**2.2.3 Multisource satellite data processing method**

According to the data characteristics mentioned above, the different projections and resolutions data need to be projected in the same project that could provide a same base for the later analysis. We select the equal latitude and longitude project to provide a more effective understanding for the China mid-latitude area. A tool has been developed to processing the EASE\_Grid, Polar Stereographic Projections into the 0.05 degree latitude and longitude map. Fig. 6 shows the transform scheme from the multi-projection to the equal latitude and longitude.

When all of these data products are resampled, we analyse the onset and duration of the data from the SCA products (named: IMS and NISE) using the accumulating, the first and the end day of the snow. The monthly SWE products are used to calculate the climatological characteristics over China by the averaging method.

### **2.2.4 Onset, duration of the snow cover over China**

After all of the data mentioned above is projected into the same equal latitude and longitude grid. The IMS and NISE daily snow cover data are processed to the onset, duration and the end time map, the base-time for IMS product is 31/May, and the day of the year 183 (almost 31/May) for NISE products. The Global Monthly EASE-Grid Snow Water Equivalent

Satellite-Based Snow Cover Analysis and the

Snow Water Equivalent Retrieval Perspective over China 55

Fig. 6. The resampling processing in the transition process (multi-projection to equal latitude and longitude)

Climatology data is reprocessed to analyse the averaged monthly climatologic characteristics. The data quality control has been done to make sure that the representativeness suit for statistical analysis. The China area is defined as 150N-560N, and 670E-1360E, includes all of the Chinese land area, part of the center-Asia, Mongolia, and part of the southern Russia, where the snow often appear.

#### **a. Onset of the snow cover over China**

The onset of the snow cover is plotted using the data from IMS and NISE products for fourteen (1997~2011) and sixteen (1995~2011) at whole year respectively (Fig.7 just shows the corresponding 4 years of these two dataset). From fig.7, the snow cover over Tibet High Mountain and the Centre Asia Mountain is always influenced much by the mountain glaciers, the mostly early snow are showed in the northern part and *Tibet Plateau* area of high mountains marked the permanent snow area (the onset data value is 1). Along with the latitude which changes from south to north, the snow appearance shows its latitude dependency over land area, the high latitude experience early snow cover compared to the low latitude area. The NISE and IMS onset of the snow cover all show postpone in the first snow occurrence, while the IMS records give an explicit result.

These two products show the same regime of the onset of the snow cover but they have explicit difference when compare together (compare these two column in fig.7). The data from NISE take larger area as blank or snow-free area, such as the Yellow River area at Central Plains China. There is more snow record at the beginning of the 31/May at the south margin of the Tibet Plateau that is not suit for the rain forest area in the Northern Indian Mountains. Over the Khrebet Kropotkina area and the northern glacier rich areas the NISE products show the early records about the snow appearance. Overall, the NISE

Fig. 6. The resampling processing in the transition process (multi-projection to equal latitude

Climatology data is reprocessed to analyse the averaged monthly climatologic characteristics. The data quality control has been done to make sure that the

The onset of the snow cover is plotted using the data from IMS and NISE products for fourteen (1997~2011) and sixteen (1995~2011) at whole year respectively (Fig.7 just shows the corresponding 4 years of these two dataset). From fig.7, the snow cover over Tibet High Mountain and the Centre Asia Mountain is always influenced much by the mountain glaciers, the mostly early snow are showed in the northern part and *Tibet Plateau* area of high mountains marked the permanent snow area (the onset data value is 1). Along with the latitude which changes from south to north, the snow appearance shows its latitude dependency over land area, the high latitude experience early snow cover compared to the low latitude area. The NISE and IMS onset of the snow cover all show postpone in the first

These two products show the same regime of the onset of the snow cover but they have explicit difference when compare together (compare these two column in fig.7). The data from NISE take larger area as blank or snow-free area, such as the Yellow River area at Central Plains China. There is more snow record at the beginning of the 31/May at the south margin of the Tibet Plateau that is not suit for the rain forest area in the Northern Indian Mountains. Over the Khrebet Kropotkina area and the northern glacier rich areas the NISE products show the early records about the snow appearance. Overall, the NISE

0E, includes all of the Chinese land area, part of the center-Asia, Mongolia, and part

0N-56

0N, and

representativeness suit for statistical analysis. The China area is defined as 15

of the southern Russia, where the snow often appear.

snow occurrence, while the IMS records give an explicit result.

**a. Onset of the snow cover over China** 

and longitude)

67 0E-136

Satellite-Based Snow Cover Analysis and the

Snow Water Equivalent Retrieval Perspective over China 57

Fig. 7. The onset time of the snow appearance over China, left column is from the IMS products, and the right column is from NISE products. We just give the winter of 4 years, 1998-1999, 2002-2003, 2006-2007 and 2010-2011.

products give out a relative late onset time than that of IMS over flat area which could be attributed to the rugged mountain's influence in the microwave signal in NISE. While for the IMS products, the coverage area are larger than that from NISE (SSM/I) products, and show the early first snow occurrences. The IMS spatial distribution of the snow are possibly more accuracy than that of NISE, such as the Korea Island and the southern China where there is snowy in the January or February.

#### **b. Duration of Snow cover over China**

The duration of snow cover for a region is also a sign of climate condition. The duration of the snow is derived from the snow products, IMS and NISE. From fig.8, the duration distribution of the snow is inhomogeneous. The high land area experiences the longest time of the snow cover, such as the expected *Tibet Plateau* and the northern glacier rich area. The duration of the snow have direct relationship with the latitude (higher latitude, longer snow duration), and the southeastern China has the least time of snow cover where the climate is temperate continental climate.

The snow duration data from these two dataset are similar distributed but the NISE (i.e. SSM/I) product shows the longer time when compare the same region in the lower latitude at high land, for example, the snow over Tibet Plateau. Over the high latitude area, the timespan of the snow existence from IMS is somewhat longer than that from NISE. These aspects reveal that the satellite snow products of optical and microwave estimation are different in northern part of China and high land of Tibet Plateau, which is similar with the finding of Wang (2007).

From fig.8, the time series of the snow cover duration is increasing over the patchy snow cover areas, such as the low land of the China area, e.g. south-east of China and the Yellow river area. It seems that there is somewhat a little bit of longer and longer duration of the

Fig. 7. The onset time of the snow appearance over China, left column is from the IMS products, and the right column is from NISE products. We just give the winter of 4 years,

products give out a relative late onset time than that of IMS over flat area which could be attributed to the rugged mountain's influence in the microwave signal in NISE. While for the IMS products, the coverage area are larger than that from NISE (SSM/I) products, and show the early first snow occurrences. The IMS spatial distribution of the snow are possibly more accuracy than that of NISE, such as the Korea Island and the southern China where

The duration of snow cover for a region is also a sign of climate condition. The duration of the snow is derived from the snow products, IMS and NISE. From fig.8, the duration distribution of the snow is inhomogeneous. The high land area experiences the longest time of the snow cover, such as the expected *Tibet Plateau* and the northern glacier rich area. The duration of the snow have direct relationship with the latitude (higher latitude, longer snow duration), and the southeastern China has the least time of snow cover where the climate is

The snow duration data from these two dataset are similar distributed but the NISE (i.e. SSM/I) product shows the longer time when compare the same region in the lower latitude at high land, for example, the snow over Tibet Plateau. Over the high latitude area, the timespan of the snow existence from IMS is somewhat longer than that from NISE. These aspects reveal that the satellite snow products of optical and microwave estimation are different in northern part of China and high land of Tibet Plateau, which is similar with the finding of

From fig.8, the time series of the snow cover duration is increasing over the patchy snow cover areas, such as the low land of the China area, e.g. south-east of China and the Yellow river area. It seems that there is somewhat a little bit of longer and longer duration of the

1998-1999, 2002-2003, 2006-2007 and 2010-2011.

there is snowy in the January or February. **b. Duration of Snow cover over China** 

temperate continental climate.

Wang (2007).

Satellite-Based Snow Cover Analysis and the

Snow Water Equivalent Retrieval Perspective over China 59

Fig. 8. The duration of the snow appearance over China, left column is from the IMS products, and the right column is from NISE products

snow over Tibet Plateau area from IMS records for fourteen years, but an ambiguous trend is for the 14 or 16 years. Over the southeastern China, the snow obviously exits in every winter time, but the NISE products does not record for its empirical ancillary data in the algorithm, we could get that the data from IMS is more reliable for the situation over China than that from NISE (SSM/I).

#### **c. The monthly climatologic characteristics over China**

The monthly snow climatology map is derived from the EASE-Grid Snow Water Equivalent (SWE) for about 30 years' satellite records. From fig.9, the seasonal snow change is obvious in the most area of China. The winter and early spring time from December to the March of next year is the snowiest over the northern China. The maximum snow cover area is in January. From May to September, the snow cover became less and less except the high altitude of *Tibet Plateau* area. The minimum snow cover area is in August. While the maximum SWE and snow cover area of northern China and *Tibet Plateau* area is quite different, the SWE (mm) reach its peak in November over *Tibet Plateau*, and the northeastern China suffered its maximum SWE (mm) in February. The snow cover area in Qinghai-Xizang (Tibet) experiences the largest snow cover in January, which is consistent with Qin's result (2006), while the western area (Xinjiang province) of China reaches its maximum snow in February along with the maximum SWE (mm) which is earlier than that of Qin's (2006). The southeastern China is almost snow-free for the all year time.

The climatological characteristic of *Tibet plateau* area is different than that of the low land area of China, especially the northern part of China. The latitude dependency is obvious in the northern China. Another control factor is the altitude, especially over the Northern Mongolia when compared with the ASTER Global Digital Elevation Map (http://asterweb.jpl.nasa.gov/images/GDEM-10km-colorized.png).

Fig. 8. The duration of the snow appearance over China, left column is from the IMS

snow over Tibet Plateau area from IMS records for fourteen years, but an ambiguous trend is for the 14 or 16 years. Over the southeastern China, the snow obviously exits in every winter time, but the NISE products does not record for its empirical ancillary data in the algorithm, we could get that the data from IMS is more reliable for the situation over China

The monthly snow climatology map is derived from the EASE-Grid Snow Water Equivalent (SWE) for about 30 years' satellite records. From fig.9, the seasonal snow change is obvious in the most area of China. The winter and early spring time from December to the March of next year is the snowiest over the northern China. The maximum snow cover area is in January. From May to September, the snow cover became less and less except the high altitude of *Tibet Plateau* area. The minimum snow cover area is in August. While the maximum SWE and snow cover area of northern China and *Tibet Plateau* area is quite different, the SWE (mm) reach its peak in November over *Tibet Plateau*, and the northeastern China suffered its maximum SWE (mm) in February. The snow cover area in Qinghai-Xizang (Tibet) experiences the largest snow cover in January, which is consistent with Qin's result (2006), while the western area (Xinjiang province) of China reaches its maximum snow in February along with the maximum SWE (mm) which is earlier than that of Qin's (2006). The southeastern China is almost snow-free for the all

The climatological characteristic of *Tibet plateau* area is different than that of the low land area of China, especially the northern part of China. The latitude dependency is obvious in the northern China. Another control factor is the altitude, especially over the Northern Mongolia when compared with the ASTER Global Digital Elevation Map

(http://asterweb.jpl.nasa.gov/images/GDEM-10km-colorized.png).

products, and the right column is from NISE products

**c. The monthly climatologic characteristics over China** 

than that from NISE (SSM/I).

year time.

Satellite-Based Snow Cover Analysis and the

**a. Time-series climatological analysis** 

**the last ten years** 

area.

Snow Water Equivalent Retrieval Perspective over China 61

**2.2.5 SWE from AMSR-E/Aqua and SCA MODIS/Terra (Aqua) over Tibetan Plateau for** 

From the above analysis, the Tibet Plateau area is quite special in the seasonal snow cover not only for the SCA (Squa. km) but also for the SWE (mm). We consider the high land of *Tibet Plateau* as one whole area by filtering the atmosphere pressure that is lower than 700 hPa, which includes all of the Tibet, China, part of the Qinghai province and the Center Asia mountain areas (see Fig.10). The AMSR-E/Aqua L3 Global Snow Water Equivalent EASE-Grids and the MODIS/Aqua Snow Cover 8-Day L3 Global 0.05Deg Climate Modeling Grid (CMG) data are employed to analyse the snow time series trend over the Tibetan Plateau

Fig. 10. Definition of *Tibet Plateau* area- according to the air pressure (when < 700hpa)

The AMSR-E/Aqua provides 8 years' monthly average SWE for the study area, and the total area of the pixels covered by snow is also presented monthly. The time series analysis is in Fig.11, which give a slightly increasing trend for 8 years from 2002 (launch time) to summer, 2010. From fig.11, the average monthly SWE (mm) reach the max value in February (2002/2003, 2003/2004, 2005/2006, 2009/2010) or March (2006/2007, 2007/2008, 2008/2009), and the minimum value appear in August except the summer in 2005, which is quite similar with the section in 2.2.4 c. When we check the SCA from AMSR-E/Aqua, the SCA (Squa. km) reach its maximum extent in January except the winter of 2005/2006, the minimum extent is in July (2002, 2005, 2008) or August (2003, 2004, 2006, 2007, 2009). These tells a positive trend of SCA and SWE over the high altitude region (<700hpa) of *Tibet Plateau* area.

Fig. 9. The monthly averaged SWE (mm) of the snow appearance over China, data is from Global Monthly EASE-Grid Snow Water Equivalent Climatology for 1978-2007

Fig. 9. The monthly averaged SWE (mm) of the snow appearance over China, data is from

Global Monthly EASE-Grid Snow Water Equivalent Climatology for 1978-2007

#### **2.2.5 SWE from AMSR-E/Aqua and SCA MODIS/Terra (Aqua) over Tibetan Plateau for the last ten years**

From the above analysis, the Tibet Plateau area is quite special in the seasonal snow cover not only for the SCA (Squa. km) but also for the SWE (mm). We consider the high land of *Tibet Plateau* as one whole area by filtering the atmosphere pressure that is lower than 700 hPa, which includes all of the Tibet, China, part of the Qinghai province and the Center Asia mountain areas (see Fig.10). The AMSR-E/Aqua L3 Global Snow Water Equivalent EASE-Grids and the MODIS/Aqua Snow Cover 8-Day L3 Global 0.05Deg Climate Modeling Grid (CMG) data are employed to analyse the snow time series trend over the Tibetan Plateau area.

Fig. 10. Definition of *Tibet Plateau* area- according to the air pressure (when < 700hpa)

#### **a. Time-series climatological analysis**

The AMSR-E/Aqua provides 8 years' monthly average SWE for the study area, and the total area of the pixels covered by snow is also presented monthly. The time series analysis is in Fig.11, which give a slightly increasing trend for 8 years from 2002 (launch time) to summer, 2010. From fig.11, the average monthly SWE (mm) reach the max value in February (2002/2003, 2003/2004, 2005/2006, 2009/2010) or March (2006/2007, 2007/2008, 2008/2009), and the minimum value appear in August except the summer in 2005, which is quite similar with the section in 2.2.4 c. When we check the SCA from AMSR-E/Aqua, the SCA (Squa. km) reach its maximum extent in January except the winter of 2005/2006, the minimum extent is in July (2002, 2005, 2008) or August (2003, 2004, 2006, 2007, 2009). These tells a positive trend of SCA and SWE over the high altitude region (<700hpa) of *Tibet Plateau* area.

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Snow Water Equivalent Retrieval Perspective over China 63

Fig. 11. Time series of the averaged AMSR-E Snow Water Equivalence (SWE) (Average snow cover area, the SCA plot is not showed here).

#### **b. The monthly averaged SWE (mm) and SCA(Squa. km) from 2002.6 to 2010.7**

The time series analysis for the averaged SWE (mm) is presented in Fig.12, the fluctuation for each month in the near eight years is small but can find that the slightly trend (see Table 1). The trend analysis shows that the SWE (mm) experience a slightly increasing in this eight years from June to September, which is almost in the summer and autumn time of one year in China, while other time (winter and spring) are decreasing in the averaged SWE (mm). The SCA parameter of the study area shows the same trend as the averaged SWE (see table1 at right column). This climatological characteristic is fit for the Warming and Wetting of the *Tibet Plateau* (Bao, Q., 2010) for the increasing precipitation in the summer time, while the increasing precipitation could not influence the winter and spring snow-rich situation.

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Fig. 11. Time series of the averaged AMSR-E Snow Water Equivalence (SWE) (Average

**b. The monthly averaged SWE (mm) and SCA(Squa. km) from 2002.6 to 2010.7** 

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Satellite-Based Snow Cover Analysis and the

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0.0 5.0x10<sup>5</sup> 1.0x10<sup>6</sup> 1.5x10<sup>6</sup> 2.0x10<sup>6</sup> 2.5x10<sup>6</sup> 3.0x10<sup>6</sup> 3.5x10<sup>6</sup> 4.0x10<sup>6</sup> 4.5x10<sup>6</sup> 5.0x10<sup>6</sup>

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 Terra SCF 100% 90-100% 80-90% 70-80% 60-70% 50-60% 40-50% 30-40% 20-30% 10-20% 0-10%

 Aqua SCF 100% 90-100% 80-90% 70-80% 60-70% 50-60% 40-50% 30-40% 20-30% 10-20% 0-10%

Snow Water Equivalent Retrieval Perspective over China 65


Table 1. The trend slope for the average SWE (mm) and SCA (Squa. km) from AMSR-E/Aqua eight years records

#### **c. The time series of the monthly snow cover fraction**

For the snow cover fraction area (SFC) statistic for *Tibet Plateau* study area in Fig.13. The snow data from MODIS/Aqua (Terra) can provide the snow cover fractional distribution in different time at the same day (morning and afternoon). In Fig. 13, the time series of the SCFs are plotted for different span (0-10%, 10-20%, 20-30%, 30-40%, 40-50%, 50-60%, 60-70%, 70-80%, 80-90%, 90-100% and 100%) for two satellites (MODIS/Terra and Aqua). Compared these two figures, the SCF area from Aqua satellite is general larger than that of Aqua with the same seasonal characteristic possibly for its different overpass time (morning and afternoon) at mid-latitude area. The summer time (almost in later August or early September) has the least area for the SCF which is greater than 20%, while the winter time (especially in the February) has the maximum area. When focus on the SCF less than 20%, the situation is a different result than that more than 10%, the summer time has the greater area than that in winter time for these two satellites, due to the summer patchy snow fractional pixels influence the satellite estimation. The time series analysis trends for these different SCF's range are showed in Table.2. The changing rate indicates a positive trend for the last ten year, especially for the large SFC which almost distribute in the high altitude mountain area. The largest increasing rate is the SFC between 90% and 100% which indicate the high mountain area suffering an increasing snow cover because the full cover areas are mostly in the high elevation mountain area. Another aspect is that the changing rate for MODIS/Terra record is larger than MODIS/Aqua's, but the reason has not discovered in this study.


Table 2. The slope coefficients for different SCF range during ten year (Terra) and eight year (Aqua) running

Month Rate of Changing Averaging SWE (mm) Rate of Changing SCA (Squa. KM)

Jan. -0.00037 -7.36287 Feb. -0.00035 -17.9450 Mar. -0.00062 -17.4998 Apr. -0.00012 -29.1789 May -0.00021 -89.2610 Jun. 0.000049 26.7051 Jul. 0.00014 23.9970 Aug. 0.00012 20.2665 Sep. 0.00004 -48.7194 Oct. -0.00017 -14.1196 Nov. -0.00030 21.7514 Dec. -0.00021 -2.1661

Table 1. The trend slope for the average SWE (mm) and SCA (Squa. km) from AMSR-

For the snow cover fraction area (SFC) statistic for *Tibet Plateau* study area in Fig.13. The snow data from MODIS/Aqua (Terra) can provide the snow cover fractional distribution in different time at the same day (morning and afternoon). In Fig. 13, the time series of the SCFs are plotted for different span (0-10%, 10-20%, 20-30%, 30-40%, 40-50%, 50-60%, 60-70%, 70-80%, 80-90%, 90-100% and 100%) for two satellites (MODIS/Terra and Aqua). Compared these two figures, the SCF area from Aqua satellite is general larger than that of Aqua with the same seasonal characteristic possibly for its different overpass time (morning and afternoon) at mid-latitude area. The summer time (almost in later August or early September) has the least area for the SCF which is greater than 20%, while the winter time (especially in the February) has the maximum area. When focus on the SCF less than 20%, the situation is a different result than that more than 10%, the summer time has the greater area than that in winter time for these two satellites, due to the summer patchy snow fractional pixels influence the satellite estimation. The time series analysis trends for these different SCF's range are showed in Table.2. The changing rate indicates a positive trend for the last ten year, especially for the large SFC which almost distribute in the high altitude mountain area. The largest increasing rate is the SFC between 90% and 100% which indicate the high mountain area suffering an increasing snow cover because the full cover areas are mostly in the high elevation mountain area. Another aspect is that the changing rate for MODIS/Terra record is larger than MODIS/Aqua's, but the reason has not discovered in

0-10% 10-20% 10-20% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100% 100%

MODIS/Aqua -0.05629 1.82353 1.62721 1.77621 1.51221 1.54443 2.00536 2.14856 3.00974 7.61426 2.35225

MODIS/Terra 1.52948 1.63037 1.14803 1.2696 1.29663 1.36101 1.52654 1.59566 2.20187 8.62898 4.85512

Table 2. The slope coefficients for different SCF range during ten year (Terra) and eight year

**c. The time series of the monthly snow cover fraction** 

E/Aqua eight years records

this study.

(Aqua) running

Fig. 13. The time series analysis for different snow cover fractions derived from MODIS/terra and MODIS/Aqua

#### **2.3 Conclusions**

From what we have analyzed, the climatological characteristics show that the onset time of snow over China area are slightly postponed, while the duration is undecided by the satellite record of NISE (SSM/I) and IMS, the monthly climatology analysis reveals that the snow distribution is quite different in the altitude and latitude, the Tibet Plateau area experiences the maximum SWE in November. The northern China and lower land reach the maximum area in December and January.

Satellite-Based Snow Cover Analysis and the

series data comparison.

**3.2.1 Snow depth data** 

**3.2 Snow depth and AMSR-E brightness temperature** 

Snow Water Equivalent Retrieval Perspective over China 67

artificial neural network methodology (Yungang Cao, 2008) without more physical explanation. From the Fig.14, the distribution of the meteorological stations in the Tibet Plateau can be seen to be very sparse, especially over the main part of the Plateau. Furthermore, many of them are near areas of human activity, and provide few measurements for a long time span with very shallow snow depth values (see Fig.15 example for DanXung Station) (Che, 2004). Armstrong (2001) notes that passive microwave remote sensing tends to underestimate the snow in the fall and early winter due to the weak signal of thin snow with the 36.5GHz and 18.7GHz (Armstrong, 2001), while the situation is the opposite over Tibet Plateau. Matthew H. Savoie (2009) improved the accuracy of the snow measurement by considering the atmospheric influence to some extent; Qiu etc. (2009)

paid attention to the atmosphere influence via the experiment and model simulation.

Due to the thin snow (snow occurrence) is often seen over western China, especially over the Tibet Plateau, more comprehensive analysis is urgent with the station observation data and microwave Tbs. In this work, we consider the shallow snow situation, and try to explain the discrepancy between the in situ time series measurement of snow (snow depth, SD) and the values retrieved from passive microwave remote sensing with the traditional difference between the brightness temperature at 36.5GHz and 18.7GHz, and that from 89.0GHz-18.7GHz and 18.7GHz-10.7GHz. Then, we analyze the ability of the higher frequencies in snow parameter retrieval over the Tibet Plateau (e.g. 89.0GHz at AMSR-E) using the time

We selected the snow depth measurements at the NamCo station over 4700m in altitude, which is located beside the NamCo Lake and the Mt. Nyainqenttanglha (Fig.14, circle). The Institute of Tibetan Plateau Research, Chinese Academy of Sciences, operates a station in the

Fig. 14. The distribution of the selected meteorological station over Tibet Plateau and western China (from China Meteorological Data Sharing Server System, data used in this work) and the geographic location of the Namco station site (30046.44'N, 90059.31'E).

The products of SCA and SWE could provide a long time series data and derived snow climatological analysis, when compared the optical and microwave remote sensing products of snow, IMS SCA and NISE SCA show difference each other, the blank area in Tibet and northwestern China could not enough to provide analytical result, though these are some clues on it. The snow product of IMS seems provide more reliable results over China area, and it is recommended that a new snow algorithm from satellite is needed for the accuracy assessment.

In the traditional view, the satellite data could provide more reliable large-scale snow parameters than the local observational station, the trend from several snow products provides the same continental regime over Northern American, it looks like snow cover gets a negative response to the global warming, while, a near local look over the Tibet Plateau, the result shows that the snow cover area appears a positive trend with snow equivalent water from PSW dataset, and the situation is also same over the China West Area.

From the monthly snow water equivalent (mm) which is recorded from the AMSR-E/Aqua, two snow parameters are derived, one is the averaged SWE monthly and another is the snow cover area (squa. km). The result reveals the positive trend of the averaged SWE (mm) and snow cover area (squa. km) over the Tibet Plateau area, which is the same situation with the result of western China (Qin, 2006 and Xu, 2007). While the monthly trend for more than ten years, we can find some interest results (see b part in 2.2.5). The averaged SWE and Snow cover area experience slightly increasing trend in the summer and autumn time (June, July, August and September), while in the winter and spring time (from October to next May), these parameters shows its negative trend.

From the MODIS SCF time series analysis according to the different percentage pixels, we can find that the SCF less than 20% are quite variable with more pixels in summer time than that in the winter time, while all of the pixels that contain more snow indicate a similar positive trend, and less pixels in summer time than that in winter time. The higher of the SCF, the higher trend value for the line. The data from the MODIS/Aqua show very similar result as that of MODIS/Terra, but larger area than Terra's.

It is hoped that China mainland area whose cryosphere is a major element in the climate now undertake national programs designed to address questions of global environment change.
