A major benefit of multitemporal, remotely sensed images is their applicability to change detection over time. Because of concerns about global and environmental change, these data are becoming increasingly more important. However, to maximize the usefulness of the data from a multitemporal point of view, an easy-to-use, cost-effective, and accurate radiometric calibration and correction procedure is needed. The atmosphere effects the radiance received at the satellite by scattering, absorbing, and refracting light; corrections for these effects, as well as for sensor gains and offsets, solar irradiance, and solar zenith angles, must be included in radiometric correction procedures that are used to convert satellite-recorded digital counts to ground reflectances. To generate acceptable radiometric correction results, a model is required that typically uses in-situ atmospheric measurements and radiative transfer code (RTC) to correct for atmospheric effects. The main disadvantage of this type of correction procedure is that it requires in-situ field measurements during each satellite overflight. This is unacceptable for many applications and is often impossible, as when using historical data or when working in very remote locations. The optimum radiometric correction procedure is one based solely on the digital image and requiring no in-situ field measurements during the satellite overflight. The darkobject subtraction (DOS) method, a strictly image-based technique, is an attempt to achieve this ideal procedure. However, the accuracy is not acceptable for many applications, mostly because it corrects only for the additive scattering effect and not for the multiplicative transmittance effect. This paper presents an entirely image-based procedure that expands on the ~10s model by including a simple multiplicative correction for the effect of atmospheric transmittance. Two straightforward methods to derive the multiplicative transmittance-correction coefficient are presented. The COSITZ) or COST method uses the cosine of the solar zenith angle, which, to a first order, is a good approximation of the atmospheric transmittance for the dates und sites used in this study. The default TAUS method uses the average of the transmittance values computed by using in-situ atmospheric field measurements made during seven different satellite overflights. Published and unpublished data made available for this study by Moran et al. (1992) are used, and my model results are compared with their results. The corrections generated by the entirely image-based COST model are as accurate as those generated by the models that used in-situ atmospheric field measurements and RTC software.

/pdf/image-based-atmospheric-corrections-revisited-and-improved-1jx25ea6e7.pdf

Image-Based Atmospheric Corrections - Revisited and Improved

The Arctic climate is changing. Permafrost is warming, hydrological processes are chang- ing and biological and social systems are also evolving in response to these changing conditions. Knowing how the structure and function of arctic terrestrial ecosystems are responding to recent and persistent climate change is paramount to understanding the future state of the Earth system and how humans will need to adapt. Our holistic review presents a broad array of evidence that illustrates con- vincingly; the Arctic is undergoing a system-wide response to an altered climatic state. New extreme and seasonal surface climatic conditions are being experienced, a range of biophysical states and pro- cesses influenced by the threshold and phase change of freezing point are being altered, hydrological and biogeochemical cycles are shifting, and more regularly human sub-systems are being affected. Importantly, the patterns, magnitude and mechanisms of change have sometimes been unpredictable or difficult to isolate due to compounding factors. In almost every discipline represented, we show

/pdf/evidence-and-implications-of-recent-climate-change-in-1rey4qwtm8.pdf

Evidence and implications of recent climate change in northern Alaska and other arctic regions.

Evidence and Implications of Recent Climate Change in Northern Alaska and Other Arctic Regions

[1] The presence of seasonal snow cover during the cold season of the annual air temperature cycle has significant influence on the ground thermal regime in cold regions. Snow has high albedo and emissivity that cool the snow surface, high absorptivity that tends to warm the snow surface, low thermal conductivity so that a snow layer acts as an insulator, and high latent heat due to snowmelt that is a heat sink. The overall impact of snow cover on the ground thermal regime depends on the timing, duration, accumulation, and melting processes of seasonal snow cover; density, structure, and thickness of seasonal snow cover; and interactions of snow cover with micrometeorological conditions, local microrelief, vegetation, and the geographical locations. Over different timescales either the cooling or warming impact of seasonal snow cover may dominate. In the continuous permafrost regions, impact of seasonal snow cover can result in an increase of the mean annual ground and permafrost surface temperature by several degrees, whereas in discontinuous and sporadic permafrost regions the absence of seasonal snow cover may be a key factor for permafrost development. In seasonally frozen ground regions, snow cover can substantially reduce the seasonal freezing depth. However, the influence of seasonal snow cover on seasonally frozen ground has received relatively little attention, and further study is needed. Ground surface temperatures, reconstructed from deep borehole temperature gradients, have increased by up to 4°C in the past centuries and have been widely used as evidence of paleoclimate change. However, changes in air temperature alone cannot account for the changes in ground temperatures. Changes in seasonal snow conditions might have significantly contributed to the ground surface temperature increase. The influence of seasonal snow cover on soil temperature, soil freezing and thawing processes, and permafrost has considerable impact on carbon exchange between the atmosphere and the ground and on the hydrological cycle in cold regions/cold seasons.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2004RG000157

Influence of the seasonal snow cover on the ground thermal regime: An overview

Cloud detection algorithm comparison and validation for operational Landsat data products

Measurements from the subarctic snowpack are used to explore the relationship between grain growth and vapor flow, the fundamental processes of dry-snow metamorphism. Due to extreme temperature gradients, the subarctic pack undergoes extensive depth-hoar metamorphism. By the end of the winter a five-layered structure with a pronounced weak layer near the base of the snow evolves. Grain-size increases by a factor of 2-3, while the number of grains per unit mass decreases by a factor of 10. Observed growth rates require significant net inter-particle vapor fluxes. Stable-isotope ratios show that there are also significant net layer-to-layer vapor fluxes. Soil moisture enters the base of the pack and mixes with the bottom 10 cm of snow, while isotopically light water vapor fractionates from the basal layer and is deposited up to 50 cm higher in the pack. End-of-winter density profiles for snow on the ground, compared with snow on tables, indicate the net layer-to-layer vapor flux averages 6 x 10 -7 kg m 2 s -1, , though detailed measurements show the net flux is episodic and varies with time and height in the pack, with peak net fluxes ten times higher than average. A model, driven by observed temperature profiles, reproduces the layer-to-layer flux pattern and predicts the observed weak layer at the base of the snow. Calculated layer-to-layer vapor fluxes are ten times higher than inter-particle fluxes, which implies that depth-hoar grain growth is limited by factors other than the vapor supply. This finding suggests that gain and loss of water molecules due to sublimation from grains takes place at a rate many times higher than the rate at which grains grow, and it explains why grains can metamorphose into different forms so readily.

Vapor transport, grain growth and depth-hoar development in the subarctic snow

The hydrologic cycle of an Arctic watershed is dominated by such physical elements as snow, ice, permafrost, seasonally frozen soils, wide fluctuations in surface energy balance, and phase change of snow and ice to water. Hydrologic data collected over a 5-year period (1985-1989) from an Arctic watershed in northern Alaska are presented. Processes related to snow accumulation, redistribution, ablation, evaporation and subsequent snowmelt-generated runoff are discussed. The total water content of the snowpack at the end of winter had comprised between 28 and 40% of the annual precipitation. Redistribution of snow on the ground by the wind is a major factor in increasing the snowmelt runoff. Much of the redistributed snow accumulates in the valley bottom along the stream and also along depressions on the hillslopes. These depressions are small surface drainage channels that are referred to as water tracks. Partitioning of the snowmelt into runoff, evaporation and increased soil water storage in the active layer was carried out on the plot and watershed scale. Over a 5-year period, the volume of snowmelt runoff varied from 50 to 66% of the average watershed snowpack, while evaporation varied from 20 to 34% and soil moisture storage increased between 10 and 19%.more » Much greater variation in these hydrologic components occurred at the plot scale.« less

Snow hydrology of a headwater Arctic basin: 1. Physical measurements and process studies

A methodology for adjusting the daily precipitation measured by the U.S. National Weather Service (NWS) 8-inch standard gauge for wind-induced undercatch, wetting loss, and trace amount of precipitation is provided. The application of the proposed adjustment procedures was made at 10 NWS climate stations in Alaska for 1982 and 1983. The results show the following: (1) Daily adjustment for wind-induced undercatch, wetting loss, and trace amount of precipitation increases the gauge-measured annual precipitation by 65–800 mm for the 2 years (about 10-140% of the gauge-measured yearly total) at the 10 stations in Alaska; (2) compared to wetting loss and trace amount of precipitation, wind-induced undercatch is the source of greatest error, although wetting loss and trace amount of precipitation are also significant systematic errors in the northern Alaska regions of low precipitation; (3) in the similar climate condition, the NWS 8-inch standard gauges with an Alter wind shield have a much lower adjustment for wind-induced undercatch than the unshielded gauges at nearby stations, and the unshielded gauges placed on the roof of the weather office building have a higher adjustment for wind-induced errors than those gauges mounted on the ground; (4) monthly adjustment factors (adjusted/measured precipitation) differ by station, and at an individual station by type of precipitation; (5) considerable intra-annual variation of the magnitude of the adjustments has been found in Alaska owing to the fluctuation of wind speed, air temperature, and frequency of snowfall. Using the constant correction factors (derived at a single intercomparison site) to the archived monthly precipitation records produces significant undercorrection of the wind-induced errors at high wind sites and overcorrecting of the errors at low wind sites. To avoid the undercorrection or overcorrection of the wind-induced errors, a constant correction factor should not be applied to gauge-measured snow data. Daily adjustments for systematic errors need to be applied to the archived precipitation data. It is expected that the adjustments will have an impact on climate monitoring.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/97WR02681

Adjustment of daily precipitation data at 10 climate stations in Alaska: Application of World Meteorological Organization intercomparison results

https://modis-snow-ice.gsfc.nasa.gov/uploads/pap_assmnt98.pdf

Assessment of Snow-Cover Mapping Accuracy in a Variety of Vegetation-Cover Densities in Central Alaska

The different snow and ice types on a glacier may be subdivided according to the glacier-facies concept The surficial expression of some facies may be detected at the end of the balance year by the use of visible and near-infrared image data from the Landsat multispectral scanner (MSS) and thematic mapper (TM) sensors Ice and snow can be distinguished by reflectivity differences in individual or ratioed TM bands on Bruarjokull, an outlet glacier on the northern margin of the Vatnajokull ice cap, Iceland The Landsat scene shows the upper limit of wet snow on 24 August 1986 Landsat-derived reflectance is lowest for exposed ice and increases markedly at the transient snow line Above the slush zone is a gradual increase in near-infrared reflectance as a result of decreasing grain-size of the snow, which characterizes drier snow Landsat data are useful in measuring the areal extent of the ice facies, the slush zone within the wet-snow facies, the snow facies (combined wet-snow, percolation and dry-snow facies), and the respective positions of the transient snow line and the slush limit In addition, fresh snowfall and/or airborne contaminants, such as soot and tcphra, can limit the utility of Landsat data for delineation of the glacier facies in some cases

/pdf/analysis-of-glacier-facies-using-satellite-techniques-43nxprvvpa.pdf

Carl S. Benson

Papers

Vapor transport, grain growth and depth-hoar development in the subarctic snow

Snow hydrology of a headwater Arctic basin: 1. Physical measurements and process studies

Adjustment of daily precipitation data at 10 climate stations in Alaska: Application of World Meteorological Organization intercomparison results

Assessment of Snow-Cover Mapping Accuracy in a Variety of Vegetation-Cover Densities in Central Alaska

Analysis of glacier facies using satellite techniques