Jeff Dozier

Spectral Signature of Alpine Snow Cover from the Landsat Thematic Mapper

Mapping of snow and estimation of snow characteristics from satellite remote sensing data require that we distinguish snow from other surface cover and from clouds and compensate for the effects of the atmosphere and rugged terrain. The spectral signature of the snowpack is calculated from a radiative transfer model, accounting for scattering and absorption by the ice grains, water inclusions, and particulates. In interpreting the spectral reflectance measured from the Thematic Mapper, or from any other satellite, we need to account for topographic effects without requiring that satellite data be precisely registered to digital elevation data, because the poor quality of most digital elevation data introduces considerable noise into calculations of slope and azimuth. For a range of snow types, atmospheric profiles, and topographic illumination conditions, I estimate typical spectral signatures for the Landsat Thematic Mapper. TM images of the southern Sierra Nevada are analyzed to distinguish several classes of snow from other surface covers. Snow can be reliably distinguished from other surfaces and from clouds at all sun angles encountered in the mid-latitudes. In TM Band 1 or 2, snow is brighter than other natural surfaces, and in Band 5 clouds are usually much more reflective than snow. Therefore, the intersection of the following criteria maps the snow cover: planetary reflectance Rp in TM Band 1 is greater than about 0.16; Rp (TM5) is less than about 0.2; the normalized Band 2 – 5 difference, [Rp(TM2) − Rp(TM5)] / [Rp(TM2) + Rp(TM5)], is greater than about 0.4. Large surface grain sizes can be distinguished from areas where the grain size is finer at the snow surface, using TM Bands 2, 4 and 5. Because of saturation in TM Band 1, estimation of the degree of contamination by absorbing aerosols is not feasible. [Rp(TM2) − Rp(TM5)]/[Rp(TM2) + Rp(TM5)], is

The Effect of Grain Size on Spectral Mixture Analysis of Snow-Covered Area from AVIRIS Data

Abstract We developed a technique to improve spectral mixture analysis of snow-covered area in alpine regions through the use of multiple snow endmembers. Snow reflectance in near-infrared wavelengths is sensitive to snow grain size while in visible wavelengths it is relatively insensitive. Snow-covered alpine regions often exhibit large surface grain size gradients due to changes in aspect and elevation. The sensitivity of snow spectral reflectance to grain size translates these grain size gradients into spectral gradients. To spectrally characterize a snow-covered image domain with mixture analysis, the variable spectral nature of snow must be accounted for by use of multiple snow endmembers of varying grain size. We performed numerical simulations to demonstrate the sensitivity of mixture analysis to grain size for a range of sizes and snow fractions. From Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data collected over Mammoth Mountain, CA on 5 April 1994, a suite of snow image endmembers spanning the imaged region’s grain size range were extracted. Mixture models with fixed vegetation, rock, and shade were applied with each snow endmember. For each pixel, the snow fraction estimated by the model with least mixing error (RMS) was chosen to produce an optimal map of subpixel snow-covered area. Results were verified with a high spatial resolution aerial photograph demonstrating equivalent accuracy. Analysis of fraction under/overflow and residuals confirmed mixture analysis sensitivity to grain size gradients.

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.

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.

Earth Observing System

The Earth Observing System (EOS), the centerpiece of NASAs Mission to Planet Earth, is to study the interactions of the atmosphere, land, oceans, and living organisms, using the perspective of space to observe the earth as a global environmental system. To better understand the role of clouds in global change, EOS will measure incoming and emitted radiation at the top of the atmosphere. Then, to study characteristics of the atmosphere that influence radiation transfer between the top of the atmosphere and the surface, EOS wil observe clouds, water vapor and cloud water, aerosols, temperature and humidity, and directional effects. To elucidate the role of anthropogenic greenhouse gas and terrestrial and marine plants as a source or sink for carbon, EOS will observe the biological productivity of lands and oceans. EOS will also study surface properties that affect biological productivity at high resolution spatially and spectrally.

Snow Property Measurements Correlative to Microwave Emission at 35 GHz

Snow microstructure, measured by plane section analysis, and snow wetness, measured by the dilution method, are used to calculate input parameters for a microwave emission model that uses the radiative transfer method. The scattering and absorbing properties are calculated by Mie theory. The effects of different equivalent sphere conversions, adjustments for near-field interference, and different snow wetness characterizations are compared for various snow conditions.

Preparation of serial sections in dry snow specimens

Pore space in a dry snow specimen is filled with a water‐insoluble liquid which is frozen solid. The specimen is microtomed, polished with carbon dust, and micrographed through a photomicroscope mounted directly over the microtome. Serial sections are prepared and micrographed at the rate of about ten per hour. The micrographs are automatically digitized, and converted to binary images for microstructural analysis.

Stereological characterization of dry alpine snow for microwave remote sensing

Abstract A persistent problem in investigations of electromagnetic properties of snow, from reflectance at visible wavelengths to emission and backscattering in the microwave, has been the proper characterization of the snows physical properties. We suggest that the granular and laminar structure of snow can be measured in its aggregated state by stereology performed on sections prepared from snow specimens, and that these kinds of measurements can be incorporated into models of the electromagnetic properties. With careful sampling, anisotropy in the snow microstructure at various scales can be quantified. We show how stereological parameters can be averaged over orientation and optical depth for radiative transfer modeling.

Spectral emissivity measurements of land-surface materials and related radiative transfer simulations

Abstract Spectral radiance measurements have been made in the laboratory and in the field for deriving spectral emissivities of some land cover samples with a spectroradiometer and an auxiliary radiation source in the wavelength range 2.5–14.5 μm. A easy and quick four-step method (four step to measure the sample and a diffuse reflecting plate surface under sunshine and shadowing conditions, respectively) has been used for simultaneous determination of surface temperature and emissivity. We emphasized in-situ measurements in combination with radiative transfer simulations, and an error analysis for basic assumptions in deriving spectral emissivity of land-surface samples from thermal infrared measurements.

Towards predicting temporal changes of the spectral signature of snow in visible and near-infrared wavelengths

This study links two models, one that simulates changes in snow microstructure and one that recovers microstructure properties from measurements of snow reflectance. An energy and mass transfer model, SNTHERM.89, was used to calculate snow grain growth. Grain-sizes from the model and measurements of grain bond areas provided estimates of the surface-to-volume ratio of the bulk snow, which were transformed to geometrically-equivalent sphere sizes. An inversion technique based on a discrete-ordinate model of the directional reflectance recovered optically-equivalent sphere sizes from reflectance measurements at 1.075 j1.m. The predictions of equivalent sphere sizes from the snow model and the recovered optical sphere sizes from the inversion method were compared with stereological measurements from snow sections. The geometrically-equivalent and optically-equivalent grain-sizes showed good agreement with each other and with stereological measurements from snow a few days old. The predictions of the reflectance inversion method also compared favorably with geometrically-equivalent grain-sizes measured from a melt-freeze surface crust. This investigation showed the potential for fully coupling snow property simulations with models to predict the spectral reflectance of snow.