Mary J. Brodzik

Recent northern hemisphere snow extent: A comparison of data derived from visible and microwave satellite sensors

During the past four decades much important information on Northern Hemisphere snow extent has been provided by the NOAA weekly snow extent charts derived from visible-band satellite imagery. Passive microwave satellite remote sensing can enhance snow measurements based on visible data alone because of the ability to penetrate clouds, provide data during darkness and the potential to provide an index of snow depth or water equivalent. We compare the fluctuation of Northern Hemisphere snow cover over the past twenty years using these two satellite remote sensing techniques. Results show comparable inter-annual variability with similar long-term hemispheric-scale trends indicating decreases in snow extent of approximately 0.2 percent per year. The passive microwave snow algorithm applied in this study indicates less snow-covered area than the visible data during fall and early winter when the snow is shallow. New algorithms designed to reduce this apparent error are being developed and tested.

An earth-gridded SSM/I data set for cryospheric studies and global change monitoring

Abstract The National Snow and Ice Data Center (NSIDC) has distributed DMSP Special Sensor Microwave Imager (SSM/I) brightness temperature grids for the Polar Regions on CD-ROM since 1987. In order to expand this product to include all potential snow covered regions, the area of coverage is now global. The format for the global SSM/I data set is the Equal Area SSM/I Earth Grid (EASE-Grid) developed at NSIDC. The EASE-Grid has been selected as the format for the NASA/NOAA Pathfinder Program Level 3 Products which include both SSM/I and SMMR (Scanning Multichannel Microwave Radiometer) data (1978–1987). Providing both data sets in the EASE-Grid will result in a 15 year time-series of satellite passive microwave data in a common format. The extent and variability of seasonal snow cover is recognized to be an important parameter in climate and hydrologic systems and trends in snow cover serve as an indicator of global climatic changes. Passive microwave data from satellites afford the possibility to monitor temporal and spatial variations in snow cover on the global scale, avoiding the problems of cloud cover and darkness. NSIDC is developing the capability to produce daily snow products from the DMSP-SSM/I satellite with a spatial resolution of 25 km. In order to provide a standard environment in which to validate SSM/I algorithm output, it is necessary to assemble baseline data sets using other, more direct, methods of measurement. NSIDC has compiled a validation data set of surface station measurements for the northern hemisphere with specific focus on the United States, Canada, and the former Soviet Union. Digital image substraction is applied to compare the surface station and satellite measurements.

EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets

Defined in the early 1990s for use with gridded satellite passive microwave data, the Equal-Area Scalable Earth Grid (EASE-Grid) was quickly adopted and used for distribution of a variety of satellite and in situ data sets. Conceptually easy to understand, EASE-Grid suffers from limitations that make it impossible to format in the widely popular GeoTIFF convention without reprojection. Importing EASE-Grid data into standard mapping software packages is nontrivial and error-prone. This article defines a standard for an improved EASE-Grid 2.0 definition, addressing how the changes rectify issues with the original grid definition. Data distributed using the EASE-Grid 2.0 standard will be easier for users to import into standard software packages and will minimize common reprojection errors that users had encountered with the original EASE-Grid definition.

Hemispheric-scale comparison and evaluation of passive-microwave snow algorithms

Abstract Passive-microwave satellite remote sensing can greatly enhance large-scale snow measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. This study provides preliminary results from the comparison and evaluation of several different passive-microwave algorithms. These algorithms represent examples which include both mid- and high-frequency channels, vertical and horizontal polarizations and polarization-difference approaches. In our comparisons we utilize larger, more comprehensive, validation datasets which can be expected to provide a full range of snow/climate conditions rather than limited data which may only represent a snapshot in time and space. Evaluation of snow extent derived from passive-microwave data is undertaken through comparison with the U.S. National Oceanic and Atmospheric Administration (NOAA) Northern Hemisphere snow charts which are based on visible-band satellite data. Results clearly indicate those time periods and geographic regions where the two techniques agree and where they tend to consistently disagree. Validation of snow water equivalent derived from passive-microwave data is undertaken using measurements from snow-course transects in the former Soviet Union. Preliminary results indicate a general tendency for nearly all of the algorithms to underestimate snow water equivalent.

Correction: Brodzik, M.J., et al. EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets. ISPRS International Journal of Geo-Information 2012, 1, 32–45

causes some mapping software to transform locations along the left edge to longitude 180.0 and locations along the right edge to −180.0. The desirable behavior is to transform the left edge to longitude −180.0 and the right edge to longitude 180.0. We have therefore decided to define the 25 km cylindrical

Automated mapping of Earth's annual minimum exposed snow and ice with MODIS

] Global snow and ice have been diminishing during theAnthropocenebutwestilllackacompletemappingofannualminimumexposedsnowandicewithaconsistent,repeatablealgorithm. The Global Land Ice Measurements from Space(GLIMS) project has compiled digital glacier outlines andrelated metadata for the majority of the world’s glaciers butinconsistency among product algorithms and time periodsrepresented precludes the production of a consistentlyderived global data set. Here we present the MODIS Persis-tent Ice (MODICE) algorithm that leverages the time seriesof fractional snow and ice cover from the MODIS SnowCovered Area and Grain size (MODSCAG) algorithm. Theend product of MODICE is a consistently derived map ofannual minimum exposed snow and ice. Comparisons ofMODICE with GLIMS glacier outlines derived from SPOT,ASTER, and Landsat Thematic Mapper show strong agree-ment with the higher resolution outlines subject to uncer-tainties with spatial resolution, deep mountain shadows, andGLIMS interpretation errors.

Optimum Image Formation for Spaceborne Microwave Radiometer Products

This paper considers some of the issues of radiometer brightness image formation and reconstruction for use in the NASA-sponsored Calibrated Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature Earth System Data Record project, which generates a multisensor multidecadal time series of high-resolution radiometer products designed to support climate studies. Two primary reconstruction algorithms are considered: the Backus-Gilbert approach and the radiometer form of the scatterometer image reconstruction (SIR) algorithm. These are compared with the conventional drop-in-the-bucket (DIB) gridded image formation approach. Tradeoff study results for the various algorithm options are presented to select optimum values for the grid resolution, the number of SIR iterations, and the BG gamma parameter. We find that although both approaches are effective in improving the spatial resolution of the surface brightness temperature estimates compared to DIB, SIR requires significantly less computation. The sensitivity of the reconstruction to the accuracy of the measurement spatial response function (MRF) is explored. The partial reconstruction of the methods can tolerate errors in the description of the sensor measurement response function, which simplifies the processing of historic sensor data for which the MRF is not known as well as modern sensors. Simulation tradeoff results are confirmed using actual data.

A custom EASE-Grid SSM/I processing system

A task fundamental to advancing global change research is the availability of a standard reference system for direct digital comparison and interuse of remote sensing data sets on varying spatial and temporal scales. The availability of a standard gridding scheme is an essential requirement for systematic time-series studies. Such a scheme also supports the direct comparison of different remote sensing products, as well as the validation of algorithms, through digital comparison with surface station and other ancillary data sets which have been processed into the common grid. New software now allows users of Special Sensor Microwave/Imager (SSM/I) data to produce their own Equal-Area Scalable Earth Grid (EASE-Grid) image data from Temperature Data Record (TDR) format swath data. Overlapping orbit data are separately retained for the highest possible temporal resolution, and data for specific pixels can be extracted. The modular processor also simplifies testing of alternative resampling and gridding algorithms. The EASE-Grid is a standard gridding scheme now used with a variety of satellite datasets, including SSM/I data, its original application. Although the specific method used to interpolate from native sensor coordinates to a fixed Earth grid is unique to each sensor, the fundamental projection and gridding concept of the EASE-Grid provides the basis for a standard interuse gridding method. Thus, the SSM/I EASE-Grid is composed of two fundamental parts: i) a grid and projection scheme and, 2) a specific method to interpolate SSM/I data from swath space to Earth gridded coordinates. The projection and gridding scheme is independent of the satellite sensor or data type.

Enhanced snow cover mapping using combined optical and passive microwave data

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Over the past several decades both optical and passive microwave satellite data have been utilized for snow mapping at the regional to global scale. Snow mapping using optical data is based primarily on the magnitude of the surface reflectance, and in some cases on specific spectral signatures, while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. We present results which describe the respective advantages and disadvantages of these two types of satellite data for snow cover mapping and demonstrate how a multi-sensor product is optimal. Passive microwave and optical data sets for the Northern Hemisphere indicate similar patterns of inter-annual variability, although annual maximum snow extents and monthly variability derived from the optical data consistently exceed those provided by the microwave data. Because of the significant differences between snow products derived from passive microwave and optical data, especially during early winter, our current efforts to produce climate data records for snow cover focus on methods which combine the advantages of both data types. For the period 1978 to 2004 we combine data from the manually generated NOAA weekly snow charts, produced primarily from AVHRR and GOES data, with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend NASA EOS MODIS and AMSR-E data sets. Prototype snow cover maps from AMSR-E compare well with maps derived from SSM/I. Our current EASE-Grid blended product incorporates MODIS data from the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) with microwave-derived SWE at 25 km. resulting in a blended product that includes percent snow cover in the larger grid cell whenever the microwave snow water equivalent (SWE) signal is absent

A comparison of Northern Hemisphere snow extent climatologies derived from passive microwave and visible remote sensing data

The extent and variability of seasonal snow cover are recognized as important parameters in climate and hydrologic systems. At its average maximum extent snow covers more than 45 percent of the land surface of the Northern Hemisphere and is the single greatest source of annual and interannual albedo fluctuations, both in terms of magnitude as well as spatial extent. Trends in snow cover are also expected to serve as an indicator of global climatic changes. Any decrease in snow resulting from a warming trend results in increased absorption of solar radiation and additional heat to melt additional snow. This results in the classic positive temperature albedo feedback mechanism which is included in nearly all climate models. In addition to the albedo effect, snow cover represents a significant heat sink during the warming period of the seasonal cycle due to a relatively high latent heat of fusion. As a result, the seasonal snow cover provides a major source of thermal inertia within the total climate system as it takes in and releases large quantities of energy with little or no fluctuation in temperature.