**2.2.3 Ground stations data**

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

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.

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

Predictability of Water Sources Using Snow

(Hall et al., 1998).

should be done),

**2.3.2 Cloud isolation** 

**2.3.3 Heat masking** 

Imagery should be taken in day light,

1. High cloud index introduces it as cloud 2. Heat difference index consider it as cloud

2003):

Snow map algorithm includes following thresholds:

If NDSI ≥ 0.4; and MODIS band2 > 11%; and MODIS band 4 0.4.

Maps Extracted from the Modis Imagery in Central Alborz, Iran 361

NDSI index is used to recognize snow and ice and also to differentiate between cumulus and ice or snow. In fact this index represents relative differential reflectance value of visible and short wave-infrared channels emitted from snow. Pure snow has a high NDSI value but other materials such as soil, smoke, etc cause a NDSI reduction. The mentioned threshold for band 4 of MODIS is a key tool to prevent identifying pixels with low reflection, for example dark Cypress, instead of snow. Water and cloud are separable using mentioned threshold for band 2 of MODIS and finally, NDSI has the key role in investigating snow

Necessary eligibilities of pixels for applying snow map algorithm are as follows (Riggs et al.,

Pixels should have level1B reflection (geo-reference process and radiometric correction

Their approximate temperature should be less than 2830 K (applying temperature mask)

Liberal cloud masking just uses 7 out of 36 MODIS bands as well as 4 out of 18 old cloud masking algorithm criteria. Before performing snow map as one of the preprocessing steps, liberal cloud masking was applied. With regards to spectral resemblances between snow and cloud, applying mask on image is inevitable. In liberal cloud masking, a pixel will be

3. Visual bands reflection index proves the existence of cloud when reflection of band (1/625, 1/628, µm) is more than 20 percent and visual band threshold is applied. 4. NDSI ≥ 0.4 and reflection of band 6 is more than 20 percent (Riggs and Hall, 2002).

In this research, since the study area is located on terrestrial area and consequently discrimination of snow from cloud is very important and also all MODIS imagery were taken in day time, thresholds related to terrestrial region in day time were applied.

Heat masking is the final step before using snow map algorithm. This method was introduced on 3rd October 2001 and resulted in eliminating many of incorrect land cover classified as snow. In MODIS version 3, a threshold of 2770 K was used whereas in version 4 this value increased to 2830 K. Every individual pixel of band 31 with a temperature more than threshold of version 4 is not classified as snow (Kamanpoon, 2004). Heat masking is used to remove ambiguity between snow and other phenomena such as water bodies, sand

They should belong to terrestrial region or water bodies surrounded by lands,

Imagery should not be covered by cloud (applying cloud mask),

considered as cloud provided that it covers one of the following criteria:

Furthermore, water bodies were eliminated before image processing.

#### **2.3 Research method**

Figure 2 illustrates overall flowchart of this research methodology.

Fig. 2. The flowchart of this research methodology

#### **2.3.1 Snow map algorithm**

Snow map algorithm benefits from Normalized Difference Snow Index (NDSI). Because of low reflection of snow in infrared bands and high reflection in visible bands, NDSI can be useful for discrimination of snow from other phenomena. NDSI is calculated by equation below (Hall et al., 1998):

NDSI = (band4-band6) / (band4+ band6)

Snow map algorithm includes following thresholds:

If NDSI ≥ 0.4; and MODIS band2 > 11%; and MODIS band 4 0.4.

NDSI index is used to recognize snow and ice and also to differentiate between cumulus and ice or snow. In fact this index represents relative differential reflectance value of visible and short wave-infrared channels emitted from snow. Pure snow has a high NDSI value but other materials such as soil, smoke, etc cause a NDSI reduction. The mentioned threshold for band 4 of MODIS is a key tool to prevent identifying pixels with low reflection, for example dark Cypress, instead of snow. Water and cloud are separable using mentioned threshold for band 2 of MODIS and finally, NDSI has the key role in investigating snow (Hall et al., 1998).

Necessary eligibilities of pixels for applying snow map algorithm are as follows (Riggs et al., 2003):


## **2.3.2 Cloud isolation**

360 Remote Sensing – Applications

MODIS Data

MOD09 MOD02

Corrections Corrections

Snow Map

Snow Map Cloud mask Thermal mask

Figure 2 illustrates overall flowchart of this research methodology.

Atmospheric correction

Snow cover image Snow cover image

Ground data

Discussion and conclusion

Fig. 2. The flowchart of this research methodology

NDSI = (band4-band6) / (band4+ band6)

Snow map algorithm benefits from Normalized Difference Snow Index (NDSI). Because of low reflection of snow in infrared bands and high reflection in visible bands, NDSI can be useful for discrimination of snow from other phenomena. NDSI is calculated by equation

**2.3.1 Snow map algorithm** 

below (Hall et al., 1998):

**2.3 Research method** 

Liberal cloud masking just uses 7 out of 36 MODIS bands as well as 4 out of 18 old cloud masking algorithm criteria. Before performing snow map as one of the preprocessing steps, liberal cloud masking was applied. With regards to spectral resemblances between snow and cloud, applying mask on image is inevitable. In liberal cloud masking, a pixel will be considered as cloud provided that it covers one of the following criteria:


In this research, since the study area is located on terrestrial area and consequently discrimination of snow from cloud is very important and also all MODIS imagery were taken in day time, thresholds related to terrestrial region in day time were applied. Furthermore, water bodies were eliminated before image processing.

#### **2.3.3 Heat masking**

Heat masking is the final step before using snow map algorithm. This method was introduced on 3rd October 2001 and resulted in eliminating many of incorrect land cover classified as snow. In MODIS version 3, a threshold of 2770 K was used whereas in version 4 this value increased to 2830 K. Every individual pixel of band 31 with a temperature more than threshold of version 4 is not classified as snow (Kamanpoon, 2004). Heat masking is used to remove ambiguity between snow and other phenomena such as water bodies, sand

Predictability of Water Sources Using Snow

Snow map

 Accuracy %

 Accuracy %

February, 13th and 15th of March

Snow No snow

compatible with data gathered from ground-based stations.

**4. Discussion** 

Date of acquisition

Maps Extracted from the Modis Imagery in Central Alborz, Iran 363

In this part, using NDSI, topographic data and data gathered from snow measurement stations, snow map algorithm alone and together with Liberal cloud masking were separately interpreted. Ground-based snow measurement data and their corresponding points on images resulted from snow map algorithm as well as images resulted from snow map algorithm together with Liberal cloud masking in different dates are illustrated in table

2. In fact error matrix is drawn for each image and results have been surveyed.

Total Accuracy %

13/3/2006 Snow 12 2 14 77 Snow 12 0 12 100 No snow 0 4 4 100 No snow 0 6 6 100 Total 12 6 18 Total 12 6 18

100 66 88 Accuracy

15/3/2006 Snow 12 2 14 85 Snow 12 0 12 100 No snow 0 4 4 100 No snow 0 6 6 100

Total 12 6 18 Total 12 6 18

100 66 88 Accuracy

Table 2. Evaluation of accuracy of snow gauge obtained from snow map algorithm and snow map algorithm with attending the Liberal cloud mask using earth data in 6th of

Results demonstrate that in both images of 13th and 15th March in which snow map was applied, the number of points classified as snow is more than the time when applying snow map algorithm; adding cloud masking to snow map algorithm reduces this number. It means that regions which are incorrectly classified as snow by snow map algorithm can be categorized as cloud after adding cloud masking. Furthermore, no snow regions identified as snow in snow-gauging station and snow map algorithm with Liberal cloud masking are more than those no snow regions that are not classified as snow without applying Liberal cloud masking. So it can be concluded that snow map algorithm shows some regions as snow despite the fact that they are clouds. However, cloud masking can detect them and classify as cloud. Error matrix demonstrates that accuracy of snow map algorithm increases by applying cloud mask (Riggs and Hall, 2002; Ault et al., 2006; Hall and Riggs, 2007). Overall, results from snow map algorithm together with Liberal cloud masking are more

One of the factors affecting accuracy of snow detection is clouds which cover snow surface. These clouds are distinguishable by Liberal cloud masking provided that they are transparent and thin (Riggs and Hall, 2002; Ault et al., 2006). In images related to 21st February and 8th March, the observed cloud is thick and far from the Earth. False color images show that clouds are far from the Earth surface in both mentioned images so they can be detected and classified correctly by Liberal cloud masking. However, there is snow under these clouds and should be considered as snow. Data from ground-based snow

Snow map (masking)

%

%

Snow No snow

100 100 100

100 100 100

Total Accuracy %

and cloud (Zhou et al., 2005). In this part using calculated apparent temperature for band 31 and applying 2830K threshold, heat masking is performed after new cloud masking algorithm and before snow map algorithm.

### **3. Results**

Images resulted from snow map algorithm before and after applying Liberal cloud mask related to February and March are illustrated in Figure 3. Right column shows images before liberal cloud masking and middle column show them after masking. Left column shows false color images which are made by combining visual and infrared channels of MODIS according to method introduced by Miller et al. (2004). Lands without snow cover, with snow cover, low height clouds and higher clouds appear as green, white, yellow and violet tones, respectively. False color image help to recognize cloudy regions on image as well as cloud height.

Fig. 3. Snow area before applying the liberal cloud mask (right column) and after applying the Liberal cloud mask (middle column) and false color composite (left column).
