Mapping and monitoring of the snow cover fraction over North America

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D16, 8619, doi: /2002jd003142, 2003 Mapping and monitoring of the snow cover fraction over North America Peter Romanov, 1,2 Dan Tarpley, 1 Garik Gutman, 3 and Thomas Carroll 4 Received 5 November 2002; revised 3 March 2003; accepted 24 March 2003; published 30 August [1] Automated snow maps over North America have been produced at the National Environmental Satellite Data and Information Service (NESDIS) of the National Oceanic and Atmospheric Administration (NOAA) since The developed snow-mapping system is based on observations in the visible, middle infrared, infrared, and microwave spectral bands from operational geostationary and polar orbiting meteorological satellites and generates daily maps of snow cover at a spatial resolution of 4 km. Recently, the existing snow-mapping technique was extended to derive the fractional snow cover. To obtain snow fraction, we use measurements of the Imager instrument on board Geostationary Operational Environmental Satellite (GOES). The algorithm treats every cloud-clear image pixel as a mixed scene consisting of a combination of snow-covered and snow-free land surface. To determine the portion of the pixel that is covered with snow, we employ a linear mixture approach, which relies on the Imager measurements in the visible spectral band. The estimated accuracy of subpixel snow fraction retrievals is about 10%. In this paper, we present a description of the snow cover and snow fraction mapping algorithms. Application of the developed algorithms over North America for three winter seasons from to has shown that the spatial distribution of the fractional snow cover over areas affected by seasonal snow closely corresponds to the distribution of the forest cover. The fraction of snow in the middle of the winter season generally varied from 100% over croplands, grasslands, and other nonforested areas to 20 30% over dense boreal forests. The snow fraction over dense boreal forests exhibited a slight intraseason variability; however, no obvious correlation of these changes with snowfalls was noticed. Over areas with no or sparse tree vegetation cover (croplands, grasslands), snow fraction showed a noticeable correlation with snow depth for snow depths up to cm. INDEX TERMS: 1863 Hydrology: Snow and ice (1827); 1640 Global Change: Remote sensing; 9350 Information Related to Geographic Region: North America; KEYWORDS: fractional snow cover, satellite remote sensing Citation: Romanov, P., D. Tarpley, G. Gutman, and T. Carroll, Mapping and monitoring of the snow cover fraction over North America, J. Geophys. Res., 108(D16), 8619, doi: /2002jd003142, Introduction [2] Snow cover substantially affects the land surface physical and optical properties, and therefore the surface energy exchange. Water accumulated in the snowpack during a winter season and released during snowmelt makes snow cover an important element of the global hydrological cycle. Regarding the surface radiation balance, the major 1 Office of Research and Applications, National Environmental Satellite Data and Information Service, National Oceanic and Atmospheric Administration, Camp Springs, Maryland, USA. 2 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, USA. 3 NASA Headquarters, Washington, D. C., USA. 4 National Operational Hydrologic Remote Sensing Center, National Weather Service, National Oceanic and Atmospheric Administration, Chanhassen, Minnesota, USA. This paper is not subject to U.S. copyright. Published in 2003 by the American Geophysical Union. effect of snow cover consists of an increase of the land surface albedo from 0.05 to 0.4, typical for bare soils and snow-free vegetated surfaces, to up to 0.9 corresponding to the albedo of the pure snow [Nolin and Liang, 2000]. In practice, however, the albedo of snow-covered surfaces rarely reaches such a high value [Robinson and Kukla, 1985; Jin et al., 2002]. First, the snowpack, particularly during the snowmelt, may develop a patchy configuration, which brings down the reflectance of the mixed surface. Second, snow cover is often littered by vegetation debris or masked by the canopy. Because of these latter factors only a portion of the snow-covered area is directly exposed to the atmosphere and thus contributes to the reflective properties of the land surface. [3] The subject of this study is the fractional snow cover or snow fraction. This term is used here as a reference to the portion of the land surface, which is seen by a satellite or an airborne imaging instrument as being covered with snow. Therefore the snow fraction is determined by both factors GCP 14-1

2 GCP 14-2 ROMANOV ET AL.: MAPPING AND MONITORING OF SNOW COVER FRACTION mentioned above, i.e., a nonuniform, fractal distribution of the snow cover and snow masking by vegetation. This perception of the fractional snow cover is mostly inherent in radiation balance calculations since it directly relates the snow fraction to the land surface albedo [Slater et al., 2001]. There also exists another definition of the fractional snow cover, which accounts for the effect of a nonuniform distribution of snow, but not for the snow masking and therefore includes all snow cover on the ground surface (both exposed to the atmosphere and masked by the canopy). The use of the snow fraction defined this way is limited primarily to hydrological applications when assessing the snowmelt water runoff [e.g., Nagler and Rott, 1999]. [4] The necessity to account for a fractional feature of the snow cover when calculating the surface albedo in global climate, hydrological and numerical weather prediction models is generally acknowledged by the modeling community [e.g., Liston, 1995]. Snow fraction is not measured in situ; therefore, in the models, it is usually introduced as a prognostic variable dependent on the snow depth (or snow water equivalent) and the surface roughness (or vegetation properties). However, there is a substantial variability in particular parameterizations of the snow fraction and hence of the albedo of the snow-covered land surface used in the radiation balance calculations [Slater et al., 2001]. This fact was found to be one of the principal sources of a substantial discrepancy in mass, duration and the extent of snow cover simulated within different general circulation models [Foster et al., 1996]. Unavailability of snow fraction observations and a consequent indeterminacy in the snow fraction parameterization obviously limits the ability of current models to accurately represent the seasonal dynamics of snow cover, especially during transition months. [5] For over three decades observations from satellites have been actively used for large-scale monitoring of the snow cover distribution. Since 1978 global observations of the snow cover extent have been performed using microwave measurements from Nimbus-7 Scanning Multichannel Microwave Radiometer (SSMR) [Chang et al., 1987] and later, since 1987, with the Special Sensor Microwave/ Imager (SSM/I) flown by the Defense Meteorological Satellite Program (DMSP) [Grody and Basist, 1996]. There are well known limitations of microwave observations, which consist primarily in a relatively coarse (currently, km) spatial resolution and in difficulty in detecting melting snow [Basist et al., 1996]. However, these measurements still present a highly valuable snow remote sensing tool because of their all-weather imaging capability and potentials (although, rather limited) to provide information on the snow depth and the snow water equivalent [Chang et al., 1991; Derksen et al., 2002]. In contrast to microwave measurements, satellite observations in the visible spectral band allow for a more accurate estimates of the snow cover and offer an opportunity for mapping and monitoring it at a much higher spatial resolution [Basist et al., 1996]. Snow cover exhibits a specific spectral reflectance pattern with high values in the visible and low reflectance in the shortwave infrared and middle infrared part of spectrum [Wiscombe and Warren, 1980], which is different from the spectral reflectance of many other natural surfaces and clouds. Because of the availability of spectral channels centered in these spectral bands in many current satellite sensors, this feature is widely used in automated and semiautomated techniques to identify snow in the satellite imagery [Carroll, 1990; Klein et al., 1998; Romanov et al., 2000; Allen et al., 1990; Rosenthal and Dozier, 1996]. Examples of application of these techniques for regional and global automated mapping of snow cover are presented in particular by Hall et al. [2002], Xiao et al. [2001], and Romanov and Tarpley [2003]. It is important that snow retrievals are limited only to cloud-clear conditions since clouds are typically opaque in the visible and infrared spectral range. [6] The majority of existing snow-mapping algorithms consider every cloud-clear mage pixel as either completely snow covered or snow free and thus provide a binary, snow/ no snow classification of satellite images. Only few attempts have been made to extend the image analysis technique to assess the snow cover at a subpixel level, or snow fraction. Typically the fractional snow cover of an image pixel is estimated within a linear mixture approach, where the reflectance of a mixed pixel is represented as the reflectance sum of every land cover type or endmember, weighted by their respective area proportion in the instrument field of view. The primary difference between various techniques proposed to derive the snow fraction concerns the selection of end-members and methods to determine their spectral features. Rosenthal and Dozier [1996] have utilized Landsat TM data to estimate the snow fraction over a mountainous area in the Sierra Nevada, California. An unsupervised linear unmixing technique was employed in this work, with end-members determined from the image data using a principal component analysis. Manually selected end-members including multiple endmembers representing snow cover with different grain size were utilized by Nolin et al. [1993] to analyze mixed pixels in the imagery acquired with the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS). Kaufman et al. [2002] proposed to derive the fractional snow cover with a combination of spectral measurements at 0.66 mm and 2.1 mm. The visible reflectance of snow-free land surface is inferred from the observed reflectance at 2.1 mm, whereas the brightest pixels in the visible image are utilized to estimate the visible reflectance of snow. As given by Rosenthal and Dozier [1996], snow fraction retrievals in the two latter studies were confined to a small area covering the Sierra Nevada, California. Vikhamar and Solberg [2002] developed a method to retrieve the snow fraction over a forested area. In this work the reflectance spectra of endmembers (snow cover and different forest cover types) was determined using a physically based model along with the results of in situ surface reflectance measurements. Another technique has been proposed by Barton et al. [2000] who estimated the snow fraction from the value of the Normalized Difference Snow Index (NDSI) derived from measurements of Landsat TM. A similar approach was applied by Appel and Salomonson [2002] to the data of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra satellite. In the latter study the statistical relationship between the snow fraction and NDSI was established through comparing MODIS scenes with coregistered classified images from Landsat TM. The relationship between NDSI and snow fraction, however, was found to vary substantially from one geographical area to the other.

3 ROMANOV ET AL.: MAPPING AND MONITORING OF SNOW COVER FRACTION GCP 14-3 [7] Most of the papers mentioned above present promising results of the snow fraction retrieval. It is important to note, however, that all of the proposed methodologies, except the one of Appel and Salomonson [2002], were developed and tested over a limited area. None of the techniques was applied over a variety of land surface cover types and atmospheric conditions, for different viewing and solar illumination geometries. No long-enough time series of snow fraction data have been produced to comprehensively assess the performance of the algorithms. Therefore further studies are required to justify application of these techniques in a continental to global-scale routine monitoring of snow cover from satellites. [8] At NOAA NESDIS the primary snow product is the map of snow cover over the Northern Hemisphere, which is prepared using an interactive technique: snow maps are drawn by analysts who rely on visible imagery from geostationary and polar orbiting satellites [Ramsay, 1998]. Interactive snow charts are derived daily at a spatial resolution of approximately 25 km and are used as an input to NOAA regional and global numerical weather prediction models and in climate studies. To facilitate NOAA snow cover monitoring and analysis, three years ago we developed and launched a system which employs a fully automated technique to identify snow in satellite imagery and to generate maps of snow cover distribution [Romanov et al., 2000]. This system uses combined observations in the visible, infrared and microwave spectral bands from both polar-orbiting and geostationary satellite platforms and produces daily maps of snow cover over North America at a nominal spatial resolution of 4 km. Recently, we extended the automated system to generate maps of fractional snow cover. Snow fraction is derived from observations in the visible spectral band made by the Imager instrument on board GOES satellites. Measurements in the microwave were not considered in the snow fraction retrieval primarily because of their coarse spatial resolution. The algorithm used to estimate the fraction of the satellite image pixel covered with snow employs a linear mixture model with two end-members representing a snow-free and snow-covered land surface. In contrast to the previous studies, the reflective properties of end-members were determined with respect to varying viewing and illumination geometry of observations. We have also established and used in the model location-specific values of the visible reflectance corresponding to the snow-free land surface. The model of snow reflectance was formulated with regard to a variable surface temperature. All these improvements provided a more realistic characterization of spectral features of the end-members and thus helped to achieve a better accuracy in the derived snow fraction. In this paper, we describe in detail the technique for snow fraction retrieval and present the results of snow fraction mapping and monitoring over North America. 2. Technique [9] Observations from GOES satellites comprise an important component of the global climate observing system. Three of the five spectral channels of the Imager instrument on board GOES satellites are centered in the visible, middle infrared and infrared spectral band. With this combination of spectral channels, GOES measurements can be effectively used to identify and map snow cover. Snow detection capability of the GOES Imager is extensively exploited in the NOAA/NESDIS automated multisensor snow mapping system, where GOES-based maps of snow cover are combined with observations in the visible, mid-infrared, infrared and microwave from other satellite platforms to derive daily snow cover maps over North America. [10] The new technique for snow fraction retrieval relies on the snow cover identification with the existing GOES snow cover mapping algorithm and uses Imager observations in the visible spectral band to determine the actual proportion of the snow cover in every image pixel. Since clouds are generally opaque in the visible, mid-infrared and infrared spectral bands, the retrieval of daily snow fraction data is limited to cloud-clear scenes. A complete data processing scheme to derive maps of snow fraction involves three stages, which are the data preprocessing, snow detection and snow fraction estimation. The first two stages comprising the GOES snow-mapping procedure were described in our earlier paper [Romanov et al., 2000]. However, several substantial changes to both data preprocessing and the snow detection algorithm have been introduced since then to improve the system performance. Therefore we give a detailed description of all three stages of the algorithm in this paper Data Preprocessing [11] The GOES system consists of two satellites, GOES East (currently GOES-8) and GOES-West (currently GOES-10), which provide complete coverage of the North American continent up to N latitude. In order to identify snow-covered pixels in the satellite imagery and, further, to derive snow fraction, the NOAA NESDIS automated snow mapping system uses all available daytime measurements in GOES Imager channel 1 (centered in the visible, with a wave band covering mm), 2 (middle infrared, mm) and 4 (infrared, mm). Satellite observations are acquired every 30 min at a nominal spatial resolution of 4 km in the middle-infrared and infrared and 1 km in the visible. At the preprocessing stage (see Figure 1) all images obtained during a day are registered to a latitude-longitude projection with a 0.04 by 0.04 grid size, or approximately 4 km spatial resolution. GOES data are processed over the area extending from 25 N to 66 N and from 50 W to 170 W. Within this area we utilize GOES-10 coverage west of 100 W and GOES-8 east of 100 W. Observations in the Imager channel 1 are calibrated in reflectance (R 1 ), whereas observations in channels 2 and 4 are calibrated in brightness temperature (T 2 and T 4, respectively). Radiances measured in the Imager channels 2 and 4 are used to estimate the surface reflectance in the middle infrared (R 2 )[Allen et al., 1990]. [12] In contrast to infrared sensors, the visible sensor of the GOES Imager does not have an onboard calibration mechanism. This causes significant problems when trying to compare and combine observations from different satellite platforms. The sensor responsivity decreases with time after launch at a satellite-specific rate and therefore the preflight calibration becomes invalid soon after the launch. In our work, to ensure consistency in the observations and hence in snow cover derived from the two GOES satellites, the

4 GCP 14-4 ROMANOV ET AL.: MAPPING AND MONITORING OF SNOW COVER FRACTION Figure 1. fraction. Flowchart of the algorithm used to derive snow response of the visible sensor of GOES-10 Imager is normalized to fit GOES-8 visible reflectance. The normalization coefficient was determined and routinely updated through intercomparison of collocated simultaneous observations taken at the same viewing and illumination geometry. The satellite-observed visible reflectance was also corrected for a sensor sensitivity loss with time following Rao and Zhang [1999]. [13] The daily data processing routine further includes compositing of the GOES imagery, (stage II in Figure 1) where for every image pixel we retain the measurement with the highest infrared brightness temperature obtained during a day. Since the warmest observation is most often the least cloud-contaminated, this procedure provides an effective cloud clearing of the composited image. While testing the compositing procedure, we found that the composited image tended to accumulate corrupted measurements with an unrealistically high brightness temperature reading, which sometimes occur in the satellite imagery. As a remedy to this problem, we complemented the algorithm with a procedure determining the second warmest observation during a day. The warmest observation is rejected as a corrupted measurement and the second warmest is retained in the composite if the difference between the two corresponding infrared brightness temperature values exceeds 25 K Snow Identification [14] Both the daily composited image and all individual instantaneous images acquired during a day are utilized in the snow identification procedure. This procedure uses both spectral signatures and temporal stability criteria to ensure the most accurate image classification and snow mapping. As a first step (stage III in Figure 1), the daily composited image is subjected to a threshold-based decision-tree unsupervised spectral-based classification, which separates the image pixels into snow, snow-free land surface and cloud categories. Besides the brightness temperature in GOES Imager channel 4 (T 4 ), visible and mid-infrared reflectance (R 1 and R 2 respectively) the classification algorithm utilizes a snow index (SI, defined as the ratio R 1 /R 2 ). The idea of using the ratio of the visible to the middle-infrared or short-wave infrared reflectance to identify snow in satellite images was put forward about two decades ago [see Bunting and d Entremont, 1982]. Because of a low reflectance of the snow cover in the middle infrared and a high reflectance in the visible, the snow index enhances the difference of the spectral response of the snow cover from the response of other surfaces and is thus beneficial for snow detection. [15] The snow identification process starts with a general cloud test, which assigns cloud flags to all pixels with T 4 below 230 K (see Figure 2). Next, snow cover is identified with a suite of tests involving all spectral features mentioned above. In general, larger R 1 and SI values distinguish snow cover from snow-free land surface, whereas discrimination of snow from clouds is possible mostly owing to its small reflectance in the middle infrared (R 2 ). At this stage a set of fixed threshold values is used for SI (SI T =5),T 4 (T 4T = 290 K) and R 2 (R 2T = 5%). For the visible reflectance, the threshold value (R 1T ) was assumed to be location dependent and was defined for every grid cell of the map. To establish R 1T values, we have used statistics of GOES cloud-clear observations accumulated during snowfree periods of 1998 and The visible threshold for a grid cell was set equal to the value exceeding the average visible reflectance for this grid cell by twice the standard deviation. The value of the infrared brightness temperature Figure 2. algorithm. Flowchart of the GOES image classification

5 ROMANOV ET AL.: MAPPING AND MONITORING OF SNOW COVER FRACTION GCP 14-5 threshold (T 4T = 290 K) used to separate snow-covered and snow-free pixels may seem too high; however, our analysis of satellite imagery has shown that in spring in the daytime, mixed surfaces consisting of snow and rocks (primarily in the mountains) or the snow cover partially masked by the canopy sometimes exhibit the scene brightness temperature exceeding the freezing level by 10 K to 15 K. [16] After snow-covered pixels are separated, the image classification procedure continues with discriminating nonsnow pixels into clouds and snow-free land surface. Observations having a low (below 265 K) brightness temperature or a moderate brightness temperature (within 265 K to 285 K) along with a high visible (R 1 ) or middle infrared (R 2 ) reflectance are labeled as cloudy. All remaining image pixels are assigned a snow-free land surface flag. [17] Experience with numerous GOES Imager scenes has shown that some clouds exhibit spectral features similar to snow and thus cannot be distinguished from snow cover only from instantaneous spectral measurements. To resolve this ambiguity, we complemented the image classification algorithm with a temporal stability test (stage IV in Figure 1). In this test an intraday temporal variability of the scene temperature and reflectance is employed as a predictor to distinguish between cloudy and cloud clear pixels. The test is applied only to those image pixels, which were classified as snow covered according to their spectral response. For every snowcovered pixel, the warmest observation retained in the daily composite is compared to all observations over this location acquired during the day. The pixel is confirmed as snow, if three or more instantaneous observations are found, which are spectrally similar to the warmest one. Observations are considered similar if corresponding values of R 1, R 2 and T 4 are within 5%, 1% and 8 K, respectively. These threshold values were determined empirically through a visual examination of satellite imagery and quantitative analysis of daily time series of satellite observations over selected targets representing different surface types. It should be noted that the values of thresholds given above are very close to corresponding threshold values proposed by Key and Barry [1989] to detect clouds over snow-covered land surface in the polar area from a series of daily NOAA AVHRR images. [18] A detailed evaluation of snow maps derived from GOES data revealed that even the use of both spectral and temporal stability tests still does not prevent from occasional classification errors, which mostly consist in misinterpretation of clouds as snow. To remove these residual errors from the product, we implemented two tests based on the available climatology of the land surface temperature and snow cover. The first test utilizes monthly values of surface temperature, derived from 10 years ( ) of NOAA AVHRR observations [Gutman et al., 1995]. Snow cover detected in the GOES imagery is regarded as a classification error and labeled as cloud if the scene infrared brightness temperature (T 4 ) is over 20 K below the climatic value for the given location and the time of the year. The second test employs statistics of snow cover occurrence for 2 by 2 grid cells derived from NOAA 30- year time series of weekly interactive snow cover charts. Weekly snow charts are available from the NOAA Climate Prediction Center (CPC) at snow/. In this latter test, for every pixel classified as snow we examine the frequency of occurrence of snow cover within the 2 by 2 grid cell covering this pixel as well as in the immediately adjacent grid cells. The detected snow is rejected if for the week, corresponding to the date of the observation, as well as for the preceding and the subsequent week of the year, snow cover was never observed in any of the nine inspected grid cells. [19] A considerable difficulty in classifying the satellite imagery is caused by surface-atmospheric bidirectional effects inherent to the reflected visible radiance measured by satellite sensors. Observations from geostationary satellites used for snow detection are performed at quite different viewing angles and in varying illumination conditions; therefore, to compare them and to use in classification, they have to be brought to the same viewing-illumination geometry. In the automated snow mapping system, we normalized the GOES-measured visible reflectance to a reference viewing-illumination geometry using a semi-empirical kernel-driven model of Roujean et al. [1992]. The model is governed by two coefficients, which are the loadings on the kernels representing correspondingly volumetric scattering and surface geometrical effects, and a constant. The two model coefficients defining the reflectance anisotropy for the snow-free land surface were assumed to be location independent and were derived from the statistics of the visible reflectances over North America accumulated during the 1998 and Thus this normalization can be considered an average normalization independent of the surface type and atmospheric conditions. Application of this model in the GOES-based snow identification was justified in our earlier paper [Romanov et al., 2000]. As a reference viewing-illumination geometry we adopted the measurement configuration with a satellite viewing zenith angle (q sat ) and solar zenith angle (q sol ) both equal to 50 and a relative solar-satellite azimuth (j) corresponding to a complete backscatter (0 ). These angular values generally correspond to the average viewing and illumination conditions inherent to observations from geostationary satellites over seasonally snow-covered areas Snow Fraction Retrieval [20] In the data processing flow the snow fraction retrieval immediately follows the image classification procedure and is performed only for pixels labeled as snow. To determine the fraction of a pixel covered with snow (F), we have used GOES Imager observations in the visible spectral band and employed a linear mixture technique, i.e., F ¼ ðr 1 R 1land Þ= ðr 1snow R 1land Þ; ð1þ where R 1 is the observed visible reflectance of the scene, R 1land and R 1snow, are the reflectance of end-members representing a snow-free and a snow-covered land surface, respectively. In this work, we defined the first end-member, R 1land, for every grid cell and set it equal to the value of the average visible reflectance of the snow-free land surface. The procedure used to establish the value of the average visible reflectance for every grid cell of the map and the model representation of anisotropical reflectance properties

6 GCP 14-6 ROMANOV ET AL.: MAPPING AND MONITORING OF SNOW COVER FRACTION Figure 3. Test sites used to determine the reflectance of snow. of the snow-free land surface was described above. Reflective properties of snow were determined in a similar way; however, the snow reflectance (R 1snow ) was supposed to be independent of location. [21] Snow cover exhibits a highly anisotropical angular reflectance because of its strong forward scattering [Steffen, 1987]. This reflectance pattern is completely different from the one of the vegetated land surface, which typically exhibits a maximum reflectance in the backscatter direction [Gutman et al., 1989]. To determine R 1snow, statistics of satellite-observed snow reflectance were collected over five specially selected target areas (see Figure 3). All sites represent a generally flat terrain with almost no tree vegetation, which is required to avoid specular reflection effects as well as snow masking and shadowing by the vegetation. To ensure that all low-level vegetation is covered with snow at the time of observations, we selected the cases when the depth of the snowpack exceeded 30 cm. The University of Maryland land surface classification data set was used at this stage to prescribe the type of the vegetation cover [Hansen et al., 2000], whereas information on the snow depth was obtained from available ground based observations and from daily snow cover depth analysis of the Canadian Meteorological Center [Brassnet, 1999]. Satellite observations acquired over the test sites shown in Figure 3 during the two winter seasons of and were utilized to determine the kernel loadings for the bidirectional reflectance model of Roujean et al. [1992] and thus to establish a model description for the top-of-theatmosphere (TOA) anisotropical reflectance of snow. To reduce the effect of snow metamorphism enhanced by snow melting on the modeled snow reflectance, at this stage, we have used only satellite measurements with the infrared brightness temperature below 265 K. [22] Figure 4 illustrates the angular anisotropy of the modeled TOA snow reflectance. The results are presented only for the backscatter direction, since no forward scatter observations from GOES are available during daytime over snow-covered surfaces in the middle and high latitude. The angular anisotropy of the snow reflectance in Figure 4 is represented with an anisotropic reflectance factor (ARF), which is defined here as a ratio of the angular reflectance to the nadir-viewed reflectance. For moderate solar zenith angles (q sol <50 ), the top-of-the-atmosphere snow ARF closely corresponds to the angular reflectance of a pure snow observed in situ and simulated in models [e.g., Jin and Simpson, 1999; Han et al., 1999]. It remains almost unchanged for satellite viewing angles below 70 and slightly decreases for larger q sat. With the Sun closer to the horizon (q sol >60 ), the snow ARF exhibits a substantial increase with the view angle due to an increasing atmospheric contribution to the TOA reflectance. [23] Application of the established bidirectional reflectance model of snow cover to satellite measurements results in a substantial reduction of angular effects in the observed reflectance. This fact is evident from Figure 5, which compares the frequency distribution of satellite-observed and angular corrected visible reflectance for snow-covered areas. Histograms include data obtained over the five test areas shown in Figure 3 during two winter seasons of and Corrected reflectances were brought to a reference viewing-illumination geometry (q sol = 50, q sat = 50, j = 0 ). For the overall statistics of observations, the anisotropic correction provided a 22% reduction of the scatter in the snow reflectance with the corresponding standard deviation decreased from 6.8% to 5.3% (see Figure 5a). An even more substantial reduction of over 33% (corresponding to the change from 11.3% to 7.6% of the standard deviation) was achieved in the snow reflectance observed over the test area in Greenland (test site 5 in Figure 3). Better correction results in Figure 5b are mostly due to the higher stability and homogeneity of the physical and, hence optical properties of the snow cover in this area. [24] Even with the anisotropic correction applied, there is still a substantial scatter remaining in the statistics of the visible reflectance over snow. This fact indicates that the visible reflectance of the end-member, representing a completely snow-covered land surface, cannot be established unambiguously. The reasons for the scatter include a vari-

7 ROMANOV ET AL.: MAPPING AND MONITORING OF SNOW COVER FRACTION GCP 14-7 Figure 4. Top-of-the-atmosphere anisotropic reflectance factor (ARF) for snow. Results are shown for the backscatter in the principle plane. able snow grain size, vegetation or rocks that are not completely covered by the snowpack, littering of snow cover and its shadowing due to the surface roughness, nondetected semitransparent clouds as well as limited ability of the adopted kernel-driven model to accurately represent the bidirectional reflectance of snow. Most of these factors (i.e., cloudiness, vegetation, shadows) tend to decrease the reflectance of the scene as compared to the reflectance of the pure snow. In this work we assumed that in the whole statistics collected over snow-covered areas only the highest 5% of the observed visible reflectance values correspond to observations of an unobstructed snow cover. Therefore the reflectance corresponding to the 95th percentile of the frequency distribution (see Figure 5a) was taken as an estimate of reflectance of a nonforested, completely snowcovered land surface. This criterion for determining R 1snow is much alike the one of Kaufman et al. [2002], who proposed to use the reflectance value of 1% to 5% brightest pixels in the winter-time satellite image as an estimate of the visible reflectance of a completely snow-covered land surface. In our work, the value of the top of the atmosphere reflectance of snow in the GOES Imager visible channel brought to the reference viewing-illumination geometry (q sol =50, q sat =50, j =0 ) was found equal to 71%. [25] Another factor, which should be taken into account when modeling the reflective properties of snow, is the surface temperature. The snow reflectance generally decreases with the increase of the temperature because of the snow metamorphism processes [Sergent et al., 1993]. The statistical analysis of available GOES observations over snow-covered surfaces did not reveal any noticeable variability of snow reflective properties, when the scene brightness temperature was below 265 K. With the increasing temperature, the TOA snow reflectance was found to decrease, dropping by about 1.5% at 268 K and by about 7% when the temperature approached the melting point (273 K). In this work we approximated the temperature dependence of snow reflectance with a quadratic function and used it to correct the snow reflectance model. The reflectance anisotropy of snow was assumed to be independent of the surface temperature. The latter is justified by the fact that the snow metamorphism mostly affects the snow grain size, whereas the grain size has little effect on the ARF in the visible spectral band [Grenfell et al., 1994]. [26] The established models for the bidirectional reflectance of the snow-free land surface and snow cover were used to calculate the snow cover fraction with equation (1). Figure 5. Frequency distribution of the observed and angular-corrected visible reflectance for snow. (a) Overall statistics for five test sites in Figure 3 and (b) observations over the test area in Greenland. Corrected reflectance is brought to the reference geometry of observations (q sol =50, q sat =50, j =0 ). Scenes exhibiting the reflectance above the value corresponding to the 95th percentile of the corrected frequency distribution (71%) are considered to be completely snow covered.

8 GCP 14-8 ROMANOV ET AL.: MAPPING AND MONITORING OF SNOW COVER FRACTION In the calculations the reflectance of the snow-free and snow-covered land surface (R 1land and R 1snow ) was brought to the current Sun-target-satellite geometry of observations. Therefore the derived snow fraction was specific to particular viewing-illumination conditions. [27] Theoretically, errors in the estimate of the snow cover fraction are determined by uncertainty in the top of the atmosphere (TOA) reflectances in the right side of equation (1). The error associated with R 1 is mostly due to the measurement noise (about 0.1%) and to a variable atmospheric (primarily, aerosol) composition, which is not accounted for in the algorithm. Changes in the aerosol concentration may be responsible for about 5% variability in the TOA visible reflectance observed in the backscatter direction for light to moderate values of the aerosol loading [Moulineaux et al., 1998; Li et al., 1997]. Errors in R 1land and R 1snow result from indeterminacy in the spectral reflectance of end-members and inability of a simple two-kernel model used in this study to accurately represent the bidirectional reflectance of the land surface over a wide range of satellite viewing and illumination geometries. According to Walter- Shea et al. [1992] changes in the leaf area index and soil wetness may cause up to 6% change in the reflectance of the snow-free land surface. A similar effect on the visible reflectance of snow (about 5%) may have a varying snow grain size [Painter et al., 1998]. The accuracy of representation of the bidirectional reflectance for a vegetated land surface with kernel-driven semi-empirical models typically ranges within 2 to 3% [Li et al., 1997]. It is reasonable to assume that approximately the same errors are inherent to the model representation of the bidirectional reflectance of snow. Finally, a bias in the derived snow fraction may occur because of the use of a statistical approach to determine R 1snow, which consists in assigning it the value of a predefined (95th) percentile of the frequency distribution of the observed reflectance of the snow-covered land surface. We estimate the error in R 1snow associated with the implementation of this approach equal to 4%, which corresponds to the difference between the values of the 95th and 99th percentiles (71% and 75%, respectively) of the reflectance frequency distribution (Figure 5a). Being combined, all errors listed above yield the estimated error of the snow fraction retrieval ranging from 10% for low values of snow fraction (0.3 and below) to around 13% when the snow fraction exceeds 0.9. It should be noted that the presented error budget does not include several other factors, such as nondetected semi-transparent clouds, subresolution water bodies with non-frozen water, specular reflectance due to a rugged terrain, etc. A possible cumulative impact of these latter factors on the accuracy of the derived snow fraction is hard to assess quantitatively. From a long-term perspective, the accuracy of the instrument post-launch calibration, especially its visible sensor, may also become an important issue when trying to derive consistent multiyear time series of the fractional snow cover. 3. Results of Snow Fraction Monitoring [28] Daily maps of fractional snow cover over North America have been routinely produced since late We also reprocessed GOES observations since late 1999, thus a 3-year time series of snow fraction retrievals has become available for analysis. An example of snow fraction retrievals is shown in Figure 6. The maps presented in this figure illustrate changes of the fractional snow cover in the middle and late November 2000 over the area of approximately 1500 km by 1000 km in the Northern Great Plains. The analyzed period of time immediately followed a series of snowfalls, which occurred during 7 11 November 2000 and was characterized by a rapid snowmelt due to relatively warm weather. Several daily images during this period were almost completely covered with clouds and thus are not shown. The retrieval results give a consistent picture of the time change of snow fraction: over a large area the fractional snow cover gradually decreases before the snow cover disappears completely. Some pixels in the images are identified as 100% snow covered, which indicates that the snowpack reached the height sufficient to mask practically all of the vegetation. The vegetation cover in the area shown in the snow maps in Figure 6 consists mostly of grasslands, croplands, and bare soil with very few trees, thus it is quite possible for the snowpack to exceed the height of most of the canopy. [29] Although the image compositing procedure that we use in the data processing algorithm effectively reduces the cloud amount in the product, a substantial part of the daily snow fraction map still remains obscured by clouds. Snow fraction maps for the whole North America given in Figure 6 for the first and the last day of the analyzed time period provide an apt illustration for this problem. Cloud cover appears as a major factor, which hampers a timely detection of changes in the snow fraction using GOES Imager observations. Conditions for observations of snow cover and snow fraction become more favorable in spring, when the cloud amount over middle and high latitudes decreases as compared to the fall season. [30] The primary factor determining the snow fraction over nonforested areas is the depth of the snowpack. Two monthly time series of collocated satellite and surface observations shown in Figure 7 give a clear evidence of a close correlation between the two parameters. Both locations are characterized as cropland/grassland with a negligible percent of forest cover. To reduce variability in the time series of the satellite snow fraction, we averaged it within 3 by 3 grid cell (or approximately 12 km by 12 km) area centered at the station location. There is some small variability in the derived snow fraction, which is not supported by corresponding changes in the reported snow depth. However, these variations do not exceed 7%, and, hence are within the estimated error range. The snow fraction time series have noticeable data gaps, which are caused by a persistent cloud cover. On the average, cloud free observations over the two sites were available every second or third day yielding from 12 to 14 snow fraction retrievals during a month-long period. [31] An overall statistics of correspondence between the snow fraction and the snow depth over areas with little or no tree cover is presented in Figure 8. The statistics include about 4,000 collocated satellite-surface measurements for more than 170 stations in North America collected during two winter seasons of and It is important that most of the collocations (over 90%) used in the comparison came from the Great Plains area (area 3 in Figure 3). This is the largest grassland area in North

9 ROMANOV ET AL.: MAPPING AND MONITORING OF SNOW COVER FRACTION GCP 14-9 Figure 6. Snow fraction retrievals in the end of November 2000 over Great Plains and Canadian Prairies.

10 GCP ROMANOV ET AL.: MAPPING AND MONITORING OF SNOW COVER FRACTION Figure 7. Time series of collocated snow depth and snow fraction observations for two sites in Great Plains for November America, which receives a substantial amount of seasonal snow and at the same time has a high-density network of first-order and U.S. Cooperative observing stations. As is seen in Figure 8, the fastest increase of the snow fraction corresponds to the snow depth increase from 0 to approximately 10 cm. After that, the growth of the snow fraction continues at a gradually decreasing rate, but it is still noticeable for the snow depth of up to cm. [32] There is a substantial scatter in the snow fraction values corresponding to the same snow depth, which is mostly due to the different land surface cover type at the station locations. In the absence of the tree cover, the height of the grass is the primary factor, which determines the land surface reflectance and hence the apparent snow fraction. Other factors, which potentially contribute to the scatter, include snow removal in urbanized areas and high spatial inhomogeneity of the snow depth. It is important that in the comparison of satellite and surface observations, no quality control was applied to snow depth reports from ground-based stations. A selective analysis of available station data has shown that depending on a particular station, the percent of obvious errors in the reported snow depth may reach 2 3%. These errors may also adversely affect the correspondence between the snow depth and the snow fraction. The overall correlation between the snow fraction and snow depth for areas generally free of forest cover was [33] A broader analysis of satellite and surface observations has shown that snow fraction remains sensitive to changes in the snow depth over lightly forested areas, with the tree cover fraction below 20% (see Figure 9). Over areas with a moderate (20% 59%) or dense (over 60%) tree cover, the positive correlation between the snow depth and the snow fraction is still noticeable, but the response of the snow fraction to variations in the snow depth is much less pronounced, than in the cases with a lighter forest cover. The statistics presented in Figure 9 included about 18,000 observations from over 1700 ground-based stations. Most of these stations (about 90%) belong to the U.S. Cooperative network and are located within the territory of conterminous United States. Information on the tree cover distribution was taken from the percent tree cover data set prepared at the University of Maryland [DeFries et al., 2000]. [34] The graphs in Figure 9 show that the maximum observed snow fraction gradually decreases with the increase of the tree cover fraction. For the most densely forested locations, the observed snow fraction varies within 20% 30%, showing only a slight increase with the increase of the snow depth. Correlation between the snow fraction and the snow depth within 0 to 40 cm drops from 0.56 and 0.40 for treeless and lightly forested areas, respectively, to for the sites located in moderately and heavily forested region. It should be noted, however, that even for the heavily forested areas, the observed correlation remains statistically significant at significance level. All errors responsible for a scatter in the snow depth-snow fraction relationship over nonforested areas may also be the reason for a nonmonotonic nature of the curves in Figure 9. Another factor, which influences the correspondence between the snow depth and the snow Figure 8. Correspondence of snow fraction to snow depth. Bars show 1 standard deviation.

11 ROMANOV ET AL.: MAPPING AND MONITORING OF SNOW COVER FRACTION GCP Figure 9. Snow fraction versus snow depth for different tree cover fraction. fraction over the forest, is littering of the snowpack with vegetation debris. The effect of litter on the reflectance of a snow-covered forest becomes most pronounced in the middle and especially in the end of the winter season, during snowmelt [Melloh et al., 2001]. [35] Figure 10 illustrates the complexity in the snow fraction-snow depth relationship over a forested area. The time series of matched satellite and surface observations presented in this figure cover the winter season of the year The graphs clearly indicate that snow fraction follows the increase of the snow depth only in the beginning of the winter season, during November and December. Later on, despite a continuing snow accumulation, the snow fraction does not exhibit any further growth and even decreases. In the middle of the winter season (January to March) there exists a noticeable variability in the derived snow fraction, which has no clear correlation with changes in the snow depth. This may be attributed to a variable shadowing of snow cover by the canopy due to variable solar illumination conditions. The effect of shadows on snow fraction is discussed in more detail later in this section. Both of the factors mentioned above (littering and shadowing) make it difficult to obtain an unambiguous estimate of the snow depth from snow fraction measurements over a forested area. [36] Another factor, which may add to the day-to-day variability in the time series of the observed snow fraction over a forested location (Figure 10), is inaccuracy of the satellite image navigation and registration. GOES Imager pixel registration error between repeated images within a 24-hour period generally comprises about 6 km for a subsatellite point and increases with the increase of the Figure 10. Seasonal change of snow depth and snow fraction for the station located at N, W. The corresponding forest cover fraction for this location is 59%.

12 GCP ROMANOV ET AL.: MAPPING AND MONITORING OF SNOW COVER FRACTION Figure 11. Blended map of (a) maximum snow fraction and (b) the map of tree cover fraction [DeFreis et al., 2000] for areas affected by a substantial seasonal snow. satellite view angle. The effect of these errors on the derived time series of snow fraction obviously increases with the increase in the spatial inhomogeneity of the forest cover distribution. [37] Forest cover is obviously the primary factor that controls the spatial distribution of the snow fraction at a continental scale. Figure 11 shows a blended map of the maximum snow fraction for North America, which was derived by combining all daily images obtained during three winter seasons from to The maximum snow fraction was estimated from a statistical distribution of the observed snow fraction for every map grid cell. To eliminate outliers, caused by occasional errors in individual retrievals of snow fraction, we chose to represent the maximum snow fraction within a grid cell using the value of the 90th percentile of the corresponding snow fraction frequency distribution. A grid cell was assigned a particular value of the snow fraction if during the last three winter seasons nonzero snow fraction over this location was observed during 20 days. In other words, the fractional snow cover map in Figure 11a represents the distribution of the maximum snow fraction only over areas affected by a substantial seasonal or perennial snow cover. [38] Qualitative comparison of the maximum snow fraction map with the map of the tree cover fraction (see Figure 11) reveals their similarity. Because of the snow masking by trees, forests appear as areas of reduced fractional snow cover. Over the Great Plains, Canadian prairies, tundra zones and other areas with little or no trees the maximum snow fraction reaches %. The derived fractional snow cover also exhibits high values in the mountains. However, over the rugged terrain it may be overestimated because of specular reflectance effects, which are not accounted for in the algorithm. An overall correlation between the snow fraction and fractional forest cover calculated on a pixel-by-pixel basis was found equal to [39] A close correlation between the snow fraction and the tree cover fraction provides grounds for using wintertime satellite data in the forest cover monitoring. To study the feasibility of this approach, we established an empirical relationship between these two land surface characteristics. A straightforward point-by-point matching of the two data