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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
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NASA NTRS1999

Using Spaceborne Ku-Band Scatterometer for Global Snow Cover Monitoring

We demonstrate for the first time the utility of spaceborne Ku-band scatterometer for global snow cover monitoring. Satellite radar data were collected over the globe by the NASA Scatterometer (NSCAT) operated at 14 GHz on board the Japanese ADEOS spacecraft from September 1996 to June 1997, spanning the 1997 seasonal snow season. First, we present backscatter signature of dry and wet snow to facilitate the interpretation of NSCAT backscatter evolution over snow cover regions. Surface field experiments indicated that dry snow backscatter at Ku band is approximately 40 times stronger than that at C band. Thus, Ku-band scatterometer measurements are sensitive to snow cover, which is typically transparent to C-band scatterometer returns. Furthermore, Ku-band backscatter does not saturate for most of natural snow depths as compared to radar responses at 19 GHz and 37 GHz or higher frequencies which have more limited penetration depths into snow. Ku-band backscatter is also sensitive to wetness in snow, which is appropriate to detect early snow melt conditions. Using the snow backscatter characteristics, we investigate NSCAT backscatter evolution over global snow cover regions throughout the 1997 snow season. The results reveal detail delineations between different regional snow areas. We show the correlation of these delineations with the boundaries of different global snow classes defined by the U.S. Army Cold Regions Research and Engineering Laboratory snow classification system. Using in-situ snow depth data from the U.S. National Climatic Data Center, we show that Ku-band backscatter corresponds very well to the trend of snow melt while snow mapping products (U.S. Climate Prediction Center gridded snow charts) from visible sensors does not reflect the fast snow melt trend. To illustrate the practical application of global snow monitoring with spaceborne Ku-band scatterometer, we present NSCAT backscatter response corresponding to the snow event leading to the 1997 Flood of the Century over the U.S. Northern plains and the Canadian prarie region, which caused loss of lives and several billion dollars in flood related damages and cleanup costs. Finally, we show that the fixed incidence configuration of Seawinds scatterometers, to be launched on QuickSCAT and on ADEOS-2 in the near future, is better for snow monitoring and then discuss the use of future high-resolution scatterometers for global snow mapping.

Nghiem, S. V.↗
NASA NTRS2009

Enhancement of the MODIS Daily Snow Albedo Product

The MODIS daily snow albedo product is a data layer in the MOD10A1 snow-cover product that includes snow-covered area and fractional snow cover as well as quality information and other metadata. It was developed to augment the MODIS BRDF/Albedo algorithm (MCD43) that provides 16-day maps of albedo globally at 500-m resolution. But many modelers require daily snow albedo, especially during the snowmelt season when the snow albedo is changing rapidly. Many models have an unrealistic snow albedo feedback in both estimated albedo and change in albedo over the seasonal cycle context, Rapid changes in snow cover extent or brightness challenge the MCD43 algorithm; over a 16-day period, MCD43 determines whether the majority of clear observations was snow-covered or snow-free then only calculates albedo for the majority condition. Thus changes in snow albedo and snow cover are not portrayed accurately during times of rapid change, therefore the current MCD43 product is not ideal for snow work. The MODIS daily snow albedo from the MOD10 product provides more frequent, though less robust maps for pixels defined as "snow" by the MODIS snow-cover algorithm. Though useful, the daily snow albedo product can be improved using a daily version of the MCD43 product as described in this paper. There are important limitations to the MOD10A1 daily snow albedo product, some of which can be mitigated. Utilizing the appropriate per-pixel Bidirectional Reflectance Distribution Functions (BRDFs) can be problematic, and correction for anisotropic scattering must be included. The BRDF describes how the reflectance varies with view and illumination geometry. Also, narrow-to-broadband conversion specific for snow on different surfaces must be calculated and this can be difficult. In consideration of these limitations of MOD10A1, we are planning to improve the daily snow albedo algorithm by coupling the periodic per-pixel snow albedo from MCD43, with daily surface ref|outanoom, In this paper, we compare a daily version of MCD43B3 with the daily albedo from MOD10A1. and MCD43B3 with a 16-day average of MOD10A1, over Greenland. We also discuss some near-future planned enhancements to MOD10A1.

Hall, Dorothy K.↗
NASA NTRS1995

Satellite Snow-Cover Mapping: A Brief Review

Satellite snow mapping has been accomplished since 1966, initially using data from the reflective part of the electromagnetic spectrum, and now also employing data from the microwave part of the spectrum. Visible and near-infrared sensors can provide excellent spatial resolution from space enabling detailed snow mapping. When digital elevation models are also used, snow mapping can provide realistic measurements of snow extent even in mountainous areas. Passive-microwave satellite data permit global snow cover to be mapped on a near-daily basis and estimates of snow depth to be made, but with relatively poor spatial resolution (approximately 25 km). Dense forest cover limits both techniques and optical remote sensing is limited further by cloudcover conditions. Satellite remote sensing of snow cover with imaging radars is still in the early stages of research, but shows promise at least for mapping wet or melting snow using C-band (5.3 GHz) synthetic aperture radar (SAR) data. Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data beginning with the launch of the first EOS platform in 1998. Digital maps will be produced that will provide daily, and maximum weekly global snow, sea ice and lake ice cover at 1-km spatial resolution. Statistics will be generated on the extent and persistence of snow or ice cover in each pixel for each weekly map, cloudcover permitting. It will also be possible to generate snow- and ice-cover maps using MODIS data at 250- and 500-m resolution, and to study and map snow and ice characteristics such as albedo. been under development. Passive-microwave data offer the potential for determining not only snow cover, but snow water equivalent, depth and wetness under all sky conditions. A number of algorithms have been developed to utilize passive-microwave brightness temperatures to provide information on snow cover and water equivalent. The variability of vegetative Algorithms are being developed to map global snow and ice cover using Earth Algorithms to map global snow cover using passive-microwave data have also cover and of snow grain size, globally, limits the utility of a single algorithm to map global snow cover.

Hall, Dorothy K.↗