THE VALIDATION OF THE SNOW COVER MAPPING DERIVED FROM NOAA AVHRR/3 OVER TURKEY

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1 THE VALIDATION OF THE SNOW COVER MAPPING DERIVED FROM NOAA AVHRR/3 OVER TURKEY Aydın Gürol Ertürk 1, İbrahim Sönmez 1, A. Ünal Şorman 2 1 Turkish State Meteorological Service, Remote Sensing Division, Kalaba, Ankara, 2 Middle East Technical University, Dept. of Civil Engineering, Water Resources Lab., Ankara Abstract Clouds representing the similar spectral characteristics in optical satellites with snow have been the challenging point for the snow cover mapping algorithms. Since 1.6 microns is useful in separating snow and snow free land, The NOAA AVHRR/3 (Advanced Very High Resolution Radiometer) instrument containing 3A (1.6 microns) with time-share 3B (3.7 microns) channel provides promising opportunities. In the frame of EUMETSAT H-SAF (Satellite Application Facilities on Support to Operational Hydrology and Water Management) project, the developed snow cover product algorithm derived from NOAA AVHRR/3 data is introduced. The daily snow cover products are validated over Turkey by using 262 synoptic and climate stations for the time period of 15 November March The performance of the algorithm is analyzed in daily basis by using probability of detection (POD), hit rate (HR) and critical success index (CSI) statistics. Higher success rates such as, HR varying from 63.31% to 97.33% with the overall accuracy of 87.44% is observed. Key Words: NOAA, AVHRR, Snow Cover, Validation and Turkey. Introduction The determination of the spatial and temporal variation of the snow is essential for various reasons. On the one hand, the information of the snow cover is crucial since the water amount originated from snowmelt is the backbone of the hydrological models (e.g., Grayson et al., 2002; Udnaes et al., 2007; Parajaka et al., 2007). On the other hand, snow cover is one of the important components in the radiative transfer models and climate studies (Cohen, 1994). For this reason, various satellite sensors such as, MERIS(Malcher et al., 2003), MODIS(Hall et al., 2002; Parajka and Blosch 2008; Tekeli et al., 2005), SEVIRI (Ramanov et al., 2003; de Ruyter de Wildt et al., 2006) and ASTER(e.g., Logar et al., 1998), are employed to provide snow-cover product. Additional to other sensors, AVHRR sensor on board NOAA polar orbit satellite is used for the snow cover parameter (e.g., Foppa et al., 2005). After successful improvement of AVHRR/3 instrument, the optical and infrared channels became much more potential for snow detection. However, cloud obstruction (Parajka and Blosch 2008) still remains as the main limitation of such application. In this study, snow cover product generated by using the NOAA AVHRR/3 Level 1b data is validated by using the 262 synoptic and climate in-situ data over Turkey. The categorical statistics of POD(probability of detection), HR (Hit Rate) and CSI(Critical Success Index) product are performed daily basis for the time period from 15 November 2007 to 31 March The temporal variation of each statistic is introduced.

2 Snow Depth Measurements Snow depth measurements from the total number of 262 in-situ sites over Turkey of which 125 are synoptic and 137 are climate observation sites are used as the ground truth for validation. The spatial distribution of the stations is provided in Figure 1. Snow depth measurements from the synop stations are reported at 6:00 UTC. On the other hand, the same parameter is reported by the climate stations at 7:00 am local time which corresponds to time period varying from 04:00 UTC to 06 UTC for the study domain. All these sites have been operated by Turkish State Meteorological Service (TSMS). The amount of the non-zero snow depth amount reported at the time mentioned above is assumed to be remained during the day. Even the snow depth amount is subject to vary during the day; the presence of the snow on the ground is the main concern for this study. To avoid any snow depth amount reaching null depth during the daytime, 1 cm is chosen to be the threshold and any snow depth observations less than this amount are excluded from the analysis. Figure 1. In-situ site distribution used for the validation. Snow Cover Product Separation of the purely snow-covered and snow-free land is comparatively easy since the spectral characteristics of them are significantly different. On the other hand, pixels containing mixture of snow, forest, rock, etc. lead to inaccurate classifications (Daly et al., 2000). These errors are mainly originated because of the assumption of homogenous pixel content which indeed is generally not the case. To clarify the problem, various studies conducted for better snow cover detection at sub-pixel scale (Painter et al., 1998; Metsamaki et al., 2002; Vikhamar and Solberg, 2002). Due to the similar spectral characteristics, the other challenging classification occurs between snow and clouds. Various snow and cloud extraction methodologies for the AVHRR data is introduced in the literature (e.g., Saunders and Kriebel, 1988; Gesell,; Derrien et al., 1993;).

3 For the snow and ice pixels, the reflectance of channel 3A is relatively lower than Channel 1 reflectance. This difference is significantly demonstrated in the ratio of Channel 3A and Channel 1. Tuning the NDSI proposed by Hall et al. (2002), the snow cover classification using NOAA AVHRR/3 data is performed on the variation of this ratio described by Ramanov et al. (2003) as follows; Channel_1 > 0.15 AND Channel_3A / Channel_1 < 0.4 AND Channel_4< 288 K In addition, the cloud coverage classification is determined using the threshold method proposed by Derrein and Gléau, (2003) including the support of Channel 4 brightness temperatures. 1) Channel_4 < 253 K OR 2) Channel_3A / Channel_1 > 0.4 AND Channel_3A / Channel_1 < 1.6 AND Channel_1 > 0.2 AND Channel_3A > 0.25 By using the classification criteria mentioned above, a sample snow cover product map for the date 22 Feb 2008 is illustrated in Figure 2. Figure 2. A sample snow cover map for the date 22 Feb The white, green aqua, gray and blue corresponds snow, land, cloud, no data and water respectively. The SCA products have been producing regularly since November 2007 at TSMS, Remote Sensing Division product center. Validation Methodology The snow cover product generated by using the NOAA 17 Level 1b data is validated by using the snow depth observations from 262 in-situ sites. Daily-generated

4 NOAA snow product and in-situ observations are compared for the period from 15 November 2007 to 31 March 2008 (101 observations). For this period, some days are excluded from the analysis due to NOAA data reception problem or having not any insitu observations exceeding 1 cm of snow depth. Categorical statistics of POD, HR and CSI are performed to test the validity of the NOAA snow cover product. For each comparison day, in-situ observations and the snow cover product for the corresponding NOAA pixels are used to determine the total number of the contingency table elements (a,b,c and d) in Table 1. Table 1 Contingency table representation for the snow cover product validation. In-situ Observation Snow Presence Snow Cover Product Snow Presence a None None c d b If any in-situ observation does not exist, confirmed as unreliable or product pixel is labeled as no data/cloud, then the corresponding pair is not included in the determination of the a, b, c and d totals, At the final step, categorical statistics of the POD, HR and CSI are determined for the considered day by the following equations. POD = a / (a+b) (1) HR = (a+d) / (a+b+c+d) (2) CSI = a / (a+b+c) (3) Due to the possibility of the uncertainties originated by the latitude and longitude of the in-situ sites or the geolocation of the NOAA AVHRR pixels, 3x3 and 5x5 pixel comparison of the snow cover product with the in-situ observations is also performed as part of the validation procedure. In 3X3 and 5x5 cases, the pixel containing the in-situ site is located in the middle. For the 3X3 case, the product is considered as snow if any of the 3X3 pixels (9 in total) indicates snow. The product is considered as snow free if all of the 3X3 pixels indicate no-snow. The same procedure is followed for the 5X5 case as well. Finally, the POD, HR and CSI statistics are performed as described above. Results Totally 101 daily snow cover products is validated in this study for the time period from 15 November 2007 to 31 March Among these, contingency table elements for the date 31 Jan, 21 Feb and 28 Feb are provided in Table 2 for the 5X5 grid configuration as an example. For the Jan 31 th, 134 of the in-situ sites reported non-zero, greater than 1cm, snow cover amount of which 121 of them is successfully detected by the NOAA snow cover product with the percentage POD value of In the same way,

5 more than 90% POD values are obtained for the other two dates. Also, more than 85% of HR and more than 80% of CSI amounts are achieved for the mentioned dates (Table 2). Table 2: Categorical statistics for the sample 3 dates for 5X5 grid configuration. Year Month Day a b c d POD% HR% CSI% The performance effect of the, 1X1, 3X3 and 5X5 pixel configurations for the categorical statistics are also analyzed in Table 3. The overall averages of the POD, HR and CSI percentages are presented for 1X1, 3X3 and 5X5 pixel configurations considering the whole study period. Significantly increasing percentages are observed with the increasing grid configuration. Table 3: Average POD, HR and CSI percentages for the whole study period.. 1 X 1 3 X 3 5 X 5 POD % HR % CSI % Temporal variations of the categorical statistics for the considered pixel configurations are also given in Table 4 for the sub-study period of Jan 2008 March In monthly basis, highest POD statistic is obtained in February. Climatologically, January and February are the highest snowy period for Turkey and the melting period starts in March. The findings of the categorical statistics prove that the algorithm performs better during the snowy period. Table 4: Average POD, HR and CSI percentages (including 67 days of analysis). January 2008 February 2008 March X 1 3 X 3 5 X 5 1 X 1 3 X 3 5 X 5 1 X 1 3 X 3 5 X 5 POD % HR % CSI % Lastly, temporal variation of the POD, HR and CSI statistics are introduced in Figure 3, 4 and 5 with respect to 1X1, 3X3 and 5X5 pixel configurations. Relatively higher percentages are observed in the order of 5X5, 3X3 and 1X1 pixel configuration for each day. Analysis indicated that cloud obstruction was the dominant parameter affecting the performance of each configuration. Higher percentages are observed for the days with the lower cloudy pixel amounts and vice versa.

6 POD Pod X 1 3 X 3 5 X Days Figure 3. Temporal variation of the POD percentages. Hit Rate HR X 1 3 X 3 5 X Days Figure 4. Temporal variation of the HR percentages. CSI CSI X 1 3 X 3 5 X Days Figure 5 Temporal variation of the CSI percentages.

7 The higher POD, HR and CSI percentages prove the validity of the NOAA AVHRR snow cover product. In case of the lower percentages of the categorical statistics, presence of the ice clouds may be considered as the main concern. On the other hand, highest percentages are observed in the snowy days of January and February. The overall performance of NOAA snow cover products may be considered reliable according to the higher categorical statistic percentages. This result indicates that this product may be used as an input component for the hydrological models and may positively contribute to the performance of the model for estimating runoffs resulting from snowmelt. Meanwhile, comparison of another satellite derived snow cover product called IMS (Interactive Multisensor Snow and Ice Mapping System) is considered as a future work. References Cohen, J. (1994). Snow and climate. Weather, 49, Daly, S.F., R. Davis, E. Ochs and T. Pangburn, (2000) An approach to spatially distributed snow modelling of the Sacramento and San Joaquin basins, California. Hydrological Processes. 14(18), pp: Derrien, M., Farki, B., Harang, L., Legleau, H., Noyalet, A., Pochic, D. & Sairouni, A. (1993): Automatic Cloud Detection Applied to NOAA-11 / AVHRR Imagery. Remote Sensing of Environment, Vol. 46, pp Derrien, M., H. Le Gléau, (2003). SAFNWC/MSG SEVIRI Cloud Product, Proceeding of the EUMETSAT Meteorological Satellite Conference, de Ruyter de Wildt M, G Seiz & A Grün, (2006). Snow mapping using multi-temporal Meteosat-8 data. EARSeL eproceedings, 5(1): Foppa, N., A. Hauser, D. Oesch, S. Wunderle, R. Meister and A. Stoffel (2005). Validation of operational AVHRR sub-pixel snow cover maps for the European Alps. EGU General Assembly 2005, Vienna, Austria. Grayson, R. B., G. Bloschl, A. Western, and T. McMahon (2002), Advances in the use of observed spatial patterns of catchment hydrological response, Adv. Water Resour., 25, Hall, D. K., Riggs, G. A., Salomonson, V. V., DeGirolamo, N. E., Bayr, K. J., & Jin, J. M. (2002). MODIS Snow-cover products. Remote Sensing of Environment, 83, Logar, A.M.; Lloyd, D.E.; Corwin, E.M.; Penaloza, M.L.; Feind, R.E.; Berendes, T.A.; Kwo-Sen Kuo; Welch, R.M. (1998). The ASTER polar cloud mask. IEEE Transactions on Geoscience and Remote Sensing, Vol. 36:

8 Malcher, P.; Floricioiu, D.; Rott, H. (2003). Snow mapping in Alpine areas using medium resolution spectrometric sensors. Proceedings. IEEE IGRASS03 (Toulouse) 4630 pp. Vol. 4: Metsamaki, S., J. Vepsalainen, J. Pullinainen and Y. Sucksdor_. (2002). Improved linear interpolation method for the estimation of snow-covered area from optical data. Remote Sensing Environ., 82(1), Painter, T., D. Roberts, R. Green and J. Dozier. (1998). The E_ect of Grain Size on Spectral Mixture Analysis of Snow-Covered Area from Aviris Data. Remote Sensing Environ., 65(3), Parajka, J., R. Merz, and G. Bloschl (2007), Uncertainty and multiple objective calibration in regional water balance modelling - Case study in 320 Austrian catchments, Hydrol. Processes, 21, Parajka, J., G. Bloschl (2008), Spatio-temporal combination of MODIS images potential for snow cover mapping, Water Resources Research, 44, W Romanov P., Tarpley D., Gutman G. & Carroll T.R, (2003). Mapping and monitoring of the snow cover fraction over North America. Journal of Geophysical Research, 108:D16: 8619 Saunders, R.W. & Kriebel, K.T. (1988): An Improved Method for Detecting Clear Sky and Cloudy Radiances from AVHRR Data. International Journal of Remote Sensing, Vol. 9, No. 1, pp Tekeli, A. E., Z. Akyurek, A. A. Sorman, A. Sensoy, and A. U.Sorman (2005), Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey, Remote Sens. Environ., 97, Udnaes, H. Ch., E. Alfnes, and L. M. Andreassen (2007), Improving runoff modeling using satellite-derived snow cover area?, Nordic Hydrol., 38(1), Vikhamar, D. and R. Solberg. (2002). Subpixel mapping of snow cover in forests by optical remote sensing. Remote Sensing Environ., 84(1),