Near concurrent MIR, SSM/T-2, and SSM/I observations over snow-covered surfaces

The airborne Millimeter-wave Imaging Radiometer (MIR) and MODIS Airborne Simulator (MAS) measurements over the Arctic region and the Midwest region of the US are used to derive surface emissivities ξ(ν) for three frequencies, ν=89, 150, and 220 GHz, as well as Normalized Difference Snow Index (NDSI) and R87 (0.87-μm reflectance). These derived parameters are compared with parameters estimated from near concurrent measurements made by the SSMI and SSM/T-2 over snow-covered areas. It is shown that the MIR-estimated ξ(ν) values at ν=89 and 150 GHz agree well with those estimated from the SSM/T-2 at ν=91 and 150 GHz, respectively. Low MIR-estimated ξ(ν) values are generally associated with high NDSI and R87 over the snow-covered areas. Over forested areas, more fluctuations in the values of MIR-estimated ξ(ν), NDSI and R87, as well as a reduction in polarization index (PI) at 37 and 85 GHz are observed.Both observations and results from radiative transfer calculations show a change in the difference between brightness temperatures (T_b) at 19 and 37 GHz, as well as PI at 37 and 85 GHz, when measured at satellite altitudes and at the surface. The amplitude of the T_b difference and PI is reduced by about 10-15% from surface to high altitudes when integrated water vapor is ≤1.5 g/cm². This effect is readily correctable and requires consideration when validating satellite retrieval algorithms based on surface and low-elevation aircraft measurements.     

Correlation of the ERS and SSM/I observations over Snowpack and Numerical Simulation

Observations by active ERS scatterometer and passive SSM/I radiometers on the same area and for the same time period are correlated using the data measured by ERS-1 and multichannel SSM/I over snowpack located in Greenland, Siberia, the Alps, etc. It is demonstrated that multichannel analysis by both active and passive sensors is very helpful for monitoring temporal variation of complex snow covers. Back-scattering from multilayer, strongly fluctuating random media is calculated for ERS data simulation. The radiative transfer of thermal emission from multilayer media of dense scatterers is solved for SSM/I data simulation. Simulations of both back-scattering and emission show good correlation and functional dependence on highly-variable snowcovers. Theoretical results also compare well with observation data.     

A comparison between SSM/I passive microwave melt onset dates and satellite-derived albedo melt onset dates in the Arctic

Arctic sea ice undergoes a very strong annual cycle. This study sets out to look at the transition when the Arctic sea ice starts to melt using satellite-obtained passive microwave brightness temperatures and satellite-derived albedo data for 13 points within the Arctic, including both first-year and multiyear ice locations, for 1995–2000. Special sensor microwave imager (SSM/I) brightness temperature differences are used to determine melt onset dates once surface temperatures approach freezing. Independently, satellite-derived albedo data are obtained and a melt onset date is derived. Generally, the two methods produce the same date for melt onset with optimum conditions. However, in most cases there are clouds present, which for the albedo data restrict observations and generate melt dates that are several days later than the passive microwave melt onset which is not affected by cloud cover. Melt onset dates, determined from the passive microwave brightness temperatures, are compared to those from the albedo observation to determine differences between the two methods. For first-year ice (FYI) locations, the average differences in melt onset dates for the study locations between the passive microwave and albedo-derived methods are +/?3 days. The average difference for multiyear ice (MYI) locations melt onset dates is around 8 days, slightly longer than the (FYI) locations, however, this is due to more cloudy conditions. The results indicate that the passive microwave-derived melt onset dates and albedo-derived dates are very close and either method could be used to determine melt. The advantage of using microwave data would be the independence of having to have cloud free conditions.     

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.     

The influence of spatial resolution enhancement a implied to SSM/I data on pattern recognition

The impact of spatial resolution enhancement on pattern recognition based on SSM/I measurements is evaluated. The instrument ground footprints for the 19, 22 and 37 GHz channels have considerable overlap. An objective technique can be applied to enhance spatial resolution of measurements to the spatial resolution of the 37 GHz channel. The authors utilize a Backus-Gilbert matrix transform approach. Different validation procedures have been performed to demonstrate the effectiveness of the method with the aim to ameliorate the boundary detection on pattern recognition and specially to cloud classification improvement.     

An improved look-up table technique for geophysical parameters from SSM/I

An improved look-up table technique is developed to calculate meteorological parameters from Special Sensor Microwave/Imager (SSM/I) measurements. The method, which is based on a look-up table and an extrapolation and interpolation technique used in the weather prediction model, gives results comparable to or better than the regression method for the total precipitable water (TPW), surface wind speed (V), and cloud liquid water path (LWP). Applied to a noise-free data set (dependent test) TPW, V and LWP are retrieved with a rms. accuracy of 0.26 kg m^-2, 0.28 m s^-1 and 0.002 kg m^-2, respectively. If the random noise of the SSM/I radiometer is taken into account in the retrieval, the r.m.s. increases to 0.84 kg m^-2, 1.08 m s^-1 and 0.013 kg m^-2, respectively. The method is applied to a set of over 520 SSM/I measurements from the DMSP-F8 satellite for which collocated radiosondes launched from ships are available. The rms. (bias) of TPW and V was 2.91 (-0.61) kg m^-2 and 2.75 (-0.13) m s^-1, respectively. We use the improved look-up table technique to calculate the monthly mean global distribution of surface wind for August 1989 and compare the results with the Comprehensive Ocean-Atmosphere Data Set (COADS) for the same month. The rms. error and mean differences for the monthly mean values of sea surface wind speed between the retrievals and COADS data are 1.01 m s^-1 and 0.03 m s^-1, respectively. We also calculate LWP for October 1987 and compare it with the LWP derived from cloud optical thicknesses of International Satellite Cloud Climatology Project (ISCCP) dataset. Good agreement is obtained. The extension of the method to calculate cloud water and water vapour profiles is discussed.     

The modelling of sea ice melt-water ponds for the High Arctic using an Airborne line scan camera,and applied to the Satellite Special Sensor Microwave/Imager (SSM/I)

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.     

从SSM/I亮温反演海洋上大气可降水量

利用日本NASDA提供的SSM/I和相应的海岛气象探空资料,对几种有代表性的SSM/I反演大气可降水量算式的反演结果进行了比较分析。结果表明,目前业务反演采用的算式过高地估计了低大气可降水量,而对于高大气可降水量(>4.5g/cm2)又过低地估计了;对反演值进行一个三次多项式订正,从总体上并不改善反演效果。针对以上问题,提出一个改进的混合分段反演算式。     

The Kamishak Gap wind as depicted in DMSP OLS and SSM/I data

Abstract

DMSP visual and infrared Operational Line Scanner (OLS) data reveal several effects of a mountain gap wind generated by wind flow through the Kamishak Gap on the west side of Cook Inlet, Alaska. The existence of the wind can be inferred through one of three separate effects apparent in the data: (1) a roughened sea effect, appearing as a dark grey shade swath within a sunglint pattern; (2) an anomalous grey shade effect induced by sea spray and aerosols, and, (3) a multiple cloud line effect originating as a result of a moisture flux from the sea to the air in a high speed, cold air, coastal outbreak. The Special Sensor Microwave Imager (SSM/I) of the DMSP satellite provides another means to detect the Kamishak Gap wind due to the sensitivity of this sensor to changes in microwave emission of the sea surface as a result of sea spray and foam in high wind speed areas and to associated changes in integrated water vapour content (oceanic total precipitable water). Developed algorithms provide a methodology to quantify from a satellite perspective some of the characterisitics of such events.     

Snow depth inverted by scattering indices of SSM/I channels in a mesh graph

The accuracy of snow depth estimation is affected significantly by the regional surface type. We have developed a theoretical model of vector radiative transfer (VRT) for snowpack/vegetation canopies at SSM/I channels. The vegetation canopy is modelled by a layer of nonspherical particles, and the snowpack is modelled as a layer of dense spherical particles. By numerically solving two coupled VRT equations for multi-layer models of different surface types such as tree/snow, grass/snow and snowpack only, two scattering indices SI1 = T B19v - T B37v and SI2 = T B22v - T B85v are obtained for a variety of snow depths (SD) and ice-grain sizes. These results are combined as a mesh graph in the figure of SI1 versus SI2 . When the SSM/I TB data is observed, its location in the mesh graph can indicate the estimation of SD. Our results compare well with the SSM/I data of the U.S.A. east coast January blizzard, 1996.     

Estimation of Arctic glacier motion with satellite L-band SAR data

Offset fields between pairs of JERS-1 satellite SAR data acquired in winter with 44 days time interval were employed for the estimation of Arctic glacier motion over Svalbard, Novaya Zemlya and Franz-Josef Land. The displacement maps show that the ice caps are divided into a number of clearly defined fast-flowing units with displacement larger than about 6 m in 44 days (i.e. 50 m/year). The estimated error of the JERS-1 offset tracking derived displacement is on the order of 20 m/year. Occasionally, azimuth streaks related to auroral zone ionospheric disturbances were detected and dedicated processing steps were applied to minimize their influence on the estimated motion pattern. Our analysis demonstrated that offset tracking of L-band SAR images is a robust and direct estimation technique of glacier motion. The method is particularly useful when differential SAR interferometry is limited by loss of coherence, i.e. for rapid and incoherent flow and large acquisition time intervals between the two SAR images. The JERS-1 results, obtained using SAR data acquired by a satellite operated until 1998, raise expectations of L-band SAR data from the ALOS satellite launched in early 2006.     

Investigation of effect of heterogeneities in vegetation and rainfall on simulated SSM/I brightness temperatures

Land surface characteristics: soil and vegetation and rainfall inputs are distributed in nature. Representation of land surface characteristics and inputs in models is lumped at spatial scales corresponding to the grid size or observation density. Complete distributed representation of these characteristics or inputs is infeasible due to excessive computational costs or costs associated with maintaining dense observational networks. The measurements of microwave brightness temperatures by the SSM/I (Special Sensor Microwave Imager) are at resolutions of the order of 56km 56km for 19 GHz and 33 km 33 km for 37 GHz. At these resolutions, soil moisture and vegetation are not homogeneous over the measurement area. The experiments carried out in this study determine the effect of heterogeneities in vegetation (leaf area index) and input rainfall on simulated soil moisture and brightness temperatures and the inversion of brightness temperatures to obtain soil moisture estimates. This study would help us to understand the implications of using the SSM/I microwave brightness temperatures for soil moisture estimation. The consequences of treating rainfall inputs and vegetation over large land surface areas in a lumped fashion is examined. Simpler methods based on dividing the leaf area index or input rainfall into classes rather than explicit representation for representing heterogeneities in leaf area index and spatial distribution of rainfall is tested. It is seen that soil moisture is affected by the representation (lumped vs distributed) of rainfall and not leaf area index. The effect of spatially distributed soil moisture on the inversion of observed SSM/I brightness temperatures to obtain soil moisture estimates is investigated. The inversion process does not exhibit biases in the retrieval of soil moisture. The methodology presented in this paper can be used for any satellite sensor for purposes of analysis and evaluation.     

Monitoring sandstorms and desertification in northern China using SSM/I data and Getis statistics

A massive sandstorm enveloped most of northern China during Spring 2002. Monitoring the evolution of sandstorm and desertification has become one of the most serious problems for China's environment. Since 1989, one of the most advanced and operational passive microwave sensors is the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) operated at seven channels (19, 37, 85?GHz with vertical and horizontal polarization and 22?GHz with vertical polarization only). In this paper, the sandstorm and desertification indexes, SDI and DI, are derived from the radiative transfer equation, and are employed with multi-channel measurements of the DMSP SSM/I for monitoring the sandstorm and desertification in northern China. Some SSM/I data in 1997 and 2001 are employed. The algorithm of the Getis statistics is developed to categorize the spatial correlation and its evolution during these days. It is demonstrated that the SSM/I indexes, SDI and DI, and its Getis statistics are well applicable for monitoring the sandstorm and desertification.     

Initial observations on using SAR to monitor wildfire scars in boreal forests

Abstract

Initial observations on the effects of wildfires in black spruce forests on radar backscatter are presented. Airborne and spaceborne SAR imagery are utilized to illustrate two distinct fire signatures. A theory is presented to explain these differences.     

Model-based interpretation of ERS-1 SAR images of Arctic sea ice

Interpretation of Synthetic Aperture Radar (SAR) images of sea ice is complex because of the natural variability of sea ice and sensor-induced effects, such as speckle. Most of the research on SAR image interpretation has focused on the winter months and algorithms were developed to classify sea ice successfully under cold conditions. However, interpretation of SAR images during the seasonal transitions has proved difficult due to rapidly changing weather conditions. In this paper we address the application of SAR during the transition from summer to the fall freeze-up. This period is important because it signals the start of significant new ice growth, which affects the air-ocean heat exchange and injects brine into the upper layers of the ocean. We have interpreted SAR images of the sea ice in terms of the basic ice characteristics by using shipborne radar measurements of sea ice during the freeze-up and models derived from these measurements. We have shown that the model-based approach is effective in interpreting SAR images during this seasonal transition. This work also provides the physical mechanisms responsible for the large increase in backscatter observed at the end of the summer melt season.     

Total precipitable water over the Arabian Ocean and the Bay of Bengal using SSM/I data

Using Special Sensor Microwave/Imager (SSM/I) data, the total precipitable water (TPW) over the Arabian Ocean and the Bay of Bengal has been estimated. The monthly and seasonal variations of TPW show a very systematic pattern that correlates closely with the monsoon.     

A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia.

The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987–1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is − 0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient − 0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America (where the correlation coefficient decreases to 0.29).     

Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM/I data

The retrieval of snow water equivalent (SWE) and snow depth is performed by inverting Special Sensor Microwave Imager (SSM/I) brightness temperatures at 19 and 37 GHz using artificial neural network ANN-based techniques. The SSM/I used data, which consist of Pathfinder Daily EASE-Grid brightness temperatures, were supplied by the National Snow and Ice Data Centre (NSIDC). They were gathered during the period of time included between the beginning of 1996 and the end of 1999 all over Finland. A ground snow data set based on observations of the Finnish Environment Institute (SYKE) and the Finnish Meteorological Institute (FMI) was used to estimate the performances of the technique. The ANN results were confronted with those obtained using the spectral polarization difference (SPD) algorithm, the HUT model-based iterative inversion and the Chang algorithm, by comparing the RMSE, the R², and the regression coefficients. In general, it was observed that the results obtained through ANN-based technique are better than, or comparable to, those obtained through other approaches, when trained with simulated data. Performances were very good when the ANN were trained with experimental data.     

A decision tree algorithm for surface soil freeze/thaw classification over China using SSM/I brightness temperature

This paper reports the development of a decision tree algorithm to classify the surface soil freeze/thaw states. The algorithm uses SSM/I brightness temperatures recorded in the early morning. Three critical indices are used as classification criteria—the scattering index (SI), the 37 GHz vertical polarization brightness temperature (T_37V), and the 19 GHz polarization difference (PD₁₉). The thresholds of these criteria were obtained from samples of frozen soil, thawed soil, desert, and snow. The algorithm is capable of distinguishing between frozen soil, thawed soil, desert, and precipitation. In-situ 4-cm deep soil temperatures on the Qinghai-Tibetan Plateau were used to validate the classification results, and the average classification accuracy was found to be 87%. Regarding the misclassified pixels, about 40% and 73% of them appeared when the surface soil temperature ranged from − 0.5 °C to 0.5 °C and from − 2.0 °C to 2.0 °C, respectively, which means that most misclassifications occurred near the soil freezing point. In addition, misclassifications mainly occurred from April to May and September to October, the transition periods between warm and cold seasons. A grid-to-grid Kappa analysis was also conducted to evaluate the consistency between the map of the actual number of frozen days obtained using the decision tree classification algorithm and the reference map of geocryological regionalization and classification in China. The overall classification accuracy was 91.7%, and the Kappa index was 80.5%. The boundary between the frozen and thawed soil was consistent with the southern limit of seasonally frozen ground from the reference map. The statistics show that the maximum area of frozen soil is about 6.82 × 10⁶ km² in late January, accounting for 69% of total Chinese land area.