Anne W. Nolin

New Directions in Earth Observing: Scientific Applications of Multiangle Remote Sensing

The physical interpretation of simultaneous multiangle observations represents a relatively new approach to remote sensing of terrestrial geophysical and biophysical parameters. Multiangle measurements enable retrieval of physical scene characteristics, such as aerosol type, cloud morphology and height, and land cover (e.g., vegetation canopy type), providing improved albedo accuracies as well as compositional, morphological, and structural information that facilitates addressing many key climate, environmental, and ecological issues. While multiangle data from wide field-of-view scanners have traditionally been used to build up directional “signatures” of terrestrial scenes through multitemporal compositing, these approaches either treat the multiangle variation as a problem requiring correction or normalization or invoke statistical assumptions that may not apply to specific scenes. With the advent of a new generation of global imaging spectroradiometers capable of acquiring simultaneous visible/near-IR...

A Hyperspectral Method for Remotely Sensing the Grain Size of Snow

Abstract We have developed a robust, accurate inversion technique for estimating the grain size in a snowpacks surface layer from imaging spectrometer data. Using a radiative transfer model, the method relates an ice absorption feature, centered at λ=1.03 μm, to the optically equivalent snow grain size. Because the interpretation is based on the area—not depth—of the absorption feature scaled to absolute reflectance, the method is insensitive to instrument noise and does not require a topographic correction. We tested the method using Airborne Visible/Infrared Imaging Spectrometer data over the eastern Sierra Nevada, California, and we validated it with a combination of ground-based spectrometer data and grain size measurements.

Mapping “At Risk” Snow in the Pacific Northwest

Abstract One of the most visible and widely felt impacts of climate warming is the change (mostly loss) of low-elevation snow cover in the midlatitudes. Snow cover that accumulates at temperatures close to the ice-water phase transition is at greater risk to climate warming than cold climate snowpacks because it affects both precipitation phase and ablation rates. This study maps areas in the Pacific Northwest region of the United States that are potentially at risk of converting from a snow-dominated to a rain-dominated winter precipitation regime, under a climate-warming scenario. A data-driven, climatological approach of snow cover classification is used to reveal these “at risk” snow zones and also to examine the relative frequency of warm winters for the region. For a rain versus snow temperature threshold of 0°C the at-risk snow class covers an area of about 9200 km2 in the Pacific Northwest region and represents approximately 6.5 km3 of water. Many areas of the Pacific Northwest would see an increa...

Estimating snow grain size using AVIRIS data

Abstract Estimates of snow grain size for the near-surface snow layer were calculated for the Tioga Pass region and Mammoth Mountain in the Sierra Nevada, California, using an inversion technique and data collected by the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS). The inversion method takes advantage of the sensitivity of near-infrared snowpack reflectance to snow grain size. The Tioga Pass and Mammoth Mountain single-band AVIRIS radiance images were atmospherically corrected to obtain surface reflectance. Given the solar and viewing geometry for the time and location of each AVIRIS overflight, a discrete-ordinate model was used to calculate directional reflectance as a function of snowpack grain size, for a wide range of snow grain radii. The resulting radius vs. reflectance curves were each fit using a nonlinear least-squares technique which provided a means of transforming surface reflectance in each AVIRIS image to optically equivalent grain size on a per-pixel basis. This inversion technique has been validated using a combination of ground-based reflectance measurements and grain size measurements derived from stereologic analysis of snow samples for a wide range of snow grain sizes. The model results and grain size estimates derived from the AVIRIS data show that, for solar incidence angles between 0° and 30°, the technique provides good estimates of grain size. Otherwise, the local angle of solar incidence must be known more exactly. This work provides the first quantitative estimates for grain size using data acquired from an airborne remote sensing instrument and is an important step in improving our ability to retrieve snow physical properties independent of field measurements.

Recent advances in remote sensing of seasonal snow

Remote sensing offers local, regional and global observations of seasonal snow, providing key information on snowpack processes. This brief review highlights advancements in instrumentation and analysis techniques that have been developed over the past decade. Areas of advancement include improved algorithms for mapping snow-cover extent, snow albedo, snow grain size, snow water equivalent, melt detection and snow depth, as well as new uses of instruments such as multiangular spectroradiometers, scatterometry and lidar. Limitations and synergies of the instruments and techniques are discussed, and remaining challenges such as multisensor mapping, scaling issues, vegetation correction and data assimilation are identified. Remote sensing is a powerful tool that offers the ability to quantitatively examine the physical properties of snow in remote or otherwise inaccessible areas where measurements may be expensive and dangerous. Moreover, the global coverage and regular repeatability of measurements offered by satellite remote sensing allows scientists to monitor the vast temporal and spatial variability of snow cover. Satellite and airborne remote sensing augments the relatively sparse in situ observations, thereby affording important spatial context for such measurements. Routinely carried out since the 1960s, remote sensing of snow has created a multi- decadal archive of variability and trends in snow cover. Innovations in sensor technology and digital image proces- sing allow scientists to visualize and monitor snow cover for hydrology and water resources management, climatology and ecosystem science. Early work on remote sensing of snow and glaciers has been covered in the excellent review by Konig and others (2001) and the valuable text by Rees (2006). Remote sensing of glaciers and ice sheets is rather extensive and beyond the scope of this paper. This review concentrates on recent advances in satellite, airborne and ground-based remote sensing of seasonal snow. In many ways, the research questions involving seasonal snow have not changed over the past several decades since the inception of remote sensing. We still want to know the spatial extent of snow cover, snowpack properties such as grain size and albedo, snow depth, snow water equivalent, and the onset of snowmelt. However, the accuracy with which we can answer these questions has developed significantly. The following sections are arranged with regard to these questions, highlighting advances within the past decade.

Comparison of AVHRR-derived and in situ surface albedo over the Greenland ice sheet

Abstract This paper discusses a methodology for computing the clear-sky surface albedo over the Greenland ice sheet by using advanced very high resolution radiometer (AVHRR) global area coverage visible and near-infrared reflectances. This approach is then used to map the monthly changes in albedo over the entire Greenland ice sheet during the spring and .summer months. The methodology includes corrections for the intervening atmosphere by using the 6S radiative transfer model. Additional corrections for the anisotropic nature o f snow reflectance and the conversion from a. marrow- into a broadband albedo are made based on in .site measurements from the Swiss Federal Institute of Technology/University of Colorado camp at 69°34′N, 49°18′W Comparison with surface albedo field ineasureinents indicates that before the onset of snoic melt, agreement is good between the satellite-derived and measured surface albedo. After melt begins, it is difficult to compare the satellite values with the field data owing to melt water ponding on the ice sheet surface. With appropriate modifications, the 6S radiative transfer model is suitable for atmospherically correcting AVHRR visible and near-infrared radiances atmospherically polar regions.

Mapping alpine snow using a spectral mixture modeling technique

Remote sensing has provided a means of obtaining estimates of snow-covered area, yet traditional methods have had difficul ty mapping snow in shaded and vegetated areas. Spectral mixture analysis is a linear mixture modeling technique that shows promise for mapping land surface covers, particularly when imaging spectrometer data are used. Applying this technique to AVIRIS data collected over the Sierra Nevada, California, we have estimated the fraction of snow cover in each pixel, even in areas that are shaded or forested. This modeling technique enables us to map snow cover at the sub-pixellevel and provides a means of estimating the errors associated with the calculation.

Progress in bidirectional reflectance modeling and applications for surface particulate media: Snow and soils

Surface particulate media such as soil and snow have significant impacts on global systems because of their contributions to the radiation balance of the Earth system, their roles in the hydrologic cycle, and their extensive spatial distributions. At regional and global scales, characteristics of soils and snow cover can best be mapped and analyzed using satellite‐based sensors that acquire both spectral and angular reflectance information. Advances in surface reflectance models over the past decade now provide improved interpretation of remote sensing imagery over snow. However, advances have been slower in remote sensing of soil properties and a number of key issues remain. This article begins with an overview of the similarities and differences between particulate media modeling approaches for soil and snow. We review common modeling methods: radiative transfer approximations, numerical solutions, and geometrical optics. Model inversion and validation experiments are summarized and research priorities are discussed.

Surface roughness characterizations of sea ice and ice sheets: case studies with MISR data

This work is an examination of potential uses of multiangular remote sensing imagery for mapping and characterizing sea ice and ice sheet surfaces based on surface roughness properties. We use data from the Multi-angle Imaging SpectroRadiometer (MISR) to demonstrate that ice sheet and sea ice surfaces have characteristic angular signatures and that these angular signatures may be used in much the same way as spectral signatures are used in multispectral classification. Three case studies are examined: sea ice in the Beaufort Sea off the north coast of Alaska, the Jakobshavn Glacier on the western edge of the Greenland ice sheet, and a region in Antarctica south of McMurdo station containing glaciers and blue-ice areas. The MISR sea ice image appears to delineate different first-year ice types and, to some extent, the transition from first-year to multiyear ice. The MISR image shows good agreement with sea ice types that are evident in concurrent synthetic aperture radar (SAR) imagery and ice analysis charts from the National Ice Center. Over the Jakobshavn Glacier, surface roughness data from airborne laser altimeter transects correlate well with MISR-derived estimates of surface roughness. In Antarctica, ablation-related blue-ice areas, which are difficult to distinguish from bare ice exposed by crevasses, are easily detected using multiangular data.

New methods to infer snow albedo from the MISR instrument with applications to the Greenland ice sheet

Snow-covered surfaces have a very high surface albedo, thereby allowing little energy to be absorbed by the snowpack. As the snowpack ages and/or begins to melt, the snow albedo decreases and more solar energy is absorbed by the snowpack. Therefore, accurate estimation of snow albedo is essential for monitoring the state of the cryosphere. This paper examines the retrieval of snow albedo using data from the Multi-angle Imaging SpectroRadiometer (MISR) instrument over the Greenland ice sheet. Two different methods are developed and examined to derive the snow albedo: one based on the spectral information from MISR and one utilizing the angular information from the MISR instrument. The latter method is based on a statistical relationship between in situ albedo measurements and the MISR red channel reflectance at all MISR viewing angles and is found to give good agreement with the ground-based measurements. Good agreement is also found using the spectral information, although the method is more sensitive to instrument calibration, snow bidirectional reflectance distribution function models, and narrowband-to-broadband relationships. In general, using either method retrieves snow surface albedo values that are within about 6% of that measured at the stations in Greenland.