%0 Journal Article %T Detection of the Snow Cover Area Using NOAA-AVHRR in Shahcheraghi Dam Basin %J Physical Geography Research %I University of Tehran %Z 2008-630X %A Banihabib, Mohammad Ebrahim %A Jamali, Farimah Sadat %A Saghafian, Bahram %D 2013 %\ 11/22/2013 %V 45 %N 3 %P 13-29 %! Detection of the Snow Cover Area Using NOAA-AVHRR in Shahcheraghi Dam Basin %K Brightness Temperature %K NOAA-AVHRR %K remote sensing %K Shahcheraghi Reservoir %K Snow Cover Area Trend %R 10.22059/jphgr.2013.35832 %X Extended AbstractIntroductionSnow, as one of the basic factors of water supply, plays an important role in water resourcesmanagement, especially in areas with cold winters and warm summers. The data obtained fromsnow gauges as well as temperature and precipitation time series data are generally being usedto develop experimental models in order to estimate the spatial and temporal distribution ofsnow in watersheds. However, when reliable snow or other necessary climatic data records donot exist, using proper substitutes becomes essential. Hence, the snow cover area (SCA) derivedfrom satellite images can be used as a representative of the amount of snow in a basin.Moreover, Remote Sensing (RS) is a useful tool in identifying snow and calculating SCA inmountainous regions with low accessibility and deficiency of snow gauges. Accordingly, theSCA time series data can then be used as input dataset in flow forecasting by hydrologicmodels.This paper aims to study the snow cover area of Shahcheraghi Dam basin in order to collectthe necessary input data for developing dam inflow forecasting models. The basin is located inthe north of Semnan province, Iran. The area of the basin is 1373km2 and the annualprecipitation and mean temperature of the basin are 124mm and 12°c, respectively. Since there  is no active snow gauges within the basin and also there is only one weather station with reliabletemperature records in the region, NOAA satellite images have been used for defining the SCA.MethodologyIn this paper snow cover area detection in Shahcheraghi dam basin has been studied usingNOAA-AVHRR images in a 22-year period from 1986 to 2007. In order to improve theprecision of calculated monthly SCAs, an image per 10 days was processed (3 images permonth). The highest value of SCA among the three calculated values in each month is selectedas the final SCA data of the month. Since during this period of time two different sensors ofAVHRR-2 and AVHRR-3 have recorded data in different spectral bands, it is necessary to usedifferent algorithms in separating snow from other phenomena including cloud and land cover.By employing the differences between the spectral characteristics of snow compared with otherphenomena, the snow covered area can be separated. Therefore, two threshold algorithms areused to separate SCAs. These algorithms are based on grouped conditions of comparing albedoof bands 1 and 2 and brightness temperature values of thermal bands. The most significantdifference between the conditions in these methods is using the albedo of band 3A (1.6μm) inAVHRR-3.On the other hand, it is necessary to evaluate the numerical difference among the snowseparation methods as they may significantly affect the statistic parameters of the time series.Moreover, two trend detection methods are used to examine whether significant trends in thetime series exist. The hypothesis-based linear regression and non-parametric Mann-Kendallmethods are applied to the maximum annual SCA data.Results and DiscussionBased on the NOAA-AVHRR image properties, snow cover area is detected by theaforementioned threshold algorithms. The results show that the maximum amount of SCAoccurs in January. Generally the snow settlement in the basin is from December to April whilethere is no record of snow from May to September, which is due to the abrupt air temperaturerise in spring. Furthermore, the difference between the snow separation methods is analyzed bycomparing two successive images of the basin, taken by different sensors on 5th November2003. One of the images contains channel 3B which includes thermal infrared band and theother contains channel 3A that scans near infrared wavelengths. Accordingly, the SCA ofAVHRR-3 sensor which contains channels 3A has been calculated 4% more than the SCA ofAVHRR-2 which records channel 3B. Moreover, the result of applying trend detection testsshows that the SCA time series has no evident linear or monotonic trend.ConclusionThe trend analysis on the SCA dataset has demonstrated that no significant statistic trend existsin the SCA time series. Moreover, the difference between calculated values of the SCA derivedfrom two different AVHRR-2 and AVHRR-3 sensors does not affect the reliability of the SCAdataset, considering the area of the basin. Hence, as a representative of the snow in Shahcheraghi basin, it is possible to consider the calculated snow cover area data as an appropriate input for hydrologic flow forecasting models. %U https://jphgr.ut.ac.ir/article_35832_fdb196bb2844c79863e8bc05222a6775.pdf