Accuracy of wet snow mapping using simulated Radarsat backscattering coefficients from observed snow cover characteristics

Wet snow cover mapping by means of airborne and spaceborne SAR is operational today and successfully applied in rugged high mountain terrain and in agricultural area. This paper proposes a numerical study to estimate the accuracy of wet snow mapping by using a radar backscattering model that simulates backscattering from a multi-layer snowpack for various snow cover conditions and for SAR parameters specific to Radarsat (C-HH). Field measurements carried out in numerous sites during the winters of 1994 to 1996 in several areas of Quebec (Canada) have allowed to choose some typical snow profiles and the corresponding snow/soil parameters. Results indicate that under the assumptions used in the model and the simulations, for the standard mode S1 of Radarsat (20 to 27.4) and in the case of wet snow cover with liquid water content of 1%, the optimum relative under-and over-estimation of wet snow pixels are of the order of 23.9% and 13.4%, respectively. For wet snow cover at 2%, the algorithm operates with a relative under-estimation of wet snow pixels around 8.5% and a relative over-estimation of the order of 1.7%. For wet snow with liquid water content of 4%, the relative under-and over-estimation of wet snow pixels is around 0.8% and 0.3%, respectively. They are negligible for wet snow with liquid water content higher than 4%. With the standard mode S7 of Radarsat (44.9 to 49.4), the wet snow mapping algorithm leads to a slightly lower performance than with the standard mode S1. The accuracy of the method for wet snow mapping demonstrates the high potential of SAR for snow monitoring. It is considered sufficient when the liquid water content of the snowpack is higher than 1% for actual snow conditions similar to those eight observed conditions used in this study.     

Fully polarimetric airborne SAR and ERS SAR observations of snow: implications for selection of ENVISAT ASAR modes

Snow cover has a substantial impact on processes involved in the interaction between atmosphere and surface, and the knowledge of snow parameters is important in both climatology and weather forecasting. With the upcoming launch of Advanced Synthetic Aperture Radar (ASAR) instruments on Envisat, enhanced snow-mapping capabilities are foreseen. In this paper fully polarimetric C- and L-band airborne SAR data, ERS SAR and auxiliary data from various snow conditions in mountainous areas are analysed in order to determine the optimum ASAR modes for snow monitoring. The data used in this study are from the Norwegian part of the snow and ice experiment within the European Multi-sensor Airborne Campaign (EMAC'95) acquired in the Kongsfjellet area, located in Norway, 66°?N, 14°?E. Fully polarimetric C- and L-band SAR data from ElectroMagnetic Institute SAR (EMISAR), an airborne instrument operated by the Danish Center for Remote Sensing (DCR), were acquired in March, May, and July 1995. In addition, several ERS SAR, airborne photos, field and auxiliary data were acquired.

A larger separation between wet snow and bare ground in EMISAR C-VV polarisation data was found at high incidence angle (55°) compared to lower incidence angle (45°). Cross-polarized observations from bare ground, dry and wet snow in the incidence angle range 35° to 65° are below the specified Envisat ASAR noise floor of –20–22 dB. The backscattering angular dependency for wet snow and bare ground derived from EMISAR C-VV and ERS SAR data corresponds well, and agrees to some extent with volume and surface scattering model results. The C-band is more sensitive to variation in snow properties than the L-band.     

Mapping dry/wet snow cover in the Indian Himalayas using IRS multispectral imagery

Application of remote sensing data has been made to differentiate between dry/wet snows in a glacierized basin. The present study has been carried out in the Gangotri glacier, Himalayas, using IRS-LISS-III multispectral data for the period March-November 2000 and the digital elevation model. The methodology involves conversion of satellite sensor data into reflectance values, computation of NDSI, determination of the boundary between dry/wet snows from spectral response data, and threshold slicing of the image data. The areas of dry snow cover and wet snow cover for different dates of satellite overpasses have been computed. The dry snow area has been compared with non-melting area obtained from the temperature lapse rate method, and the two are found to be in close mutual correspondence (< 15%). It is observed that there occur four water-bearing zones in the glacierized basin: dry snow zone, wet snow zone, exposed glacial ice and moraine-covered glacial ice, each of which possesses unique hydrological characteristics and can be distinguished and mapped from satellite sensor data. It is suggested that input of data on the position and extent of specifically wet snow and exposed glacial ice, which can be directly derived from remote sensing, should improve hydrological simulation of such basins.     

C波段SAR山区积雪面积提取研究

合成孔径雷达(SAR)不仅具有穿云透雾,全天候观测地表的能力,而且可穿透地表覆盖一定深度获取地表覆盖物内部特征信息。利用2011年10景ENVISAT\|ASAR可变极化模式精细图像(ASA_APP_1P)数据,分析比较了黑河上游祁连山冰沟流域不同时段积雪SAR后向散射特性,应用同期的MODIS积雪面积产品确定研究区积雪的累积和消融背景信息。研究表明:由于融雪期积雪含水量上升,SAR图像后向散射系数相比干雪或无雪图像明显降低,经过分析认为广泛应用的-3 dB阈值会明显低估湿雪覆盖范围,-2 dB阈值更适合该地区湿雪面积参数提取。山区积雪融化过程中低海拔区域积雪融化而高海拔山区积雪仍可能为干雪,在提取湿雪像元的基础上,根据Sigmoid函数阈值获取的像元湿雪百分比及DEM信息来提取干雪像元,最终获取整个流域积雪面积信息。通过与Landsat ETM+图像积雪面积分类结果进行比较,总体精度达到78%。积雪累积和消融背景信息的分析表明:误差主要源于流域东北部与西北部低海拔区域积雪快速消融。     

Investigation of C- and X-band backscattering signatures of Baltic Sea ice

Backscattering signatures of various Baltic Sea ice types and open water leads were measured with the helicopter-borne C- and X-band Helsinki University of Technology scatterometer (HUTSCAT) during six ice research campaigns in 1992–1997. The measurements were conducted at incidence angles of 23° and 45°. The HUTSCAT data were assigned by video imagery into various surface type categories. The ground data provided further classification of the HUTSCAT data into different snow wetness categories (dry, moist and wet snow). Various basic statistical parameters of backscattering signature data were used to study discrimination of open water leads and various ice types. The effect of various physical parameters (e.g. polarization, frequency, snow condition) on the surface type discrimination was investigated. The results from the data analysis can be used to help the development of sea ice classification algorithms for space-borne SAR data (e.g. Radarsat and Envisat). According to the results from the maximum likelihood classification it is not possible to reliably distinguish various surface types in the SAR images only by their backscatter intensity. In general, the best ice type discrimination accuracy is achieved with C-band VH-polarization σ° at an incidence angle of 45°.     

Evaluation of RADARSAT-1 data for identification of burnt areas in Southern Europe

This study presents the combined analysis of RADARSAT products of different spatial resolutions acquired under different incidence angles for mapping burnt scars on forested areas of Central Portugal. Prior to the SAR data analysis, a number of pre-processing procedures were carried out. Noise was eliminated by adaptive texture preserving filtering. A specific algorithm for the geocoding of SAR images, based on a range-Doppler approach, enabled precise geocoding of the SAR data by means of a single very accurate ground control point. A novel incidence-angle-normalization for SAR imagery was applied to analyze the backscatter coefficient to a given incidence angles. Further, a backscatter coefficient analysis was performed according to the slope on forested areas and fire disturbed areas. A qualitative and quantitative investigation of the backscattering as related to the slope angle and time changes was performed. As a result of this analysis, the scenes that allowed maximizing the discrimination of burnt areas were selected as the input for the neural network classification. The investigation on the effect of the SAR incidence angle in burnt area discrimination determined that low incidence angles are required for discriminating burnt areas in hilly regions. It was also demonstrated that topography influences the level of discrimination of burnt areas since areas affected by forest fires on face-slopes presents higher backscatter coefficient than those back-slopes. Therefore, SAR data can play a significant role for burnt area mapping in Europe in those areas where optical data cannot be used due to persistent cloud cover.     

Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island

Multi-temporal TerraSAR-X, ASAR/ENVISAT and PALSAR SAR data acquired at various incidence angles and polarizations were analyzed to study the potential of these new spaceborne SAR systems for monitoring sugarcane crops. The sensitivity of different radar parameters (wavelength, incidence angles, and polarization) to sugarcane growth stages was analyzed to determine the most suitable radar configuration for better characterisation of sugarcane fields and in particular the monitoring of sugarcane harvest.Correlation between backscattered signals and crop height was also carried out. Radar signal increased quickly with sugarcane height until a threshold height, which depended on radar wavelength and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is higher with longer wavelengths (L-band in comparison with C- and X-bands) and higher incidence angles (~ 40° in comparison with ~ 20°).The radar backscattering coefficients (σ°) were also compared to the Normalized Difference Vegetation Index (NDVI) calculated from SPOT-4/5 images. Results showed a high correlation between the behaviors of σ° and NDVI as a function of sugarcane crop parameters. A decrease in NDVI for fully mature sugarcane fields due to drying of the sugarcane (water stress) was also observed in the radar signal. This decrease in radar signal was of the same order as the decrease in radar signal after the sugarcane harvest. In general, it is more suitable to monitor the sugarcane harvest using high incidence angles regardless of the radar wavelength. SAR data in L- and C-bands showed an ambiguity between the signals of ploughed fields and those of fields in vegetation because of the high sensitivity of the radar signal at these wavelengths to surface roughness of bare soils. Indeed, sometimes the radar signal of ploughed fields was of the same order as that of harvested or mature sugarcane fields. Results showed better discrimination between ploughed fields and sugarcane fields in vegetation (sugarcane canopy) when using TerraSAR-X data (X-band).Concerning the influence of radar polarization, results showed that the co-polarizations channels (HH and VV) were well correlated, but had slightly less potential than cross-polarization channels (HV and VH) for the detection of the sugarcane harvest. Finally, SAR data at high spatial resolution were shown to be useful and necessary for better analysis of SAR images when the fields were of small size.     

Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery

Soil moisture is important information in semiarid rangelands where vegetation growth is heavily dependent on the water availability. Although many studies have been conducted to estimate moisture in bare soil fields with Synthetic Aperture Radar (SAR) imagery, little success has been achieved in vegetated areas. The purpose of this study is to extract soil moisture in sparsely to moderately vegetated rangeland surfaces with ERS-2/TM synergy. We developed an approach to first reduce the surface roughness effect by using the temporal differential backscatter coefficient (Δσ_wet-dry⁰). Then an optical/microwave synergistic model was built to simulate the relationship among soil moisture, Normalized Difference Vegetation Index (NDVI) and Δσ_wet-dry⁰. With NDVI calculated from TM imagery in wet seasons and Δσ_wet-dry⁰ from ERS-2 imagery in wet and dry seasons, we derived the soil moisture maps over desert grass and shrub areas in wet seasons. The results showed that in the semiarid rangeland, radar backscatter was positively correlated to NDVI when soil was dry (m_v<10%), and negatively correlated to NDVI when soil moisture was higher (m_v>10%). The approach developed in this study is valid for sparse to moderate vegetated areas. When the vegetation density is higher (NDVI>0.45), the SAR backscatter is mainly from vegetation layer and therefore the soil moisture estimation is not possible in this study.     

Application of incoherent target decomposition theorems to classify snow cover over the Himalayan region

Snow cover is an important parameter for hydrological modelling and climate change modelling. Various methods are available only for wet snow-cover mapping using conventional synthetic aperture radar (SAR) data. Total snow (wet + dry) cover mapping with SAR data is still a topical research area. Therefore, incoherent target decomposition theorems have been implemented on fully polarimetric SAR data to characterize the scattering of various targets. Further classification techniques – both unsupervised and supervised – have been applied for accurate mapping of total snow cover. For this purpose, Advanced Land Observing Satellite – phased array-type L-band SAR (ALOS–PALSAR) data (12 May 2007) have been analysed for snow classification of glaciated terrain in and around Badrinath region in Himalaya. An ALOS-Advanced Visible and Near Infrared Radiometer (AVNIR)-2 image (6 May 2007) was also used to provide assistance in the selection of different training classes. It has been found that the application of incoherent target decomposition theorems such as H/A/α and four-component scattering mechanism models are good for extracting the desired information of snow cover from fully polarimetric PALSAR data. Finally, based on these target decomposition theorems and the Wishart classifier, PALSAR data have been classified into snow or non-snow cover, and the user accuracy of snow classes was found to be better than the user accuracy of other classes. Hence, the application of incoherent target decomposition theorems with full polarimetric ALOS-PALSAR data is useful for snow-cover mapping.     

Routine Monitoring of Changes in the Columbia Glacier, Alaska, with Synthetic Aperture Radar

This study focuses on the use of spaceborne Synthetic Aperture Radar (SAR) from the European Remote Sensing Satellite (ERS-1) and the Canadian RADARSAT satellite to monitor retreating glaciers, specifically the Columbia Glacier, and the hazards caused by their recession on a routine basis. The Columbia Glacier is an important tidewater glacier to monitor because of its present rapid retreat, its generation of icebergs, and its vicinity to oil-tanker traffic in and out of the Port of Valdez, Alaska. We have established routine monitoring of the Prince William Sound area using SAR aboard the Canadian RADARSAT on a semi-weekly basis. To demonstrate the use of this data, four ERS-1 and seven RADARSAT SAR images from 1992 to 1998 are used in this study to monitor the long-term retreat of the Columbia Glacier. A loss of 17.7±0.2 square kilometers is measured from 1992 to 1997, with 38.4% of this change occurring from 1993 to 1994. Also, hazards such as icebergs are monitored in near real-time. SAR is an efficient and cost-effective means of monitoring glaciers, where cloud cover and logistical costs and difficulties hamper other data-collection efforts. These data are available to U.S. Government users and would be of benefit to the Prince William Sound region if used operationally.     

Rice field mapping and monitoring with RADARSAT data

The aim of this paper is to assess the use of RADARSAT data for rice field mapping and monitoring. The specific characteristics of RADARSAT data, i.e. the radar polarization, the spatial resolution, the range of incidence angles and the repeat cycle, have been studied. Experimental data have been collected over one of the most important irrigated flood plains of Indonesia and analysed along with RADARSAT data. The radar backscatter coefficient sigma of rice fields appears to have a significant temporal variation. However, the dynamic range of RADARSAT data is found lower than that of the ERS data due to a higher backscatter at HH than at VV polarization at early stages. At a higher incidence angle (42 compared with 23), the sigma of the fine resolution F3 mode reaches a saturation level earlier compared with the standard resolution mode, which appears consequently to be most appropriate for rice field mapping. The mapping result, based on the temporal change of the radar backscatter, was found to have an accuracy of 87% compared with the available map. In order to follow the growing cycles and improve the management of irrigated perimeters, a methodology for Golongan (fields with the same irrigation scheme) mapping have also been developed. The results appear promising and rice mapping operations using RADARSAT data can be foreseen.     

Spatial and temporal variations of summer surface temperatures of high-arctic tundra on Svalbard — Implications for MODIS LST based permafrost monitoring

The ground surface temperature is one of the key parameters that determine the thermal regime of permafrost soils in arctic regions. Due to remoteness of most permafrost areas, monitoring of the land surface temperature (LST) through remote sensing is desirable. However, suitable satellite platforms such as MODIS provide spatial resolutions that cannot resolve the considerable small-scale heterogeneity of the surface conditions characteristic for many permafrost areas. This study investigates the spatial variability of summer surface temperatures of high-arctic tundra on Svalbard, Norway. A thermal imaging system mounted on a mast facilitates continuous monitoring of approximately 100 × 100 m of tundra with a wide variability of different surface covers and soil moisture conditions over the entire summer season from the snow melt until fall. The net radiation is found to be a control parameter for the differences in surface temperature between wet and dry areas. Under clear-sky conditions in July, the differences in surface temperature between wet and dry areas reach up to 10 K. The spatial differences reduce strongly in weekly averages of the surface temperature, which are relevant for the soil temperature evolution of deeper layers. Nevertheless, a considerable variability remains, with maximum differences between wet and dry areas of 3 to 4 K. Furthermore, the pattern of snow patches and snow-free areas during snow melt in July causes even greater differences of more than 10 K in the weekly averages. Towards the end of the summer season, the differences in surface temperature gradually diminish. Due to the pronounced spatial variability in July, the accumulated degree-day totals of the snow-free period can differ by more than 60% throughout the study area. The terrestrial observations from the thermal imaging system are compared to measurements of the land surface temperature from the MODIS sensor. During periods with frequent clear-sky conditions and thus a high density of satellite data, weekly averages calculated from the thermal imaging system and from MODIS LST agree within less than 2 K. Larger deviations occur when prolonged cloudy periods prevent satellite measurements. Furthermore, the employed MODIS L2 LST data set contains a number of strongly biased measurements, which suggest an admixing of cloud top temperatures.We conclude that a reliable gap filling procedure to moderate the impact of prolonged cloudy periods would be of high value for a future LST-based permafrost monitoring scheme. The occurrence of sustained subpixel variability of the summer surface temperature is a complicating factor, whose impact needs to be assessed further in conjunction with other spatially variable parameters such as the snow cover and soil properties.     

The effect of anisotropic reflectance on imaging spectroscopy of snow properties

How does snow's anisotropic directional reflectance affect the mapping of snow properties from imaging spectrometer data? This sensitivity study applies two spectroscopy models to synthetic images of the spectral hemispherical-directional reflectance factor (HDRF) with prescribed snow-covered area and snow grain size. The MEMSCAG model determines both sub-pixel snow-covered area and the grain size of the fractional snow cover. The Nolin/Dozier model analyzes the ice absorption feature that spans wavelength λ≅1.03 μm to estimate snow grain radius when the pixel is fully snow-covered. Retrievals of subpixel snow-covered area with MEMSCAG are progressively more sensitive to the HDRF as grain size decreases, solar zenith angle increases, and fractional snow cover increases. The model overestimates snow cover in the forward reflectance angles by up to +20% and underestimates it in the backward reflectance angles by as much as −15%. Grain size retrievals from both MEMSCAG and Nolin/Dozier are more sensitive to anisotropy as grain size and solar zenith angle increase. MEMSCAG retrievals of grain size are insensitive to snow-covered area. The largest inferred grain sizes occur around a peak in the backward reflectance angles and the smallest generally occur at the largest view angles in the forward direction. Retrievals of albedo from MEMSCAG and Nolin/Dozier are similarly sensitive to anisotropy, with albedo errors up to 5% for a 30° solar zenith angle and up to 10% at 60°. The albedo differences between the two models are less than 0.015 for all grain sizes and solar zenith angles.     

Ship detection performance using simulated dual-polarization RADARSAT constellation mission data

The RADARSAT Constellation Mission (RCM) planned to be launched in 2018 is designed to support maritime surveillance requirements in which ice, wind, oil pollution, and ships are to be monitored by providing wide swath beam modes. In this article, we introduce the first analysis of ship detection performance using simulated RCM data. We report ship detection performance using a likelihood ratio test (LRT) for three wide swath RCM imaging modes: Ship Detection (25 m), Low Resolution (100 m), and Medium Resolution (50 m). These beam modes were assessed for a number of dual-polarimetric (dual-pol) systems, including the standard linear polarizations as well as compact polarimetry (CP). These data were simulated from RADARSAT-2 Fine Quad (FQ) mode in the three RCM modes. Furthermore, the detection performance of the pseudo-quad data reconstructed from the simulated circular transmit, linear receive data is also investigated and compared to the other systems’ performance for the three RCM modes. The receiver operating characteristic curves are used in this study as the basic measure of detection performance for all beam modes and all detectors. We found that the compact polarimetric SAR detectors outperform the conventional linear dual-pol detectors at the three RCM modes for ship detection for medium to high incidence angles. At steep angles, the performance of the two polarization configurations was comparable. Our study confirmed that detection performance improved as incidence angle and spatial resolution increased. We also investigated the impact of the ship orientation with respect to the radar beam and found that detection performance was generally higher when ship was oriented perpendicular to the radar beam.     

Development of a winter snow water equivalent algorithm using in situ passive microwave radiometry over snow-covered first-year sea ice

A snow water equivalent (SWE) algorithm has been developed for thin and thick snow using both in situ microwave measurements and snow thermophysical properties, collected over landfast snow covered first-year sea ice during the Canadian Arctic Shelf Exchange Study (CASES) overwintering mission from December 2003 to May 2004. Results showed that the behavior of brightness temperatures (T_bs) in thin snow covers was very different from those in a thick snowpack. Microwave SWE retrievals using the combination of T_b 19 GHz and air temperature (multiple regression) over thick snow are quite accurate, and showed very good agreement with the physical data (R² = 0.94) especially during the cooling period (i.e., from freeze up to the minimum air temperature recorded) where the snow is dry and cold. Thin snow SWE predictions also showed fairly good agreement with field data (R² = 0.70) during the cold season. The differences between retrieved and in situ SWE for both thin and thick snow cover are mainly attributable to the variations in air temperature, snow wetness and spatial heterogeneity in snow thickness.     

Influence of snow shovel shaft configuration on lumbosacral biomechanics during a load-lifting task

Lower-back injury from snow shovelling may be related to excessive joint loading. Bent-shaft snow shovels are commonly available for purchase; however, their influence on lower back-joint loading is currently not known. Therefore, the purpose of this study was to compare L5/S1 extension angular impulses between a bent-shaft and a standard straight-shaft snow shovel. Eight healthy subjects participated in this study. Each completed a simulated snow-lifting task in a biomechanics laboratory with each shovel design. A standard motion analysis procedure was used to determine L5/S1 angular impulses during each trial, as well as peak L5/S1 extension moments and peak upper body flexion angle. Paired-samples t-tests (α = 0.05) were used to compare variables between shovel designs. Correlation was used to determine the relationship between peak flexion and peak moments. Results of this study show that the bent-shaft snow shovel reduced L5/S1 extension angular impulses by 16.5% (p = 0.022), decreased peak moments by 11.8% (p = 0.044), and peak flexion by 13.0% (p = 0.002) compared to the straight-shaft shovel. Peak L5/S1 extension moment magnitude was correlated with peak upper body flexion angle (r = 0.70). Based on these results, it is concluded that the bent-shaft snow shovel can likely reduce lower-back joint loading during snow shovelling, and thus may have a role in snow shovelling injury prevention.     

A new empirical model to retrieve soil moisture and roughness from C-band radar data

A new empirical model for the retrieval, at a field scale, of the bare soil moisture content and the surface roughness characteristics from radar measurements is proposed. The derivation of the algorithm is based on the results of three experimental radar campaigns conducted under natural conditions over agricultural areas. Radar data were acquired by means of several C-band space borne (SIR-C, RADARSAT) or helicopter borne (ERASME) sensors, operating in different configurations of polarization (HH or VV) and incidence angle. Simultaneously to radar acquisitions, a complete ground truth data base was built up with different surface condition measurements of the mean standard deviation (rms) height s, the correlation length l, and the volumetric surface moisture M_v. This algorithm is more specifically developed using the radar cross-section σ⁰ (HH polarization and 39° incidence angle off nadir), namely, σ_0HH,39, and the differential (HH polarization) radar cross-section Δσ₀=σ_0,23°−σ_0,39° in terms of an original roughness parameter, Z_s, namely Z_s=s²/l, and M_v. A good agreement is observed between model outputs and backscattering measurements over different test fields. Eventually, an inversion technique is proposed to retrieve Z_s and M_v from radar measurements.     

The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle

In response to the urgent need for improved mapping of global biomass and the lack of any current space systems capable of addressing this need, the BIOMASS mission was proposed to the European Space Agency for the third cycle of Earth Explorer Core missions and was selected for Feasibility Study (Phase A) in March 2009. The objectives of the mission are 1) to quantify the magnitude and distribution of forest biomass globally to improve resource assessment, carbon accounting and carbon models, and 2) to monitor and quantify changes in terrestrial forest biomass globally, on an annual basis or better, leading to improved estimates of terrestrial carbon sources (primarily from deforestation); and terrestrial carbon sinks due to forest regrowth and afforestation. These science objectives require the mission to measure above-ground forest biomass from 70° N to 56° S at spatial scale of 100-200 m, with error not exceeding ± 20% or ± 10 t ha^− 1 and forest height with error of ± 4 m. To meet the measurement requirements, the mission will carry a P-Band polarimetric SAR (centre frequency 435 MHz with 6 MHz bandwidth) with interferometric capability, operating in a dawn-dusk orbit with a constant incidence angle (in the range of 25°-35°) and a 25-45 day repeat cycle. During its 5-year lifetime, the mission will be capable of providing both direct measurements of biomass derived from intensity data and measurements of forest height derived from polarimetric interferometry. The design of the BIOMASS mission spins together two main observational strands: (1) the long heritage of airborne observations in tropical, temperate and boreal forest that have demonstrated the capabilities of P-band SAR for measuring forest biomass; (2) new developments in recovery of forest structure including forest height from Pol-InSAR, and, crucially, the resistance of P-band to temporal decorrelation, which makes this frequency uniquely suitable for biomass measurements with a single repeat-pass satellite. These two complementary measurement approaches are combined in the single BIOMASS sensor, and have the satisfying property that increasing biomass reduces the sensitivity of the former approach while increasing the sensitivity of the latter. This paper surveys the body of evidence built up over the last decade, from a wide range of airborne experiments, which illustrates the ability of such a sensor to provide the required measurements.At present, the BIOMASS P-band radar appears to be the only sensor capable of providing the necessary global knowledge about the world's forest biomass and its changes. In addition, this first chance to explore the Earth's environment with a long wavelength satellite SAR is expected to make yield new information in a range of geoscience areas, including subsurface structure in arid lands and polar ice, and forest inundation dynamics.     

Radar imaging of Kelvin arms of ship wakes

The wave pattern generated by a moving ship is formed by two dominant features: the turbulent wake and a 'V'-shaped pattern trailing the ship, consisting of the two Kelvin arms. In this paper we investigate the radar imaging mechanism of Kelvin arms, which are formed by the cusp waves. A composite surface model for the radar backscattering at the ocean surface is used. The radar signatures of Kelvin arms can be attributed to tilt and hydrodynamic modulation of Bragg waves by the cusp waves. The proposed model allows the computation of the normalized radar backscattering cross-section (NRCS) as a function of radar frequency, polarization, incidence angle, wind speed and direction, and wavelength, direction, and slope of the cusp waves. By using this imaging model, radar signatures of cusp waves are calculated for several spaceborne Synthetic Aperture Radars (SARs): (1) the SEASAT L-band HH-polarized SAR, (2) the ERS-1/-2 VV-polarized SAR, (3) the RADARSAT C-band HH-polarized SAR, and (4) the X-, C- and L-band multipolarization SARs of the Space Radar Laboratory flown on the space shuttle during the SIRC/X-SAR mission in 1994. The results of the simulations are compared with SEASAT and SIR-C/X-SAR imagery of ship wake patterns. It is shown that the dependence of the observed radar signatures of Kelvin arms on radar look direction is consistent with the proposed imaging theory and that the measured relative mean NRCS values induced by Kelvin arms can be fairly well reproduced by the proposed model. The simulations indicate that ship wake signatures should be more clearly visible on SEASAT L-band SAR than on ERS-1/-2 or RADARSAT C-band SAR images. The radar signatures of Kelvin arms are strongest at low wind speeds and are not very sensitive to wind direction.     

MODIS snow albedo bias at high solar zenith angles relative to theory and to in situ observations in Greenland

In situ measurements of snow albedo at five stations along a north-south transect in the dry-snow facies of the interior of Greenland follow the theoretically expected dependence of snow albedo with solar zenith angle (SZA). Greenland Climate Network (GC-Net) measurements from 1997 through 2007 exhibit the trend of modest surface brightening with increasing SZA on both diurnal and seasonal timescales. SZA explains up to 50% of seasonal albedo variability. The two other environmental factors considered, temperature and cloudiness, play much less significant roles in seasonal albedo variability at the five stations studied. Compared to the 10-year record of these GC-Net measurements, the five-year record of MODIS satellite-retrieved snow albedo shows a systematic negative bias for SZA larger than about 55°. Larger bias of MODIS snow albedo exists at more northerly stations. MODIS albedos successfully capture the snow albedo dependence on SZA and have a relatively good agreement with GC-Net measurements for SZA < 55°. The discrepancy of MODIS albedo with in situ albedo and with theory is determined mainly by two related factors, SZA and retrieval quality. While the spatiotemporal structure, especially zonal features, of the MODIS-retrieved albedo may be correct for large SZA, the accuracy deteriorates for SZA > 55° and often becomes physically unrealistic for SZA > 65°. This unphysical behavior biases parameterizations of surface albedo and restricts the range of usefulness of the MODIS albedo products. Seasonal-to-interannual trends in surface brightness in Greenland, and in polar (i.e., large SZA) regions in general, and model simulations of these trends, should be evaluated in light of these limitations.