**1. Introduction**

354 Remote Sensing – Applications

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#### **1.1 Snow reserves and remote sensing**

Snow reserves in mountainous basins are important and reliable water resources in Iran. Identification of their quality is necessary because of an increasing value of freshwater and utilization of water recourses. About 60 percent of surface water and 57 percent of ground water sources in Iran flows in snowy regions (Rayegani, 2005). The water produced from snowmelt process provides soil water, ground water reserves and water in lakes and rivers. Since snow cover is one of the most important sources of provided water, an accurate prediction and timing of snow runoff is necessary for the efficient management and decision- making in water supply.

The science of snow hydrology, compared to other branches of hydrology science, has a relatively shorter history due to difficulties accompanied with snow measurement. The correct analysis of snow issues needs a set of observations and statistics in snow-gauging. Currently, however, there are no regular and comprehensive snow measurement procedures in most parts of Iran. Measurements are only limited to those snowy basins recharging important dams; even these measurements are carried out in scattered points rather than an entire dam catchment area.

The measurement range of these stations is limited to 2000-3000 m asl heights. Thus, in mountainous Iran, current distribution of stations would not seem to be adequate. In such conditions, study of snow reserves and identification of snow melting trend in most basins would be accompanied with limitations. Consequently, measuring snow cover using ground methods will be difficult and costly. Remote sensing technology has many applications in various environmental and earth resources studies including ice and snow research. These applications have been increased recently as a result of unique technical advantages such as multi-temporal imagery acquired in various wavelengths, extent of spatial coverage, and improvement of computer hardwares for interpretation and extraction of information. Regarding snow research, remote sensing technology can provide

<sup>\*</sup> Corresponding Author

Predictability of Water Sources Using Snow

the above-mentioned maps.

or SNOTEL had the highest accuracy.

algorithm will be removed (Taghvakish, 2005; Adhami, 2005).

well, they will reduce the accuracy of snow map.

2. This method is a completely automatic algorithm. 3. This algorithm is applicable for all regions in the globe. 4. This method is simple, accurate and easy to understand.

Maps Extracted from the Modis Imagery in Central Alborz, Iran 357

Snow Map algorithm uses normalized subtractive index (NDSI). Lee et al. (2001) compared MODIS snow maps created with NDSI index with maps prepared by National Operational Hydrologic Remote Sensing Center (NOHRSC, prepared automatically by GOES and NOAA images) in upper region of Rio Grande reservoir. In NOHRSC, the teta algorithm is used. In this algorithm, two classified images are subtracted to identify snow surface. In teta algorithm, two separate threshold limit is introduced for each image. Lee et al. (2001) concluded that both images are affected by cloudy condition and the main error is cloud coverage. They also mentioned that maps produced from MODIS were more accurate than

Ault et al. (2006) concluded that MOD10-L2 snow surface product, MODIS sensor, in clear sky condition had the highest accuracy. They showed that the highest error was associated with those conditions that snow depth was lower than 1 cm; thus the higher was the snow depth, the higher was the accuracy. Hall et al. (2000) also showed a low accuracy of lowmass and patchy snows in New England. Klein and Barnett (2003) carried out a snow cover study using MODIS in Rio Grande reservoir during the 2000-2001 period and compared their results to the ground-measuring methods such as snowpack telemetry (SNOTEL) and NOHRSC models. They ultimately concluded that the highest error associated with maps prepared by MODIS was related to the beginning and end of snowfall period. They showed that when the surface was completely covered by snow with no mixed cloud, ground survey

It can be mentioned that MODIS sensor and NDSI index are appropriate in snow map preparation, although cloud coverage and classification are regarded as constraints (Klein and Barnett, 2003; Zhou et al., 2005). In fact, in spite of various advantages, Snow Map algorithm has some limitations due to inseparability of snow cover from cloud and similarity of cloud behaviours to snow cover. This algorithm cannot completely distinguish clouds from snow (of course, this problem is relatively removable by using Cloud Mask algorithm). Also, this algorithm cannot detect coastal terrains which are similar to snow from viewpoint of whiteness and brightness. However, temperature can act as factor to discriminate snow from these terrains using MODIS bands 31 and 32. Since Cloud Mask and thermal Mask are used before applying algorithm, some error sources in snow map

In snow-gauging using satellite imagery, the existence of cloud is problematic due to the following reasons (Riggs and Hall, 2002): first, clouds conceal Earth information; second, clouds create shades on area and change reflectance. Indeed, if clouds cannot be detected

Clouds and snow have generally similar spectral reflectional properties in range of visible and infra-red spectra, so thermal properties is not enough for distinguishing them from each other as clouds may be cooler or hotter than snow (Singh and Singh, 2001). In order to detect clouds, a procedure called Cloud Mask algorithm is being used. Akerman et al. (1998) introduced MOD35 Cloud Mask algorithm. MOD35 algorithm is based on obstruction of Earth surface affected by cloud or dust particles that identifies water body, land and atmosphere (Strabala,

continuous information layers with higher accuracy and lower cost compared to the ground survey, so it can fill the information gaps in snow hydrological statistics. However, using ground data can increase the efficiency of remotely-sensed measurement of snow-gauging. Satellites are appropriate tools for gauging snow coverage, because of high reflection of snow that creates proper contrast to most of natural surfaces (with the exception of clouds). Therefore, using satellite imagery and GIS modeling one can produce snow-cover maps, assess the changes in snow cover area with various time series, discriminate snow from other features, and model it in a catchment area. These simplify decision-making process for engineers and hydrology managers.

One of the important issues in remotely-sensed snow-gauging is the selection of sensor. Some of optical sensors that have ever used in snow-measuring include sensors mounted to satellites namely TIROS-1 (1960), ESSA\_3, NOAA (1996), LANDSAT (MSS and ETM), and MODIS (2000). Since each sensor has unique properties, a sensor with appropriate spectral, temporal and spatial resolution for snow-gauging must be selected. Since snow is a phenomenon with noticeable surface changes over time, it is necessary to select a sensor that produces proper multi-temporal series. Snow-gauging is done in vast areas, and snow surface is generally even; therefore, MODIS is an appropriate imagery for this purpose. From the view point of spectral resolution, MODIS is one of the best optical sensors for studying snow and discrimination of snow from phenomena such as cloud which has similar spectral reflectance.

One of the purposes of designing of MODIS is a global identification of various types of clouds; hence, several bands have been considered for it to identify various types of cloud cover, optical thickness, effective radius and thermal phase (King et al., 2004).

NASA (National Aeronautics and space administration) launched TERRA satellite to space on December 18th 1999, and MODIS as one of the five sensors mounted on TERRA transferred the first information to Earth on February 24th 2000. MODIS has 36 various bands in visible, infra-red and thermal parts of electromagnetic spectrum including 2 visible bands with 250 m resolution, 5 infra-red bands with 500 m resolution, and 29 thermal bands with 1000 m resolution (Hall et al., 2000).

#### **1.2 A review on remotely-sensed snow measurement**

Various methods have been used to estimate snow surface such as classification methods, threshold limit, decision-based methods, etc. One of the most applicable algorithms used to estimate snow surface is MODIS snow map algorithm. It was introduced in 1998 as a decision-based algorithm which uses group tests of threshold limit for detection of snow. This algorithm has very small volume from the calculation viewpoint and simple from the conceptual viewpoint, thus user can track how product has been created. In addition, this algorithm has an appropriate efficiency with global application (Hall et al., 1998).

Totally the properties of this algorithm include:

1. The precision of this method for various types of snow-covered surfaces for identification of snow surface is higher than other methods such as supervised classification, unsupervised classification and sub-pixel methods provided that atmospheric correction is considered (Dadashi Khaneghah, 2008).

2. This method is a completely automatic algorithm.

356 Remote Sensing – Applications

continuous information layers with higher accuracy and lower cost compared to the ground survey, so it can fill the information gaps in snow hydrological statistics. However, using ground data can increase the efficiency of remotely-sensed measurement of snow-gauging. Satellites are appropriate tools for gauging snow coverage, because of high reflection of snow that creates proper contrast to most of natural surfaces (with the exception of clouds). Therefore, using satellite imagery and GIS modeling one can produce snow-cover maps, assess the changes in snow cover area with various time series, discriminate snow from other features, and model it in a catchment area. These simplify decision-making process for

One of the important issues in remotely-sensed snow-gauging is the selection of sensor. Some of optical sensors that have ever used in snow-measuring include sensors mounted to satellites namely TIROS-1 (1960), ESSA\_3, NOAA (1996), LANDSAT (MSS and ETM), and MODIS (2000). Since each sensor has unique properties, a sensor with appropriate spectral, temporal and spatial resolution for snow-gauging must be selected. Since snow is a phenomenon with noticeable surface changes over time, it is necessary to select a sensor that produces proper multi-temporal series. Snow-gauging is done in vast areas, and snow surface is generally even; therefore, MODIS is an appropriate imagery for this purpose. From the view point of spectral resolution, MODIS is one of the best optical sensors for studying snow and discrimination of snow from phenomena such as cloud which has

One of the purposes of designing of MODIS is a global identification of various types of clouds; hence, several bands have been considered for it to identify various types of cloud

NASA (National Aeronautics and space administration) launched TERRA satellite to space on December 18th 1999, and MODIS as one of the five sensors mounted on TERRA transferred the first information to Earth on February 24th 2000. MODIS has 36 various bands in visible, infra-red and thermal parts of electromagnetic spectrum including 2 visible bands with 250 m resolution, 5 infra-red bands with 500 m resolution, and 29 thermal bands

Various methods have been used to estimate snow surface such as classification methods, threshold limit, decision-based methods, etc. One of the most applicable algorithms used to estimate snow surface is MODIS snow map algorithm. It was introduced in 1998 as a decision-based algorithm which uses group tests of threshold limit for detection of snow. This algorithm has very small volume from the calculation viewpoint and simple from the conceptual viewpoint, thus user can track how product has been created. In addition, this

1. The precision of this method for various types of snow-covered surfaces for identification of snow surface is higher than other methods such as supervised classification, unsupervised classification and sub-pixel methods provided that

algorithm has an appropriate efficiency with global application (Hall et al., 1998).

atmospheric correction is considered (Dadashi Khaneghah, 2008).

cover, optical thickness, effective radius and thermal phase (King et al., 2004).

engineers and hydrology managers.

similar spectral reflectance.

with 1000 m resolution (Hall et al., 2000).

Totally the properties of this algorithm include:

**1.2 A review on remotely-sensed snow measurement** 


Snow Map algorithm uses normalized subtractive index (NDSI). Lee et al. (2001) compared MODIS snow maps created with NDSI index with maps prepared by National Operational Hydrologic Remote Sensing Center (NOHRSC, prepared automatically by GOES and NOAA images) in upper region of Rio Grande reservoir. In NOHRSC, the teta algorithm is used. In this algorithm, two classified images are subtracted to identify snow surface. In teta algorithm, two separate threshold limit is introduced for each image. Lee et al. (2001) concluded that both images are affected by cloudy condition and the main error is cloud coverage. They also mentioned that maps produced from MODIS were more accurate than the above-mentioned maps.

Ault et al. (2006) concluded that MOD10-L2 snow surface product, MODIS sensor, in clear sky condition had the highest accuracy. They showed that the highest error was associated with those conditions that snow depth was lower than 1 cm; thus the higher was the snow depth, the higher was the accuracy. Hall et al. (2000) also showed a low accuracy of lowmass and patchy snows in New England. Klein and Barnett (2003) carried out a snow cover study using MODIS in Rio Grande reservoir during the 2000-2001 period and compared their results to the ground-measuring methods such as snowpack telemetry (SNOTEL) and NOHRSC models. They ultimately concluded that the highest error associated with maps prepared by MODIS was related to the beginning and end of snowfall period. They showed that when the surface was completely covered by snow with no mixed cloud, ground survey or SNOTEL had the highest accuracy.

It can be mentioned that MODIS sensor and NDSI index are appropriate in snow map preparation, although cloud coverage and classification are regarded as constraints (Klein and Barnett, 2003; Zhou et al., 2005). In fact, in spite of various advantages, Snow Map algorithm has some limitations due to inseparability of snow cover from cloud and similarity of cloud behaviours to snow cover. This algorithm cannot completely distinguish clouds from snow (of course, this problem is relatively removable by using Cloud Mask algorithm). Also, this algorithm cannot detect coastal terrains which are similar to snow from viewpoint of whiteness and brightness. However, temperature can act as factor to discriminate snow from these terrains using MODIS bands 31 and 32. Since Cloud Mask and thermal Mask are used before applying algorithm, some error sources in snow map algorithm will be removed (Taghvakish, 2005; Adhami, 2005).

In snow-gauging using satellite imagery, the existence of cloud is problematic due to the following reasons (Riggs and Hall, 2002): first, clouds conceal Earth information; second, clouds create shades on area and change reflectance. Indeed, if clouds cannot be detected well, they will reduce the accuracy of snow map.

Clouds and snow have generally similar spectral reflectional properties in range of visible and infra-red spectra, so thermal properties is not enough for distinguishing them from each other as clouds may be cooler or hotter than snow (Singh and Singh, 2001). In order to detect clouds, a procedure called Cloud Mask algorithm is being used. Akerman et al. (1998) introduced MOD35 Cloud Mask algorithm. MOD35 algorithm is based on obstruction of Earth surface affected by cloud or dust particles that identifies water body, land and atmosphere (Strabala,

Predictability of Water Sources Using Snow

coordinates system, PCI Geomatica software was used.

**2.2.2 Digital Elevation Model** 

**2.2.3 Ground stations data** 

Maps Extracted from the Modis Imagery in Central Alborz, Iran 359

implemented in way that atmospheric diffusion and reflection were minimum. Since data with higher wavelength are being less influenced by aerosols, suspended particles and non-

Digital Elevation Model (DEM) obtained from SRTM Shuttle was used. These data that was in format of GeoTiff had Lambert image system. For transformation of this data to UTM

Snow-gauge station data were obtained from Water Organization- Department of Surface Water. Snow-gauge stations of Central Alborz are located in five basins namely Lattian, Lar, Taleghan, Karaj and Golpayegan basins. In some cases, ground data survey time was not consistent with the time of image acquisition. For solving this problem, in those dates that no ground statistics were available, previous and next day's information were interpolated. Of course interpolation was carried out for those stations where sampling time was close to image acquisition date, and the station had snow cover during the period (February and March).

It is necessary to mention that snow depth data has been used to examine the presence or absence of snow cover. Those gauging stations located far from human interfering features (e.g. buildings) were selected and their snow depth measured. The snow surface was defined an area where surface is regular and even, with the minimum wind blowing effect to increase measurement accuracy (Pfister and Schneebli, 1999). The output spatial resolution of snow map algorithm and Cloud Mask Algorithm is one kilometre. Around each station, up to two kilometres was regarded to include 9 to 13 pixels covering snow. The most repeated pixel shows snowy or not snowy condition. Finally, snow surface obtained from Snow Map Algorithm with and without Liberal Cloud Mask was compared to ground data. Figure 1 illustrates distribution pattern of snow-gauge stations within five basins in Central Alborz.

Among sampled stations, 18 stations with above-mentioned conditions were selected.

Fig. 1. The position Of snow gauge stations in Alborz-e-Markazi

selective diffusion phenomenon, thermal bands of MOD02 imagery were used.

2003). In this process, based on land type, geographical position and available data, Cloud Mask algorithm uses 14 bands amongst 36 bands of MODIS to test 18 spectral and spatial features (Hall and Riggs, 2002). However, this procedure was modified by Hall and Riggs (2002) who presented a new version of Cloud Mask algorithm (Liberal). This algorithm can analyse the pixels located under thin and transparent clouds (Zhou et al., 2005; Ault et. al., 2006). This procedure identifies the darkness and if it faces to such darkness, it means that sun angle is higher than 85º. This algorithm is called Liberal Cloud Mask algorithm. In fact, Liberal Cloud Mask algorithm functions as subset of spectral tests of old Cloud Mask algorithm (MOD35) and uses 7 bands of MODIS and set 4 criteria (Hall and Riggs, 2002, 2004).
