Polarimetric Scanning Radiometer Research Articles

Intercomparison of two trapezoid-based soil moisture downscaling methods using three scaling factors

ABSTRACTThis paper compared two soil moisture downscaling methods using three scaling factors. Level 3 soil moisture product of advanced microwave scanning radiometer for EOS (AMSR-E) is downscaled from 25 to 1 km. The downscaled results are compared with the soil moisture observations from polarimetric scanning radiometer (PSR) microwave radiometer and field sampling. The results show that (1) the scaling factor of normalized soil thermal inertia (NSTIs) and vegetation temperature condition index (VTCI) are better than soil evaporative efficiency in reflecting soil moisture; (2) for method 1, NSTIS is the best in the downscaling of soil moisture. For method 2, VTCI is the best; (3) no significant differences of the correlation coefficients (R2) and the biases were found between the two methods for the same scaling factors. However, method 2 shows a better potential than method 1 in the time-series applications of the downscaling of soil moisture; (4) compared with the relationship between the area-averaged soil moisture of AMSR-E and that of PSR, R2 of the 6 sets of the downscaled soil moisture almost do not decrease, which suggests the validity of the downscaling of soil moisture with the two downscaling methods using the three scaling factors.

A physically based hydrological connectivity algorithm for describing spatial patterns of soil moisture in the unsaturated zone

AbstractHydrologic connectivity has been proposed as an important concept for understanding local processes in the context of catchment hydrology. It can be useful for characterizing the soil moisture variability in complex heterogeneous landscapes. The current land surface models (e.g., Community Land Model, CLM) could not completely account for flow path continuity and connected patterns of subsurface properties in the unsaturated zone. In this study, we developed a physically based hydrologic connectivity algorithm based on dominant physical controls (e.g., topography, soil texture, and vegetation) to better understand the spatially distributed subsurface flow and improve the parameterization of soil hydraulic properties in hydrological modeling. We investigated the effects of mixed physical controls on soil moisture spatial variability and developed hydrologic connectivity using various thresholds. The connectivity was used for identifying the soil moisture variability and applied in a distributed land surface model (CLM) for calibrating soil hydraulic properties and improving model performance for estimating spatially distributed soil moisture. The proposed concept was tested in two watersheds (Little Washita in Oklahoma and Upper South Skunk in Iowa) comparing estimated soil moisture with the airborne remote sensing data (Electronically Scanning Thinned Array Radiometer and Polarimetric Scanning Radiometer). Our finding demonstrated that the spatial variations of soil moisture could be described well using physically based hydrologic connectivity, and the land surface model performance was improved by using the calibrated (distributed) soil hydraulic parameters. In addition, we found that the calibrated soil hydraulic parameters significantly affect model outputs not only on the water cycle but also on surface energy budgets.

Soil moisture inversion from aircraft passive microwave observations during SMEX04 using a single-frequency algorithm

Soil moisture plays a key role in global water cycles. In the study, soil moisture retrievals from airborne microwave radiometer observations using a single-frequency algorithm were presented. The algorithm is based on a simplified radiative transfer (tau-omega) model and the influence of both the roughness and vegetation is combined into a single parameter in the algorithm. The microwave polarization difference index (MPDI) is used to eliminate the effects of temperature. Then soil moisture is obtained through a nonlinear iterative procedure by making the absolute value of the differences between the simulated and observed MPDI minimum. The algorithm was validated with aircraft passive microwave data from the Polarimetric Scanning Radiometer (PSR) at the Arizona during the Soil Moisture Experiment 2004 (SMEX04). The results show that the soil moisture retrieved by the algorithm is in good agreement with ground measurements with a small bias and an overall accuracy of 0.037m3m−3.

A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km × 50 km) coordinated aircraft and AMSR-E snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an AMSR-E simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59.

Comparison of Statistical and Multifractal Properties of Soil Moisture and Brightness Temperature From ESTAR and PSR During SGP99

Global soil moisture products are based on passive microwave sensors of brightness temperature at different frequencies, including Land C-bands. Two airborne sensors used to develop and test retrieval algorithms for soil moisture estimation are the L-band Electronically Scanned Thinned Array Radiometer (ESTAR) and the C-band Polarimetric Scanning Radiometer (PSR). In this letter, we compare the statistical and multifractal properties of soil moisture and brightness temperature from ESTAR and PSR during the Southern Great Plains Experiment in 1999. We show that differences between products are minimized at a support scale close to the satellite resolution. Then, we compare the subfootprint variabilities of each product in eight coarse domains of 25.6 × 25.6 km2. Scale-invariance and multifractal properties were found in all the three available soil moisture products, but multifractality was negligible in brightness temperature fields, implying that the nonlinear transformations in retrieval algorithms introduce the multifractal behavior in soil moisture. Subfootprint variability and the multifractal behavior for diverse wetness conditions are also different for the three products. Since discrepancies between the subfootprint variabilities of PSR and ESTAR brightness temperatures are small, the factors leading to the differences among the soil moisture products are mainly due to the level of detail of the retrieval algorithm and to the spatial variability of forcing data and parameters related to surface roughness and vegetation.

Estimation of Snow Water Equivalence Using the Polarimetric Scanning Radiometer From the Cold Land Processes Experiments (CLPX03)

In this letter, we investigated an inversion technique to estimate snow water equivalence (SWE) under Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor configurations. Through our numerical simulations by the advanced integral equation model (AIEM), we found that the ground surface emission signals at 18.7 and 36.5 GHz were highly correlated regardless of the ground surface properties (dielectric and roughness properties) and can be well described by a linear function. It leads to a new development for describing the relationship between snow emission signals observed at 18.7 and 36.5 GHz as a linear function. The intercept (A) and slope (B) of this linear equation depend only on snow properties and can be estimated from the observations directly. This development provides a new technique that separates the snowpack and ground surface emission signals. With the parameterized snow emission model from a simulated database that was derived using a multiscattering microwave emission model (dense medium radiative transfer model-AIEM-matrix doubling) over dry snow covers, we developed an algorithm to estimate the SWE using the microwave radiometer measurements. Evaluations on this technique using both the model simulated data and the field experimental data with the airborne Polarimetric Scanning Radiometer data from National Aeronautics and Space Administration Cold Land Processes Experiment 2003 showed promising results, with root-mean-square errors of 32.8 and 31.85 mm, respectively. This newly developed inversion method has the advantages over the AMSR-E SWE baseline algorithm when applied to high-resolution airborne observations.

Soil Moisture Retrieval Using a Two-Dimensional L-Band Synthetic Aperture Radiometer in a Semiarid Environment

Surface soil moisture was retrieved from the L-band radiometer data collected in semiarid regions during the Soil Moisture Experiment in 2004. The 2-D synthetic aperture radiometer (2D-STAR) was flown over regional-scale study sites located in AZ, USA, and Sonora, Mexico (SO). The study sites are characterized by a range of topographic relief with a land cover that varies from bare soil to grass and scrubland and includes areas with high rock fraction near the soil surface. The 2D-STAR retrieval of soil moisture was in good agreement with the ground-based estimates of surface soil moisture in both AZ (raise = 0.012 m3 m-3) and SO (rmse = 0.011 m3 m-3). The 2D-STAR also showed a good performance in the Walnut Gulch Experimental Watershed (rmse = 0.014 m3 m-3) where the surface soil featured high rock fraction was as high as 60%. Comparison of the results with the Polarimetric Scanning Radiometer at the Cand X-band data indicates the superior soil moisture retrieval performance of the L-band data over the regions with high rock fraction and moderate vegetation density.

Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 1: Field trial results from the Wakasa Bay experiment

Abstract. Tomographic methods offer great potential for retrieving three-dimensional spatial distributions of cloud liquid water from radiometric observations by passive microwave sensors. Fixed tomographic systems require multiple radiometers, while mobile systems can use just a single radiometer. Part 1 (this paper) examines the results from a limited cloud tomography trial with a single-radiometer airborne system carried out as part of the 2003 AMSR-E validation campaign over Wakasa Bay of the Sea of Japan. During this trial, the Polarimetric Scanning Radiometer (PSR) and Microwave Imaging Radiometer (MIR) aboard the NASA P-3 research aircraft provided a useful dataset for testing the cloud tomography method over a system of low-level clouds. We do tomographic retrievals with a constrained inversion algorithm using three configurations: PSR, MIR, and combined PSR and MIR data. The liquid water paths from the PSR retrieval are consistent with those from the MIR retrieval. The retrieved cloud field based on the combined data appears to be physically plausible and consistent with the cloud image obtained by a cloud radar. We find that some vertically-uniform clouds appear at high altitudes in the retrieved field where the radar shows clear sky. This is likely due to the sub-optimal data collection strategy. This sets the stage for Part 2 of this study that aims to define optimal data collection strategies using observation system simulation experiments.

NASA Cold Land Processes Experiment (CLPX 2002/03): Airborne Remote Sensing

Abstract This paper describes the airborne data collected during the 2002 and 2003 Cold Land Processes Experiment (CLPX). These data include gamma radiation observations, multi- and hyperspectral optical imaging, optical altimetry, and passive and active microwave observations of the test areas. The gamma observations were collected with the NOAA/National Weather Service Gamma Radiation Detection System (GAMMA). The CLPX multispectral optical data consist of very high-resolution color-infrared orthoimagery of the intensive study areas (ISAs) by TerrainVision. The airborne hyperspectral optical data consist of observations from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Optical altimetry measurements were collected using airborne light detection and ranging (lidar) by TerrainVision. The active microwave data include radar observations from the NASA Airborne Synthetic Aperture Radar (AIRSAR), the Jet Propulsion Laboratory’s Polarimetric Ku-band Scatterometer (POLSCAT), and airborne GPS bistatic radar data collected with the NASA GPS radar delay mapping receiver (DMR). The passive microwave data consist of observations collected with the NOAA Polarimetric Scanning Radiometer (PSR). All of the airborne datasets described here and more information describing data collection and processing are available online.

Near‐surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithms: 2. Using airborne remote sensing during SGP97 and SMEX02

Pixel‐based effective soil hydraulic parameters are crucial inputs for large‐scale hydroclimatic modeling. In this paper, we extend/apply a genetic algorithm (GA) approach for estimating these parameters at the scale of an airborne remote sensing (RS) footprint. To estimate these parameters, we used a time series of near‐surface RS soil moisture data to invert a physically based soil‐water‐atmosphere‐plant (SWAP) model with a (multipopulated) modified‐microGA. Uncertainties in the solutions were examined in two ways: (1) by solving the inverse problem under various combinations of modeling conditions in a respective way; and (2) the same as the first method but the inverse solutions were determined in a collective way aimed at finding the robust solutions for all the modeling conditions (ensembles). A cross validation of the derived soil hydraulic parameters was done to check their effectiveness for all the modeling conditions used. For our case studies, we considered three electronically scanned thinned array radiometer (ESTAR) footprints in Oklahoma and four polarimetric scanning radiometer (PSR) footprints in Iowa during the Southern Great Plains 1997 (SGP97) Hydrology Experiment and Soil Moisture Experiment 2002 (SMEX02) campaigns, respectively. The results clearly showed the promising potentials of near‐surface RS soil moisture data combined with inverse modeling for determining average soil hydrologic properties at the footprint scale. Our cross validation showed that parameters derived by method 1 under water table (bottom boundary) conditions are applicable also for free‐draining conditions. However, parameters derived under free‐draining conditions generally produced too wet near‐surface soil moisture when applied under water table conditions. Method 2, on the other hand, produced robust parameter sets applicable for all modeling conditions used. These results were validated using distributed in situ soil moisture and soil hydraulic properties measurements, and texture‐based data from the UNSODA database. In this study, we conclude that inverse modeling of RS soil moisture data is a promising approach for parameter estimation at large measurement support scale. Nevertheless, the derived effective soil hydraulic parameters are subject to the uncertainties of remotely sensed soil moisture data and from the assumptions used in the soil‐water‐atmosphere‐plant modeling. Method 2 provides a flexible framework for accounting these sources of uncertainties in the inverse estimation of large‐scale soil hydraulic properties. We have illustrated this flexibility by combining multiple data sources and various modeling conditions in our large‐scale inverse modeling.

An Evaluation of Soil Moisture Retrievals Using Aircraft and Satellite Passive Microwave Observations During SMEX02

The Soil Moisture Experiments conducted in Iowa in the summer of 2002 (SMEX02) had many remote sensing instruments that were used to study the spatial and temporal variability of soil moisture. The sensors used in this paper (a subset of the suite of sensors) are the AQUA satellite-based AMSR-E (Advanced Microwave Scanning Radiometer- Earth Observing System) and the aircraft-based PSR (Polarimetric Scanning Radiometer). The SMEX02 design focused on the collection of near simultaneous brightness temperature observations from each of these instruments and in situ soil moisture measurements at field- and domain- scale. This methodology provided a basis for a quantitative analysis of the soil moisture remote sensing potential of each instrument using in situ comparisons and retrieved soil moisture estimates through the application of a radiative transfer model. To this end, the two sensors are compared with respect to their estimation of soil moisture.

High-Resolution Airborne Polarimetric Microwave Imaging of Snow Cover During the NASA Cold Land Processes Experiment

We present a detailed analysis of the airborne passive microwave remote-sensing data that were collected at a broad range of microwave bands and at a high spatial resolution during the 2002 and 2003 National Aeronautics and Space Administration Cold Land Processes Experiment (CLPX). An accurate measurement of snowpack properties using passive microwave observations requires the detailed knowledge of the relationship between snowpack geophysical parameters and the upwelling polarimetric brightness signature. The principle microwave instrument used for the CLPX was the polarimetric scanning radiometer (PSR), which provided ~100-m resolution maps of the snow emissivity at all Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) bands during several intensive observation periods over the Colorado Rocky Mountains. The observed conditions included drought, normal snowpack, and spring snowmelt. The PSR and related ground-based observations of snowpack properties made during the 2002 and 2003 CLPX campaigns provide a comprehensive high-resolution passive microwave data set. Results show that the high-resolution PSR data exhibit emissivity modes that are similar to those observed in the historical data sets, and that the empirical relationships between the emissivity and the snow water equivalent (SWE), after the effects of macrovegetation are removed, closely match those found in the past theoretical studies. The use of the 89-GHz channel in the empirical relationships provides improved accuracy under dry snow conditions and a small SWE; however, the variability of the SWE-emissivity relationships increases with an increasing SWE. A summary of the observed relationships between the emissivity spectra of snow and snowpack properties is presented. Comparison of the total water content from the AMSR-E and PSR observations shows that the satellite measurements underestimated the total volume of water storage from airborne observations on the average by a factor of five.

Characterization of Errors in a Coupled Snow Hydrology–Microwave Emission Model

Abstract Traditional approaches to the direct estimation of snow properties from passive microwave remote sensing have been plagued by limitations such as the tendency of estimates to saturate for moderately deep snowpacks and the effects of mixed land cover within remotely sensed pixels. An alternative approach is to assimilate satellite microwave emission observations directly, which requires embedding an accurate microwave emissions model into a hydrologic prediction scheme, as well as quantitative information of model and observation errors. In this study a coupled snow hydrology [Variable Infiltration Capacity (VIC)] and microwave emission [Dense Media Radiative Transfer (DMRT)] model are evaluated using multiscale brightness temperature (TB) measurements from the Cold Land Processes Experiment (CLPX). The ability of VIC to reproduce snowpack properties is shown with the use of snow pit measurements, while TB model predictions are evaluated through comparison with Ground-Based Microwave Radiometer (GBMR), aircraft [Polarimetric Scanning Radiometer (PSR)], and satellite [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E)] TB measurements. Limitations of the model at the point scale were not as evident when comparing areal estimates. The coupled model was able to reproduce the TB spatial patterns observed by PSR in two of three sites. However, this was mostly due to the presence of relatively dense forest cover. An interesting result occurs when examining the spatial scaling behavior of the higher-resolution errors; the satellite-scale error is well approximated by the mode of the (spatial) histogram of errors at the smaller scale. In addition, TB prediction errors were almost invariant when aggregated to the satellite scale, while forest-cover fractions greater than 30% had a significant effect on TB predictions.

Aircraft based soil moisture retrievals under mixed vegetation and topographic conditions

An unresolved issue in global soil moisture retrieval using passive microwave sensors is the spatial integration of heterogeneous landscape features to the nominal 50 km footprint observed by most low frequency satellite systems. One of the objectives of the Soil Moisture Experiments 2004 (SMEX04) was to address some aspects of this problem, specifically variability introduced by vegetation, topography and convective precipitation. Other goals included supporting the development of soil moisture data sets that would contribute to understanding the role of the land surface in the concurrent North American Monsoon System. SMEX04 was conducted over two regions: Arizona — semi-arid climate with sparse vegetation and moderate topography, and Sonora (Mexico) — moderate vegetation with strong topographic gradients. The Polarimetric Scanning Radiometer (PSR/CX) was flown on a Naval Research Lab P-3B aircraft as part of SMEX04 (10 dates of coverage over Arizona and 11 over Sonora). Radio Frequency Interference (RFI) was observed in both PSR and satellite-based (AMSR-E) observations at 6.92 GHz over Arizona, but no detectable RFI was observed over the Sonora domain. The PSR estimated soil moisture was in agreement with the ground-based estimates of soil moisture over both domains. The estimated error over the Sonora domain (SEE = 0.021 cm 3/cm 3) was higher than over the Arizona domain (SEE = 0.014 cm 3/cm 3). These results show the possibility of estimating soil moisture in areas of moderate and heterogeneous vegetation and high topographic variability.

Spatial sampling uncertainty in SMEX04 soil moisture fields: A data-based resampling experiment

A data-based resampling experiment is performed to estimate sampling errors of area-averaged soil moisture estimates due to spatial sampling by ground-based sensors. The data consists of high-resolution soil moisture images derived from the Polarimetric Scanning Radiometer (PSR/CX) sensor flown on an aircraft as part of the summer field experiment (SMEX04 — Soil Moisture Experiment 2004) in the monsoon region of Sonora, Mexico. The sampling characteristics are investigated by accounting for random networks and evenly spaced networks. For random network designs, we develop a simple model that can be used to estimate the sampling uncertainty (expressed as standard deviation of sampling error as a percentage of the areal mean soil moisture) as a function of the number of sensors, mean soil moisture content and averaging area. This model is valid for five or more sensors. The model should prove useful to those wishing to assess the area-averaged performance of a soil moisture network. Furthermore, the method of analysis is applicable to other study regions (Oklahoma, Iowa, Alabama, Georgia, and Arizona) where soil moisture fields have been mapped at high resolution using airborne passive microwave remote sensors.

Comparison of ground-based and remotely-sensed surface soil moisture estimates over complex terrain during SMEX04

Comparisons to ground-based surface soil moisture estimates are necessary to evaluate the capability of remote sensors to determine soil moisture and its spatiotemporal variability. Soil moisture can be especially variable in regions of complex terrain which exhibit large variations in vegetation, soil properties and hydrologic conditions. The objective of this study is to evaluate the spatiotemporal variability of soil moisture in a mountainous basin in northwestern Mexico. Soil moisture estimates from ground sampling over a topographic transect and high resolution retrievals from the Polarimetric Scanning Radiometer are compared during a two week period in August 2004 as part of the Soil Moisture Experiment 2004. Results indicate that the soil moisture estimates exhibit similar variability with mean water content. Statistical analysis, however, reveals clear differences in soil moisture in the basin, in particular for wet periods and high elevations. Despite these differences, the temporal persistence of soil moisture from the estimates agrees well and indicates locations that capture the basin-averaged conditions. Furthermore, the spatiotemporal soil moisture characteristics from the two products are linked to terrain attributes. As a result, a hypsometric technique is shown to improve comparisons between basin-averaged values derived from ground data and remote sensing, as compared to arithmetic averaging. To our knowledge, this study is the first attempt to evaluate PSR/CX retrievals with respect to ground observations over a region of high topographic and vegetation variability using statistical, time-stability and terrain analysis techniques.

Temporal dynamics of PSR-based soil moisture across spatial scales in an agricultural landscape during SMEX02: A wavelet approach

Abstract In this study, we examined the characteristics of soil moisture dynamics of wet and dry fields across hierarchical spatial scales within the region of Soil Moisture Experiment 2002 (SMEX02) hydrology campaign in Iowa. The Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface (∼ 0–5 cm) soil moisture at 800 m × 800 m resolution was used in this study. Wavelet-based multiresolution technique decomposed the soil moisture into large-scale mean soil moisture fields and fluctuations in horizontal, diagonal, and vertical directions at hierarchical spatial resolutions. Results suggested linearity in the log–log dependency of the variance of soil moisture up to a resolution of 6400 m × 6400 m on PSR sampling dates during SMEX02. The wet fields (with high soil moisture) show almost similar variance for all the resolutions signifying the strong spatial correlation. Analysis of the dry fields (with low soil moisture) indicated a log–log linearity of moments with various scales, and the slopes of these relationships exhibit a concave functional form with the order of moments, typically representing a multiscaling process. The scaling exponent of soil moisture during dry-down suggests a transition from simple scaling (in wet fields) to multiscaling (in dry fields) behavior. The fluctuation components of multiresolution analysis in the horizontal, diagonal, and vertical directions for dry and wet fields exhibited self-similarity. Another important finding of this study is the increase of subpixel soil moisture variability with increasing resolution, especially for the wet fields. These findings will help develop appropriate up-and down-scaling schemes of remotely sensed soil moisture data for various hydrologic and environmental modeling applications.

L‐band microwave observations over land surface using a two‐dimensional synthetic aperture radiometer

Antenna size is a major factor that has limited realization of the potential capabilities of L‐band (1.4 GHz) microwave radiometry to estimate surface soil moisture from space. However, emerging interferometric technology, called aperture synthesis, has been developed to address this limitation. The Soil Moisture and Ocean Salinity (SMOS) mission will apply the technique to monitor these parameters at a global‐scale in the near future. The first airborne experiment using an aircraft prototype called the Two‐Dimensional Synthetic Aperture Radiometer (2D‐STAR) was performed during Soil Moisture Experiment in 2003 (SMEX03). To gain insight, the L‐band brightness temperature data acquired by 2D‐STAR in Alabama was compared with C‐band data collected simultaneously by another aircraft instrument called the Polarimetric Scanning Radiometer (PSR), and also compared with surface soil moisture measurements from in‐situ observations sites. Results show that there was fairly good radiometric sensitivity of the synthetic aperture radiometer to the soil moisture both in pasture and also in forest areas. The overall performance of the L‐band synthetic aperture radiometer in the presence of vegetation appears to be better than the C‐band real aperture radiometer.

Snow Depth and Ice Thickness Measurements From the Beaufort and Chukchi Seas Collected During the AMSR-Ice03 Campaign

In March 2003, a field validation campaign was conducted on the sea ice near Barrow, AK. The goal of this campaign was to produce an extensive dataset of sea ice thickness and snow properties (depth and stratigraphy) against which remote sensing products collected by aircraft and satellite could be compared. Chief among these were products from the Polarimetric Scanning Radiometer (PSR) flown aboard a NASA P-3B aircraft and the Aqua Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The data were collected in four field areas: three on the coastal sea ice near Barrow, AK, and the fourth out on the open ice pack 175 km northeast of Barrow. The snow depth ranged from 9.4-20.8 cm in coastal areas (n=9881 for three areas) with the thinnest snow on ice that had formed late in the winter. Out in the main pack ice, the snow was 20.6 cm deep (n=1906). The ice in all four areas ranged from 138-219 cm thick (n=1952), with the lower value again where the ice had formed late in the winter. Snow layer and grain characteristics observed in 118 snow pits indicated that 44% of observed snow layers were depth hoar; 46% were wind slab. Snow and ice measurements were keyed to photomosaics produced from low-altitude vertical aerial photographs. Using these, and a distinctive three-way relationship between ice roughness, snow surface characteristics, and snow depth, strip maps of snow depth, each about 2 km wide, were produced bracketing the traverse lines. These maps contain an unprecedented level of snow depth detail against which to compare remote sensing products. The maps are used in other papers in this special issue to examine the retrieval of snow properties from the PSR and AMSR-E sensors

Spatial Variability of Barrow-Area Shore-Fast Sea Ice and Its Relationships to Passive Microwave Emissivity

Aircraft-acquired passive microwave data, laser radar height observations, RADARSAT synthetic aperture radar imagery, and in situ measurements obtained during the AMSR-Ice03 experiment are used to investigate relationships between microwave emission and ice characteristics over several space scales. The data fusion allows delineation of the shore-fast ice and pack ice in the Barrow area, AK, into several ice classes. Results show good agreement between observed and Polarimetric Scanning Radiometer (PSR)-derived snow depths over relatively smooth ice, with larger differences over ridged and rubbled ice. The PSR results are consistent with the effects on snow depth of the spatial distribution and nature of ice roughness, ridging, and other factors such as ice age. Apparent relationships exist between ice roughness and the degree of depolarization of emission at 10, 19, and 37 GHz. This depolarization would yield overestimates of total ice concentration using polarization-based algorithms, with indications of this seen when the NT-2 algorithm is applied to the PSR data. Other characteristics of the microwave data, such as effects of grounding of sea ice and large contrast between sea ice and adjacent land, are also apparent in the PSR data. Overall, the results further demonstrate the importance of macroscale ice roughness conditions such as ridging and rubbling on snow depth and microwave emissivity