Evaluating the potential of a blended passive microwave‐interactive multi‐sensor product for improved mapping of snow cover and estimations of snow water equivalent

The retrieval of Snow Water Equivalent (SWE) from remote sensing satellites continues to be a very challenging problem. In this paper, we evaluate the accuracy of a new SWE product derived from the blending of a passive microwave SWE product based on the Advanced Microwave Sounding Unit (AMSU) with a multi‐sensor snow cover extent product based on the Interactive Multi‐sensor Snow and Ice Mapping System (IMS). The microwave measurements have the ability to penetrate the snow pack, and thus, the retrieval of SWE is best accomplished using the AMSU. On the other hand, the IMS maps snow cover more reliably due to the use of multiple satellite and ground observations. The evolution of global snow cover from the blended, the AMSU and the IMS products was examined during the 2006 snow season. Despite the overall good inter‐product agreement, it was shown that the retrievals of snow cover extent in the blended product are improved when using IMS, with implications for improved microwave retrievals of SWE. In a separate investigation, the skill of the microwave SWE product was also examined for its ability to correctly estimate SWE globally and regionally. Qualitative evaluation of global SWE retrievals suggested dependence on land surface temperature: the lower the temperature, the higher the SWE retrieved. This temperature bias was attributed in part to temperature effects on those snow properties that impact microwave response. Therefore, algorithm modifications are needed with more dynamical adjustments to account for changing snow cover. Quantitative evaluation over Slovakia in central Europe, for a limited period in 2006, showed reasonably good performance for SWE less than 100 mm. Sensitivity to deeper snow decreased significantly. Copyright © 2007 John Wiley & Sons, Ltd.     

Combining MODIS and AMSR‐E observations to improve MCD43A3 short‐time snow‐covered Albedo estimation

Land surface albedo plays an important role in the radiation budget and global climate models. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) provide 16‐day albedo product with 500‐m resolution every 8 days (MCD43A3). Some in‐situ albedo measurements were used as the true surface albedo values to validate the MCD43A3 product. As the 16‐day MODIS albedo retrievals do not include snow observations when there is ephemeral snow on the ground surface in a 16‐day period, comparisons between MCD43A3 and 16 day averages of field data do not agree well. Another reason is that the MODIS cannot detect the snow when the area is covered by clouds. The Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) data are not affected by weather conditions and are a good supplement for optical remote sensing in cloudy weather. When the surface is covered by ephemeral snow, the AMSR‐E data can be used as the additional information to retrieve the snow albedo. In this study, we developed an improved method by using the MODIS products and the AMSR‐E snow water equivalent (SWE) product to improve the MCD43A3 short‐time snow‐covered albedo estimation. The MODIS daily snow products MOD10A1 and MYD10A1 both provide snow and cloud information from observations. In our study region, we updated the MODIS daily snow product by combining MOD10A1 and MYD10A1. Then, the product was combined with the AMSR‐E SWE product to generate new daily snow‐cover and SWE products at a spatial resolution of 500 m. New SWE datasets were integrated into the Noah Land Surface Model snow model to calculate the albedo above a snow surface, and these values were then utilized to improve the MODIS 16‐day albedo product. After comparison of the results with in‐situ albedo measurements, we found that the new corrected 16‐day albedo can show the albedo changes during the short snowfall season. For example, from January 25 to March 14, 2007 at the BJ site, the albedo retrieved from snow‐free observations does not indicate the albedo changes affected by snow; the improved albedo conforms well to the in‐situ measurements. The correlation coefficient of the original MODIS albedo and the in‐situ albedo is 0.42 during the ephemeral snow season, but the correlation coefficient of the improved MODIS albedo and the in‐situ albedo is 0.64. It is concluded that the new method is capable of capturing the snow information from AMSR‐E SWE to improve the short‐time snow‐covered albedo estimation. Copyright © 2012 John Wiley & Sons, Ltd.     

MODIS Terra Collection 6 fractional snow cover validation in mountainous terrain during spring snowmelt using Landsat TM and ETM+

Daily swath MODIS Terra Collection 6 fractional snow cover (MOD10_L2) estimates were validated with two‐day Landsat TM/ETM + snow‐covered area estimates across central Idaho and southwestern Montana, USA. Snow cover maps during spring snowmelt for 2000, 2001, 2002, 2003, 2005, 2007, and 2009 were compared between MODIS Terra and Landsat TM/ETM + using least‐squared regression. Strong spatial and temporal map agreement was found between MODIS Terra fractional snow cover and Landsat TM/ETM + snow‐covered area, although map disagreement was observed for two validation dates. High‐altitude cirrus cloud contamination during low snow conditions as well as late season transient snowfall resulted in map disagreement. MODIS Terra's spatial resolution limits retrieval of thin‐patchy snow cover, especially during partially cloudy conditions. Landsat's image acquisition frequency can introduce difficulty when discriminating between transient and resident mountain snow cover. Furthermore, transient snowfall later in the snowmelt season, which is a stochastic accumulation event that does not usually persist beyond the daily timescale, will skew decadal snow‐covered area variability if bi‐monthly climate data record development is the objective. As a quality control step, ground‐based daily snow telemetry snow‐water‐equivalent measurements can be used to verify transient snowfall events. Users of daily MODIS Terra fractional snow products should be aware that local solar illumination and sensor viewing geometry might influence fractional snow cover estimation in mountainous terrain. Cross‐sensor interoperability has been confirmed between MODIS Terra and Landsat TM/ETM + when mapping snow from the visible/infrared spectrum. This relationship is strong and supports operational multi‐sensor snow cover mapping, specifically climate data record development to expand cryosphere, climate, and hydrological science applications. Copyright © 2013 John Wiley & Sons, Ltd.     

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km²) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.     

Accuracy assessment of the MODIS snow products

A suite of Moderate‐Resolution Imaging Spectroradiometer (MODIS) snow products at various spatial and temporal resolutions from the Terra satellite has been available since February 2000. Standard products include daily and 8‐day composite 500 m resolution swath and tile products (which include fractional snow cover (FSC) and snow albedo), and 0·05° resolution products on a climate‐modelling grid (CMG) (which also include FSC). These snow products (from Collection 4 (C4) reprocessing) are mature and most have been validated to varying degrees and are available to order through the National Snow and Ice Data Center. The overall absolute accuracy of the well‐studied 500 m resolution swath (MOD10_L2) and daily tile (MOD10A1) products is ～93%, but varies by land‐cover type and snow condition. The most frequent errors are due to snow/cloud discrimination problems, however, improvements in the MODIS cloud mask, an input product, have occurred in ‘Collection 5’ reprocessing. Detection of very thin snow (<1 cm thick) can also be problematic. Validation of MOD10_L2 and MOD10A1 applies to all higher‐level products because all the higher‐level products are all created from these products. The composited products may have larger errors due, in part, to errors propagated from daily products. Recently, new products have been developed. A fractional snow cover algorithm for the 500 m resolution products was developed, and is part of the C5 daily swath and tile products; a monthly CMG snow product at 0·05° resolution and a daily 0·25° resolution CMG snow product are also now available. Similar, but not identical products are also produced from the MODIS on the Aqua satellite, launched in May 2002, but the accuracy of those products has not yet been assessed in detail. Published in 2007 by John Wiley & Sons, Ltd.     

A new approach of dynamic monitoring of 5‐day snow cover extent and snow depth based on MODIS and AMSR‐E data from Northern Xinjiang region

Taking the Northern Xinjiang region as an example, we develop a snow depth model by using the Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E) horizontal and vertical polarization brightness temperature difference data of 18 and 36 GHz bands and in situ snow depth measurements from 20 climatic stations during the snow seasons November–March) of 2002–2005. This article proposes a method to produce new 5‐day snow cover and snow depth images, using Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products and AMSR‐E snow water equivalent and daily brightness temperature products. The results indicate that (1) the brightness temperature difference (Tb_18h–Tb_36h) provides the most accurate and precise prediction of snow depth; (2) the snow, land and overall classification accuracies of the new images are separately 89.2%, 77.7% and 87.2% and are much better than those of AMSR‐E or MODIS products (in all weather conditions) alone; (3) the snow classification accuracy increases as snow depth increases; and (4) snow accuracies for different land cover types vary as 88%, 92.3%, 79.7% and 80.1% for cropland, grassland, shrub, and urban and built‐up, respectively. We conclude that the new 5‐day snow cover–snow depth images can provide both accurate cloud‐free snow cover extent and the snow depth dynamics, which would lay a scientific basis for water management and prevention of snow‐related disasters in this dry and cold pastoral area. After validations of the algorithms over other regions with different snow and climate conditions, this method would also be used for monitoring snow cover and snow depth elsewhere in the world. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluating the utility of the EUMETSAT HSAF snow recognition product over mountainous areas of eastern Turkey

Abstract

Monitoring snow parameters (e.g. snow-cover area, snow water equivalent) is challenging work. Because of its natural physical properties, snow strongly affects the evolution of weather on a daily basis and climate on a longer time scale. In this paper, the snow recognition product generated from the MSG-SEVIRI images within the framework of the Hydrological Satellite Facility (HSAF) Project of EUMETSAT is presented. Validation of the snow recognition product H10 was done for the snow season (from 1 January to 31 March) of the water year 2009. The MOD10A1 and MOD10C2 snow products were also used in the validation studies. Ground truth of the products was obtained by using 1890 snow depth observations from 20 meteorological stations, which are mainly located in mountainous areas and are distributed across the eastern part of Turkey. The possibility of 37% cloud cover reduction was obtained by merging 15-min observations from MSG-SEVIRI as opposed to using only one daily observation from MODIS. The coarse spatial resolution of the H10 product gave higher commission errors compared to the MOD10A1 product. Snow depletion curves obtained from the HSAF snow recognition product were compared with those derived from the MODIS 8-day snow cover product. The preliminary results show that the HSAF snow recognition product, taking advantage of using high temporal frequency measurement with spectral information required for snow mapping, significantly improves the mapping of regional snow-cover extent over mountainous areas.

Citation Surer, S. and Akyurek, Z., 2012. Evaluating the utility of the EUMETSAT HSAF snow recognition product over mountainous areas of eastern Turkey. Hydrological Sciences Journal, 57 (8), 1–11.     

Evaluation of the snow‐covered area data product from MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS), flown on board the Terra Earth Observing System (EOS) platform launched in December 1999, produces a snow‐covered area (SCA) product. This product is expected to be of better quality than SCA products based on operational satellites (notably GOES and AVHRR), due both to improved spectral resolution and higher spatial resolution of the MODIS instrument. The gridded MODIS SCA product was compared with the SCA product produced and distributed by the National Weather Service National Operational Hydrologic Remote Sensing Center (NOHRSC) for 46 selected days over the Columbia River basin and 32 days over the Missouri River basin during winter and spring of 2000–01. Snow presence or absence was inferred from ground observations of snow depth at 1330 stations in the Missouri River basin and 762 stations in the Columbia River basin, and was compared with the presence/absence classification for the corresponding pixels in the MODIS and NOHRSC SCA products. On average, the MODIS SCA images classified fewer pixels as cloud than NOHRSC, the effect of which was that 15% more of the Columbia basin area could be classified as to presence–absence of snow, while overall there was a statistically insignificant difference over the Missouri basin. Of the pixels classified as cloud free, MODIS misclassified 4% and 5% fewer overall (for the Columbia and Missouri basins respectively) than did the NOHRSC product. When segregated by vegetation cover, forested areas had the greatest differences in fraction of cloud cover reported by the two SCA products, with MODIS classifying 13% and 17% less of the images as cloud for the Missouri and Columbia basins respectively. These differences are particularly important in the Columbia River basin, 39% of which is forested. The ability of MODIS to classify significantly greater amounts of snow in the presence of cloud in more topographically complex, forested, and snow‐dominated areas of these two basins provides valuable information for hydrologic prediction. Copyright © 2003 John Wiley & Sons, Ltd.     

Comparison and validation of MODIS standard and new combination of Terra and Aqua snow cover products in northern Xinjiang,China

Taking northern Xinjiang, China, as an example, this study first compares the standard MODIS Terra and Aqua snow cover classifications, and then compares the accuracy of the standard MODIS daily and 8‐day snow cover products with the new daily and multi‐day snow cover combination of MODIS Terra and Aqua observations using in situ measurements. Under clear sky in both products, the agreement of land classification from MODIS Terra and Aqua daily and 8‐day snow cover products is close to 100% for a entire water year. In contrast, the agreement of snow classification from MODIS Terra and Aqua is high only in the winter months, decreasing in the rest of the period. The high agreement mainly concentrates in land or snow‐dominated areas, and major disagreements take place in the transitions zones from snow to land. The disagreement (mainly snow–land) in the 8‐day products is higher than that in the daily products. In addition, both MODIS Terra and Aqua cloud masks tend to map more areas in the transition zones as cloud. Under clear sky conditions, the three daily products have similar accuracy of snow and land classification, and the 8‐day standard products and the multi‐day combination product also have similar accuracy of snow and land classification. This further suggests that the algorithm in the combination of Terra and Aqua snow cover products is valid. Moreover, in the actual weather/cloud conditions, the combination products from Terra and Aqua reduce cloud blockage and improve snow classification accuracy against either MODIS Terra or Aqua (51% against 44% and 34% for daily and 92% against 87% and 78% for 8‐day, respectively), although Terra snow product (daily or 8‐day) has slightly better accuracy than the Aqua snow product. The new combination products can provide better mapping of spatiotemporal variation of snow cover/glacier and for snow‐melting modeling. Copyright © 2008 John Wiley & Sons, Ltd.     

Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.     

Assessment of methods for mapping snow cover from MODIS

Characterization of snow is critical for understanding Earth’s water and energy cycles. Maps of snow from MODIS have seen growing use in investigations of climate, hydrology, and glaciology, but the lack of rigorous validation of different snow mapping methods compromises these studies. We examine three widely used MODIS snow products: the “binary” (i.e., snow yes/no) global snow maps that were among the initial MODIS standard products; a more recent standard MODIS fractional snow product; and another fractional snow product, MODSCAG, based on spectral mixture analysis. We compare them to maps of snow obtained from Landsat ETM+ data, whose 30 m spatial resolution provides nearly 300 samples within a 500 m MODIS nadir pixel. The assessment uses 172 images spanning a range of snow and vegetation conditions, including the Colorado Rocky Mountains, the Upper Rio Grande, California’s Sierra Nevada, and the Nepal Himalaya. MOD10A1 binary and fractional fail to retrieve snow in the transitional periods during accumulation and melt while MODSCAG consistently maintains its retrieval ability during these periods. Averaged over all regions, the RMSE for MOD10A1 fractional is 0.23, whereas the MODSCAG RMSE is 0.10. MODSCAG performs the most consistently through accumulation, mid-winter and melt, with median differences ranging from −0.16 to 0.04 while differences for MOD10A1 fractional range from −0.34 to 0.35. MODSCAG maintains its performance over all land cover classes and throughout a larger range of land surface properties. Characterizing snow cover by spectral mixing is more accurate than empirical methods based on the normalized difference snow index, both for identifying where snow is and is not and for estimating the fractional snow cover within a sensor’s instantaneous field-of-view. Determining the fractional value is particularly important during spring and summer melt in mountainous terrain, where large variations in snow, vegetation and soil occur over small distances and when snow can melt rapidly.     

Estimating the distribution of snow water equivalent using remotely sensed snow cover data and a spatially distributed snowmelt model: A multi-resolution,multi-sensor comparison

Time series of fractional snow covered area (SCA) estimates from Landsat Enhanced Thematic Mapper (ETM+), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) data were combined with a spatially distributed snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3419 km²). In this reconstruction approach, modeled snowmelt over each pixel is integrated during the period of satellite-observed snow cover to estimate SWE. Due to underestimates in snow cover detection, maximum basin-wide mean SWE using MODIS and AVHRR were, respectively, 45% and 68% lower than SWE estimates obtained using ETM+ data. The mean absolute error (MAE) of SWE estimated at 100-m resolution using ETM+ data was 23% relative to observed SWE from intensive field campaigns. Model performance deteriorated when MODIS (MAE = 50%) and AVHRR (MAE = 89%) SCA data were used. Relative to differences in the SCA products, model output was less sensitive to spatial resolution (MAE = 39% and 73% for ETM+ and MODIS simulations run at 1 km resolution, respectively), indicating that SWE reconstructions at the scale of MODIS acquisitions may be tractable provided the SCA product is improved. When considering tradeoffs between spatial and temporal resolution of different sensors, our results indicate that higher spatial resolution products such as ETM+ remain more accurate despite the lower frequency of acquisition. This motivates continued efforts to improve MODIS snow cover products.     

Investigation of the snow‐cover dynamics in the Upper Euphrates Basin of Turkey using remotely sensed snow‐cover products and hydrometeorological data

The Euphrates and Tigris rivers serve as the most important water resources in the Middle East. Precipitation in this region falls mostly in the form of snow over the higher elevations of the Euphrates Basin and remains on the ground for nearly half of the year. This snow‐covered area (SCA) is a key element of the hydrological cycle, and monitoring the SCA is crucial for making accurate forecasts of snowmelt discharge, especially for energy production, flood control, irrigation, and reservoir‐operation optimization in the Upper Euphrates (Karasu) Basin. Remote sensing allows the detection of the spatio‐temporal patterns of snow cover across large areas in inaccessible terrain, such as the eastern part of Turkey, which is highly mountainous. In this study, a seasonal evaluation of the snow cover from 2000 to 2009 was performed using 8‐day snow‐cover products (MOD10C2) and the daily snow‐water equivalent (SWE) product. The values of SWE products were obtained using an assimilation process based on the Helsinki University of Technology model using equal area Special Sensor Microwave Imager (SSM/I) Earth‐gridded advanced microwave scanning radiometer—EOS daily brightness‐temperature values. In the Karasu Basin, the SCA percentage for the winter period is 80–90%. The relationship between the SCA and the runoff during the spring period is analysed for the period from 2004 to 2009. An inverse linear relationship between the normalized SCA and the normalized runoff values was obtained (r = 0·74). On the basis of the monthly mean temperature, total precipitation and snow depth observed at meteorological stations in the basin, the decrease in the peak discharges, and early occurrences of the peak discharges in 2008 and 2009 are due to the increase in the mean temperature and the decrease in the precipitation in April. Copyright © 2011 John Wiley & Sons, Ltd.     

A comparison of MODIS and NOHRSC snow‐cover products for simulating streamflow using the Snowmelt Runoff Model

Remote sensing is an important source of snow‐cover extent for input into the Snowmelt Runoff Model (SRM) and other snowmelt models. Since February 2000, daily global snow‐cover maps have been produced from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS). The usefulness of this snow‐cover product for streamflow prediction is assessed by comparing SRM simulated streamflow using the MODIS snow‐cover product with streamflow simulated using snow maps from the National Operational Hydrologic Remote Sensing Center (NOHRSC). Simulations were conducted for two tributary watersheds of the Upper Rio Grande basin during the 2001 snowmelt season using representative SRM parameter values. Snow depletion curves developed from MODIS and NOHRSC snow maps were generally comparable in both watersheds: satisfactory streamflow simulations were obtained using both snow‐cover products in larger watershed (volume difference: MODIS, 2·6%; NOHRSC, 14·0%) and less satisfactory streamflow simulations in smaller watershed (volume difference: MODIS, −33·1%; NOHRSC, −18·6%). The snow water equivalent (SWE) on 1 April in the third zone of each basin was computed using the modified depletion curve produced by the SRM and was compared with in situ SWE measured at Snowpack Telemetry sites located in the third zone of each basin. The SRM‐calculated SWEs using both snow products agree with the measured SWEs in both watersheds. Based on these results, the MODIS snow‐cover product appears to be of sufficient quality for streamflow prediction using the SRM in the snowmelt‐dominated basins. Copyright © 2005 John Wiley & Sons, Ltd.     

Evaluation of the MODIS snow cover fraction product

Eleven years of daily 500 m gridded Terra Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD10A1) snow cover fraction (SCF) data are evaluated in terms of snow presence detection in Colorado and Washington states. The SCF detection validation study is performed using in‐situ measurements and expressed in terms of snow and land detection and misclassification frequencies. A major aspect addressed in this study involves the shifting of pixel values in time due to sensor viewing angles and gridding artifacts of MODIS sensor products. To account for this error, 500 m gridded pixels are grouped and aggregated to different‐sized areas to incorporate neighboring pixel information. With pixel aggregation, both the probability of detection (POD) and the false alarm ratios increase for almost all cases. Of the false negative (FN) and false positive values (referred to as the total error when combined), FN estimates dominate most of the total error and are greatly reduced with aggregation. The greatest POD increases and total error reductions occur with going from a single 500 m pixel to 3×3‐pixel averaged areas. Since the MODIS SCF algorithm was developed under ideal conditions, SCF detection is also evaluated for varying conditions of vegetation, elevation, cloud cover and air temperature. Finally, using a direct insertion data assimilation approach, pixel averaged MODIS SCF observations are shown to improve modeled snowpack conditions over the single pixel observations due to the smoothing of more error‐prone observations and more accurately snow‐classified pixels. Copyright © 2012 John Wiley & Sons, Ltd.     

博斯腾湖流域积雪变化及影响因素

积雪是西北干旱地区河流的主要补给源,是绿洲的生命线.积雪的时空变化是全球变化的区域响应敏感因子之一,同时也是影响西北干旱地区地表水资源变化的主要因子之一.本研究利用MODIS雪盖产品、地表温度、SSM/I雪深、DEM等数据,通过GIS空间分析及地统计分析功能,系统分析了博斯腾湖流域雪盖、雪深的时空变化规律及其与影响因素之间的关系.研究表明,研究区雪深和雪盖多年月平均值从8月份到1月份达到最大值,到7月份降到最低值.但月最大雪深却出现在3月份.雪盖、雪深与地温相关系数分别达到-0.878、-0.853,与分布高程均值相关系数分别达到-0.626和-0.791.雪深最大值受海拔影响有明显的陡坎效应.从12月到8月份随着时间的推移雪的深度在降低,陡坎向高海拔方向移动.9-11月份雪深在加深,陡坎向低海拔方向移动.同一高程段雪深的变幅反应坡向对雪深的影响,变幅越宽坡向影响越大.并且变幅也有先从低海拔到高海拔移动,然后再回到低海拔的特点.本研究对了解该研究区积雪特性的研究有很大作用,可为在该地区开展融雪径流模拟等研究提供重要的参考信息.     

Development of a rain‐on‐snow detection algorithm using passive microwave radiometry

Currently observed climate warming in the Arctic has numerous consequences. Of particular relevance, the precipitation regime is modified where mixed and liquid precipitation can occur during the winter season leading to rain‐on‐snow (ROS) events. This phenomenon is responsible for ice crust formation, which has a significant impact on ecosystems (such as biological, hydrological, ecological and physical processes). The spatially and temporally sporadic nature of ROS events makes the phenomenon difficult to monitor using meteorological observations. This paper focuses on the detection of ROS events using passive microwave (PMW) data from a modified brightness temperature (T_B) gradient approach at 19 and 37 GHz. The approach presented here was developed empirically for observed ROS events with coincident ground‐based PMW measurements in Sherbrooke, Quebec, Canada. It was then tested in Nunavik, Quebec, with the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E). We obtained a detection accuracy of 57, 71 and 89% for ROS detection for three AMSR‐E grid cells with a maximum error of 7% when considering all omissions and commissions with regard to the total number of AMSR‐E passes throughout the winter period. Copyright © 2016 John Wiley & Sons, Ltd.     

Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada

In this study, the spatial and temporal variabilities of terrestrial water storage anomaly (TWSA) and snow water equivalent anomaly (SWEA) information obtained from the Gravity Recovery and Climate Experiment (GRACE) twin satellites data were analysed in conjunction with multisource snow products over several basins in the Canadian landmass. Snow water equivalent (SWE) data were extracted from three different sources: Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Canadian Meteorological Centre (CMC). The objective of the study was to understand whether SWE variations have a significant contribution to terrestrial water storage anomalies in the Canadian landmass. The period was considered from December 2002 to March 2011. Significant relationships were observed between TWSA and SWEA for most of the 15 basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products compared with satellite-based SWE data. Stronger relationships were found in snow-dominated basins (R_s > = 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in the north of Quebec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behaviour was observed in the Western Hudson Bay basin. In both regions, it was found that the contribution of non-SWE compartments including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the Water-Global Assessment and Prognosis Global Hydrology Model (WGHM) simulations and then subtracted from GRACE observations. The GRACE-derived SWEA correlation results showed improved relationships with three SWEA products. The improvement is particularly important in the sub-basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE-derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). Overall, the results indicated the important role of SWE on terrestrial water storage variations.     

Passive microwave remote sensing of the historic February 2010 snowstorms in the Middle Atlantic region of the USA

The snowfall in the Baltimore/Washington metropolitan area during the winter of 2009/2010 was unprecedented and caused serious snow‐related disruptions. In February 2010, snowfall totals approached 2 m, and because maximum temperatures were consistently below normal, snow remained on the ground the entire month. One of the biggest contributing factors to the unusually severe winter weather in 2009/2010, throughout much of the middle latitudes, was the Arctic Oscillation. Unusually high pressure at high latitudes and low pressure at middle latitudes forced a persistent exchange of mass from north to south. In this investigation, a concerted effort was made to link remotely sensed falling snow observations to remotely sensed snow cover and snowpack observations in the Baltimore/Washington area. Specifically, the Advanced Microwave Scanning Radiometer onboard the Aqua satellite was used to assess snow water equivalent, and the Advanced Microwave Sounding Unit‐B and Microwave Humidity Sounder were employed to detect falling snow. Advanced Microwave Scanning Radiometer passive microwave signatures in this study are related to both snow on the ground and surface ice layers. In regard to falling snow, signatures indicative of snowfall can be observed in high frequency brightness temperatures of Advanced Microwave Sounding Unit‐B and Microwave Humidity Sounder. Indeed, retrievals show an increase in snow water equivalent after the detection of falling snow. Yet, this work also shows that falling snow intensity and/or the presence of liquid water clouds impacts the ability to reliably detect snow water equivalent. Moreover, changes in the condition of the snowpack, especially in the surface features, negatively affect retrieval performance. Copyright © 2011. This article is a U.S. Government work and is in the public domain in the USA.     

Intercomparison of snow water equivalent observations in the Northern Great Plains

In the Northern Great Plains, melting snow is a primary driver of spring flooding, but limited knowledge of the magnitude and spatial distribution of snow water equivalent (SWE) hampers flood forecasting. Passive microwave remote sensing has the potential to enhance operational river flow forecasting but is not routinely incorporated in operational flood forecasting. We compare satellite passive microwave estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) to the National Oceanic and Atmospheric Administration Office of Water Prediction (OWP) airborne gamma radiation snow survey and U.S. Army Corps of Engineers (USACE) ground snow survey SWE estimates in the Northern Great Plains from 2002 to 2011. AMSR‐E SWE estimates compare favourably with USACE SWE measurements in the low relief, low vegetation study area (mean difference = ?3.8 mm, root mean squared difference [RMSD] = 34.7 mm), but less so with OWP airborne gamma SWE estimates (mean difference = ?9.5 mm, RMSD = 42.7 mm). An error simulation suggests that up to half of the error in the former comparison is potentially due to subpixel scale SWE variability, limiting the maximum achievable RMSD between ground and satellite SWE to approximately 26–33 mm in the Northern Great Plains. The OWP gamma versus AMSR‐E SWE comparison yields larger error than the point‐scale USACE versus AMSR‐E comparison, despite a larger measurement footprint (5–7 km² vs. a few square centimetres, respectively), suggesting that there are unshared errors between the USACE and OWP gamma SWE data.