Developing snow cover parameters maps from MODIS, AMSR-E and

Transcription

1 1 2 Developing snow cover parameters maps from MODIS, AMSR-E and blended snow products Abstract Snow cover onset date (SCOD), snow cover end date (SCED), and snow cover duration (SCD) are important parameters for providing comprehensive characteristics of regional or global snow cover conditions and are traditionally derived from in situ observations. In this study, we test three methods, two new (1 and 2) and one published (W_X), in deriving those parameters from remotely sensed snow cover maps, i.e. AMSR-E, cloudless MODIS multi-day combined (MODISMC8), and MODIS/AMSR-E blended. Method 1 follows a traditional definition of snow cover parameters and is used for cloud-free snow cover maps (AMSR-E and MODIS/AMSR-E blended). Method 2 sets different conditions to derive snow cover parameters from MODISMC8 maps. Method W_X also applies to the MODISMC8 maps. The accuracies of these image-derived parameters are evaluated against those from in situ measurements at 244 SNOTEL stations in the Pacific Northwestern USA, during the hydrological years. Results indicate that biases are mainly from scale difference between images and in situ observations and that the method 2/MODISMC8 combination performs the best. It is found that the three SCD maps (one for each year) have very similar distribution patterns, although the SCOD and SCED maps vary from year to year and from area to area. Overall, 2008 had the longest SCD, even though it had the latest SCOD of the 3 years analyzed. 22 1

2 Introduction The pattern of snow cover, as determined by snow accumulation and snowmelt processes, has significant impacts upon climate processes, surface hydrological cycles and ecological processes (Cherkauer et al., 2003). Snow cover parameters, such as snow cover onset date (SCOD), snow cover end date (SCED), and snow cover duration (SCD) are highly relevant to stability of permafrost, length of active growing season, cold regions ecological processes, and surface water cycle and energy balances (Rango 1997; Bamzai, 2003; Frolking et al., 2006). Spatial-temporal pattern and variability of snow cover are important factors for regional to global hydrological modeling, possible flood forecasting, and water management (Dressler et al., 2006; Wulder et al., 2007; Wang and Derksen, 2007). Time series of snow cover parameters can also reflect snow response to climatic change. For instance, the higher the temperature, the shorter the snow cover duration (Yang et al., 2003; Belchansky et al., 2004; Ye et al.,2005; Brown et al., 2007). As solar irradiance is the driver for snow melt, a modified albedo due to addition of impurities on the snow can have a much great impact on SCD than temperature (Painter et al., 2007). Traditionally, snow cover parameters are derived from in situ observations. But point measurement networks cannot provide an overall picture on regional and global scales, due to their low spatial density or even absence in inaccessible regions. Compared to conventional point observations, satellite remote sensing data are well suited to map snow over in continuous space-time scales (Pivot et al., 2002). Currently, some space-borne sensors with various spectral, spatial and temporal resolutions are available for snow cover mapping and can be used to calculate snow cover parameters. Each type of sensor and method has inherent advantages and limitations. Visible and near-infrared images (e.g. from MODIS, AVHRR, SPOT, Landsat), for example, can 2

3 provide snow information with high spatial resolution. Thermal infrared images can monitor surface temperature and provide complementary information for snow cover mapping. In this case, when mean surface temperature of three continuous days is above a certain threshold value for the first time, the corresponding first date is defined/flagged as the onset date of snow melt (Takala et al., 2008). The accuracies of optical images for snow mapping largely depend on cloud cover and illumination conditions, especially in winter seasons and higher latitude regions where strongly affected by polar nights (Simic et al., 2004; Ault et al., 2006; Wang et al., 2008). Therefore, multi-day combined snow cover products, such as NOAA NESDIS weekly snow charts, with lower cloud coverage or contamination, are highly recommended to estimate snow cover duration (Derksen et al., 2004; Brown et al., 2007; Wang and Xie, 2009; Xie et al., 2009; Gao et al., 2010a; Hall et al., 2010). Space-borne passive and active microwave radiometers are ideal for global snow monitoring since they are available in all weather conditions. Based on passive microwave sensors (e.g. SMMR, SSM/I, AMSR-E), snow cover extent and snow water equivalent can be inferred on a daily basis (Belchansky et al., 2004). The brightness temperature differences have been widely used in determining the onset dates of snow cover and melt (Takala, 2008). But there are some difficulties in accurately estimating the date of the first snowfall, especially under warm and dry winter conditions (Pivot et al., 2002). Time series of active microwave data are also used to detect terrestrial snow variability (Wang and Derksen, 2007). However, coarse spatial resolution hinders their applications in regional climatological and hydrological modeling. Therefore, multi-sensor blended snow maps were developed, which take advantage of the high spatial resolution of optical sensors and cloud penetration of microwave sensors (Foster et al., 2007 &2008; Liang et al., 2008; Gao et al., 2010a). The general motivation of this study is to derive snow cover parameters (SCOD, SCED, and 3

4 SCD) from these newly developed remote sensing images that have high spatial resolution, high accuracy, and low cloud coverage. Two new methods and one published method are tested on three types of snow maps: cloud-free AMSR-E snow maps, low-cloud MODIS multi-day combined snow cover maps, and cloud-free MODIS/AMSR-E blended snow cover maps. Specifically, the objectives of this study are (i) to define and calculate snow cover parameters (focusing on SCOD, SCED, SCD) from these three snow cover maps and ii) to evaluate those image-derived snow cover parameters by comparing with those calculated from in situ observations. In situ observations at 244 SNOTEL (automated SNOwpack TELemetry) stations over the Pacific Northwestern USA during three hydrological years from September 2005 to August 2008 are used for validation and evaluation Method Definition of Snow Cover Parameters Traditionally, snow cover parameters are calculated from a series of in situ snow depth measurements ( hereinafter referred to as traditional method. In traditional method, SCOD is defined as the first date of the first period, with at least 14 consecutive days of snow cover greater than 2 cm in depth. SCED is the last date of the last period, with at least 14 consecutive days of snow cover greater than 2 cm in depth. The 14 consecutive days is long enough to avoid the transient snowfall events (in the early fall and in the later spring), which usually cause multiple starting-dates and ending-dates of snow cover (Brown et al., 2007). The period between SCOD and SCED is called the maximum continuous snow-covered period. SCD is defined as the number of days with snow cover greater than 2 cm in depth. It includes the maximum continuous snow-covered period and other transient snowfall events. In situ SCOD, SCED, 4

5 and SCD from the 244 SNOTEL stations are derived based on the above definitions and used for validating those SCOD, SCED, and SCD values derived from remote sensing images. Methods to Derive Snow Cover Parameters from Remote Sensing Images Two new methods and one published method are tested to derive the dates of snow cover onset, end and duration of each pixel from remote sensing images in one hydrological year. One hydrological year is assumed to start on September 1 of any given year and end on August 31 of the subsequent year. For example, hydrological year 2008 is from September 1, 2007 to August 31, This assumption depends on the snow cover climatology and variability of different geographic regions (Bamzai, 2003). In this study, it is assumed that if one pixel is labeled as snow in a remote sensing image, the mean snow depth of this pixel is then greater than 2 cm. Method 1 for cloud-free snow cover maps Method 1 follows the traditional definitions of snow cover parameters and is used to calculate snow cover parameters from cloud-free snow cover maps. For SCOD, each pixel of all images starting from the first day to the last day of one hydrological year is searched to satisfy this condition: the first date when the pixel value is snow for 14 consecutive days. The first date is then defined as the SCOD of the pixel. If no such date is found throughout a year, this pixel has no SCOD and is labeled as 0. This definition will not capture ephemeral snowfall events in the fall or spring whenever snow cover lasts less than 14 consecutive days. For SCED, a pixel with SCOD will be searched from its SCOD to the last day of one hydrological year to satisfy this condition: the first date when the pixel value is land for 14 consecutive days. The first date is then defined as the SCED of that pixel. The pixels that have no SCED are labeled as 0 in the SCED map. 5

6 SCD is simply calculated as the total snow cover days for each pixel in one hydrological year. It includes the maximum continuous snow-covered period and transient snowfall events Method 2 for low-cloud snow cover maps Snow cover parameters derivation from low-cloud snow cover maps using the traditional method would be bias prone due to the cloud pixels. For example, if one pixel is labeled as snow during 14 consecutive days, the first date of this period should be the SCOD of this pixel based on the traditional method. However, if the pixel in any one of the 14 days is covered by cloud, the first date will not be defined as SCOD by the traditional method, even though it is most likely snow covered in the cloud-covered day. In order to reduce the effect of cloud pixels, we divide one hydrological year into two periods and set different conditions to derive snow cover parameters for different periods. One period is from September to February and the other is from March to August, since the maximum snow depth usually happens in late February to early March in the study area (Figure 1). During the period of the first snowfall in autumn to February, a pixel has more opportunity to be snow if it is marked as cloud cover. In contrast, from March to August, a pixel has more opportunity to be land if it is marked as cloud cover. Therefore, for SCOD, each pixel of all images starting from the first day to the last day of one hydrological year is searched to satisfy two conditions: (i) The date when the pixel value is snow; (ii) Pixel value is snow or cloud (from September to February), or is snow (from March to August) for 13 consecutive days, following that date. The date is then defined as the SCOD of the pixel. For SCED, a pixel with SCOD will be searched from its SCOD to the last day of one 6

7 hydrological year to satisfy two conditions: (i) The date when the pixel value is land; (ii) Pixel value will not be snow or cloud (from September to February), or will not be snow (from March to August) for 13 consecutive days following that date. The date is then defined as the SCED of that pixel. The period between SCOD and SCED is the maximum continuous snow-covered period of that pixel. The cloud in the maximum continuous snow-covered period is counted as snow. Therefore, SCD, the number of snow covering days, includes all days within the maximum continuous snow-covered period and snow cover days in transient snowfall events. Method W_X Wang and Xie (2009) proposed an approach (here we refer it as method W_X) to calculate snow cover parameters from low-cloud MODIS multi-day combined snow cover maps. The SCD is calculated at a pixel scale (500 m) using all images within a hydrological year from September 1 to next August 31 by 147 SCD = N i= 0 (Di2 Di1 + 1) (1) where N is the total number of available composite images within a hydrological year; D i1 and D i2 are the beginning and ending dates of each multi-day composite image, respectively. Only those snow-covered days were counted. Data from September 1 to December 1 are used to calculate SCOD (Julian Day), which is calculated by 153 ' SCOD = D SCD (2) 154 where SCD is the snow cover duration within the period from September 1 to December 1; D is the 7

8 Julian day of December 1 (D = 336 or 335) or other date when this area is supposed to be covered by snow. 157 Data from February 1 to May 1 are used to calculate SCED, which is calculated by ' SCED = D+ SCD (3) where SCD is the snow cover duration during the period from February 1 to May 1. D is the first beginning date of SCD. Here the authors supposed snow still exists on February 1, so D should be a constant (32, February 1). More details about this method are in Wang and Xie (2009) Evaluation To quantitatively assess image-derived snow cover parameters by different methods, in situ observed snow cover parameters are calculated based on traditional definitions and used as ground truth for the pixel (named as the station-pixel) that includes such an in situ station. The error (ε) of SCOD or SCED is defined as 168 ε = t (4) SCOD (SCED) image t insitu where t image is the image derived SCOD or SCED (Julian day) of the station-pixel; t insitu is in situ observed SCOD or SCED (Julian day) of a corresponding in situ station. When the SCOD or SCED is in January to August of a subsequent year, 366 or 365 needs to be added to the date. Figure 2 shows the relationship between snow cover parameters from images and in situ observations. If an image observed SCOD or SCED date is in point a or c, i.e., ε SCOD or ε SCED is less than 0, we define that the image observation advances against in situ observation. In contrast, if image observed SCOD or SCED date is in point b or d, i.e., ε SCOD or ε SCED is greater than 0, we define that the image observation is delayed against the in situ observation. 8

9 177 The error of SCD (ε SCD ) is defined as 178 ε = SCD SCD (5) SCD image insitu where SCD image is the image-derived SCD of a station-pixel; SCD insitu is in situ observed SCD of a corresponding in situ station. If ε SCD is less than 0, it means that the image observation underestimates snow cover duration (against the in situ observation). If ε SCD is greater than 0, it means that the image observation overestimates snow cover duration (against the in situ observation). Care must be taken in interpreting the results when using in situ observed snow cover parameters to validate image derived snow cover parameters (Gao et al., 2010a). Conditions at individual stations may not be representative of the larger area (corresponding station-pixels) viewed by satellites due to scaling differences (Simic et al., 2004; Klein and Barnett, 2005). It is possible that snow may cover a station, but not be present at other elevations below the station, which would make the in situ observation as snow while the image observation is no snow, and lead to Image Underestimate (IU) error (missing snow when it is present). This IU error may therefore make the image-derived SCOD delayed, SCED advanced, and SCD underestimated. Contrary, snow may be present at elevations above a station, but not at the station itself, which would make the image observation as snow while the in situ observation as no snow, and cause Image Overestimate (IO) error (mapping snow when none is present), thus making the image-derived SCOD advanced, SCED delayed, and SCD overestimated Study Area and Data Study Area The study area is located in the Pacific Northwestern USA. It includes most of Washington, 9

10 northeastern Oregon, northern and eastern Idaho, southwestern Montana, and northeastern Wyoming 2 (Figure 3). The total study area is ~565,000 km and extends from 103 W to W and from 41.5 N to 49.5 N. Elevation ranges from 0 to 4394m. The mean annual precipitation is less than 200 mm in the northwest and almost 2800 mm in the central and eastern part of this area. Land use is mainly cultivated crops and pasture in the lowlands, scrub and grassland in the middle elevation regions, and forest in the mountainous areas. This area has a large amount of high-elevation glaciers and rocks, with a very distinct tree line to separate them from other land cover types at lower elevations. Data The in situ observation datasets used in this study are daily snow depth measurements at 244 stations in the period from September 1, 2005 to August 31, They are provided by the SNOTEL network with integer format (inch) (Klein and Branett, 2003). The locations of these stations are shown in Figure 3. For the quantitative validation of the image-derived snow cover parameters, the spatial representativeness of these stations has been evaluated by Gao et al. (2010b). They demonstrated that the distribution of these SNOTEL stations is expected to capture the relative variability of snow cover distribution for this region. Three image products, cloud-free AMSR-E, MODIS/AMSR-E blended (MODISMC8_AE) snow cover maps, and low-cloud MODIS multi-day snow cover maps (MODISMC8) from September 1, 2005 to August 31, 2008 are used to calculate snow cover parameters. The AMSR-E snow cover map is converted from the daily Level-3 Snow Water Equivalent (SWE) product (AE_DySno) (Kelly, et al., 2003; Pulliainen, 2006; Markus et al., 2006), in which SWE values (representing snow-covered area) are merged into snow class, 0 (representing snow-free land) is kept as no snow 10

11 (land) class, and the data missing class is retained as data missing class. The AMSR-E snow cover map is daily snow cover map with a spatial resolution of 25km. MODIS multi-day combined snow cover product (MODISMC8) (Gao et al., 2010b) is the combination of MODIS daily snow cover products MOD10A1 and MYD10A1 (MODIS Terra/Aqua Snow Cover Daily L3 Global 500m SIN GRID V005) (Hall et al., 2002) within flexible temporal windows. The flexible temporal windows are controlled by two thresholds, maximum cloud coverage of 10% and maximum composite days of 8 days. MODISMC8 has spatial resolution of 500 m and a mean temporal resolution of 2.6 days. The MODIS/AMSR-E blended snow cover product (MODISMC8_AE) is a multi-day multi-sensor blended snow cover map, in which the remaining cloud pixels of MODISMC8 are replaced by the corresponding observation of AMSR-E. Details about these products can be found in Gao et al. (2010b). Table 1 summarizes the number of images in three years, mean annual snow percentage, cloud percentage, overall accuracy in all-sky condition, image underestimation and overestimation errors against in situ observations among different snow cover maps, and methods used for deriving snow cover parameters. Information for the MODIS daily products (MOD10A1 and MYD10A1) is also included. Detailed comparison and accuracy assessment of those products are in Gao et al. (2010b). The overall accuracy of AMSR-E maps in all sky conditions is 74.1% (Table 1). The mean IU error against ground observations of AMSR-E (24.3%) is larger than its IO error (1.6%), which means that AMSR-E snow cover maps tend to underestimate snow extent. MODISMC8 maps still have a mean cloud coverage of 7.2%, which is much less than the MODIS daily products (over 50%). Its overall accuracy in all sky condition is 79.4% and mean IU and IO errors are 6.1% and 4.6%, respectively. MODISMC8_AE has overall accuracy 86.0% in all sky conditions. The mean IU and IO 11

12 errors of MODISMC8_AE are 9.8% and 4.2%, respectively. Method 1 is used to calculate snow cover parameters from AMSR-E and MODISMC8_AE snow cover maps. Method 2 and method W_X are used to calculate snow cover parameters from MODISMC8 snow cover maps Results and Discussion Errors in Image-derived Snow Cover Parameters The errors are calculated using equation (4) and (5). The histograms of the errors between 732 pairs (244 stations 3 years) of image and in situ observed snow cover parameters are plotted in Figures 4, 5, 6 respectively for SCOD, SCED, and SCD. In each figure, a and b are results respectively for AMSR-E and MODISMC8_AE snow cover maps using method 1, while c and d are results for MODISMC8 snow cover maps using method W_X and method 2, respectively. Histograms of SCOD errors in Figure 4 show tails to the right (larger than 0). This indicates that image derivations have a tendency to delay SCOD as compared with in situ observations of SCOD. If the SCOD difference of ±10 days is defined as no difference against in situ observations, 20%, 58%, and 2% of AMSR-E derived SCOD show no difference, delay, and advance, respectively. Another 147 (~20%) AMSR-E pixels show no SCOD (i.e., no 14 consecutive snow cover days found in these station-pixels), while those corresponding in situ stations show SCOD in the period of (Julian day). This places the error of those pixels (~20%) at the -100 error zone (values less than -100 are included in this zone) (Figure 4a). Figure 4b shows 66%, 23%, and 11% of MODISMC8_AE derived SCOD with no difference, delay, and advance, respectively. For the MODISMC8 (method W_X) and MODISMC8 (method 2) derived SCOD, the results are respectively 40% and 72% as no difference, 12

13 % and 14% as image delay, and 10% and 14% as image advance (Figure 5c, d). Histograms of SCED errors in Figure 5 show tails to the left (less than 0), indicating a tendency of image-derived SCED advance in situ observations of SCED. If the difference of ±10 days is used to define no difference, the percentages of no difference in SCED of AMSR-E (method 1), MODISMC8_AE (method 1), MODISMC8 (method W_X), and MODISMC8 (method 2) are 13%, 44%, 17%, and 55%, respectively; the percentages of image advance are 84%, 52%, 81%, and 41%, respectively, while only a few percent of images show delay. Histograms of SCD errors in Figure 6 show tails to the left (less than 0), indicating that image-derived SCD tend to underestimate against in situ observations of SCD. If the difference of ±20 days is defined as no difference, the percentages of no difference in SCD of AMSR-E (method 1), MODISMC8_AE (method 1), MODISMC8 (method W_X), and MODISMC8 (method 2) are 21%, 61%, 50%, 70%, respectively (Figure 7a, b, c, d). Image underestimation are respectively 73%, 22%, 40%, and 17%, and image overestimation are respectively 6%, 17%, 10%, and 13%. These errors in image-derived snow cover parameters against in situ observations may be caused by two factors. One is the scaling difference between image and in situ observations. The other is the difference of deriving methods from images or from in situ observations. In this paper, we call error caused by scaling difference as scaling error, while error caused by method difference as method error. All image-derived snow cover maps have scaling difference against in situ observations, thus, scaling error. Snow cover maps used in this study have larger IU errors (missing snow when it is present), particularly the AMSR-E maps (Table 1). Therefore, image-derived SCOD tends to be delayed, SCED tends to be advanced, and SCD tends to be underestimated. This is what are presented in Figures 4, 5, 6. Method 1 is defined exactly the same as the traditional method used for in situ 13

14 observations. So the method 1 should not introduce method error. Method 2 and method W_X differ from the traditional method. So these two methods may introduce method error. First we compare the results of AMSR-E and MODISMC8_AE using method 1 (Figs. 4-6, a, b). The errors in difference should mainly come from the scaling difference (scaling error) because the same method is used. AMSR-E derived snow cover parameters have more bias than that of MODISMC8_AE, i.e., 58% vs. 23% of SCOD delay, 84% vs. 52% of SCED advance, and 73% vs. 22% of SCD underestimate. This clearly suggests that the coarse spatial resolution of AMSR-E (24.3% IU error, Table 1) caused the primary error. We then compare the results of MODISMC8 using method W_X and method 2 (Figure 4-6, c and d). Errors of MODISMC8 derived snow cover parameters against in situ observations should include both scaling error and method error. Since scaling difference between MODISMC8 and in situ observations should be the same for method W_X and method 2, the difference of Figure 4-6, c and d should be only caused by the method difference. All the results indicate that MODISMC8 derived snow cover parameters by method 2 have less bias than by method W_X, i.e., 14% vs. 50% of SCOD delay, 41% vs. 81% of SCED advance, and 17% vs. 40% of SCD underestimate. This suggests that method 2 has a better ability to detect snow cover parameters from low-cloud MODIS multi-day combined snow cover maps. Finally, the results of MODISMC8_AE by method 1 and MODISMC8 by method 2 are compared (Figure 4-6, b and d). The distributions of their errors are similar, although the errors form MODISMC8_AE (method 1) are only affected by scaling difference, while errors from MODISMC8 (method 2) are affected by both scaling difference and method difference. The percentages of no difference from MODISMC8_AE (method 1) against in situ versus MODISMC8 (method 2) against 14

15 in situ are respectively 66% vs. 72% of SCOD, 44% vs. 55% of SCED, and 61% vs.70% of SCD. This indicates that MODISMC8/method 2 combination is better than the MODISMC8_AE/method 1 combination, although the former includes both scaling and method errors while the latter has only the scaling error. The effect of scaling error from the MODISMC8_AE (almost 500m) must be larger than that of the scaling and method errors together from the MODISMC8 (500m). The combination of AMSR_E and MODIS images makes the spatial resolution decrease, specifically for those massive cloud-covered areas, although its pixel size is 500 m (Gao et al., 2010a). Correlation Analysis between Errors in Parameters Derivation and IU/IO Errors Above analyses indicate that the combination of MODISMC8 and method 2 has the best performance for deriving snow cover parameters. IU/IO errors can reflect scaling differences to some degree. In order to further examine the main source of errors in snow cover parameters derivation by method 2, we perform correlation analysis between error in deriving those parameters from the MODISMC8 (method 2) and MODISMC8 s IU and IO errors at 244 SNOTEL stations during hydrological years. Figures 7, 8, and 9 present respectively the results between errors of SCOD, SCED, SCD and IU/IO errors, during the hydrological years. Figure 7 shows that the correlation coefficient between SCOD delay error and IU error is 0.54 (145 stations), while the correlation coefficient between SCOD advance error and IO error is 0.61 (99 stations). Figure 8 shows the correlation coefficient of 0.74 between SCED delay error and IO error (19 stations) and 0.85 between SCED advance error and IU error (225 stations). Figure 9 shows the correlation coefficient of 0.83 between SCD overestimate error and IO error (65 stations) and 0.89 between SCD underestimate error and IU error (169 stations). These results demonstrate that the majority of station-pixels have 15

16 SCOD delay, SCED advance, and SCD underestimate, most of which are highly correlated with the underestimation (IU) error from the remote sensing images (MODISMC8). So the error in snow cover parameters derived from MODISMC8 with method 2 mainly stems from the scaling difference. Snow Cover Parameters Maps of Pacific Northwestern USA MODISMC8 (method 2) derived snow cover parameters maps of the Pacific Northwestern USA for hydrological years are produced and demonstrated here, because they have the best performances. Figure 10 shows the SCOD maps. The SCODs are categorized into 9 classes as indicated in the legend. It is clear that SCOD varies from area to area and from year to year. On the mountaintops and mountain ridge areas, SCODs begin in September or October. By the end of November, most of mountain and middle elevation areas are covered by snow. Lower elevation areas are not covered by snow until December or the next year. Some low elevation areas have no SCOD (white in color) because there are no 14 continuous days of snow cover. Comparing SCOD maps of the three years, it is found that (1) SCOD spatial distributions in 2006 and 2007 are very similar as compared with SCOD in 2008; (2) overall, more areas have later SCOD (November or later) in 2008 than in 2006 and 2007, while there are more areas with SCOD in 2008; and (3) The areas with no SCOD in 2006 is the largest, followed by 2007, and then, Figure 11 shows SCED maps. The SCEDs are categorized into 11 classes. The spatial patterns of SCED are closely related to the topography of the study region. Snow melts late in high elevation areas. At the low elevation areas, snow melts in January or even before January. At the middle elevation areas, snow cover ends in February to March. At the high elevation areas, snow covers end in April to May and at the mountaintop and mountain valley areas, snow covers end even later than 16

17 May 31. Comparing SCED maps of the three years, we can find that (1) SCED spatial distributions in 2006 and 2007 are very similar as compared with that in 2008; (2) most of the SCED in 2006 are later than those of 2007, while SCED in 2008 are the latest; and (3) in 2008, SCED in most mountainous areas are later than April and there are more areas with SCED after May. Figure 12 presents the SCD maps. The SCDs are grouped into 11 classes on a 30 days interval basis. It is interesting that the three SCD maps have very similar patterns even though their corresponding SCOD and SCED maps change a lot from year to year or from area to area. We compare the SCD maps with elevation distribution in Figure 3. It is clear that SCD has high correlation with elevation. SCD are less than 90 days (greenish) in the areas where elevations are lower than 1000m. In the middle m areas, most of SCD are in the range of days (yellowish). At the high elevation areas, SCD are larger than 150 days and even larger than 240 days in mountaintop areas (reddish). At a few mountainous peak areas where elevation is higher than 3000m, SCD even exceeded 270 days (deep red) especially in In addition, snow cover duration in northern mountainous areas is longer since temperature is usually lower, even though elevation in northern mountainous areas is relatively lower than that in southern mountainous areas Summary and conclusions The aim of this study is to derive accurate snow cover parameters maps from remote sensing images. Two new methods and one published method are tested to derive snow cover parameters from cloud-free AMSR-E, MODIS/AMSR-E blended (MODISMC8_AE) snow cover maps, and low-cloud MODIS flexible multi-day combined snow cover maps (MODISMC8) over the Pacific Northwestern USA. The results are evaluated against those from in situ observations at 244 SNOTEL stations for 17

18 the hydrological years. Method 1, applied to cloud-free AMSR-E and MODISMC8_AE maps, is a new method to derive snow cover parameters following the traditional definition from in situ observations. The error in snow cover parameter derivations by method 1 only stems from the scaling difference between imaged and in situ observations. Since the spatial resolution of AMSR-E (25 km) is coarser than that of MODISMC_AE (500m pixel size), the scaling difference between AMSR-E and in situ observations is larger. Thus only 21%, 20%, and 13% of AMSR-E (method 1) derived SCD, SCOD and SCED values have no difference with in situ observed. A total of 147 AMSR-E station-pixels have no image-derived SCOD or SCED, while the corresponding in situ stations have observed SCOD or SCED. The thin and wet snow conditions, which mainly occur at snow onset and snow melt periods, might also contribute to the underestimation of AMSR-E images (Pulliainen, 2006). The performances of MODISMC8_AE (method 1) in deriving snow cover parameters are much better, i.e., 61%, 66%, and 44% of MODISMC8_AE derived SCD, SCOD, and SCED with no difference from in situ observed, respectively. Method W_X, proposed by Wang and Xie (2009), can detect snow cover parameters from low-cloud snow cover products. It was tested in comparison to MODIS multi-day combined snow cover products in Northern Xinjiang, China and validated against the observations at 20 in situ stations from 2001 to Their results indicate that SCD maps have an overall agreement of 90% against in situ observed. The SCOD or SCED maps also have good agreement with in situ observed, with a mean value of one week advanced or one week delayed, respectively. However, the combination of MODISMC8 and the method W_X to derive snow cover parameters in this study show low agreement with in situ observed. The no differences between imaged and in situ 18

19 observations in SCD, SCOD and SCED are 50%, 40%, and 17%, respectively. One possible reason is that Method W_X only uses two special periods to calculate SCOD and SCED. For SCOD, it uses the period from September 1 to December 1. If some places are not covered by snow until after December 1, the SCOD in those places could not be detected. For SCED, it uses the period from February 1 to May 1 and supposes snow still exists even after February 1. These assumptions were suitable for their study area in Northern Xinjiang, China, but to our study area with complex terrain and wide elevation ranges, the snow cover and melt situations are much complex. As seen from SCOD and SCED maps of Figures 10 and 11, SCODs can be as later as after December, and SCEDs can be as early as before January. Method 2, applied to low-cloud MODISMC8 maps, is a new method setting different conditions for different periods to derive snow cover parameters. The combination of MODISMC8 and method 2 to derive snow cover parameters show the best performance. The no differences in SCD, SCOD and SCED are 70%, 72%, and 55%, respectively. The correlation analyses indicate that error in parameter derivations by MODISMC8/method 2 has high correlation with image (MODISMC8) underestimation error, which mainly stems from scaling difference between image and in situ observations. This suggests that, although there are differences between image-derived and in situ observed snow cover parameters, they do not necessary mean actual errors (or such big errors) existing in the remotely sensed parameters. Furthermore, even if some of those errors indeed exist, they should be relative constant from image to image and from year to year. Therefore, time series of those snow cover parameters should enable us to study temporal variations of those parameters due to climate change. Although its best performance in the study area, the accuracies of SCD, SCOD, and SCED from the MODISMC8/method 2 combination are still lower as compared with those obtained 19

20 419 at the Northern Xinjiang, China (Wang and Xie, 2009). Further evaluation studies of applying the 420 method 2/MODISMC8 to Northern Xinjing, China for the same dataset of the same period and other 421 areas are needed. 422 Nevertheless, the MODISMC8/method 2 derived maps of snow cover parameters in Pacific 423 Northwestern USA are produced and compared visually. It is clear that snow covers earlier, melts later 424 and lasts longer in the mountainous areas. These visual impressions are indeed correct because 425 mountaintop areas are usually colder. In addition, snow cover durations in northern mountainous areas 426 are longer because the temperature is usually lower, even if elevation in the northern mountainous 427 areas is relatively lower than that in the southern mountainous areas. It is expected that these snow 428 cover parameters will allow us to better study spatial-temporal variability of snow cover and its onset 429 and melt, and that they have great potential to be applied to operational snowmelt-runoff forecasting, 430 calibration or validation of hydrological models, and study of snow response to global climate change Acknowledgements Removed for peer-review purpose References Ault, T.W., K.P. Czajkowski, T. Benko, J. Coss, J. Struble,A. Spongberg, M. Templin, and C. 439 Gross,2006.Validation of the MODIS snow product and cloud mask using student and NWS 440 cooperative station observations in the Lower Great Lakes Region. Remote Sensing of 441 Environment, 105, Barnett, T. P., J. C. Adam, and D. P. Lettenmaier, Potential impacts of a warming climate on 20

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25 Table 1. Comparisons of the number of images, mean snow percentage (Ps), mean cloud percentage (Pc), overall accuracy in all-sky condition (Oa), image underestimation (IU) and overestimation (IO) errors against in situ observations among different snow cover maps, in the period of hydrological years; and the method used to derive snow cover parameters (after Gao et al., 2010b) Name Total images Ps(%) Pc(%) Oa(%) IU(%) IO(%) Method MOD10A MYD10A AMSR-E Method 1 MODISMC8_AE Method 1 MODISMC Method W_X/ Method 2 25

26 Snow Cover Onset Date (SCOD) Maximum snow depth Snow Cover End Date (SCED) 566 Sep.1 Snow accumulating period Late February to early March Snow melting period 567 Figure 1. The chart of general snow cover processes in one hydrological year. Time axis Aug.31 26

27 Figure 2. The relationship between snow cover parameters from image and in situ observations. (If image observed value is in point a or c, image observation advances against in situ observation. If image observed value is in point b or d, image observation delays against in situ observation.) 27

28 Figure 3. Topography of the study area and spatial distribution of 244 SNOTEL stations. The color symbols of stations represent the mean annual snow cover duration (days) in hydrological years. (Source: Gao et al. 2010b). 28

29 581 Frequency of station pixels (%) Frequency of station pixels (%) Figure 4. Histograms of error in image-derived SCOD against in situ observed SCOD at 244 SNOTEL stations during hydrological years. Each bar represents one 10-day slot. Snow cover product name, mean annual cloud coverage and method used are shown in the each plot. 29

30 Frequency of station pixels (%) Frequency of station pixels (%) Figure 5. Histograms of error in image-derived SCED against in situ observed SCED at 244 SNOTEL stations during hydrological years. Each bar represents one 10 day slot. 30

31 Figure 6. Histograms of error in image-derived Snow SCD against in situ observed SCD at 244 SNOTEL stations during hydrological years. Each bar represents one 10 day slot. 31

32 Figure 7. The correlation analysis between SCOD delay errors vs image underestimation errors, and SCOD advance errors vs image overestimation errors at 244 stations during hydrological years. 32

33 Figure 8. The correlation analysis between SCED advance errors vs image underestimation errors, and SCED delay errors vs image overestimation errors at 244 stations during hydrological years. 33

34 Figure 9. The correlation analysis between SCD underestimation errors vs image underestimation errors, and SCD overestimation errors vs image overestimation errors at 244 stations during hydrological years. 34

35 Figure10.The MODISMC8 (method 2) derived SCOD of the Pacific Northwestern USA for hydrological years 35

36 620 No SCED Befor Dec. Jan.1-30 Feb.1-15 Feb Mar.1-15 Mar Apr.1-15 Apr May.1-31 After May Figure 11. The MODISMC8 (method 2) derived SCED of the Pacific Northwestern USA for hydrological years 36

37 Figure 12. The MODISMC8 (method 2) derived SCD of the Pacific Northwestern USA for hydrological years 37