Research relative to angular distribution of snow reflectance/snow cover characterization and microwave emission

Remote sensing has been applied in recent years to monitoring snow cover properties for applications in hydrologic and energy balance modeling. In addition, snow cover has been recently shown to exert a considerable local influence on weather variables. Of particular importance is the potential of sensors to provide data on the physical properties of snow with high spatial and temporal resolution. Visible and near-infrared measurements of upwelling radiance can be used to infer near-surface properties through the calculation of albedo. Microwave signals usually come from deeper within the snow pack and thus provide depth-integrated information, which can be measured through clouds and does not relay on solar illumination.Fundamental studies examining the influence of snow properties on signals from various parts of the electromagnetic spectrum continue in part because of the promise of new remote sensors with higher spectral and spatial accuracy. Information in the visible and near-infrared parts of the spectrum comprise nearly all available data with high spatial resolution. Current passive microwave sensors have poor spatial resolution and the data are problematic where the scenes consist of mixed landscape features, but they offer timely observations that are independent of cloud cover and solar illumination

Data compression for data archival, browse or quick-look

Soon after space and Earth science data is collected, it is stored in one or more archival facilities for later retrieval and analysis. Since the purpose of the archival process is to keep an accurate and complete record of data, any data compression used in an archival system must be lossless, and protect against propagation of error in the storage media. A browse capability for space and Earth science data is needed to enable scientists to check the appropriateness and quality of particular data sets before obtaining the full data set(s) for detailed analysis. Browse data produced for these purposes could be used to facilitate the retrieval of data from an archival facility. Quick-look data is data obtained directly from the sensor for either previewing the data or for an application that requires very timely analysis of the space or Earth science data. Two main differences between data compression techniques appropriate to browse and quick-look cases, are that quick-look can be more specifically tailored, and it must be limited in complexity by the relatively limited computational power available on space platforms

An improved algorithm for retrieval of snow wetness using C-band AIRSAR

This study shows recent results of our efforts to develop and verify an algorithm for snow wetness retrieval from a polarimetric SAR (Synthetic Aperture Radar). Our algorithm is based on the first-order scattering model with consideration of both surface and volume scattering. It operates at C-band and requires only rough information about the ice volume fraction in snowpack. Comparing ground measurements and inferred from JPL AIRSAR data, the results showed that the relative error inferred from SAR imagery was within 25 percent. The inferred snow wetness from different looking geometries (two flight passes) provided consistent results within 2 percent. Both regional and point measurement comparisons between the ground and SAR derived snow wetness indicates that the inversion algorithm performs well using AIRSAR (Airborne Synthetic Aperture Radar) data and should prove useful for routine and large-area snow wetness (in top layer of a snowpack) measurements

Improving alpine-region spectral unmixing with optimal-fit snow endmembers

Surface albedo and snow-covered-area (SCA) are crucial inputs to the hydrologic and climatologic modeling of alpine and seasonally snow-covered areas. Because the spectral albedo and thermal regime of pure snow depend on grain size, areal distribution of snow grain size is required. Remote sensing has been shown to be an effective (and necessary) means of deriving maps of grain size distribution and snow-covered-area. Developed here is a technique whereby maps of grain size distribution improve estimates of SCA from spectral mixture analysis with AVIRIS data

Retrieval of subpixel snow covered area, grain size, and albedo from MODIS

We describe and validate a model that retrieves fractional snow-covered area and the grain size and albedo of that snow from surface reflectance data (product MOD09GA) acquired by NASA\u27s Moderate Resolution Imaging Spectroradiometer (MODIS). The model analyzes the MODIS visible, near infrared, and shortwave infrared bands with multiple endmember spectral mixtures from a library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model specific to a scene\u27s illumination geometry; spectra for vegetation, rock, and soil were collected in the field and laboratory. We validate the model with fractional snow cover estimates from Landsat Thematic Mapper data, at 30 m resolution, for the Sierra Nevada, Rocky Mountains, high plains of Colorado, and Himalaya. Grain size measurements are validated with field measurements during the Cold Land Processes Experiment, and albedo retrievals are validated with in situ measurements in the San Juan Mountains of Colorado. The pixel-weighted average RMS error for snow-covered area across 31 scenes is 5%, ranging from 1% to 13%. The mean absolute error for grain size was 51 μm and the mean absolute error for albedo was 4.2%. Fractional snow cover errors are relatively insensitive to solar zenith angle. Because MODSCAG is a physically based algorithm that accounts for the spatial and temporal variation in surface reflectances of snow and other surfaces, it is capable of global snow cover mapping in its more computationally efficient, operational mode

The Payload Advisory Panel and the Data and Information System Advisory Panel of the Investigators Working Group of the Earth Observing System: A joint report

The Payload Advisory Panel of the Investigators Working Group (IWG) for the Earth Observing System (EOS) met 4 to 6 October 1993 in Herndon, Virginia. The Panel, originally composed of the Interdisciplinary Science Principal Investigators, was expanded to include all Principal Investigators and as such is now the IWG itself. The meeting also addressed directly a report from the EOS Data and Information System (EOSDIS) Advisory Panel. The meeting focused on payload issues in the years 2000 to 2005; however, some subjects in the nearer-term, most significantly EOSDIS, were considered. The overarching theme of convergence in Earth observations set a backdrop for the entire meeting. Other themes included: atmospheric chemistry; remote sensing of the global cycles of energy, water, and carbon in EOS; ocean and land-ice altimetry; and the EOSDIS. The Totol Solar Irradiance Monitoring Report and results from the Accelerated Canopy Chemistry Program are included as appendices

Spatially distributed temperatures at the base of two mountain snowpacks measured with fiber-optic sensors

Snowpack base temperatures vary during accumulation and diurnally. Their measurement provides insight into physical, biological and chemical processes occurring at the snow/soil interface. Recent advances in Raman-spectra instruments, which use the scattered light in a standard telecommunications fiber-optic cable to infer absolute temperature along the entire length of the fiber, offer a unique opportunity to obtain basal snow temperatures at resolutions of 1m, 10 s and 0.1°C. Measurements along a 330m fiber over 24 hours during late-spring snowmelt at Mammoth Mountain, California, USA, showed basal snow temperatures of 0±0.2°C using 10 s averages. Where the fiberoptic cable traversed bare ground, surface temperatures approached 408C during midday. The durability of the fiber optic was excellent; no major damage or breaks occurred through the winter of burial. Data from the Dry Creek experimental watershed in Idaho across a small stream valley showed little variability of temperature on the northeast-facing, snow-covered slope, but clearly showed melting patterns and the effects of solar heating on southwest-facing slopes. These proof-of-concept experiments show that the technology enables more detailed spatial and temporal coverage than traditional point measurements of temperature

Social science in a water observing system

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95008/1/wrcr12336.pd

Computational provenance in hydrologic science: a snow mapping example

The article of record as published may be found at http://dx.doi.org/10.1098/rsta.2008.0187Computational provenance—a record of the antecedents and processing history of digital information—is key to properly documenting computer-based scientific research. To support investigations in hydrologic science, we produce the daily fractional snow- covered area from NASA’s moderate-resolution imaging spectroradiometer (MODIS). From the MODIS reflectance data in seven wavelengths, we estimate the fraction of each 500 m pixel that snow covers. The daily products have data gaps and errors because of cloud cover and sensor viewing geometry, so we interpolate and smooth to produce our best estimate of the daily snow cover. To manage the data, we have developed the Earth System Science Server (ES3), a software environment for data-intensive Earth science, with unique capabilities for automatically and transparently capturing and managing the provenance of arbitrary computations. Transparent acquisition avoids the scientists having to express their computations in specific languages or schemas in order for provenance to be acquired and maintained. ES3 models provenance as relationships between processes and their input and output files. It is particularly suited to capturing the provenance of an evolving algorithm whose components span multiple languages and execution environments.NASAFunded by Naval Postgraduate School.Cooperative Agreements NNG0C52A and NNG04GE66G (NASA)N00244-07-1-0013 (NPS