Correcting for the influence of frozen lakes in satellite microwave radiometer observations through application of a microwave emission model

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.     

A comparison of 18 winter seasons of in situ and passive microwave-derived snow water equivalent estimates in Western Canada

The Meteorological Service of Canada (MSC) has developed an operational snow water equivalent (SWE) retrieval algorithm suite for western Canada that can be applied to both Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) data. Separate algorithms derive SWE for open environments, deciduous, coniferous, and sparse forest cover. A final SWE value represents the area-weighted average based on the proportional land cover within each pixel. The combined SSM/I and SMMR time series of dual polarized, multichannel, spaceborne passive microwave brightness temperatures extends back to 1978, providing a lengthy time series for algorithm assessment. In this study, 5-day average (pentad) passive microwave-derived SWE imagery for 18 winter seasons (December, January, February 1978/79 through 1995/96) was compared to SWE estimates taken from a distributed network of surface measurements throughout western Canada.Results indicated both vegetative and snowpack controls on the performance of MSC algorithms. In regions of open and low-density forest cover, the in situ and passive microwave SWE data exhibited both strong agreement and similar levels of interannual variability. In locations where winter season SWE typically exceeded 75 mm, and/or dense vegetative cover was present, dataset agreement weakened appreciably, with little interannual variability in the passive microwave SWE retrievals. These results have important implications for extending the SWE monitoring capability of the MSC algorithm suite to northern regions such as the Mackenzie River basin.     

Quantifying the uncertainty in passive microwave snow water equivalent observations

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.     

Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations

The monitoring of snow water equivalent (SWE) and snow depth (SD) in boreal forests is investigated by applying space-borne microwave radiometer data and synoptic snow depth observations. A novel assimilation technique based on (forward) modelling of observed brightness temperatures as a function of snow pack characteristics is introduced. The assimilation technique is a Bayesian approach that weighs the space-borne data and the reference field on SD interpolated from discrete synoptic observations with their estimated statistical accuracy. The results obtained using SSM/I and AMSR-E data for northern Eurasia and Finland indicate that the employment of space-borne data using the assimilation technique improves the SD and SWE retrieval accuracy when compared with the use of values interpolated from synoptic observations. Moreover, the assimilation technique is shown to reduce systematic SWE/SD estimation errors evident in the inversion of space-borne radiometer data.     

被动微波遥感在青藏高原积雪业务监测中的初步应用

积雪范围、积雪深度和雪水当量等参数的遥感监测与反演对气候模式的建立以及积雪灾害的评估具有重要意义。被动微波遥感在这些参数的反演方面具有明显优势,但目前尚未应用到青藏高原地区的积雪遥感业务监测上来。2001年10月至2002年4月,利用SSM/I数据对青藏高原地区的积雪范围和积雪深度进行了实时监测,为西藏、青海遥感应用部门提供逐日的雪深分布图。对这次监测的总效果进行了分析和评价,并对发生在青海省内一次较大的降雪过程进行了遥感分析,结果表明:SSM/I反演的积雪范围变化趋势与MODIS结果总体上较为一致;SSM/I的雪深监测结果为当地遥感部门对大于10 cm的雪深做出正确判断提供了重要信息,是对雪灾定位的重要信息源。     

Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements

Taking three snow seasons from November 1 to March 31 of year 2002 to 2005 in northern Xinjiang, China as an example, this study develops a new daily snow cover product (500 m) through combining MODIS daily snow cover data and AMSR-E daily snow water equivalent (SWE) data. By taking advantage of both high spatial resolution of optical data and cloud transparency of passive microwave data, the new daily snow cover product greatly complements the deficiency of MODIS product when cloud cover is present especially for snow cover product on a daily basis and effectively improves daily snow detection accuracy. In our example, the daily snow agreement of the new product with the in situ measurements at 20 stations is 75.4%, which is much higher than the 33.7% of the MODIS daily product in all weather conditions, even a little higher than the 71% of the MODIS 8-day product (cloud cover of ~ 5%). Our results also indicate that i) AMSR-E daily SWE imagery generally agrees with MOD10A1 data in detecting snow cover, with overall agreement of 93.4% and snow agreement of 96.6% in the study area; ii) AMSR-E daily SWE imagery underestimates the snow covered area (SCA) due to its coarse spatial resolution; iii) The new snow cover product can better and effectively capture daily SCA dynamics during the snow seasons, which plays a significant role in reduction, mitigation, and prevention of snow-caused disasters in pastoral areas.     

Estimation of snow water equivalent over first-year sea ice using AMSR-E and surface observations

A SWE retrieval algorithm developed in-situ using passive microwave surface based radiometer data is applied to the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E). Snow water equivalent is predicted from two pixels located in Canadian Arctic Shelf Exchange Study (CASES) overwintering study area in Franklin Bay, N.W.T., Canada. Results show that the satellite SWE predictions are statistically valid with measured in-situ snow thickness data in both smooth and rough ice environments where predicted values range from 15 to 25 mm. Stronger correlation between measured and predicted data is found over smooth ice with R² value of 0.75 and 0.73 for both pixels respectively. Furthermore, a qualitative study of sea ice roughness using both passive and active microwave satellite data shows that the two pixels are rougher than the surrounding areas, but the SWE predictions do not seem to be affected significantly.     

Seasonal snow extent and snow mass in South America using SMMR and SSM/I passive microwave data (1979-2006)

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite and the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2006, both snow cover extent and snow water equivalent (snow mass) were investigated during the coldest months (May-September), primarily in the Patagonia area of Argentina and in the Andes of Chile, Argentina and Bolivia, where most of the seasonal snow is found. Since winter temperatures in this region are often above freezing, the coldest winter month was found to be the month having the most extensive snow cover and usually the month having the deepest snow cover as well. Sharp year-to-year differences were recorded using the passive microwave observations. The average snow cover extent for July, the month with the greatest average extent during the 28-year period of record, is 321,674 km². In July of 1984, the average monthly snow cover extent was 701,250 km² — the most extensive coverage observed between 1979 and 2006. However, in July of 1989, snow cover extent was only 120,000 km². The 28-year period of record shows a sinusoidal like pattern for both snow cover and snow mass, though neither trend is significant at the 95% level.     

A blended global snow product using visible,passive microwave and scatterometer satellite data

A joint US Air Force/National Aeronautics and Space Administration (NASA) blended global snow product that uses Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and Quick Scatterometer (QuikSCAT or QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by using a newly developed Air Force Weather Agency (AFWA)/NASA Snow Algorithm (ANSA). This initial blended snow product uses minimal modelling to expeditiously yield improved snow products, which include, or will include, snow-cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the USA, from Colorado obtained during the Cold Land Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow-cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or diurnal-amplitude variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.     

Simultaneous MSMR and SSM/I observations and analysis of sea‐ice characteristics over the Antarctic region

Polar sea ice has been monitored quasi‐continuously over the last 30 years using passive microwave radiometers onboard three satellites in polar orbit, namely Nimbus‐5, Nimbus‐7 and Defense Meteorological Satellite Program (DMSP) series. A short overlap between Scanning Multichannel Microwave Radiometer (SMMR) on Nimbus‐7 and Special Sensor Microwave Imager (SSM/I) onboard DMSP allowed inter‐calibration of the two sensors leading to a consistent series of long‐term sea‐ice measurements since 1978. With the launch of Multifrequency Scanning Microwave Radiometer (MSMR) onboard OCEANSAT‐1 in the polar sun‐synchronous orbit during 1999, India developed the capability to monitor the polar sea ice on a regular basis. The concurrent availability of SSM/I and MSMR over the last few years presents a valuable opportunity to attempt an inter‐comparison of MSMR with SSM/I measurements and derived sea‐ice parameters.

In this paper, we present an indirect validation of the brightness temperatures (T _b) observed by MSMR with near‐simultaneous measurements from SSM/I over the Antarctic and Southern Polar Ocean regions. Simultaneous MSMR and SSM/I data from two contrasting seasons—summer and winter—for the 1999–2000 period have been used. Analysis includes a comparison of T _b scatterograms to achieve confidence in the quantitative use of the T _b data to derive various geophysical parameters, e.g. sea‐ice concentration and extent. Additionally, the T _b images produced by the two sensors are compared to establish the capability of MSMR in reliable two‐dimensional portrayal of all the sea and continental ice features over the Antarctic Region. Based on a regression analysis between MSMR observed T _b in different frequency channels and polarizations, and SSM/I‐derived sea‐ice concentration (SIC) values, we have developed algorithms to estimate SIC over the Southern Polar Ocean from MSMR data. The MSMR algorithms allow estimation of SIC with better than 10% rms error. MSMR SIC images faithfully capture the observed distribution of sea ice in all the sectors of the Southern Ocean both during summer and winter periods. Using the MSMR‐derived SIC, we have also derived monthly sea‐ice extent (SIE) estimates for a period extending for about 20 months from the beginning of the launch of MSMR. These estimates show excellent agreement with values derived from SSM/I. These analyses bring out the very high level of compatibility in the measurements and derived sea‐ice parameters produced by the two sensors.     

The contribution of AMSR-E 18.7 and 10.7 GHz measurements to improved boreal forest snow water equivalent retrievals

Four seasons (2004–2007) of snow surveys across the boreal forest of northern Manitoba were utilized to determine relationships between vertically polarized Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (T_B) and ground measurements of snow water equivalent (SWE). Regression analysis identified moderate strength, yet statistically significant relationships between SWE and T_B differences (36.5–18.7; 36.5–10.7; 18.7–10.7) for individual seasons. When multiple seasons were considered collectively, however, the 36.5–18.7 and 36.5–10.7 differences were insignificant because the seasonal linear relationships shifted from year to year over the same T_B range regardless of SWE. This inter-seasonal consistency in T_B was explained through significant correlations with vegetation density as characterized by a MODIS-derived forest transmissivity dataset. More encouraging results were found for the 18.7–10.7 difference: the relationship with SWE remained statistically significant when multiple years were considered together, and the 18.7–10.7 difference was not significantly associated with vegetation density. Additional snow survey data from the Northwest Territories (2005–2007) were used to verify the 18.7–10.7 relationship with SWE across the northern boreal forest. These results suggest use of the 18.7–10.7 T_B difference, rather than the traditional 36.5–18.7 T_B difference, is necessary to capture inter-seasonal SWE variability across forested regions.     

Passive and active airborne microwave remote sensing of snow cover

The presence of snow cover affects the regional energy and water balance, thus having a significant impact on the global climate system. Temporal knowledge of the onset of snow melt and snow water equivalent (SWE) values are important variables in the prediction of flooding, as well as water resource applications such as reservoir management and agricultural activities. Microwave remote sensing techniques have been effective for monitoring snow pack parameters (snow extent, depth, water equivalent, wet/dry state). Coincident ground data, airborne polarimetric C-band (5.3 GHz) Synthetic Aperture Radar (SAR) and passive microwave radiometer data (19, 37 and 85 GHz) were collected on four dates (1 December 1997, 6 March 1998, 12 March 1998 and 9 March 1999) over two flight lines in Eastern Ontario, Canada. The multitemporal, multi-sensor data were analysed for changes in SAR polarimetric signatures and microwave brightness temperatures as a function of changing snow pack parameters. Results indicate that certain parameters such as linear polarizations and pedestal height are sensitive to changes in snow pack parameters, and respond differently to various snow conditions. SWE values derived from the passive microwave brightness temperatures compare well with ground measurements, with the exception of low snow volume and in the presence of significant ice layers.     

Correlation of precipitation estimates from spaceborne passive microwave sensors and weather radar imagery for BALTEX PIDCAP

This paper describes the evaluation of a combined radar and passive microwave dataset obtained during the PIDCAP study of the Baltic Sea Experiment (BALTEX), where three-dimensional volumes of data from the Gotland radar were obtained timed according to the overpasses of the DMSP-satellites F10 and F13. Both satellites are equipped with a Special Sensor Microwave/Imager (SSM/I), suitable for precipitation retrievals. We compare radar precipitation estimates, convolved to the native resolution of the SSM/I, at different altitudes with polarization and scattering indices ( S 85 ) derived from the SSM/I. For all 22 overpasses investigated here radar precipitation estimates at 3-4 km altitude correlate well with the SSM/I-derived S 85 (average correlation coefficient = 0.70). Although more directly linked to surface precipitation, polarization indices have been found to be less correlated with radar data, due to limitations inherent in the remote sensing of precipitation at higher latitudes. A stratification of the dataset into frontal and convective events revealed significant variations in these relationships for different types of precipitation events, thus reflecting different cloud microphysical processes associated with precipitation initialization. The relationship between S 85 and radar rain estimates at higher altitudes varies considerably for different convective and frontal events. The sensitivity of S 85 to radar-derived rain rate ranges from 3.1 K mm m 1 h m 1 for a strong convective event to about 25 K mm m 1 h m 1 for the frontal and about 70 mm m 1 h m 1 for the small-scale convective events. For extrapolated surface precipitation estimates, sensitivities decrease to 14 mm m 1 h m 1 and 25 mm m 1 h m 1 for frontal and small-scale convective precipitation, respectively.     

Comparison of snow water equivalent retrieved from SSM/I passive microwave data using artificial neural network, projection pursuit and nonlinear regressions

The snow water equivalent (SWE) for the Red River basin of North Dakota and Minnesota was retrieved from data acquired by passive microwave SSM/I (Special Sensor Microwave Imager) sensors mounted on the US Defense Meteorological Satellite Program (DMSP) satellites, physiographic and atmospheric data by an artificial neural network called Modified Counter Propagation Network (MCPN), a Projection Pursuit Regression (PPR) and a nonlinear regression. The airborne gamma-ray measurements of SWE for 1989 and 1997 were used as observed SWE, and SSM/I data of 19 and 37 GHz frequencies, in both horizontal and vertical polarization, were used for the calibration (1989 data from DMSP-F8) and validation (1997 data from DMSP-F10 and F13 of both ascending and descending overpass times were combined) of the models. The SSM/I data were screened for the presence of wet snow, large water bodies like lakes and rivers, and depth-hoar. The MCPN model produced encouraging results in both calibration and validation stages (R² was about 0.9 for both calibration (C) and validation (V)), better than PPR (R² was 0.86 for C and 0.62 for V), which in turn was better than the multivariate nonlinear regression at the calibration stage (R² was 0.78 for C and 0.71 for V). MCPN is probably better than the linear and nonlinear regression counterparts because of its parallel computing structure resulted from neurons interconnected by a parallel network and its ability to learn and generalize information from complex relationships such as the SWE-SSM/I or other relationships encountered in geosciences.     

Monitoring snowpack evolution using interferometric synthetic aperture radar on the North Slope of Alaska,USA

This research investigates the use of Interferometric Synthetic Aperture Radar (InSAR) to generate a time-series of snow water equivalent (SWE) for dry snow within the Kuparuk watershed, North Slope, Alaska, during the winter of 1993/1994. Maps depicting relative change in phase and the theoretical relative change in SWE between satellite acquisitions are created for 3-day periods at the end of March 1994 using both ascending and descending ERS-1 overpasses. The theoretical coefficient relating relative change in phase and relative change in SWE for C-band is found to be at least twice as large as what is expected when using a simple single-layer snow model for this study area and time period. Without any direct measurements of SWE on the ground, station measurements of snow depth and hourly wind are linked to each 3-day relative change in phase map. Along with a qualitative assessment, quantitative measures of the rate and magnitude of phase change around these stations are directly compared to the hourly wind data for a given 3-day period. InSAR-derived maps acquired around a measured precipitation event show a considerable relationship to the predominant direction of strong winds over each 3-day period while maps acquired around no measureable precipitation depict much less correlation between phase change and predominant direction of strong winds. Despite limited ground measurements to infer snowpack conditions, these results show continued promise for the InSAR technique to measure changes in snowpack conditions (e.g. SWE) at much higher resolutions than manual sampling methods or passive microwave remote sensing. The extension of this technique to current L-band InSAR satellite platforms is also discussed.     

风云三号微波成像仪积雪参数反演算法初步研究

选择新疆地区作为实验区,为风云三号(FY-3)微波成像仪(MWRI)发展中国区域的积雪参数半经验反演算法。使用2003年4个月的新疆地区的台站观测资料和AMSR-E 18.7 GHz,36.5GHz和89 GHz水平和垂直极化亮温作为FY-3 MWRI的模拟数据,在Chang建立的半经验模型的基础上,采用多元线性回归分析,建立一个新算法。用已有方法去除水体、降雨、湿雪、冻土的像元后,用新算法反演了新疆地区的2004年1月的积雪参数,并分别与AMSR-E雪水当量产品和台站观测值进行比较,结果表明新算法在新疆地区优于AMSR-E的反演算法。     

Intercomparison of microwave remote-sensing soil moisture data sets based on distributed eco-hydrological model simulation and in situ measurements over the North China Plain

Intercomparisons of microwave-based soil moisture products from active ASCAT (Advanced Scatterometer) and passive AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System) is conducted based on surface soil moisture (SSM) simulations from the eco-hydrological model, Vegetation Interface Processes (VIP), after it is carefully validated with in situ measurements over the North China Plain. Correlations with VIP SSM simulation are generally satisfactory with average values of 0.71 for ASCAT and 0.47 for AMSR-E during 2007–2009. ASCAT and AMSR-E present unbiased errors of 0.044 and 0.053 m³ m^?3 on average, with respect to model simulation. The empirical orthogonal functions (EOF) analysis results illustrate that AMSR-E provides more consistent SSM spatial structure with VIP than ASCAT; while ASCAT is more capable of capturing SSM temporal dynamics. This is supported by the facts that ASCAT has more consistent expansion coefficients corresponding to primary EOF mode with VIP (R = 0.825, p < 0.1). However, comparison based on SSM anomaly demonstrates that AMSR-E and ASCAT have similar skill in capturing SSM short-term variability. Temporal analysis of SSM anomaly time series shows that AMSR-E provides best performance in autumn, while ASCAT provides lower anomaly bias during highly-vegetated summer with vegetation optical depth of 0.61. Moreover, ASCAT retrieval accuracy is less influenced by vegetation cover, as it is in relatively better agreement with VIP simulation in forest than in other land-use types and exhibits smaller interannual fluctuation than AMSR-E. Identification of the error characteristics of these two microwave soil moisture data sets will be helpful for correctly interpreting the data products and also facilitate optimal specification of the error matrix in data assimilation at a regional scale.     

Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements

The key variable describing global seasonal snow cover is snow water equivalent (SWE). However, reliable information on the hemispheric scale variability of SWE is lacking because traditional methods such as interpolation of ground-based measurements and stand-alone algorithms applied to space-borne observations are highly uncertain with respect to the spatial distribution of snow mass and its evolution. In this paper, an algorithm assimilating synoptic weather station data on snow depth with satellite passive microwave radiometer data is applied to produce a 30-year-long time-series of seasonal SWE for the northern hemisphere. This data set is validated using independent SWE reference data from Russia, the former Soviet Union, Finland and Canada. The validation of SWE time-series indicates overall strong retrieval performance with root mean square errors below 40 mm for cases when SWE < 150 mm. Retrieval uncertainty increases when SWE is above this threshold. The SWE estimates are also compared with results obtained by a typical stand-alone satellite passive microwave algorithm. This comparison demonstrates the benefits of the newly developed assimilation approach. Additionally, the trends and inter-annual variability of northern hemisphere snow mass during the era of satellite passive microwave measurements are shown.     

A prototype MODIS-SSM/I snow-mapping algorithm

Data in the wavelength range 0.545-1.652 w m from the Moderate Resolution Imaging Spectroradiometer (MODIS), launched aboard the Earth Observing System (EOS) Terra in December 1999, are used to map daily global snow cover at 500 m resolution. However, during darkness, or when the satellite's view of the surface is obscured by cloud, snow cover cannot be mapped using MODIS data. We show that during these conditions, it is possible to supplement the MODIS product by mapping the snow cover using passive microwave data from the Special Sensor Microwave Imager (SSM/I), albeit with much poorer resolution. For a 7-day time period in March 1999, a prototype MODIS snow-cover product was compared with a prototype MODIS-SSM/I product for the same area in the mid-western USA. The combined MODIS-SSM/I product mapped 9% more snow cover than the MODIS-only product.