George A. Riggs

MODIS Snow-Cover Products

Abstract On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. MODIS snow and ice products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to or enhancement of the currently available operational products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. The MODIS snow-mapping algorithms are automated, which means that a consistent data set may be generated for long-term climate studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the products. The MODIS snow product suite begins with a 500-m resolution, 2330-km swath snow-cover map, which is then gridded to an integerized sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to a climate-modeling grid (CMG) product at 0.05° resolution, with both daily and 8-day composite products. Each pixel of the daily CMG contains fraction of snow cover from 40% to 100%. Measured errors of commission in the CMG are low, for example, on the continent of Australia in the spring, they vary from 0.02% to 0.10%. Near-term enhancements include daily snow albedo and fractional snow cover. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented to show some early validation work.

Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data

Abstract An algorithm is being developed to map global snow cover using Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data beginning at launch in 1998. As currently planned, digital maps will be produced that will provide daily, and perhaps maximum weekly, global snow cover at 500-m spatial resolution. Statistics will be generated on the extent and persistence of snow cover in each pixel for each weekly map, cloud cover permitting. It will also be possible to generate snow-cover maps at 250-m spatial resolution using MODIS data, and to study snow-cover characteristics. Preliminary validation activities of the prototype version of our snow-mapping algorithm, SNOMAP, have been undertaken. SNOMAP will use criteria tests and a decision rule to identify snow in each 500-m MODIS pixel. Use of SNOMAP on a previously mapped Landsat Thematic Mapper (TM) scene of the Sierra Nevadas has shown that SNOMAP is 98 % accurate in identifying snow in pixels that are snow covered by 60% or more. Results of a comparison of a SNOMAP classification with a supervised-classification technique on six other TM scenes show that SNOMAP and supervised-classification techniques agree to within about 11 % or less for nearly cloud-free scenes and that SNOMAP provided more consistent results. About 10 % of the snow cover, known to be present on the 14 March 1991 TM scene covering Glacier National Park in northern Montana, is obscured by dense forest cover. Mapping snow cover in areas of dense forests is a limitation in the use of this procedure for global snow-cover mapping. This limitation, and sources of error will be assessed globally as SNOMAP is refined and tested before and following the launch of MODIS.

Improving snow cover mapping in forests through the use of a canopy reflectance model

MODIS, the moderate resolution imaging spectroradiometer, will be launched in 1998 as part of the first earth observing system (EOS) platform. Global maps of land surface properties, including snow cover, will be created from MODIS imagery. The MODIS snow-cover mapping algorithm that will be used to produce daily maps of global snow cover extent at 500 m resolution is currently under development. With the exception of cloud cover, the largest limitation to producing a global daily snow cover product using MODIS is the presence of a forest canopy. A Landsat Thematic Mapper (TM) time-series of the southern Boreal Ecosystem-Atmosphere Study (BOREAS) study area in Prince Albert National Park, Saskatchewan, was used to evaluate the performance of the current MODIS snow-cover mapping algorithm in varying forest types. A snow reflectance model was used in conjunction with a canopy reflectance model (GeoSAIL) to model the reflectance of a snow-covered forest stand. Using these coupled models, the effects of varying forest type, canopy density, snow grain size and solar illumination geometry on the performance of the MODIS snow-cover mapping algorithm were investigated. Using both the TM images and the reflectance models, two changes to the current MODIS snow-cover mapping algorithm are proposed that will improve the algorithms classification accuracy in forested areas. The improvements include using the normalized difference snow index and normalized difference vegetation index in combination to discriminate better between snow-covered and snow-free forests. A minimum albedo threshold of 10% in the visible wavelengths is also proposed. This will prevent dense forests with very low visible albedos from being classified incorrectly as snow. These two changes increase the amount of snow mapped in forests on snow-covered TM scenes, and decrease the area incorrectly identified as snow on non-snow-covered TM scenes.

A blended global snow product using visible, passive microwave and scatterometer satellite data

A joint US Air Force/National Aeronautics and Space Administration (NASA) blended global snow product that uses Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and Quick Scatterometer (QuikSCAT or QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by using a newly developed Air Force Weather Agency (AFWA)/NASA Snow Algorithm (ANSA). This initial blended snow product uses minimal modelling to expeditiously yield improved snow products, which include, or will include, snow-cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the USA, from Colorado obtained during the Cold Land Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow-cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or diurnal-amplitude variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.

Land and cryosphere products from Suomi NPP VIIRS: Overview and status

[1] The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA’s Earth Observing System’s Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA- and NOAA-funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA’s focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar-Orbiting Environmental Satellite System. The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to-date findings of the NASA Science Team’s evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS.

Assessment of the Relative Accuracy of Hemispheric-Scale Snow-Cover Maps

Abstract There are several hemispheric-scale satellite-derived snow-cover maps available, but none has been fully validated. For the period 23 October–25 December 2000, we compare snow maps of North America derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and operational snow maps from the U.S. National Oceanic and Atmospheric Administration (NOAA) National Operational Hydrologic Remote Sensing Center (NOHRSC), both of which rely on satellite data from the visible and near-infrared parts of the spectrum; we also compare MODIS maps with Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) passive-microwave snow maps for the same period. The maps derived from visible and near-infrared data are more accurate for mapping snow cover than are the passive-microwave-derived maps, but discrepancies exist as to the location and extent of the snow cover even between operational snow maps. The MODIS snow-cover maps show more snow in each of the 8 day periods than do the NOHRSC maps, in part because MODIS maps the effects of fleeting snowstorms due to its frequent coverage. The large (~30 km) footprint of the SSM/I pixel, and the difficulty in distinguishing wet and shallow snow from wet or snow-free ground, reveal differences up to 5.33 x 106 km2 in the amount of snow mapped using MODIS vs SSM/I data. Algorithms that utilize both visible and passive-microwave data, which would take advantage of the all-weather mapping capability of the passive-microwave data, will be refined following the launch of the Advanced Microwave Scanning Radiometer (AMSR) in the fall of 2001.

Detection of canopy water stress in conifers using the Airborne Imaging Spectrometer

Abstract Imagery was acquired by the Airborne Imaging Spectrometer (AIS-2) over adjacent plots of control (natural) and water-stressed canopies of Norway spruce and white pine, and analyzed for differences in near-infrared reflectance features. Water stress had been induced in the trees by severing the sapwood and was assessed with shoot water potential (ψ) and relative water content (RWC) measurements. Stressed Norway spruce was found to have approximately 20% greater relative reflectance in the 1.0–1.3 μm region compared to the control canopy in the image obtained at 0925 h 13 days after stress induction. The difference in ψ and RWC between stressed and control was measured at 1.2 MPa and estimated at 7%, respectively. However, the difference in reflectance decreased to insignificance in the image taken at 1219 h that same day when the differences in ψ and RWC were approximately 0.72 MPa and 5%. In white pine, no significant differences in reflectance between stressed and control canopies were found in images obtained 14 days and 20 days after treatment with estimated differences in ψ and RWC of 0.3 MPa and 6%. Because extensive ground data was required at times of AIS-2 overflights to detect these small reflectance differences, we believe that water stress in conifer canopies may not be routinely detectable at an operational landscape scale.

Assimilation of Remotely Sensed Soil Moisture and Snow Depth Retrievals for Drought Estimation

AbstractThe accurate knowledge of soil moisture and snow conditions is important for the skillful characterization of agricultural and hydrologic droughts, which are defined as deficits of soil moisture and streamflow, respectively. This article examines the influence of remotely sensed soil moisture and snow depth retrievals toward improving estimates of drought through data assimilation. Soil moisture and snow depth retrievals from a variety of sensors (primarily passive microwave based) are assimilated separately into the Noah land surface model for the period of 1979–2011 over the continental United States, in the North American Land Data Assimilation System (NLDAS) configuration. Overall, the assimilation of soil moisture and snow datasets was found to provide marginal improvements over the open-loop configuration. Though the improvements in soil moisture fields through soil moisture data assimilation were barely at the statistically significant levels, these small improvements were found to translat...

Sea Ice Extent and Classification Mapping with the Moderate Resolution Imaging Spectroradiometer Airborne Simulator

An algorithm for mapping sea ice extent and generalized classification of sea ice by reflective and temperature characteristics with Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) data is presented. The algorithm was tested using a MAS scene over the Bering Sea near St. Lawrence Island, Alaska, USA, acquired 8 April 1995. Clouds were masked with the University of Wisconsin cloud masking algorithm. Ice surface temperature was estimated with a split-window technique. Sea ice extent and generalized type of sea ice were identified based on reflective characteristics and estimated ice surface temperature using a grouped criteria technique. Resulting maps were consistent with visual interpretation and with sea ice extent and type information reported in prior studies of the region.

A snow index for the Landsat Thematic Mapper and Moderate Resolution Imaging Spectroradiometer

Describes the snow mapping algorithm being developed for use with the Earth Observing System (EOS) MODerate resolution Imaging Spectroradiometer (MODIS). A key component of this snow mapping algorithm is the normalized difference snow index (NDSI). The NDSI employs Landsat Thematic Mapper (TM) visible (0.56 /spl mu/m) and near-infrared (1.65 /spl mu/m) data. The snow algorithm uses the NDSI in combination with near-infrared reflectance to identify snow cover and discriminate snow from clouds. The NDSI-based snow algorithm functions with a simple set of decision rules for snow, and can be run in an automated fashion on any TM scene without a priori knowledge of surface characteristics. Consistent identification of snow cover in a variety of TM scenes has been attained with the algorithm. Snow mapping results from TM imagery of the Glacier National Park, Montana region are presented. This algorithm is expected to generate global snow cover data products in the EOS era beginning in 1998.<<ETX>>