Estimation of snow surface temperature for NW Himalayan regions using passive microwave satellite data

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1 Indian Journal of Radio & Space Physics Vol 42, February 2013, pp Estimation of snow surface temperature for NW Himalayan regions using passive microwave satellite data K K Singh 1,$,*, V D Mishra 1, Dhiraj Kumar Singh 2 & A Ganju 1 1 Snow and Avalanche Study Establishment (SASE), Chandigarh , India 2 Rayat Institute of Engineering & Information Technology, S B S Nagar, Punjab , India $ kksmer@rediffmail.com Received 23 July 2012; revised 7 January 2013; accepted 14 January 2013 In this paper Special Sensor Microwave Imager (SSM/I) data has been used to estimate snow surface temperature (SST) of different ranges of North West (NW) Himalaya. The average values of emissivity of snow have been estimated for Karakoram, Great Himalaya and Pir-Panjal ranges. The emissivity values are further used to estimate SST values. It is observed that 85 GHz (H) frequency is best suited for estimation of SST. Observations from reported emissivity values for snow from non-himalayan region are found to be not well suited for SST estimation of Himalayan snow. However, root mean square error has been observed to be less in satellite derived SST values for Himalayan region (3.8, 3.9 and 4.3 K for Great Himalaya, Karakoram and Pir-Panjal, respectively) by using emissivity values estimated from ground collected SST data. A good correlation (0.83) has been observed between the satellite derived SST values and manually observed SST values for different ranges of NW Himalaya. Keywords: Brightness temperature, Snow surface temperature, Emissivity PACS Nos: ed; vw; hv 1 Introduction Snow covered North West (NW) Himalayan region has spatial and temporal variation in snow surface temperature (SST) because of its topography and climatic changes. SST is one of the important parameters responsible for the physical processes taking place between snow pack and atmosphere 1,2 and plays an important role in avalanche related studies. It is very difficult to collect manual observations of SST from vast and remote Himalayan terrain. However, in India, a few organizations [e.g. Snow and Avalanche Study Establishment (SASE)] have been monitoring snow cover area by a sparse snow and meteorological observatory network and Automatic Weather Stations (AWS) spread over North West (NW) Himalaya 3,4. It is difficult to maintain manual observatories and AWS in harsh weather conditions. Hence, it is not practical to maintain a high density network of observatories. However, satellite data based analysis techniques can be used to obtain SST from remote areas of NW Himalaya. Both optical and passive microwave satellite data can be used to estimate SST. Optical satellite data has the limitations in cloudy conditions as its lower wavelength cannot penetrate through the cloud cover. Thus, during cloudy days, optical data is not capable of providing any useful information. However, microwave satellite data can be used in all weather conditions because of its higher penetration power 5. In microwave, passive microwave satellite data is suitable for snow cover monitoring because of its daily availability. However, its coarse resolution is the limitation. A number of studies have been reported for estimation of SST using passive microwave satellite data but mostly these are for plain areas. Land surface temperature (LST) of prairie areas in the Northern Great Plains has been estimated 6 using Scanning Multichannel Microwave Radiometer (SMMR) data. A good correlation has been observed between satellite derived and ground observed land surface temperature (LST) 7 in central plains of the United States using Special Sensor Microwave Imager (SSM/I) data. The regression analysis between brightness temperature (T B ) of SSM/I and LST has been done for Saudi Arabia 8. However, it has also been observed that surface emissivity plays an important role in LST estimation 9. The characteristics of T B of snow have been analyzed using SSM/I data of Indian Himalaya region 10. Algorithms for surface temperature 11 of snow and ice-free area have been developed using horizontal and vertical polarization channels of 19 and 37 GHz frequencies. The T B data of

2 28 INDIAN J RADIO & SPACE PHYS, FEBRUARY 2013 Advanced Microwave Scanning Radiometer Earth (AMSR-E) sensor along with Moderate Resolution Imaging Spectroradiometer (MODIS) derived LST values have been used to develop algorithm for LST estimation over China 12. SSM/I T B data has been used to calculate soil wetness index 13 over the Indian subcontinent. Remote sensing data can also be used for snow cover estimation in sub pixel level 14. Snow parameters, i.e. snow surface temperature, snow water equivalence, scattering index, emissivity and snow depth have been estimated for Indian Himalaya using SSM/I satellite data 15. AMSR-E data of Antarctica has been used to develop the empirical relations between T B and ground data; and these relations were further used to estimate the air and surface temperature of the region 16. The present paper reports application of SSM/I sensor data for estimation of SST. In the present study 85 GHz frequency is used for SST estimation. The main reason for choosing this frequency for SST estimation is its lower penetration power in snow in comparison to other available frequencies. The average emissivity values of snow in different ranges of Himalaya have been estimated using T B values and SST data collected from the respective field observatories of SASE. The estimated and reported (Grody et al. 17 ) emissivity values are used for estimation of SST and the results obtained from both emissivity values are further compared. 2 Data used The satellite data of SSM/I (descending mode) along with SST data (collected from different field observatories of SASE) for the period is used in the present study. The SSM/I was launched under Defense Meteorological Satellite Program (DMSP) of United States in June SSM/I was carried abroad DMSP satellite, which is in a circular sun synchronous, near polar orbit at an altitude of 833 km with an inclination of 98.8 and an orbital period of 102 minutes, which results in 14.1 full orbit revolutions per day. With a swath width of almost 1400 km, the SSM/I provides near global coverage every day. SSM/I scans the earth surface at 19.3, 37.0 and 85.5 GHz frequencies in vertical and horizontal polarizations and at 22.2 GHz in the vertical polarization. The incidence angle at the surface is 53.3, the effective fields of view ranges from km (19 GHz) to km (85 GHz). 3 Study area The present study is concentrated in Indian Himalayan region shown in Fig. 1, which stretches from east to west for about 2500 km across 72 E to 80 E longitude and 29 N to 37 N latitude. Indian Himalaya has been categorized in three ranges 3, i.e. lower Himalaya (Pir-Panjal range), middle Himalaya (Great Himalayan range), and upper Himalaya (Karakoram range). The average altitude of Pir-Panjal range lies between 2000 and 4000 m. The mean seasonal temperature estimated from 19 years data in this range (Dhundi sector of Pir-Panjal range) varies between -1.5 o C and 2.8 o C. The average altitude of Great Himalayan range varies between 3500 and 5300 m. In Great Himalayan range (Drass sector of Great Himalayan range) generally the temperature remains lower in comparison to Pir-Panjal range. The mean seasonal temperature of this sector remains below -10 C. The snowfall in this range is also reported lower than Pir-Panjal range. Very low temperatures have been observed in Karakoram range and thus snow remains dry most of the time. Its average altitude is more than 5000 m. This range is highly glaciated and very cold during winter. The mean seasonal temperature of Karakoram range (Siachen sector) varies between o C and o C for northern glacier, -14 o C and o C for central glacier and -13 o C and o C for southern glacier Methodology The SSM/I satellite data was obtained directly from National Snow and Ice Data Center (NSIDC) website and processing of the data has been done using ArcGIS and ENVI software. The methodology adopted in the present study is shown in Fig. 2. T B values at 85 GHz (H) frequency were estimated for Fig. 1 Study area in Indian Himalaya with different mountain ranges and SASE field observatories locations

3 SINGH et al.: SNOW SURFACE TEMPERATURE FOR NW HIMALAYAN REGIONS 29 different observatory locations of SASE in NW Himalaya. In the analysis, ground collected SST data of 13 field observatories from lower Himalaya (Pir-Panjal), 9 field observatories each from middle (Great Himalaya) and upper Himalaya (Karakoram range) have been used. Dial type thermometer with a bimetal system has been used for collecting SST data. This SST data in different field observatories locations was measured at the time of satellite pass over the respective area. During SST data collection, the snow surface was made shady and the thermometer is inserted 5 to 10 cm in the snowpack. Total 3-4 readings were collected from the same area and then the average SST of the area was estimated. In crusty or very hard snow, an ice pick has been used to punch a guide hole for thermometer. These SST values were used to estimate the average value of emissivity for different Himalayan ranges. Emissivity values at 85 GHz frequency plays important role in the estimation of SST as at this frequency the emissions are mainly from the top surface of the snow pack. The emissivity of snow in different Himalayan ranges has been estimated using the equation: T = SST B Emissivity, ε (1) where, SST, is snow surface temperature. The emissivity values of snow in Pir-Panjal (wet snow), Great Himalaya (moist snow) and Karakoram range (dry snow) are compared with reported emissivity values by Grody et al. 17. Further, these average values of emissivity both from analysis and reported ones are used to estimate the snow surface temperature using the equation: Snow surface temperature, TB SST = (2) ε 5 Results and Discussion The comparison of manually measured and satellite data estimated SST values for different ranges of NW Himalaya have been shown in Fig. 3. Here, the satellite derived SST values are estimated by using emissivity values reported by Grody et al., i.e. 0.98, 0.88 and 0.78 for wet snow (Pir-Panjal), moist snow (Great Himalaya) and dry snow (Karakoram), respectively. These values of emissivities are reported at 85 GHz frequency. From analysis, a good correlation has been observed between manually observed and satellite derived SST values. A maximum (0.92) correlation coefficient is observed for Karakoram range and minimum (0.72) for Pir- Panjal range. The lower correlation in Pir-Panjal range may be because of the presence of the forest Fig. 2 Flow chart of methodology

4 30 INDIAN J RADIO & SPACE PHYS, FEBRUARY 2013 cover, as the emissions from the forest area may add up with the emission from the snow pack and because of this, it can reduce the accuracy of the results. However, due to higher altitude, most of the area of Fig. 3 Comparison of satellite data derived SST (using reported emissivity) and ground observed SST during the year 2000 for: (a) Pir-Panjal range; (b) Great Himalayan range; and (c) Karakoram range Karakoram and Great Himalayan range is devoid of forest and hence, the accuracy of the results is much higher. From analysis as summarized in Table 1, it is observed that standard deviation and the RMS error is much higher in results while using the emissivity values reported by Gordy et al. This error, observed in estimated SST values, shows that these emissivity values by Grody et al. cannot be used in Indian Himalayan conditions. The average emissivity values for different ranges of Himalaya were estimated using satellite and field observatories data for the period Total 825 measurements of snow surface temperature from Pir-Panjal and 628 measurements, each from Great Himalaya and Karakoram range were used to estimate average value of emissivity for the respective range. The average emissivity values for Pir-Panjal, Great Himalaya and Karakoram ranges at 85 GHz frequency were estimated as 0.89, 0.80 and 0.79, respectively as shown in Table 1. This variation in snow emissivity values of different Himalayan range is mainly because of different temperature and snow characteristics. The ambient temperature and snow pack temperature in Karakoram range, generally, remains low in comparison to rest two ranges and because of this, snow mostly remains dry in Karakoram. Hence, due to low temperatures and dry condition, the emissivity of this range is lowest. However, in Great Himalaya, the ambient and snow pack temperature remains slightly higher in comparison to Karakoram and the snow is mostly between dry and moist during the entire season except in late winter when the snow becomes wet. The snow emissivity value in Great Himalaya is slightly higher in comparison to Karakoram range. However, the change in emissivity between Karakoram and Great Himalayan snow is not very significant. The average altitude of Pir-Panjal range is less in comparison to Great Himalaya and Karakoram range and some of the area of Pir-Panjal range lies below tree line also. The ambient and snow pack temperature of this range are higher in comparison to Karakoram and Great Himalaya. The snow pack generally remains moist /wet in this region. Due to Table 1 Comparison between satellite derived SST [85 GHz (H)] using ground estimated and reported (Grody et al.) emissivity values for NW Himalayan ranges NW-Himalayan Emissivity R 2 Standard deviation, K RMS error in SST, K ranges Ground NC Grody analysis Ground NC Grody analysis Ground NC Grody analysis Ground NC Grody analysis Pir-Panjal range Great Himalaya Karakoram range

5 SINGH et al.: SNOW SURFACE TEMPERATURE FOR NW HIMALAYAN REGIONS 31 higher temperature of this range, the estimated snow emissivity is also significantly higher in comparison to rest two ranges (Table 1). The present results of average emissivity values of snow in different ranges of Himalaya were further used for the estimation of SST from satellite data. Figure 4 (a, b and c) shows the scatter plots between ground observed and satellite derived SST values for Pir-Panjal, Great Himalaya and Karakoram range, respectively at 85 GHz frequency. Good correlation has been observed between ground and satellite SST values in all Himalayan ranges. The linear least square fit for each range are expressed as: SST(G) Pir-Panjal = SST(S) (3) SST(G) Great Himalaya = SST(S) (4) SST(G) Karakoram = SST(S) (5) where, SST(G), is ground observed SST values; and SST(S), satellite derived SST values. Satellite derived SST values were further used in Eqs (3-5) to estimate SST(G) values. The comparison of satellite derived SST(G) and manually measured SST(G) values is shown in Fig. 5. It was observed that Fig. 4 Scatter plot between ground observed and satellite derived SST for: (a) Pir-Panjal range; (b) Great Himalayan range; and (c) Karakoram range Fig. 5 Comparison of satellite data derived SST (using ground emissivity) and ground observed SST during the year 2000 for: (a) Pir- Panjal range; (b) Great Himalayan range; and (c) Karakoram range

6 32 INDIAN J RADIO & SPACE PHYS, FEBRUARY 2013 Table 2 Comparison between satellite derived SST using different frequencies, i.e. 19, 37 and 85 GHz NW -Himalayan region Channel used Emissivity R 2 Standard Deviation, K RMS error in SST, K Pir-Panjal range Great Himalaya Karakoram range 19 H H H H H H H H H satellite derived SST(G) are much closer to the measured SST(G) in comparison to the SST value obtained by using the emissivity values of Grody et al. The satellite derived SST(G) of Great Himalayan range matches quite well with actual SST(G) data. However, it was also observed that satellite derived SST(G) values in Karakoram and Great Himalaya matched more accurately with actual SST than in Pir-Panjal range. The possible reason of less accuracy of the developed model for Pir-Panjal is its lower elevation because of which there are areas in this range which may be devoid of snow and they may have enhanced the difference between the satellite derived and ground observed SST values. The observed RMS errors in satellite derived SST for Pir-Panjal range, Great Himalayan range and Karakoram range were ~ 4.3 K, ~ 3.8 K and ~ 3.99 K (Table 1), respectively. Apart from 85 GHz (H) frequency, 19 GHz (H) and 37 GHz (H) channels were also used for estimation of SST. The results of this exercise have been shown in Table 2. From the analysis, it is observed that the RMSE in satellite derived SST is higher for 19 and 37 GHz frequencies in comparison to 85 GHz frequency. This increase in RMSE is observed in all ranges of NW Himalaya. This may be due to the fact that out of 19, 37 and 85 GHz, only 85 GHz is the true representative of the snow surface. However, the other frequencies have the contribution from deep inside the snow pack. 6 Conclusions Space-borne microwave radiometer (SSM/I) had the potential to estimate the snow surface temperature of the rough terrain of NW Himalaya. The global available emissivity values cannot be used directly to estimate SST for NW Himalayan region. However, the emissivities values derived using ground data from NW Himalayan provide better results of SST. These emissivity values can be further used for estimation of SST of NW Himalaya and will also be helpful in avalanche forecasting. The 85 GHz (H) frequency is best suited for estimation of SST by using satellite data. The proposed technique for estimation of SST is important because of the all weather working capability of the SSM/I sensor. By using this technique, SST can be retrieved throughout the year from Himalayan terrain which in winter, generally, remains under cloud cover. Acknowledgement The authors are thankful to those involved in providing technical support during preparation of the manuscript. The authors would also like to acknowledge SASE staff for collecting the ground data. Thanks are due to Dr Sahil Sood, Senior Research Fellow, SASE for dedicated help and discussion. The SSM/I data, made available by the National Snow and Ice data center, University of Colorado, Boulder, is thankfully acknowledged. References 1 Singh A K, A Mathematical model for the study of temperature profile with a snow cover, Proceedings of the SNOWSYMP-94 (Snow and Avalanche Study Establishment, Manali, India), 1994, pp Upadhayay D S, Seasonal snow cover, in Cold climate hydrometeorology (New Age Int, New Delhi), Sharma S S & Ganju A, Complexities of avalanche forecasting in Western Himalaya - an overview, Cold Reg Sci Technol (USA), 31 (2000) pp Shekhar M S, Chand H, Kumar S, Srinivasan K & Ganju A, Climate change studies in the Western Himalaya, Ann Glaciol (UK), 51 (2010) pp Armstrong R L & Brodzik M J, Recent northern hemisphere snow extent: A comparison of data derived from visible and microwave satellite data, Geophys Res Lett (USA), 28 (19) pp , doi: /2000GL Lambert V M & McFarland M J, Land surface temperature estimation over the northern Great Plains using dual polarized passive microwave data from the Nimbus 7, Paper Presented

7 SINGH et al.: SNOW SURFACE TEMPERATURE FOR NW HIMALAYAN REGIONS 33 at the 1987 Summer Meeting American Society of Agricultural Engineers (ASAE, Baltimore, USA), 1987, pp McFarland M J, Miller R L & Neal C M U, Land surface temperature derived from the SSM/I passive microwave brightness temperatures, IEEE Trans Geosci Remote Sens (USA), 28 (1990) pp Mashat A & Alamodi A, Surface temperature estimation using Special Sensor Microwave/Imager (SSM/I) data over Saudi Arabia, Meteorol, Environ Arid Land Agric Sci (Saudi Arabia), 8 (1997) pp Jones A S & Vonder Haar T H, Retrieval of microwave surface emittance over land using coincident microwave and infrared satellite measurements. J Geophys Res (USA), 102 (1997) pp Singh R P, Mishra N C, Dash P & Mohrana B K, Snow characterization using SSM/I data, Curr Sci (India), 77 (1999) pp Fily M, Royer A, Goita K & Prigent C, A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas, Remote Sens Environ (USA), 85 (2003) pp Wu S, Zhu X & Yang H, A simple method for land surface temperature retrieval from AMSR-E, Geoscience and Remote Sensing IEEE International Symposium (IGARSS) (IEEE, Boston, USA), 2008, pp Singh R P, Kumar R & Tare V, Variability of soil wetness and its relation with floods over the Indian subcontinent, Can J Remote Sens (Canada), 35 (2009) pp Mishra V D, Negi H S, Rawat A K, Chaturvedi A & Singh R P, Retrieval of sub-pixel snow cover information in the Himalayan region using medium and coarse resolution remote sensing data, Int J Remote Sens (UK), 30 (2009) pp Mishra V D, Mathur P & Singh R P, Qualitative and quantitative analysis of snow parameters using passive microwave remote sensing, J Indian Soc Remote Sens (India), 33 (2005), pp Brogioni M, Pettinato S & Montomoli F, Estimation of air and surface temperature evolution of the East Antarctic sheet by means of passive microwave remote sensing, Geoscience and Remote Sensing IEEE International Symposium (IGARSS) (IEEE, Vancouber, Canada), 2011, pp Grody N C, Microwave remote sensing: Land and ocean surface applications, Meteorology Education and Training, COMET Program, University Corporation for Atmospheric Research (UCAR), 18 Gusain H S, Chand D, Thakur N K, Singh A & Ganju A, Snow avalanche climatology of Indian Western Himalaya, Proceedings of International Symposium on Snow and Avalanches (Snow and Avalanche Study Establishment, Manali, India), 2009, pp

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