Field Based Spectral Reflectance Studies to Develop NDSI Method for Snow Cover Monitoring

Transcription

1 Photonirvachak Journal of the Indian Society of Remote Sensing, Vol. 30, No. 1&2, 2002 Field Based Spectral Reflectance Studies to Develop NDSI Method for Snow Cover Monitoring A V KULKARNP, J SRINIVASULU 1, S S MANJUL 2, AND P MATHUR 3 Marine and Water Resources Group, Space Applications Centre, Ambawadi Vistar (P.O.), Ahmedabad Electro-Optical Systems Group, Space Applications Centre, Ahmedabad Snow and Avalanche Study Establishment, Him Parisar, Sector-37A, Chandigarh ABSTRACT Snow is highly reflective in the visible region of the electromagnetic spectrum making it possible to easily distinguish on a satellite image. However, cloud cover and mountain shadows pose a serious problem in the identification of snow in a mountainous region. Therefore, to identify snow in such an environment, a Normalized Difference Snow Index (NDSI) has been applied. The NDSI is based on the high reflectance of snow in the visible region and its low reflectance in the SWIR region, whereas, reflectance of cloud remains high compared to snow in the SWIR region. Efforts have been made to carry out field observations on reflectance of various land features near Manali in Himachal Pradesh (HP) to develop NDSI values for identifying snow. Field data have been collected using three field radiometers, viz., Multi-band Ground Truth Radiometer (GTR) operating in the 12 spectral bands ranging from visible to near-infrared wavelengths, Near-Infrared Ground Truth Radiometer (NIGTR) operating in the S'~IR range, and Ratio-Radiometer (RR) operating in two spectral bands, one in the visible range, and another band in the SWIR range. All these three field radiometers have been designed and developed indigenously at the Space Applications Centre (ISRO), Ahmedabad. NDSt values fbr all types of snow, such as, fresh, clear, patchy and wet, have been found to be in the range 0.9 to In addition, the NDSI value for snow under mountain shadow is found to be more than 0.9. This suggests the use of NDSI method for snow cover monitoring under mountain shadow. NDSI values for other land features such as soil, vegetation, and rock were substantially different than snow. However, water bodies have NDSI values close to snow and they need to be masked during snow cover delineation using NIR band. Recd. 1 Dec., 2001; in final form 12 April., 2002

2 74 A.V. Kulkarni et al. Introduction Snow was one of the first objects to be observed by satellite. The unique signature of snow as compared to other land features is its very high reflectance in the visible region (O'Brian and Munis, 1975) and this makes it possible to easily distinguish snow from other land features. The majority of operational snow mapping projects employing satellite data have experienced difficulties with the separation of snow from cloud, the detection of snow under cloud cover and mountain shadow (Canover, 1965). Cloud cover presents a number of obstacles to operational snow cover monitoring. In the mountainous regions like the Himalayas, mountain shadow, is also a major problem in mapping of seasonal snow cover in winter season. During winter, due to lower solar elevation, many areas under mountain shadow can be misclassified as non-snow. By considering these aspects, a NDSI based technique is needed to differentiate snow from cloud and to map snow in mountain shadow. Therefore to understand the usefulness of NDSI method for identifying snow under mountain shadow, field investigation was carried out near Manali (HP). Normalized Difference Snow Index (NDSI) is estimated using visible, generally (Green nm), and shortwave infrared (SWIR nm) parts of the electromagnetic spectrum, NDSI Reflectance in Green - Reflectance in SWIR Reflectance in Green + Reflectance in SWIR using the following relationship (Hall et al., 1995):... (1) At present, the green and SWIR bands are available in IRS-1C/1D LISS-III, Landsat-TM, and EOS-MODIS sensors. Once NDSI values are calculated for all the different features present in a scene, it is possible to create a binary image, showing snow or non-snow areas by selecting a proper NDSI threshold value. However, sensor saturation over snow covered areas is common in the green band of LISS-Ill and TM sensors, due to which it may not be possible to arrive at proper NDSI values from these sensor data as the computed reflectances do not represent the true values. MODIS sensor data is bit and the green band (band-4), where snow reflectance is largest, do not saturate (Hall et al., 1995) and thus it is useful for NDSI estimation. Description of the Field Instruments Three field radiometers, which are indigenously designed and developed at Space Applications Centre (ISRO), have been used in this investigation. All the instruments were calibrated in the radiometric quantity, spectral radiance (mw-cm-z-sr~-mm-~), against a standard source traceable to NIST, USA. The details of three radiometers are: 1) Multi-band Ground Truth Radiometer (GTR) (Manjul and Pandya 1983) with twelve spectral bands in the spectral range from 400 nm to 1240 nm (Table 1). 2) Near-Infrared Ground Truth Radiometer (NIGTR) in the spectral range from 1550 to 1700 nm (Manjul 2000). 3) Ratio-Radiometer (RR), which gives simultaneous output in two spectral bands, one in the visible range with a central wavelength at 550 nm and a bandwidth of 40 nm, and another band in the SWIR range with a central wavelength at 1625 nm and a bandwidth of 155 nm (Manjul, 2001). Field Setup and Observations A field setup was designed for taking observations simultaneously from any two instruments. A suitable tripod and mounting assembly has been fabricated. The instruments were mounted on a metallic beam of length 2 m. This gives obstruction free ground coverage. The

3 Field Based Spectral Reflectance Studies To Develop Table 1: Spectral Details of Ground Truth Radiometer (GTR) (Manjul and Pandya 1983) Band Number Central Wavelength Bandwidth (nm) (nm) height of the instrument from ground was around l m, which views a circular ground patch of 25 cm diameter. The field observations were conducted at Snow and Avalanche Study Establishment (SASE) observatories, located near Manali and Solang in Himachal Pradesh. The investigations have been carried out in the month of February 2000 and Spectral radiances have been observed over various land features: soil under various conditions, river sand, vegetation, rock, water, pavement and snow. The observations were carried out both under mountain shadow and non-shadow conditions. About 10 to 50 observations were made over each class and for some classes, depending upon the homogeneity and the size of the target, the observations were taken at more than one location. The percent reflectances for various land features have been estimated by taking a standard Barium Sulphate Reflectance Panel as a reference. The NDSI values have been estimated using the computed reflectance values in the green and SWIR bands (Tables 2 and 3). Results and Discussion The reflectance of snow is very high in the green and very low in the SWIR regions (Table 2). The high reflectance of snow in the visible part of the spectrum is due to the multiple refractions of the incident light within the snowpack (Bohren and Barkstrom 1974). The absorption coefficient of ice is very high in the SWIR region resulting in a very low reflectance in this portion of the spectrum (Dozier et al., 1988). Water clouds and ice clouds, on the other hand, are more strongly reflecting than snow in the SWIR region (Bowker et al., 1985, Table 2), because water is less absorptive than ice, and the small ice crystals in cirrus clouds are more

4 76 A.V. Kulkami et al. reflecting than the larger snow grains (Dozier, 1985). NDSI values for all types of snow, such as fresh, clean, patchy and wet, and contaminated snow, were found to be in the range 0.9 to 0.96 (Table 2). Whereas, the NDSI values for clouds, computed from the available data of spectral reflectances (Bowker et al., 1985) were in the range 0.0 to 0.23 (Table 2). This large contrast between the NDSI values for snow and clouds suggests the usefulness of NDSI based method for discriminating snow and clouds on a satellite image. The large contrast between green and SWIR reflectance values observed for snow as compared to other targets (Figs. 1 and 2) suggests that there will not be any ambiguity in separating snow and non-snow features on a Table 2: Radiance, Reflectance and NDSI values for various landfeatures of a mountain region (without shadow), near Manali (HP). Reflectance values for clouds are taken from Bowker et al Target Radiance (mw-cnr2-srl-/mr9 Reflectance (%) NDSI Green SWIR Green SWIR Fresh and Clean Snow Snow with mild Clay Contamination Wet and Patchy Snow Dense Ice Clouds Middle Layer Clouds I 0.23 Dry and Loose River-bed Soil Moist and Loose River-bed Soil Saturated Soil near flowing River Water Dry Soil with Dry Grass Moist Soil with Dry Grass ' Dry River Sand Vegetation Dry River-bed Rock Flowing River Water Standing Water Dry Pavement Wet Pavement

5 Field Based Spectral Reflectance Studies To Develop Fig. 1. NDSI versus visible (green) reflectance for various land features of a mountain region, including under mountain shadow, near Manali (HP) Fig. 2. NDSI versus SWIR reflectances for various land features of a mountain region, including under mountain shadow, near Manali (HP)

6 78 A.V. Kulkarni et al. Table 3: Radiance, Reflectance and NDSI values for various land features of a mountain region (under mountain shadow), near Manali (HP) Target Radiance (mw-cm-2-srl-/zm-9 Reflectance (%) Green SWIR Green SWIR NDSI Snow Dry and Loose River-bed Soil Moist and Loose River-bed Soil Dry Soil with Dry Grass Dry River Sand Dry River-bed Rock River Water NDSI image, which is a binary image generated from computed reflectances in the green and SWIR bands. Except for snow and water, NDSI values for all other objects commonly found in the Himalayan terrain, such as soil, rock and vegetation are found to be less than zero (Table 2). The water bodies have NDSI values close to snow (Table 2) and which need to be masked during snow cover delineation using a suitable band. Water absorbs most of the radiation in the near-infrared region and therefore it can be easily delineated in the NIR band (Joseph and Navalgund, 1991) Fig. 3. In addition, all the land features observed under the mountain shadow, where only the diffuse radiation is available, show almost the same NDSI values as when there is no shadow (Tables 2 and 3). This is due to the reason that the normalization reduces the illumination differences. Thus, NDSI is very useful in discriminating between snow and non-snow areas under the mountain shadow. Snowpack characteristics, such as its grain size, liquid water content, and amount of contamination, influence the spectral reflectance characteristics of snow. The snow reflectance in the green band is not sensitive to grain size but is sensitive to minor amounts of contamination. In the infrared snow reflectance is sensitive to grain size but not to contamination (Dozier et al., 1988, Wiscombe and Warren, 1980 & Warren and Wiscombe, 1980). Thus, NDSI for snow may vary as the snowpack changes in its characteristics. Therefore, further investigations are necessary to derive NDSI values for various conditions of snow. Conclusion The present study demonstrates the usefulness of the indigenously developed field radiometers to develop NDSI based method of snow cover monitoring, especially, the Ratio Radiometer, which produces simultaneous outputs in the green and SWIR bands. NDSI values for all types of snow are more than 0.9 while for all other targets, except water, the NDSI is less than zero. Separation of water and snow on a satellite image may be difficult because of their similar NDSI values and therefore water bodies have to be masked out using NIR band during snow cover delineation.

7 Field Based Spectral Reflectance Studies To Develop O 9..im Snow <> Water Z 0 m i X Soil with Grass -- Pavement " Near Infrared Reflectance (%) Fig. 3. NDSI versus NIR reflectance for various land features of a mountain region, near Manali (HP) The results indicate the capability of NDSI based method to differentiate snow and non-snow regions under the mountain shadow, which is a very common problem in mapping of seasonal snow co~,er during the winter season. Further, it is found that there is a large contrast between the NDSI values of snow and clouds and therefore they can be easily discriminated on a NDSI image. Acknowledgements The authors thank Major General S.S. Sharma, KC, VSM, Director, Snow and Avalanche Study Establishment, Mr. A K S Gopalan, Director, Space Applications Centre, Mr. A. S. Kiran Kumar, Group Director, Electro- Optical Systems Group, and Ms. Gunbala, Head, Sensor Focal Plane Systems Division, for their keen interest in this investigation. We thank Dr. Shailesh Nayak, Group Director, Marine and Water Resources Group, for critical evaluation of the paper and numerous suggestions during the investigation. References Bohren, C.F. and Barkstrom, B.R. (1974). Theory of the optical properties of snow. J. Geophys. Res.. 79: Bowker, D.E., Davis, R.E., Myrick, D.L., Stacy, K. and Jones, W.T. (1985). Spectral reflectances of natural targets for use in remote sensing studies. NASA RP-1139, 191p. Canover, J.H. (1965). Cloud and terrestrial albedo determinations from Tiros satellite pictures. J. Applied Meteorology, 4(3): Dozier, J. (1985). Spectral signatures of snow in visible and near-ir wavelengths. Proc. 3rd International Colloquium on Spectral Signatures of Objects in Remote Sensing, Les Arcs, France, Dec., 1985, ESA SP-247, pp Dozier, J., Davis, R.E., Chang, A.T.C. and Brown. K. (1988). The spectral bi-directional reflectance of snow. Proc. 4 th International Colloquium on Spectral Signatures of Objects in Remote Sensing, Aussois, France, Jan, ESA SP-287, pp

8 80 A.V. Kulkami et al. Hall, D.K., Riggs, G.A. and Salomonson, V..V. (1995). Development of methods for mapping global snow cover using Moderate Resolution Imaging Spectroradiometer data. Remote Sensing Env., 54: Joseph George and Navalgund, R.R. (1991). Remote sensing-physical basis and its evolution. Glimpses of Science in India, National Academy of Sciences, Allahabad, India, 1991, pp Manjul, S.S. and Pandya, R.M. (1983). Multi-band Ground Truth Radiometer manual. RSA/SDD/ GS/02/12/83, Space Applications Centre, Ahmedabad. Manjul, S.S. (2000). Technical datasheet on Near Infrared Ground Truth Radiometer. SAC/EOSG/ SFSD/12/4/2000/IO, Space Applications Centre, Ahmedabad. Manjul, S.S. (2001). Design and development of Ratio Radiometer for snow and glacial studies. Technical Report: SAC/EOSG/ FSD/27/4/2001/06, Space Applications Centre, Ahmedabad. O'Brien, H.W. and Munis, R.H. (1975). Red and nearinfrared reflectance of snow, U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, CRREL Research Report 332, 18p. Warren, S.G. and Wiscombe, W.J. (1980). A model for the spectral albedo of snow, II, Snow containing atmospheric aerosols. J. Atmospheric Sciences, 37(12): Wiscombe, W.J. and Warren, S.G. (1980). A model for the spectral albedo of snow, I, Pure snow. J. Atmospheric Sciences, 37(12):