed at three sites. In the near-infrared wavelengths, maximum reflectance differences remained less than ??3% for 68% of the comparisons (??1 standard deviation) and less than ??6% for 95% of the comparisons (??2 standard deviation). Higher reflectance differences in the visible and near-infrared wavelengths were most likely associated with problems in the comparison, not in the model generation. ?? 2005 US Government.

International Journal of Remote Sensing↗

USGS2008

Use of landsat ETM+ SLC-off segment-based gap-filled imagery for crop type mapping

Failure of the Scan Line Corrector (SLC) on the Landsat ETM+ sensor has had a major impact on many applications that rely on continuous medium resolution imagery to meet their objectives. The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) program uses Landsat imagery as the primary source of data to produce crop-specific maps for 20 states in the USA. A new method has been developed to fill the image gaps resulting from the SLC failure to support the needs of Landsat users who require coincident spectral data, such as for crop type mapping and monitoring. We tested the new gap-filled method for a CDL crop type mapping project in eastern Nebraska. Scan line gaps were simulated on two Landsat 5 images (spring and late summer 2003) and then gap-filled using landscape boundary models, or segment models, that were derived from 1992 and 2002 Landsat images (used in the gap-fill process). Various date combinations of original and gap-filled images were used to derive crop maps using a supervised classification process. Overall kappa values were slightly higher for crop maps derived from SLC-off gap-filled images compared to crop maps derived from the original imagery (0.3–1.3% higher). Although the age of the segment model used to derive the SLC-off gap-filled product did not negatively impact the overall agreement, differences in individual cover type agreement did increase (−0.8%–1.6% using the 2002 segment model to −5.0–5.1% using the 1992 segment model). Classification agreement also decreased for most of the classes as the size of the segment used in the gap-fill process increased.

Geocarto International↗

USGS1997

Seasonal vegetation characteristics of the United States

The U.S. Geological Survey's EROS Data Center has created a prototype 1‐km resolution data base of vegetation seasonal characteristics. The characteristics are derived from time‐series NDVI data collected by the AVHRR satellite sensor. Information covering the 5 years 1989–1993 is included in the data base. Although quantitative validation of the seasonal characteristics cannot be made until several evaluation efforts are completed, general observations are possible by viewing images of the seasonal parameters. Figures 2 through 8 show several examples of the seasonal characteristics data base.

Geocarto International↗

USGS2024

Comparison of imaging flow cytometry and microscopy for freshwater algal bloom detection

Imaging flow cytometry (IFC) is an emerging tool that allows for rapid identification and enumeration of phytoplankton in freshwater systems. However, few studies have assessed the effects of preservation on IFC results or compared live IFC and microscopy results in freshwater systems. Understanding the effects of preservation and differences between IFC and microscopy will improve interpretation of these data and inform strategies to use IFC-based approaches in freshwater systems. Our study objectives were to compare IFC and phase contrast with epifluorescence microscopy as techniques for phytoplankton identification and enumeration, and the effects of sample preservation with an emphasis on taxa forming harmful cyanobacterial blooms (HCBs). During June through October 2020, samples were collected from 2 lakes in the Finger Lakes region of New York. Live and preserved samples were analyzed by laboratory-based IFC, and preserved samples were analyzed by microscopy. The IFC approach captured community dynamics while detecting potential cyanobacterial bloom-forming taxa earlier and at lower abundances than microscopy. Laboratory-based IFC allowed for an intermediate level of taxonomic information when compared to microscopy, gross techniques, such as extracted chlorophyll a or fluorescence sensors, and field-based operation of IFC approaches. The laboratory-based application of IFC in this study allowed receipt of results in 5 d or less, a substantial improvement over microscopy, which can be time-consuming to conduct. However, the laboratory-based IFC approach had some limitations. Imaging flow cytometry-estimated biovolume may be less accurate than microscopy for some taxa because of the algorithms used to calculate biovolume, particularly for chrysophytes and coccoid cyanobacteria. Colonial dissociation during preservation appeared to affect detection of Microcystis by IFC less than for other fragile bloom-forming taxa like chrysophytes. Our study results advance understanding of how IFC may translate to field-based approaches for early harmful algal bloom indicators in freshwater.

Lake and Reservoir Management↗

USGS2022

Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security

Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.

GIScience & Remote Sensing↗

USGS2018

Sentinel-2A MSI and Landsat-8 OLI radiometric cross comparison over desert sites

The Sentinel-2A and Landsat-8 satellites carry on-board moderate resolution multispectral imagers for the purpose of documenting the Earth’s changing surface. Though they are independently built and managed, users will certainly take advantage of the opportunity to have higher temporal coverage by combining the datasets. Thus it is important for the radiometric and geometric calibration of the MultiSpectral Instrument (MSI) and the Operational Land Imager (OLI) to be compatible. Cross-calibration of MSI to OLI has been accomplished using multiple techniques involving the use of pseudo-invariant calibration sites (PICS) using direct comparisons as well as through use of PICS models predicting top-of-atmosphere reflectance. A team from the University of Arizona is acquiring field data under both instruments for vicarious calibration of the sensors. This paper shows that the work done to date by the Landsat and Sentinel-2 calibration teams has resulted in stable radiometric calibration for each instrument and consistency to ~2.5% between the instruments for all the spectral bands that the instruments have in common.

European Journal of Remote Sensing↗

USGS2005

The spectral irradiance of the moon

Images of the Moon at 32 wavelengths from 350 to 2450 nm have been obtained from a dedicated observatory during the bright half of each month over a period of several years. The ultimate goal is to develop a spectral radiance model of the Moon with an angular resolution and radiometric accuracy appropriate for calibration of Earth-orbiting spacecraft. An empirical model of irradiance has been developed that treats phase and libration explicitly, with absolute scale founded on the spectra of the star Vega and returned Apollo samples. A selected set of 190 standard stars are observed regularly to provide nightly extinction correction and long-term calibration of the observations. The extinction model is wavelength-coupled and based on the absorption coefficients of a number of gases and aerosols. The empirical irradiance model has the same form at each wavelength, with 18 coefficients, eight of which are constant across wavelength, for a total of 328 coefficients. Over 1000 lunar observations are fitted at each wavelength; the average residual is less than 1%. The irradiance model is actively being used in lunar calibration of several spacecraft instruments and can track sensor response changes at the 0.1% level. ?? 2005. The American Astronomical Society. All rights reserved.

Astronomical Journal↗

USGS2016

Patterns of diel variation in nitrate concentrations in the Potomac River

The Potomac River is a large source of N to Chesapeake Bay, where reducing nutrient loads is a focus of efforts to improve trophic status. Better understanding of NO 3 – loss, reflected in part by diel variation in NO 3 – concentrations, may refine model predictions of N loads to the Bay. We analyzed 2 y of high-frequency NO 3 – sensor data in the Potomac to quantify seasonal variation in the magnitude and timing of diel NO 3 – loss. Diel patterns were evident, especially during low flow, despite broad seasonal and flow-driven variation in NO 3 – concentrations. Diel variation was ~0.01 mg N/L in winter and 0.02 to 0.03 mg N/L in summer with intermediate values in spring and autumn, equivalent to <1% of the daily mean NO 3 – concentration in winter and ~2 to 4% in summer. Maximum diel NO 3 – values generally occurred in mid- to late morning, with more repeatable patterns in summer and wider variation in autumn and winter. Diel NO 3 – loss reduced loads by 0.7% in winter and 3% in summer. These losses were less than estimates of total in-stream NO 3 – load loss across the basin that averaged 33% of the annual groundwater contribution to the river. Water temperature and discharge had stronger relationships to the daily magnitude of diel NO 3 – variation than did photosynthetically active radiation. Estimated diel areal NO 3 – loss rates were generally >1000 mg N m –2 d –1 , greater than most published values because measurements in this large river integrate over a greater depth/unit stream bottom area than do those from smaller rivers. These diel NO 3 – patterns are consistent with the influence of photoautotrophic uptake and related denitrification, but we cannot attribute these patterns to assimilation alone because the magnitude and timing of diel dynamics were affected to an unknown extent by processes, such as evapotranspiration, transient storage, and hydrodynamic dispersion. Improvements to diel loss estimates will require additional high-frequency measures, such as dissolved O 2 , dissolved organic N, and NH 4 + , and deployment of 2 measurement stations.

Potomac River↗

USGS2009

Absolute stellar photometry on moderate-resolution FPA images

An extensive database of star (and Moon) images has been collected by the ground-based RObotic Lunar Observatory (ROLO) as part of the US Geological Survey program for lunar calibration. The stellar data are used to derive nightly atmospheric corrections for the observations from extinction measurements, and absolute calibration of the ROLO sensors is based on observations of Vega and published reference flux and spectrum data. The ROLO telescopes were designed for imaging the Moon at moderate resolution, thus imposing some limitations for the stellar photometry. Attaining accurate stellar photometry with the ROLO image data has required development of specialized processing techniques. A key consideration is consistency in discriminating the star core signal from the off-axis point spread function. The analysis and processing methods applied to the ROLO stellar image database are described. ?? 2009 BIPM and IOP Publishing Ltd.

Metrologia↗

USGS2024

Canopy cover and microtopography control precipitation-enhanced thaw of ecosystem-protected permafrost

Northern high-latitudes are projected to get warmer and wetter, which will affect rates of permafrost thaw and mechanisms by which thaw occurs. To better understand the impact of rain, as well as other factors such as snow depth, canopy cover, and microtopography, we instrumented a degrading permafrost plateau in south-central Alaska with high-resolution soil temperature sensors. The site contains ecosystem-protected permafrost, which persists in unfavorable climates due to favorable ecologic conditions. Our study (2020–2022) captured three of the snowiest years and three of the four wettest years since the site was first studied in 2015. Average thaw rates along an across-site transect increased nine-fold from 6 ± 5 cm yr −1 (2015–2020) to 56 ± 12 cm yr −1 (2020–2022). This thaw was not uniform. Hummock locations, residing on topographic high points with relatively dense canopy, experienced only 8 ± 9 cm yr −1 of thaw, on average. Hollows, topographic low points with low canopy cover, and transition locations, which had canopy cover and elevation between hummocks and hollows, thawed 44 ± 6 cm yr −1 and 39 ± 13 cm yr −1 , respectively. Mechanisms of thaw differed between these locations. Hollows had high warm-season soil moisture, which increased thermal conductivity, and deep cold-season snow coverage, which insulated soil. Transition locations thawed primarily due to thermal energy transported through subsurface taliks during individual rain events. Most increases in depth to permafrost occurred below the ∼45 cm thickness seasonally frozen layer, and therefore, expanded existing site taliks. Results highlight the importance of canopy cover and microtopography in controlling soil thermal inputs, the ability of subsurface runoff from individual rain events to trigger warming and thaw, and the acceleration of thaw caused by consecutive wet and snowy years. As northern high-latitudes become warmer and wetter, and weather events become more extreme, the importance of these controls on soil warming and thaw is likely to increase.

Alaska↗