Comparison of Envisat radar and airborne laser altimeter measurements over Arctic sea ice

Sea ice thickness is a crucial, but very undersampled cryospheric parameter of fundamental importance for climate modeling. Advances in satellite altimetry have enabled the measurement of sea ice freeboard using satellite microwave altimeters. Unfortunately, validation of these new techniques has suffered from a lack of ground truth measurements. Therefore, an airborne campaign was carried out in March 2006 using laser altimetry and photo imagery to validate sea ice elevation measurements derived from the Envisat/RA-2 microwave altimeter.We present a comparative analysis of Envisat/RA-2 sea ice elevation processing with collocated airborne measurements collected north of the Canadian Archipelago. Consistent overall relationships between block-averaged airborne laser and Envisat elevations are found, over both leads and floes, along the full 1300 km aircraft track. The fine resolution of the airborne laser altimeter data is exploited to evaluate elevation variability within the RA-2 ground footprint. Our analysis shows good agreement between RA-2 derived sea ice elevations and those measured by airborne laser altimetry, particularly over refrozen leads where the overall mean difference is about 1 cm. Notwithstanding this small 1 cm mean difference, we identify a larger elevation uncertainty (of order 10 cm) associated with the uncertain location of dominant radar targets within the particular RA-2 footprint. Sources of measurement uncertainty or ambiguity are identified, and include snow accumulation, tracking noise, and the limited coverage of airborne measurements.     

On the use of the dual-frequency ENVISAT altimeter to determine snowpack properties of the Antarctic ice sheet

The primary purpose of ice-sheet altimetry is to monitor the changes in ice-sheet topography which may impact on global sea-level. However, the altimetric signal is sensitive to different properties of the snowpack, and therefore can also be used to determine these properties. The radar altimeter onboard the European Space Agency's ENVISAT satellite provides a dual-frequency dataset at Ku (13.6 GHz) and S band (3.2 GHz). In this paper, these signals are studied over the Antarctic ice-sheet during the 4 first years of the mission (2002-2006), in order to retrieve snowpack properties.The altimeter signal can be described by 4 classical waveform parameters. The 4 year time-series of all these parameters are decomposed into a linear and a seasonal time component. The linear component is almost constant. The distribution of the mean parameters over the Antarctic ice-sheet shows that the altimeter signal is sensitive to small-scale (mm) surface roughness.For the first time, the amplitudes and phases of the seasonal variations are characterized. The S band amplitudes are greater than the Ku band, and the phase varies over the entire ice-sheet. Previous studies suggested that the seasonal variations of the altitude from the altimeter are created by a decrease of the snowpack height through compaction. The dual-frequency observations shown here suggest that this hypothesis is too simple. Instead, the altitude variations observed in the altimetric signal are not created by the snowpack height change, but are more likely caused by the seasonal change of the snow properties, which cause a different response between the S and Ku bands. Therefore, both the linear and the seasonal variations of the altimetric signal can be used to retrieve snowpack properties.Here, we compare the dual-frequency ENVISAT signal with a model of the altimetric echo over the Antarctic ice-sheet. The model combines a surface model with a sub-surface model, for both the S and Ku bands. The Brown model [Brown G. S. (1977). The average impulse response of a rough surface and its applications. IEEE Transactions on Antennas and Propagation, 25, 1.] is used to describe the interaction of the radar wave with the snow surface. The backscatter coefficient of the surface is derived using the IEM method [Fung, A. K. (1994). Microwave scattering and emission models and their applications, Boston, MA: Artech House.]. The sub-surface signal takes into account both the layering effects and the scattering caused by the homogeneous media which is composed of small snow grains. The model is tested in two areas of the Antarctic plateau which present very different waveform parameters. The sensitivity of the radar signal to the different snowpack properties is investigated. The analysis of the waveform behaviours shows that the sub-surface signal can be completely masked by the small-scale surface roughness signal.Finally, the temperature and surface density effects are investigated in order to explain the seasonal variations of the altimetric signal. Both the temperature and the compaction rate of the snow change seasonally. Temperature is shown to impact on the Ku band signal. Furthermore, the compaction rate of the snow surface can explain all of the seasonal variation characteristics observed at both the S and Ku bands. The seasonal change of compaction rate in the snow creates a change in the waveform shape that can bias the altitude. In particular, the snow compaction can induce a bias in the retrieved altimetric altitude of more than 80 cm for the Ku band and 1.5 m for the S band. This work underlines that the altitude time-series needs to be corrected for the shape of the altimetric echo over ice-sheets.     

Water levels in the Amazon basin derived from the ERS 2 and ENVISAT radar altimetry missions

This study sets out to analyze the stages of water bodies in the Amazon basin derived from the processing of ERS-2 and ENVISAT satellite altimetry data. For ENVISAT, GDR measurements for both Ice-1 and Ice-2 tracking algorithms were tested. For ERS-2, the Ice-2 data produced by the OSCAR project was used. Water level time series over river segments of very different width, from several kilometers to less than a hundred of meters, were studied. The water level time series that can be derived from narrow riverbeds are enhanced by off-nadir detections. Conversely, the off-nadir effect may degrade the series over large bodies if not properly accounted for. Comparison at crossovers and with in situ gauges shows that the quality of the series can be highly variable, from 12 cm in the best cases and 40 cm in most cases to several meters in the worse cases. Cautious data selection is clearly a key point to achieve high quality series. Indeed, low quality series mostly result from inclusion of outliers in the data set finally retained for the computation of the series. Ice-2 and Ice-1 tracking algorithms in the ENVISAT data perform almost equally well. ENVISAT altimetry is clearly an improvement on ERS-2 altimetry.     

基于匹配滤波技术的特征信号提取的仿真和实现

激光测高仪的回波信号中包含目标物的粗糙度、倾斜度、反射率等信息,对于复杂地物特征的回波信号,采用多通道匹配滤波技术不仅可以获得高的信噪比,还可以保证信号的保真度。     

Seasonal and interannual variability of surface circulation in the Cape Verde region from 8 years of merged T/P and ERS-2 altimeter data

The characterisation of the geostrophic surface flow field around the Cape Verde Archipelago in the northeast Atlantic Ocean with satellite altimeter data is presented. The aim is to analyse the main current systems present in the region 3°-30°N, 40°-10°W and their seasonal and interannual variability. A merged data set of Topex/Poseidon (T/P) and ERS-2 altimeter data for an 8-year period, beginning in June 1995, has been used and corrected sea surface heights were computed by applying a homogeneous set of relevant geophysical corrections. ERS-2 data were crossover adjusted to T/P. Monthly maps of sea level anomalies were created for the whole period and were used in the computation of monthly maps of absolute dynamic topography, geostrophic currents and eddy kinetic energy (EKE). The seasonal signal of the northeast Tropical Atlantic large-scale surface circulation appears as the prevailing cause of the variability in the region, particularly in the southernmost portion of the region being studied. This signal is also present in the flow field along the African coast and in the Guinea Dome. Regions of highest EKE values are clearly associated with the North Equatorial Counter-Current and with the currents along the African coast. The significant interannual variability found for 1998 seems to be associated with the 1997-1998 ENSO Pacific event, but other anomalous periods (1996-1997 and 2001-2002) uncorrelated with ENSO are also evident.     

The impact of simulated soil temperatures on the estimation of snow depth over Siberia from SSM/I compared to a multi-model climatology

The snowpack is a key variable of the hydrological cycle. In recent years, numerous studies have demonstrated the importance of long-term monitoring of the Siberian snowpack on large spatial scales owing to evidence of increased river discharge, changes in snow fall amount and alterations with respect to the timing of ablation. This can currently only be accomplished using remote sensing methods. The main objective of this study is to take advantage of a new land surface forcing and simulation database in order to both improve and evaluate the snow depths retrieved using a dynamic snow depth retrieval algorithm. The dynamic algorithm attempts to account for the spatial and temporal internal properties of the snow cover. The passive microwave radiances used to derive snow depth were measured by the Special Sensor Microwave/ Imager (SSM/I) data between July 1987 and July 1995.The evaluation of remotely sensed algorithms is especially difficult over regions such as Siberia which are characterized by relatively sparse surface measurement networks. In addition, existing gridded climatological snow depth databases do not necessarily correspond to the same time period as the available satellite data. In order to evaluate the retrieval algorithm over Siberia for a recent multi-year period at a relatively large spatial scale, a land surface scheme reanalysis product from the Global Soil Wetness Project-Phase 2 (GSWP-2) is used in the current study. First, the high quality GSWP-2 input forcing data were used to drive a land surface scheme (LSS) in order to derive a climatological near-surface soil temperature. Four different snow depth retrieval methods are compared, two of which use the new soil temperature climatology as input. Second, a GSWP-2 snow water equivalent (SWE) climatology is computed from 12 state-of-the-art LSS over the same time period covered by the SSM/I data. This climatology was compared to the corresponding fields from the retrievals. This study reaffirmed the results of recent studies which showed that the inclusion of ancillary data into a satellite data-based snow retrieval algorithm, such as soil temperatures, can significantly improve the results. The current study also goes a step further and reveals the importance of including the monthly soil temperature variation into the retrieval, which improves results in terms of the spatial distribution of the snowpack. Finally, it is shown that further improved predictions of SWE are obtained when spatial and temporal variations in snow density are accounted for.