Analysis of in situ and satellite data shows evidence of different regional snow cover responses to the widespread warming and increasing winter precipitation that has characterized the Arctic climate for the past 40–50 years. The largest and most rapid decreases in snow water equivalent (SWE) and snow cover duration (SCD) are observed over maritime regions of the Arctic with the highest precipitation amounts. There is also evidence of marked differences in the response of snow cover between the North American and Eurasian sectors of the Arctic, with the North American sector exhibiting decreases in snow cover and snow depth over the entire period of available in situ observations from around 1950, while widespread decreases in snow cover are not apparent over Eurasia until after around 1980. However, snow depths are increasing in many regions of Eurasia. Warming and more frequent winter thaws are contributing to changes in snow pack structure with important implications for land use and provision of ecosystem services. Projected changes in snow cover from Global Climate Models for the 2050 period indicate increases in maximum SWE of up to 15% over much of the Arctic, with the largest increases (15–30%) over the Siberian sector. In contrast, SCD is projected to decrease by about 10–20% over much of the Arctic, with the smallest decreases over Siberia (<10%) and the largest decreases over Alaska and northern Scandinavia (30–40%) by 2050. These projected changes will have far-reaching consequences for the climate system, human activities, hydrology, and ecology.

Current National Oceanic and Atmospheric Administration (NOAA) operational global- and continental-scale snow cover maps are produced interactively by visual analysis of satellite imagery. This snow product is subjective, and its preparation requires a substantial daily human effort. The primary objective of the current study was to develop an automated system that could provide NOAA analysts with a first-guess snow cover map and thus to reduce the human labor in the daily snow cover analysis. The proposed system uses a combination of observations in the visible, midinfrared, and infrared made by the Imager instrument aboard Geostationary Operational Environmental Satellites (GOES) and microwave observations of the Special Sensor Microwave Imager (SSM/I) aboard the polar-orbiting Defense Meteorological Satellite Program platform. The devised technique was applied to satellite data for mapping snow cover for the North American continent during the winter season of 1998/99. To assess the system per... Abstract Current National Oceanic and Atmospheric Administration (NOAA) operational global- and continental-scale snow cover maps are produced interactively by visual analysis of satellite imagery. This snow product is subjective, and its preparation requires a substantial daily human effort. The primary objective of the current study was to develop an automated system that could provide NOAA analysts with a first-guess snow cover map and thus to reduce the human labor in the daily snow cover analysis. The proposed system uses a combination of observations in the visible, midinfrared, and infrared made by the Imager instrument aboard Geostationary Operational Environmental Satellites (GOES) and microwave observations of the Special Sensor Microwave Imager (SSM/I) aboard the polar-orbiting Defense Meteorological Satellite Program platform. The devised technique was applied to satellite data for mapping snow cover for the North American continent during the winter season of 1998/99. To assess the system per...

Automated snow maps over North America have been produced at the National Environmental Satellite Data and Information Service (NESDIS) of the National Oceanic and Atmospheric Administration (NOAA) since 1999. The developed snow‐mapping system is based on observations in the visible, middle infrared, infrared, and microwave spectral bands from operational geostationary and polar orbiting meteorological satellites and generates daily maps of snow cover at a spatial resolution of 4 km. Recently, the existing snow‐mapping technique was extended to derive the fractional snow cover. To obtain snow fraction, we use measurements of the Imager instrument on board Geostationary Operational Environmental Satellite (GOES). The algorithm treats every cloud‐clear image pixel as a “mixed scene” consisting of a combination of snow‐covered and snow‐free land surface. To determine the portion of the pixel that is covered with snow, we employ a linear mixture approach, which relies on the Imager measurements in the visible spectral band. The estimated accuracy of subpixel snow fraction retrievals is about 10%. In this paper, we present a description of the snow cover and snow fraction mapping algorithms. Application of the developed algorithms over North America for three winter seasons from 1999–2000 to 2001–2002 has shown that the spatial distribution of the fractional snow cover over areas affected by seasonal snow closely corresponds to the distribution of the forest cover. The fraction of snow in the middle of the winter season generally varied from 100% over croplands, grasslands, and other nonforested areas to 20–30% over dense boreal forests. The snow fraction over dense boreal forests exhibited a slight intraseason variability; however, no obvious correlation of these changes with snowfalls was noticed. Over areas with no or sparse tree vegetation cover (croplands, grasslands), snow fraction showed a noticeable correlation with snow depth for snow depths up to 35–40 cm.

Due to the harsh conditions of space, the detectors within satellite-based multispectral imagers are always at risk of damage or failure. In particular, 15 out of the 20 detectors that produce the 1.6- μm band 6 of Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua are either dead or noisy. In this paper, we describe a quantitative image restoration (QIR) algorithm that is able to accurately estimate and restore the data lost due to multiple-detector failure. The small number of functioning detectors is used to train a restoration function that is based on a multivariate regression using the information in a spatial-spectral window around each restored pixel. The information from other spectral bands allows QIR to perform well even when standard image interpolation breaks down due to large contiguous sections of the image being missing, as is the case for MODIS band 6 on Aqua. We present a comprehensive evaluation of the QIR algorithm by simulating the Aqua damage using the working 1.6- μm band of MODIS on Terra and then comparing the QIR restoration to the original (unbroken) Terra image. We also compare our results with other researchers' prior work that has been based on the assumption that band 6 could be approximated well solely as a function of the related band 7. We present empirical evidence that there is information in the other 500- and 250-m bands, excluding bands 6 and 7, that can inform the estimation of missing band 6 data. We demonstrate superior performance of QIR over previous algorithms as reflected by a reduced root-mean-square-error evaluation. The QIR algorithm may also be adapted to other cases and provides a powerful and general algorithm to mitigate the risks of detector damage in multispectral remote sensing.