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Passive microwave sensors (PM) onboard satellites have the capability to provide global snow observations which are not affected by cloudiness and night condition (except when precipitating events are occurring). Furthermore, they provide information on snow mass, i.e., snow water equivalent (SWE), which is critically important for hydrological modeling and water resource management. However, the errors associated with the passive microwave measurements of SWE are well known but have not been adequately quantified thus far. Understanding these errors is important for correct interpretation of remotely sensed SWE and successful assimilation of such observations into numerical models.This study uses a novel approach to quantify these errors by taking into account various factors that impact passive microwave responses from snow in various climatic/geographic regions. Among these factors are vegetation cover (particularly forest cover), snow morphology (crystal size), and errors related to brightness temperature calibration. A time-evolving retrieval algorithm that considers the evolution of snow crystals is formulated. An error model is developed based on the standard error estimation theory. This new algorithm and error estimation method is applied to the passive microwave data from Special Sensor Microwave/Imager (SSM/I) during the 1990-1991 snow season to produce annotated error maps for North America. The algorithm has been validated for seven snow seasons (from 1988 to 1995) in taiga, tundra, alpine, prairie, and maritime regions of Canada using in situ SWE data from the Meteorological Service of Canada (MSC) and satellite passive microwave observations. An ongoing study is applying this methodology to passive microwave measurements from Scanning Multichannel Microwave Radiometer (SMMR); future study will further refine and extend the analysis globally, and produce an improved SWE dataset of more than 25 years in length by combining SSMR and SSM/I measurements.  相似文献   

3.
A new technique is described that detects when and where new snow falls on ice sheets and then determines the thickness of new accumulation. Measurements of vertically polarized passive emission at 85 GHz are filtered with the Hilbert-Huang Transform to identify periods where the surface snow has changed significantly. These are shown to be commonly the result of new snow by comparison with both field observations and in situ instrumentation. Temperature, atmospheric emission and clouds all affect the passive microwave signal but each is examined and shown not to prevent the identification of new snow events. The magnitude of the brightness temperature change is not strongly correlated with snowfall amount. To quantify the amount of new snow, the spatial extent and timing of new snowfalls are examined with ICESat/GLAS laser altimetry data. Crossover differences between altimetric profiles taken before, during, and after the snowfall event provide a measure of the thickness of new snow. Specific cases are presented where 11 and 13 cm of new snow were detected over large regions.  相似文献   

4.
The spatial resolution of passive microwave observations from space is of the order of tens of kilometers with currently available instruments, such as the Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR-E). The large field of view of these instruments dictates that the observed brightness temperature can originate from heterogeneous land cover, with different vegetation and surface properties.In this study, we assess the influence of freshwater lakes on the observed brightness temperature of AMSR-E in winter conditions. The study focuses on the geographic region of Finland, where lakes account for 10% of the total terrestrial area. We present a method to mitigate for the influence of lakes through forward modeling of snow covered lakes, as a part of a microwave emission simulation scheme of space-borne observations. We apply a forward model to predict brightness temperatures of snow covered sceneries over several winter seasons, using available data on snow cover, vegetation and lake ice cover to set the forward model input parameters. Comparison of model estimates with space-borne observations shows that the modeling accuracy improves in the majority of examined cases when lakes are accounted for, with respect to the case where lakes are not included in the simulation. Moreover, we present a method for applying the correction to the retrieval of Snow Water Equivalent (SWE) in lake-rich areas, using a numerical inversion method of the forward model. In a comparison to available independent validation data on SWE, also the retrieval accuracy is seen to improve when applying the influence of snow covered lakes in the emission model.  相似文献   

5.
Melt-water ponds on sea ice in the Northeast Water Polynya (77-82 N, 1-18 W) were mapped using a line scan camera (LSC) mounted on a helicopter. Passive microwave satellite data from the Special Sensor Microwave/ Imager (SSM/I) were employed to analyse the temporal trend of radiances of shorefast ice for 1993 and sea ice during sixteen flights of the LSC (June-July). A simple, linear algorithm tailored to accommodate the summer ice regime, was developed. The LSC measurements of ice (50.9 12.5%), water and melt-water pond fractions compared very well with the SSM/I derived mean ice concentrations (50.9 12.8%). The comparison resulted in a correlation coefficient of 0.953. Combining the LSC melt-water pond fraction data with other data available from the literature provided the basis to construct a second degree polynomial function of a melt-water empirical model to correct the under estimation of SSM/I derived sea ice concentration due to the effect of melt-water ponds.  相似文献   

6.
Passive microwave-derived ice edge locations in the Antarctic are assessed against in situ observations from ships between 1989 and 2000. During the growth season (March-October), the ship data agrees with satellite data very well, with r2 values of 0.99 and 0.97 for the Bootstrap and Team algorithms, respectively. During the melt season (November-February), the agreement is not so good with the passive microwave ice edge typically 1-2° of latitude south of the observations. This is due to the low concentration and saturated nature of the ice, and the r2 values for this period are 0.92 and 0.80 for the Bootstrap and Team algorithms, respectively. Sensitivity studies show that such an offset in the summer ice edge location can cause significant errors in trend studies of the extent of sea ice cover in the Southern Ocean. The passive microwave ice concentration at the ice edge observed by ship varies greatly, averaging 14% for the Team algorithm and 19% for the Bootstrap. Comparisons between passive microwave data and SAR, Landsat and OLS data during the ice growth season show that while small-scale details in ice edge location are lost, the passive microwave data generally provide good and consistent representation of the higher resolution imagery.  相似文献   

7.
On-board detection of cryospheric change in sea ice, lake ice, and snow cover is being conducted as part of the Autonomous Sciencecraft Experiment (ASE), using classifiers developed for the Hyperion hyper-spectral visible/infrared spectrometer on-board the Earth Observing-1 (EO-1) spacecraft. This classifier development was done with consideration for the novel limitations of on-board processing, data calibration, spacecraft targeting error and the spectral range of the instrument. During on-board tests, these algorithms were used to measure the extent of cloud, snow, and ice cover at a global suite of targets. Coupled with baseline imaging, uploaded thresholds were used to detect cryospheric changes such as the freeze and thaw of lake ice and the formation and break-up of sea ice. These thresholds were used to autonomously trigger follow-up observations, demonstrating the capability of the technique for future planetary missions where downlink is a constrained resource and there is high interest in data covering dynamic events, including cryospheric change. Before upload classifier performance was assessed with an overall accuracy of 83.3% as measured against manual labeling of 134 scenes. Performance was further assessed against field mapping conducted at Lake Mendota, Wisconsin as well as with labeling of scenes that were classified during on-board tests.  相似文献   

8.
From September 2002 to October 2010, the Envisat radar altimeter surveyed Greenland and Antarctica ice sheets on a 35 day repeat orbit, providing a unique data set for ice sheet mass balance studies. Up to 85 repeat cycles are available and the whole Envisat data set may be along-track processed in order to provide height variability and trend with a good spatial resolution for the objectives of ice sheet survey.

Soon, a joint Centre National d’Etudes Spatiales/Indian Space Research Organisation mission, SARAL (Satellite with Argos and AltiKa), with the AltiKa payload on board, will be launched on exactly the same orbit (less than 1 km of the nomimal orbit in the across-track direction). This will allow an extension of previous European Remote Sensing (ERS) satellite, ERS-1 and ERS-2, and Envisat missions of the European Space Agency (ESA), in particular from the point of view of ice altimetry. However, AltiKa operates in the Ka band (36.8 GHz), a higher frequency than the classical Ku band (13.6 GHz), leading to important modifications and potential improvements in the interactions between radar wave and snow-pack.

In this paper, a synthesis is presented of all available information relevant to ice altimetry scientific purposes as derived from the Envisat mission: mean and temporal derivatives of the height ? but also of the backscatter and of the two waveform parameters ? snow-pack change corrections, across-track surface slope at 1 km scale, etc. The spatial and temporal variability of ice sheet surface elevation is investigated with the help of the high-resolution Envisat along-track observations. We show that at least 50 repeat cycles are needed to reach the required accuracy for the elevation trend. It is thus advocated as potentially highly beneficial for SARAL/AltiKa as a follow-on mission. Moreover, the novel characteristics of SARAL/AltiKa are promising in improving our understanding of snow penetration impact.  相似文献   

9.
Satellite altimetry is a powerful tool to map the ice sheet elevation as well as a number of other parameters linked to geometrical and geophysical properties of ice sheets. Irrespective of new instrumental developments like the laser altimeter on ICESat (Ice, Cloud and land Elevation Satellite) the well-established radar altimeter (RA) missions ERS-1,2 (European Remote Sensing satellites) and ENVISAT (ENVIronment SATellite) are unique in their temporal coverage over more than a decade and in their temporal and spatial sampling. Therefore, the full exploitation of these RA data by improved methods is imperative. Here we develop improved techniques to correct for topographically induced errors by a refined consideration of the relevant topographic conditions. Furthermore we improve the gridding procedure by adapting it to local conditions and thus preserving smaller-scale features. We apply our methods to the region of the subglacial Lake Vostok/Antarctica and derive digital elevation models (DEMs) for this region with the aim of improving/resolving smaller scales. The effect of our improvements is demonstrated in detail by comparing our DEMs and previously published DEMs to the ICESat laser measurements which are taken as a reference here. Due to our improvements, the standard deviation of the difference between RA-based DEMs of the Lake Vostok region and ICESat data decreases from more than 1.1 m to 0.5 m. This remaining error is chiefly the error inherent in the RA observations. Our RA-ICESat comparisons, supported by Fourier analyses, also reveal the presence and importance of small-scale features that can be detected by laser but not by the RA measurements.  相似文献   

10.
The effects of weather systems on sea-ice concentration retrieval are investigated using an advanced radiative transfer model with input data from 155radiosonde ascents together with satellite and ground based observations in the Weddell Sea in 1992. The results of the model study indicate that, using the SSM/I NASA Team algorithm, cloud liquid water increases estimates of total sea-ice concentration by the same magnitude as water vapour, i.e., up to 10 per cent, depending on surface type (open ocean, first-year ice, multiyear ice), and actual concentration. Estimates of the multiyear ice concentration are reduced by up to 80 per cent by cloud liquid water whereas the water-vapour effect is smaller (up to 6 per cent). The combined effect is less than the sum of the two. Calculations using the SMMR sea-ice algorithm were made for comparison with previous estimates by Pedersen and Maslanik. In this case study, estimates of the multiyear fraction show a smaller reduction by water vapour and a larger reduction by cloud liquid water, whereas the total concentration change is in between the two previous results.The algorithm for the SSM/I radiometer exhibits stronger effects on total ice concentration due to water vapour and cloud liquid water than that for SMMR, and atmospheric effects using the future MIMR radiometer sea-ice algorithm will be in between those from SMMR and SSM/I. Different calculated ice-concentration changes for the SSM/I due to different sets of tiepoints (emissivities) can be of the same order of magnitude as the atmospheric effect of cloud liquid water. Comparison between these modelled effects and satellite-derived concentrations from SSM/I shows good geographical and quantitative agreement in areas with extensive frontal water clouds.  相似文献   

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