In this study, we explored the potential of the multispectral and multi-temporal IRS Advanced Wide Field Sensor (AWiFS) data for mapping of the snow cover in the northwest regions of Iran. The AWiFS snow cover maps, based on the unsupervised classification method, were compared with the estimates of snow cover area derived from the moderate resolution imaging spectroradiometer (MODIS) images based on the normalized difference snow index. Good concurrence was observed with respect to the snow area between the AWiFS features and the MODIS features; however, the snow spatial distribution of the AWiFS features differed from those of the MODIS based on the nonentity of the temporal accordance between two types of features. Also, we explored the relationships between some climatic and topographic factors with the snowpack in the northwest part of Iran. Relationships between some climatic factors with snowpack specifications were obtained, which showed significant correlation only between the components of daily temperature and snow density. The other results showed that the amounts of snowpack depth have significant correlations with the height of the stations and the height classes in 1% surface and snowpack depths showed significant differences together within the different height classes.

INTRODUCTION

Water is regarded as the most essential of the world's natural resources (Vorosmarty et al. 2010). Water is perhaps the most valuable natural asset in the Middle East (Nagler et al. 2007). The Zarinerood basin is largely fed from snow precipitation, of which nearly two-thirds occurs in the winter and may remain as snow itself for some of the year. Accordingly, modeling of the snow-covered area in the mountainous regions of Western Iran, one of the major headwaters of the Orumiyeh Lake basin, bears significant importance when forecasting snowmelt discharge, especially for energy production, flood control, irrigation and optimization of reservoir operation. The snow-covered area is a fundamental parameter in the hydrologic cycle and climatology of the earth (Tekeli et al. 2005). Characteristic high albedo reflectance of snow causes the snow surface to reflect much of the incoming solar radiation (Tekeli et al. 2005). The main reason for using a spectral band in the visible part (0.4–0.7 Am) of the reflected solar radiation spectrum is associated with the high reflectance of snow at these wavelengths, thus facilitating its detection (Tekeli et al. 2005). With the high heat capacity of snow, snow cover insulates the soil surface from the atmosphere and slows down the warming process in spring (Tekeli et al. 2005). Thus, the snow has a direct impact on micro and macro atmospheric circulation models by affecting energy absorption and thermal heating of the basin (Tekeli et al. 2005). Snow cover and soil moisture are the most important variables in the heat and water vapor exchange between earth and atmosphere (Tekeli et al. 2005). The presence of snow in a basin strongly affects moisture that is stored at the surface and is available for future runoff (Maurer et al. 2003). Therefore, monitoring of the seasonal snow cover is important for several purposes such as climatology, hydrometeorology, water use and control and hydrology, including flood forecasting (Nagler et al. 2007). Detection of the snow-covered areas from space has been used since the beginning of the 1960s. Attention to snow-covered areas was increased with the studies performed by Martinec (1975), Rango & Martinec (1979) and Tekeli et al. (2005). Maps of dry/wet snow cover in the Indian Himalayas were produced by Gupta et al. (2005) using IRS-LISS-III multispectral imagery, and partitioned dry and wet snowy areas. The attendant problem with the use of observations in the visible wavelengths is the obscuration of snow cover caused by clouds and vegetation cover (Tekeli et al. 2005). One common problem that a scientist encounters in developing nations is the lack of informative data for related study (Tekeli et al. 2005). Even if such data existed, quick, on-line and timely access to the data may be problematic. As Iran is a developing country, quick access to snow data, which is essential in the analysis of the relationships between climatic and topographic factors and snowpack, is not yet possible. Governmental organizations most often perform snow measurements of the snow courses mostly over monthly periods. In these snow courses, the snow equivalent water and snow depths are measured along a track. Furthermore, most of the courses are started mainly in December and they are abandoned by the start of March when the snow ripens and starts to melt, even though some snow may still remain in upper mountainous areas. Iran can be described as a country having an abundance of snow; however, an established continuous operational snow monitoring system is not available. There exist some individual studies such as Ghanbarpour et al. (2007) and Pourhmmat et al. (2004). Thus, the high spatial and temporal variability of snow cover and remote sensing observations are particularly useful for providing spatially detailed input data for snowmelt runoff modeling. In spite of this, satellite-based snow cover information is still strongly under-utilized, in particular for operational hydrology (Tekeli et al. 2005).

In this study, we explored the potential of the multispectral and multi-temporal AWiFS data for mapping the snow cover in the northwest part of Iran. This involved a three-fold objective, viz.: (1) to document the current status of the snow cover and respective area measurements in the northwest part of Iran using the AWiFS data acquired in 2006/2007; (2) to compare the potential of the AWiFS data in identifying the snow cover based on the unsupervised classification (clustering method) with the MODIS data, based on the normalized difference snow index (NDSI); (3) investigation of the relationships between some climatic and topographic factors and the snowpack in the Zarinerood basin. Iran is a country with great diversity, having a wide range of landforms including the major mountain ranges, deserts, rich agricultural plains, and hilly jungle regions. Thus, this study would also help in the efforts to map the mountainous regions and south central Asia, as part of the goal of global snow cover mapping. Besides, the results from the snow cover type maps derived from the analysis of these AWiFS images can be used to improve the estimates of snow and non-snow areas, and support the simulations of snowmelt runoff models that require estimates of the snow area. Also, they support instances of investigating the relationships between some climatic and topographic factors with snowpack researches such as those conducted by Bloschl et al. (1991), in which a relationship between snowpack depth and the elevation was established linearly. However, Balk & Elder (2000) arrived at the relationship non-linearly (Elder et al. 1995). Also, a positive correlation between snowpack depth and elevation was presented by Shaban et al. (2004) although Erickson et al. (2005) revealed it as an adverse correlation.

STUDY AREA

The study area is chiefly limited to the Zarinerood basin (Figure 1), which is the headwater of the Zarinerood River. The basin is located in the northwest region of Iran and southeast of Orumiyeh Lake, for which the boundaries are longitudes from 45-45V03W to 47-15V28W East and latitudes 35-30V18W to 36-45V26W North. The basin has a drainage area of 13,890 km2 with an elevation ranging from 1,300 to 3,700 m. The elevation range of the basin is visible from the digital elevation model (DEM), as depicted in Figure 2. Spatial analysis operations were performed using the Arc GIS 9.2 software, such as converting the analog topography maps to a digital format, by generating the DEM and extracting the basin boundary. Long-term studies indicate that about 60–70% of the total annual volume of water comes during the snowmelt season. In the Zarinerood basin, one large dam, the Bokan dam, was designed for flood control, hydropower generation, irrigation and water supply, and its location is depicted in Figure 1. Thus, an accurate estimation of snowmelt runoff is very important for this area of interest. The measurements of the snow course made by government organizations in the western part of Iran were also used in the analysis.
Figure 1

Location of Zarinerood basin (southeast of Orumiyeh Lake) in Iran and the location of synoptic stations and SNOTEL in the basin; the location of Bokan dam is shown in the basin.

Figure 1

Location of Zarinerood basin (southeast of Orumiyeh Lake) in Iran and the location of synoptic stations and SNOTEL in the basin; the location of Bokan dam is shown in the basin.

Figure 2

Elevation range in DEM for the Zarinerood basin.

Figure 2

Elevation range in DEM for the Zarinerood basin.

IRS AWIFS AND MODIS DATA

The Indian Remote Sensing satellite IRS_P6 (RESOURCESAT) was launched in 2003 with enhanced multispectral and spatial resolution and steerable optics. The satellite carried three sensors on board: AWiFS, Liss-3, and Liss-4, with different spatial resolutions (Kandrika & Roy 2008). The AWiFS instrument is a space borne optical sensor that was designed for the observation of snow and land surfaces (Joshi et al. 2006). The AWiFS has four spectral bands: B2 (green, 0.52–0.59 μm), B3 (red, 0.62–0.68 μm), B4 (near infrared (NIR), 0.77–0.86 μm) and B5 (short-wave infrared (SWIR), 1.55–1.7 μm). The green and red bands sensitive to snow help in improving the segmentation of snow and other land covers. With a swath of 740 km, AWiFS provides temporal resolution of 5 d at 56–70 m spatial resolution (Kandrika & Roy 2008). Unlike scanner sensors (e.g. AVHRR), the AWiFS instrument uses a linear array sensor and thus produces high quality imagery at a regional/national scale for snow mapping. Compared with AVHRR, SPOT and MODIS, which are used for snow cover monitoring, AWiFS provides better resolution with acceptable temporal resolution. In this study, a 3-month data set from 2006/07 was used. An IRS AWiFS false color composite (FCC) of northwest of Iran is shown in Figure 3. In this endeavor, the Advanced Wide Field Sensor (AWiFS) aboard Resourcesat (IRS-P6) of India, which provides data at 5-day intervals in moderate spatial resolution of 56 m and 10-bit radiometry, offers a suitable sensor configuration for regional scale mapping of snow. The IRS or similar satellite images may be a useful solution for Iran, as well as for most of the developing nations in this respect, as one of the important elements in the hydrologic, meteorological forecasting models. This is especially true relative to the IRS snow products because they are produced in high resolution and on a suitable time basis. Ongoing comparison of satellite derived snow maps and surface measurements is vital for improving snow mapping algorithms. The moderate resolution imaging spectroradiometer (MODIS) on the Terra and Aqua platforms has 36 selected, narrow spectral bands with a range of ground resolutions (Tekeli et al. 2005). Yet, a lack of ground measurements commonly results in two major limitations (Simic et al. 2004); the assessments are performed within small areas, which have available local surface measurements, and/or the assessments are based on other satellite data. An algorithm was developed to map the snow cover at 500 m spatial resolution using the MODIS observations by Hall et al. (2002). The 500 m spatial resolution results from using MODIS bands with demonstrated capability for detecting snow and separating snow from clouds (Tekeli et al. 2005). The AWiFS has the ability to fill this requirement due to its spatial and temporal resolution characteristics. The automated classification studies carried out on AWiFS data recommend it for annual snow cover mapping, which can save adequate time and money while providing equally efficient snow cover data at 1:250,000 scale unlike LISS/Landsat. However, no initiative was taken to characterize snow cover for the entire country of Iran, which represents diverse snow cover and snow cover patterns. A moderate resolution data with high temporal and radiometric dimensionality over a large area certainly calls for huge computing resources as well as robust classification procedures (Kandrika & Roy 2008). Further, the classification procedures should have the ability to handle the temporal spectral variability to capture the information on various land cover classes (Kandrika & Roy 2008).
Figure 3

An IRS-P6 AWiFS FCC from northwest of Iran in January 2007, Orumiyeh lake is visible in the image. The full color version of this figure is available in the online version of this paper, at http://dx.doi.org/10.2166/wcc.2015.009.

Figure 3

An IRS-P6 AWiFS FCC from northwest of Iran in January 2007, Orumiyeh lake is visible in the image. The full color version of this figure is available in the online version of this paper, at http://dx.doi.org/10.2166/wcc.2015.009.

IMAGE PROCESSING

The image processing was performed using various enhancement techniques including contrast stretching, band rationing, principal component analysis, spatial filtering, decorrelation stretching and finally color compositing (Jensen 1996). These processed data were then displayed as FCCs of various combinations, and the best combinations, which show maximum discrimination between snow and non-snow in distinct color tones, were selected for visual interpretation (Figure 3).

Radiometric corrections

Unwanted artifacts like additive effects due to atmospheric scattering were removed through a set of pre-processing or clean-up routines. First-order radiometric corrections were applied using a dark pixel subtraction technique (Joshi et al. 2006). This technique assumes that there is a high probability that at least a few pixels within an image should be black (0% reflectance). However, because of atmospheric scattering, the imaging system records a non-zero Digital Number (DN) value at the supposedly dark-shadowed pixel location (Joshi et al. 2006). Therefore the DN value must be subtracted from the data to remove the first-order scattering component (Joshi et al. 2006).

Geometric corrections

For importing the data sets and, thereafter, processing the data, the PCI Geomatica software Version 9.1 was used. Geometric registration of these data sets was performed using an image-to-image tie down procedure through a second order polynomial fit (Kandrika & Roy 2008). Initially, the IRS AWiFS band 4 (SWIR band of 56 m resolution) was resampled and registered; then, the first three bands (56 m resolution) were resampled and registered with band 4 subsequently. These georeferenced data sets were checked both for temporal as well as spatial geo-referencing accuracies. During georectification an overall absolute positional accuracy of 2 pixels was achieved. However, while carrying out the geo-rectification of temporal AWiFS data sets, a relative accuracy of less than 1 pixel (RMSE <0.5 pixel) has been achieved (Kandrika & Roy 2008). Also, the images were resampled by the nearest neighborhood method. The study area was extracted using digital boundary data. After geometric correction, the DNs of all the data sets were converted into at-satellite reflectance values using the scaling functions and orbit parameters provided in the image header (Kandrika & Roy 2008). These outputs were re-scaled for 10-bit output using a constant scaling function across bands (Kandrika & Roy 2008). The study area was extracted using digital boundary data.

SNOW DATA DERIVED FROM IRS AWiFS

Satellite image characteristics of snow sensors in visible, NIR and SWIR regions of the electromagnetic spectrum are useful for obtaining information on parameters of snow features (Philip & Sah 2004). Spectral reflectivity of snow is dependent on various parameters such as grain size and shape, impurity content, near-surface liquid water content, depth and surface roughness and solar elevation (Choudhary & Chang 1979; Dozier et al. 1981; Dozier 1984). The albedo is one of the properties of snow, which can be utilized while remote sensing for snow. For fresh snow, the albedo value goes as high as 80% but can decrease to 20% in the case of avalanches (Philip & Sah 2004). A dirty snow surface may register values of up to 40% of the reflected and incoming radiation. For this reason, spectral signatures of fresh snow may show up more easily on the remotely sensed data compared with the other two types, which may appear relatively darker on the image. Snow is characterized by a high reflectance in the visible region and a rather strong absorption in the SWIR region (e.g. Hall et al. 1995; Nolin & Liang 2000). In this study, visible and NIR bands were used to produce the snow cover maps using the IRS AWiFS data as well as unsupervised classification (clustering method) to separate the snowy and non-snowy areas in these images. The methodology adopted is shown in Figure 4. The different stages are explained below.
Figure 4

The methodology was adopted from this study.

Figure 4

The methodology was adopted from this study.

SNOW DATA DERIVED FROM MODIS

The approach used for obtaining the snow-covered area map from MODIS images employs the advantage of the fact that snow reflectance is high in the visible (0.5–0.7 μm) wavelengths and has low reflectance in the shortwave infrared (1–4 μm) wavelengths (Hall et al. 2001; Tekeli et al. 2005). The MODIS snow-mapping algorithm is fully automated and is based on the NDSI and a set of thresholds (Hall et al. 2002; Tekeli et al. 2005). Based on the NDSI and threshold values, snow cover pixels were separated from the non-snowy areas using Equation (1): 
formula
1
The NDSI not only takes advantage of the spectral differences of snow vis-à-vis non-snow covered area (SCA) (including clouds), but it also tends to reduce the influence of atmospheric effects and viewing geometry (Salomonson & Appel 2004; Gupta et al. 2005). The NDSI utilizes the above spectral characteristics of snow and is based on the concept of the normalized difference vegetation index used in vegetation mapping from remote sensing data (Dozier 1989; Hall et al. 1995; Gupta et al. 2005). To improve the snow mapping accuracy and to eliminate the spurious snow, a thermal mask is used. Using MODIS infrared bands 31 (10.78–11.28 μm) and 32 (11.77–12.27 μm), a split window technique (Key et al. 1997) is used to estimate ground temperature (Hall et al. 2002; Tekeli et al. 2005). If the temperature of a pixel is >283 K then the pixel will not be mapped as snow (Riggs et al. 2003; Tekeli et al. 2005). The production of a MODIS snow cover map starts with a swath (scene) at a nominal pixel resolution of 500 m and a nominal swath coverage of 2,330 km (across the track) by 2,030 km (along the track) (Riggs et al. 2003; Tekeli et al. 2005). This version is supposed to have better cloud screening (Ackerman et al. 1998; Tekeli et al. 2005) and better snow determination in forest areas (Klein et al. 1998; Tekeli et al. 2005) than earlier versions. Like other MODIS products, MOD10A1 products are being produced and are distributed by the NASA Distributed Active Archive Center located at the National Snow and Ice Data Center (Tekeli et al. 2005). The image was then reprojected on the World Geodetic System 1984 (WGS84), Universal Transverse Mercator Zone 37 with a cell size of 500 m.

STATISTICAL ANALYSIS

One of the possible solutions for the estimation of snowpack spatial distribution is in relation to the changes in the snowpack specifications due to changes in the factors that affect them. The climatic factors are more effective factors on the snowpack (Elder et al. 1995). As climate was identified as an effective factor on the snowpack, thus temperature and rainfall were selected from among the climatic factors, as they are of greater importance than the other factors. Therefore, in this study, the relationships between daily rainfall (cm) and components of daily temperature (cM) including the daily medium, maximum medium, minimum medium, absolute maximum and absolute minimum temperature (cM) were obtained from synoptic stations (Figure 1). They were explored along with snowpack specificities which included snow depth (cm), snow equivalent water (cm) and snow density (gr/cm³) for 3 months (from December, January and February) of 1996–97 until the years 2005–06 in SPSS 11.5 software. Also, the relationships between daily rainfall (cm) and components of daily temperature (cM) were explored along with the discharge of snowmelt runoff (m³/s) for 3 months (December, January and February) of 1996–97 until the years 2005–06 in SPSS 11.5 software.

This study was conducted in the Zarinerood basin due to its importance and the strategic location to direct the water of the Orumiyeh province into Orumiyeh Lake. This was the reason for selecting the Zarinerood basin for investigation. Data on snow measurements in this study are obtained from the snow courses of the Iran Water Resources Management Organization (IWRMO) for the monthly periods of December until February in 1996–97 until the years 2005–06 (Figure 1). Due to the importance of the elevation factor among the topography factors, the influence of the elevation (m) on snowpack depth (cm) was studied, therefore the correlation analysis and relationship was obtained between them from this basin, in the years 2004–05. As a primary correlation, an analysis was performed between snowpack depth (cm) and elevation (m) in the eight synoptic stations using the SPSS 11.5 software. Then the elevation of the basin was classified into six classes. To enhance the significant surface, with an increase of every 200 m of height a new class was defined. This stage provided a survey of the relationship between the snowpack depth and the elevation for different ranges of height using the SPSS 11.5 software. At this stage the primary homogeneity test of variances was carried out using the one-way analysis of variance. Providing the rejection of the equivalent assumption for variance groups in 1% of surface we used Dunnett's T3 test; otherwise the Duncan test was used for credibility. Finally, for the investigation of the different changes in snowpack depth for different heights in each class, correlation analysis was performed using the SPSS 11.5 software.

ANALYSIS OF THE RESULTS AND DISCUSSION

Optimizing the suitable bands to separate snow versus non-snow in the IRS AWiFS data

For radiometric and geometric correction, corrected IRS AWiFS images were imported to the ILWIS 3.1 software. For selecting and optimizing suitable bands to distinguish between the snow and non-snow areas, a diagram of gray degrees scattering for combined binary bands was identified. The diagram showed a state of gray degree scattering of the pixels of the images in the possible ranges (0 to 255) and presented different amounts of images variance (Figures 57). Whichever image spread more number of pixels of the gray degree in the range of 0 to 255, it enhanced the variance of images and therefore increased the volume of information on the images and contrast. However, if the number of pixels of gray degree were observed within a limited range of 0 to 255, it would therefore decrease the variance and information volume and contrast of the images. Also, the diagram traced the correlations among the four bands of the IRS AWiFS images to separate the snow versus non-snow binary bands. Consequently, three suitable bands were optimized to produce the map-listed images (Figure 8(a)8(c)). A FCC of MODIS was also produced (Figure 8(d)).
Figure 5

The diagram of gray degree scattering with combined binary bands from IRS-P6 AWiFS image dated 20/12/06.

Figure 5

The diagram of gray degree scattering with combined binary bands from IRS-P6 AWiFS image dated 20/12/06.

Figure 6

The diagram of gray degree scattering with combined binary bands from IRS-P6 AWiFS image dated 17/01/07.

Figure 6

The diagram of gray degree scattering with combined binary bands from IRS-P6 AWiFS image dated 17/01/07.

Figure 7

The diagram of gray degrees scattering with combined binary bands from IRS-P6 AWiFS image dated 01/02/07.

Figure 7

The diagram of gray degrees scattering with combined binary bands from IRS-P6 AWiFS image dated 01/02/07.

Figure 8

Map-list FCC from the IRS-P6 AWiFS, with combination of three bands including visible green and red and NIR, dated 20/12/2006 (a), dated 17/1/2007 (b), dated 1/2/2007 (c), MODIS FCC dated 25/2/2007 (d) from northwest Iran.

Figure 8

Map-list FCC from the IRS-P6 AWiFS, with combination of three bands including visible green and red and NIR, dated 20/12/2006 (a), dated 17/1/2007 (b), dated 1/2/2007 (c), MODIS FCC dated 25/2/2007 (d) from northwest Iran.

Using the sample-set to produce the map-cluster in the IRS AWiFS data

In the map-list, images were obtained under conditions necessary to cluster the pixels into the sample-set stage. In this stage, the clustered pixels were in two clusters, viz., snow and non-snow. It is necessary to note that the snowfall season in the northwest of Iran did not show cloudy cover spread. For this reason, in this study we used the sample-set to produce the map-cluster in the IRS AWiFS images that had fewer numbers of cloudy covers. The SCAs are shown in the map-cluster from the northwest of Iran (Figure 9(a)9(c)). Also, the MODIS images were classified by the NDSI under four classes: snow, earth, water and cloud (Figure 9(d)).
Figure 9

Map-cluster from the IRS-P6 AWiFS dated 20/12/2006 (a), dated 17/1/2007 (b), dated 1/2/2007 (c), classifying the MODIS by NDSI dated 25/2/2007 (d) from northwest Iran.

Figure 9

Map-cluster from the IRS-P6 AWiFS dated 20/12/2006 (a), dated 17/1/2007 (b), dated 1/2/2007 (c), classifying the MODIS by NDSI dated 25/2/2007 (d) from northwest Iran.

Snow mapping

The study area was extracted from the map-cluster. At this stage, the digital boundary of the Zarinerood basin, which was obtaining from the DEM, was overlapped on the map-cluster, then the snow cover maps were separated. The SCA was estimated from snow cover maps (Table 1). Snowy and non-snowy areas are shown in the snow cover maps from the Zarinerood basin (Figure 10(a)10(c)). Also, the snow cover map produced from the MODIS was classified into four classes: snow, earth, water and cloud (Figure 10(d)). These maps showed that melting of snow accelerated at the end of January, whereas most of the snow disappeared from the end of January by the end of February. Good concurrence was observed with respect to the snow area between the AWiFS features and the MODIS features.
Table 1

SCA from the IRS-P6 AWiFS and MODIS images in the Zarinerod basin

IRS (AWiFS) Date SCA (km²) 
20/12/2006 13,511.40 
17/1/2007 12,937.56 
1/2/2007 7,026.40 
MODIS 25/2/2007 3,424.84 
IRS (AWiFS) Date SCA (km²) 
20/12/2006 13,511.40 
17/1/2007 12,937.56 
1/2/2007 7,026.40 
MODIS 25/2/2007 3,424.84 
Figure 10

Snow cover maps from IRS-P6 AWiFS data dated 20/12/2006 (a), dated 17/1/2007 (b), dated 1/2/2007 (c), snow cover map from MODIS dated 25/2/2007 (d) from the Zarinerod basin.

Figure 10

Snow cover maps from IRS-P6 AWiFS data dated 20/12/2006 (a), dated 17/1/2007 (b), dated 1/2/2007 (c), snow cover map from MODIS dated 25/2/2007 (d) from the Zarinerod basin.

Relationships between some climatic and topographic data with snowpack specifications

The results showed that in this basin, the values of the components of daily temperature (cM) indicated a significant correlation with snowpack density in 1% of the surface, whereas no significant correlation with the other snowpack characteristics was observed (Table 2). Also, no significant correlation was noted between the quantities of daily rainfall (cm) and snowpack specificities (Table 2). Thus, between the daily temperature components and maximum medium temperature (cM) a significant correlation was seen in 1% of surfaces, as well as between the medium and absolute maximum temperature. Significant correlation with the discharge of snowmelt runoff (m³/s) in 5% surfaces was observed; however, daily rainfall (cm) and the other components of temperature did not show any significant correlation with the discharge of snowmelt runoff (Table 3). The results showed that the amount of snowpack depth has a significant correlation with the elevation of the stations (m) in 1% of surfaces, whereby with increasing elevation the snow depth increased (Figure 11).
Table 2

Correlation between some climate factors and the snowpack specifications

Daily rainfall and components of temperature Snow depth Snow equivalent water Snow density 
Rainfall (cm) –0/026 –0/152 –0/240 
Medium temperature (CM) 0/050 0/105 0/542** 
Maximum medium temperature (CM) 0/030 0/172 0/651** 
Minimum medium temperature (CM) 0/058 0/050 0/413** 
Absolute maximum temperature (CM) 0/053 0/120 0/567** 
Absolute minimum temperature (CM) 0/040 0/042 0/401** 
Daily rainfall and components of temperature Snow depth Snow equivalent water Snow density 
Rainfall (cm) –0/026 –0/152 –0/240 
Medium temperature (CM) 0/050 0/105 0/542** 
Maximum medium temperature (CM) 0/030 0/172 0/651** 
Minimum medium temperature (CM) 0/058 0/050 0/413** 
Absolute maximum temperature (CM) 0/053 0/120 0/567** 
Absolute minimum temperature (CM) 0/040 0/042 0/401** 

*Signified correlation in 5% surface.

**Signified correlation in 1% surface.

Table 3

Correlation between some climate factors and the snowmelt runoff

Daily rainfall and components of temperature Daily discharge of snowmelt runoff (m³/s) 
Rainfall (cm) 0/053 
Medium temperature (CM) 0/205* 
Maximum medium temperature (CM) 0/283** 
Minimum medium temperature (CM) 0/075 
Absolute maximum temperature (CM) 0/244* 
Absolute minimum temperature (CM) −0/041 
Daily rainfall and components of temperature Daily discharge of snowmelt runoff (m³/s) 
Rainfall (cm) 0/053 
Medium temperature (CM) 0/205* 
Maximum medium temperature (CM) 0/283** 
Minimum medium temperature (CM) 0/075 
Absolute maximum temperature (CM) 0/244* 
Absolute minimum temperature (CM) −0/041 

*Signified correlation in 5% surface.

**Signified correlation in 1% surface.

Figure 11

Correlation curve between the elevation (m) from stations and the snowpack depth (cm).

Figure 11

Correlation curve between the elevation (m) from stations and the snowpack depth (cm).

A linear relationship between snowpack depth (cm) and elevation (m) for all the stations is shown in Equation (2): 
formula
2
wherein y is the snowpack depth (cm) and X is the elevation (m) of the stations. Also, the values of snowpack depth showed significant differences within the different height classes (Figure 12). The results of the correlation analysis showed a significant correlation between snowpack depth and the different height classes in 1% of surfaces (Table 4).
Table 4

Descriptive statistics of the snowpack depth (cm) and the elevation (m) from Zarinerod basin

Variable Minimum Maximum Medium Coefficient of variation 
Snowpack depth (cm) 3/0 95/0 42/58 0/46 
Elevation (m) 1,450 2,500 2003/02 0/13 
Variable Minimum Maximum Medium Coefficient of variation 
Snowpack depth (cm) 3/0 95/0 42/58 0/46 
Elevation (m) 1,450 2,500 2003/02 0/13 
Figure 12

Correlation curve between the snowpack depth (cm) and the height classification (m).

Figure 12

Correlation curve between the snowpack depth (cm) and the height classification (m).

CONCLUSIONS

The correlation between the different bands of the IRS-P6 AWiFS from 2006 to 2007 was investigated by considering diagrams of different compositions of bands. Then the most suitable bands that could separate the snow from non-snow areas were identified from among the IRS AWiFS. Finally, the snow cover maps were obtained from the IRS AWiFS by unsupervised classification (clustering method). The diagram of the IRS AWiFS combination of binary bands obtained showed that a combination of three bands included B2 (green, 0.52–0.59 μm), B3 (red, 0.62–0.68 μm) and B4 (NIR, 0.77–0.86 μm). Among the four bands of the IRS, the AWiFS enhanced the gray degree scattering of pixels of the images in the possible range (0 to 255) and enhanced images in varying amounts. Also, the greater the spread, the more the number of pixels of gray degree (in the range of 0 to 255), and therefore the greater the volume of information and image contrast generated. However, a combination of the other bands with B5 (SWIR, 1.55–1.70 μm) showed an inverse conclusion. Therefore, using the SWIR band along with the composite bands to separate the snow versus non-snow areas is not useful. Also, the correlation between the four bands showed a high correlation between the B2–B3, B3–B4 and B2–B4 bands and a low correlation between the B2–B5, B4–B5 and B3–B5 bands of the IRS AWiFS, respectively. Therefore, a combination of the three bands including the B2 (green, 0.52–0.59 μm), B3 (red, 0.62–0.68 μm), and B4 (NIR, 0.77–0.86 μm) and the four bands of the IRS AWiFS, generates the best way to separate snow from the other phenomena in these images. The green and red bands are especially sensitive to snow, and help in improving the segmentation of the snow and other land cover types. These conclusions have been presented by Philip & Sah (2004), where they showed that the satellite image characteristics of the IRS-1C/1D data in the visible and near-infrared (NIR) regions of the electromagnetic spectrum are useful for obtaining information on the parameters of snow features. They also revealed that for snow studies, the combination of the green (0.52–0.59 mm) and red (0.62–0.68 mm) in the visible and NIR (0.77–0.86 mm) regions are found to be the most useful. Spectral reflectance curves of snow in the visible and NIR wavelength regions show that in the visible region fresh snow has very high reflectance, and as it begins to age, the reflectance slightly decreases (Zheng et al. 1984). However, in the NIR region, the reflectance of snow decreases significantly compared to that of fresh snow (O'Brien & Munis 1975). The combination of the aforementioned spectral bands of satellite images, particularly of the Indian remote sensing satellite, generates false color composites (Philip & Sah 2004). These provide the basis for the snow study. In general, the utility of the specific satellite data for snow study depends upon the defined objective of the project in question. The SCA in these maps was calculated and compared to calculate the SCA of MODIS image by NDSI in the same year.

A comparison of the SCA from the MODIS with IRS AWiFS showed a decrease in the SCA from December until last February. However, in the IRS AWiFS, the earth, water and cloud were classified as non-snow. The results confirm the unsupervised classification and clustering method for estimating the SCA of the IRS satellite images. Melting and further crystallization of the snow crystals were intensely affected by temperature changes, and thus the snow density affected the melting and further crystallization. Therefore, in the area under study, the values recorded for the components of daily temperature showed a significant correlation with the snowpack density in 1% of surfaces, whereas significant correlation was observed with the other snowpack specificities. In the Zarinerood basin, from the 3 months of study in these years, only 5 rainy days were recorded; therefore, the amount of rainfall had no significant correlation with the snowpack specificities. Also, in this study the results showing a linear and positive relationship existing between snowpack depth (cm) and elevation (m) is in accordance with the results of Bloschl et al. (1991) and Shaban et al. (2004). Lastly, because of global warming and thus increasing temperature, most of the snow melts in the winter from the Zarinerood basin. As a result, snow had disappeared from the basin by March, immediately prior to the beginning of the agricultural season.

ACKNOWLEDGEMENTS

The authors are grateful to the spatial organization and army geographic organization of Iran for providing the satellite images. Also, the authors are grateful to IWRMO and Orumiyeh Water Resources Management Organization for providing the climate and hydrometry information.

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