Snow Cover Area Estimation Using Radar and Optical Satellite Information
Affiliation(s)
Department of Geography, Faculty of Humanities, National University of Comahue, Neuquén, Argentina.
Department of Geography, Faculty of Humanities, National University of Comahue, Neuquén, Argentina.
ABSTRACT
Obtaining the seasonal variation of snow cover in areas of the Argentinian Andes is important for hydrological studies and can facilitate proper planning of water resources, with regard to irrigation, supply, flood attenuation and hydroelectricity. Remote sensors that work in the visible and infrared wavelength range are operational tools for monitoring the snow in clear skies. However, microwave satellites are able to obtain data regardless of atmospheric conditions. The advantage of using radar images is that they are very useful to obtain highly accurate parameters such as snow moisture depth, density and water equivalent resulting in improved forecasting models. In this paper, we analyze an ERS-2 image of the Andes mountain range in the northern region of the Neuquén province, Patagonia, Argentina. The objective was to obtain the spatial distribution of wet and dry snow and to compare these results with data from optical sensors (LANDSAT) in order to understand the topographic variables that influence the spatial distribution of wet snow. Optical information from sensors like LANDSAT TM 5 was analyzed to obtain fractional and binary snow indexes during a passage simultaneously with radar data. Surface temperature is used to study the association between the different types of snow altitudinal ranges and surface temperature. In this paper, we selected a scene on October 8th 2005. The entire methodology was systematized in a code implemented in IDL language.
Obtaining the seasonal variation of snow cover in areas of the Argentinian Andes is important for hydrological studies and can facilitate proper planning of water resources, with regard to irrigation, supply, flood attenuation and hydroelectricity. Remote sensors that work in the visible and infrared wavelength range are operational tools for monitoring the snow in clear skies. However, microwave satellites are able to obtain data regardless of atmospheric conditions. The advantage of using radar images is that they are very useful to obtain highly accurate parameters such as snow moisture depth, density and water equivalent resulting in improved forecasting models. In this paper, we analyze an ERS-2 image of the Andes mountain range in the northern region of the Neuquén province, Patagonia, Argentina. The objective was to obtain the spatial distribution of wet and dry snow and to compare these results with data from optical sensors (LANDSAT) in order to understand the topographic variables that influence the spatial distribution of wet snow. Optical information from sensors like LANDSAT TM 5 was analyzed to obtain fractional and binary snow indexes during a passage simultaneously with radar data. Surface temperature is used to study the association between the different types of snow altitudinal ranges and surface temperature. In this paper, we selected a scene on October 8th 2005. The entire methodology was systematized in a code implemented in IDL language.
Cite this paper
Salcedo, A. and Cogliati, M. (2014) Snow Cover Area Estimation Using Radar and Optical Satellite Information. Atmospheric and Climate Sciences, 4, 514-523. doi: 10.4236/acs.2014.44047.
Salcedo, A. and Cogliati, M. (2014) Snow Cover Area Estimation Using Radar and Optical Satellite Information. Atmospheric and Climate Sciences, 4, 514-523. doi: 10.4236/acs.2014.44047.
References
[1] Gareth Rees, W. (2006) Remote Sensing of Snow And ice. Taylor & Francis, CRC Books, Boca Raton, 295p. [2] Chuvieco, E. (2006) Environmental Remote Sensing. Earth Observation from Space. Ed. Ariel Science Barcelona, 224p. [3] Shi, J. and Dozier, J. (1995) Inferring Snow Wetness Using C-Band Data from SIR-C’s Polarimetric Synthetic Aperture Radar. IEEE Transactions on Geoscience and Remote Sensing, 33, 905-914. http://dx.doi.org/10.1109/36.406676 [4] Nagler, T. and Rott, H. (2000) Retrieval of Wet Snow by Means of Multitemporal SAR Data. IEEE Transactions on Geoscience and Remote Sensing, 38, 754-765. http://dx.doi.org/10.1109/36.842004 [5] Nagler, T. and Rott, H. (2004) Snow Classification Algorithm for Envisat ASAR. Proceedings of the 2004 Envisat & ERS Symposium, Salzburg, 6-10 September 2004, ESA SP-572. [6] Pettinato, S., Poggi, P., Macelloni, G., Paloscia, S., Pampaloni, P. and Crepaz, A. (2004) Snow Cover Mapping in Alpine Areas with Envisat/SAR Images. Proceedings of SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74771N, 29 September 2009.
http://dx.doi.org/10.1117/12.830593 [7] Pettinato, S., Santi, E., Brogioni, M., Paloscia, S. and Pampaloni, P. (2009) An Operational Algorithm for Snow Cover Mapping in Hydrological Applications. IEEE International Geoscience and Remote Sensing Symposium, Cape Town, 12-17 July 2009, IV-964-IV-967.
http://dx.doi.org/10.1109/IGARSS.2009.5417539 [8] Salcedo, A.P. (2011) Estimate of Area of Snow Cover in Watersheds with High Rate of Data Fusion Using ERS-2. Master’s Thesis, Faculty of Mathematics, Physics and Astronomy and the Mario Gulich Institute for Advanced Space Studies, 103p.
http://www.famaf.unc.edu.ar/wp-content/uploads/2014/04/8-Gulich-Salcedo.pdf [9] Laur, H., Bally, P., Meadows, P., Sanchez, J., Schaettler, B., Lopinto, E. and Esteban, D. (2004) ERS SAR Calibration. Derivation of the Backscattering Coefficient in ESA ERS SAR PRI Products. Issue 2, Rev. 5f. [10] Caves, R., Turpin, O., Nagler, T. and Mille, D. (1998) The Role of Earth Observation in Snowmelt Runoff Monitoring from High Latitude Basins: SAR Aspects. IEEE International Geoscience and Remote Sensing Symposium Proceedings, 4, 1858-1860.
http://dx.doi.org/10.1109/IGARSS.1998.703675 [11] Li, F., Jackson, T., Kustas, W., Schmugge, T., French, A., Cosh, M. and Bindlish, R. (2004) Deriving Land Surface Temp from Landsat 5 and 7 during SMEX02/SMACEX. Remote Sensing of Environment, 92, 521-534. http://dx.doi.org/10.1016/j.rse.2004.02.018 [12] Landsat Project Science Office (2002) Landsat 7 Science Data User’s Handbook. NASA’s Goddard Space Flight Center, Greenbelt, 186p.
http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf [13] Barsi, J.A., Barker, J.L. and Schott, J.R. (2003) An Atmospheric Correction Parameter Calculator for a Single Thermal Band Earth-Sensing Instrument. IGARSS03, Toulouse, 21-25 July 2003. [14] Barsi, J.A., Schott, J.R., Palluconi, F.D. and Hook, S.J. (2005) Validation of a Web-Based Atmospheric Correction Tool for Single Thermal Band Instruments. SPIE Proceedings, 5882, 7p. [15] Sobrino, J.A. and Rassouni, N. (2000) Toward Remote Sensing Methods for Land Cover Dynamic Monitoring: Application to Morocco. International Journal of Remote Sensing, 21, 353-366. http://dx.doi.org/10.1080/014311600210876 [16] Valor, E. and Caselles, V. (1996) Mapping Land Surface Emissivity from NDVI: Application to European, African and South American Areas. Remote Sensing of Environment, 57, 167-184. http://dx.doi.org/10.1016/0034-4257(96)00039-9 [17] Van deGriend, A.A. and Owe, M. (1993) On the Relationship between Thermal Emissivity and the Normalized Vegetation Index for Different Natural Surfaces. International Journal of Remote Sensing, 14, 1119-1131. http://dx.doi.org/10.1080/01431169308904400 [18] Sobrino, J.A., Jimenez-Munoz, J.C. and Paolini, L. (2004) Land Surface Temperature Retrieval from LANDSAT TM5. Remote Sensing of Environment, 90, 434-440.
http://dx.doi.org/10.1016/j.rse.2004.02.003
[1] Gareth Rees, W. (2006) Remote Sensing of Snow And ice. Taylor & Francis, CRC Books, Boca Raton, 295p. [2] Chuvieco, E. (2006) Environmental Remote Sensing. Earth Observation from Space. Ed. Ariel Science Barcelona, 224p. [3] Shi, J. and Dozier, J. (1995) Inferring Snow Wetness Using C-Band Data from SIR-C’s Polarimetric Synthetic Aperture Radar. IEEE Transactions on Geoscience and Remote Sensing, 33, 905-914. http://dx.doi.org/10.1109/36.406676 [4] Nagler, T. and Rott, H. (2000) Retrieval of Wet Snow by Means of Multitemporal SAR Data. IEEE Transactions on Geoscience and Remote Sensing, 38, 754-765. http://dx.doi.org/10.1109/36.842004 [5] Nagler, T. and Rott, H. (2004) Snow Classification Algorithm for Envisat ASAR. Proceedings of the 2004 Envisat & ERS Symposium, Salzburg, 6-10 September 2004, ESA SP-572. [6] Pettinato, S., Poggi, P., Macelloni, G., Paloscia, S., Pampaloni, P. and Crepaz, A. (2004) Snow Cover Mapping in Alpine Areas with Envisat/SAR Images. Proceedings of SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74771N, 29 September 2009.
http://dx.doi.org/10.1117/12.830593 [7] Pettinato, S., Santi, E., Brogioni, M., Paloscia, S. and Pampaloni, P. (2009) An Operational Algorithm for Snow Cover Mapping in Hydrological Applications. IEEE International Geoscience and Remote Sensing Symposium, Cape Town, 12-17 July 2009, IV-964-IV-967.
http://dx.doi.org/10.1109/IGARSS.2009.5417539 [8] Salcedo, A.P. (2011) Estimate of Area of Snow Cover in Watersheds with High Rate of Data Fusion Using ERS-2. Master’s Thesis, Faculty of Mathematics, Physics and Astronomy and the Mario Gulich Institute for Advanced Space Studies, 103p.
http://www.famaf.unc.edu.ar/wp-content/uploads/2014/04/8-Gulich-Salcedo.pdf [9] Laur, H., Bally, P., Meadows, P., Sanchez, J., Schaettler, B., Lopinto, E. and Esteban, D. (2004) ERS SAR Calibration. Derivation of the Backscattering Coefficient in ESA ERS SAR PRI Products. Issue 2, Rev. 5f. [10] Caves, R., Turpin, O., Nagler, T. and Mille, D. (1998) The Role of Earth Observation in Snowmelt Runoff Monitoring from High Latitude Basins: SAR Aspects. IEEE International Geoscience and Remote Sensing Symposium Proceedings, 4, 1858-1860.
http://dx.doi.org/10.1109/IGARSS.1998.703675 [11] Li, F., Jackson, T., Kustas, W., Schmugge, T., French, A., Cosh, M. and Bindlish, R. (2004) Deriving Land Surface Temp from Landsat 5 and 7 during SMEX02/SMACEX. Remote Sensing of Environment, 92, 521-534. http://dx.doi.org/10.1016/j.rse.2004.02.018 [12] Landsat Project Science Office (2002) Landsat 7 Science Data User’s Handbook. NASA’s Goddard Space Flight Center, Greenbelt, 186p.
http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf [13] Barsi, J.A., Barker, J.L. and Schott, J.R. (2003) An Atmospheric Correction Parameter Calculator for a Single Thermal Band Earth-Sensing Instrument. IGARSS03, Toulouse, 21-25 July 2003. [14] Barsi, J.A., Schott, J.R., Palluconi, F.D. and Hook, S.J. (2005) Validation of a Web-Based Atmospheric Correction Tool for Single Thermal Band Instruments. SPIE Proceedings, 5882, 7p. [15] Sobrino, J.A. and Rassouni, N. (2000) Toward Remote Sensing Methods for Land Cover Dynamic Monitoring: Application to Morocco. International Journal of Remote Sensing, 21, 353-366. http://dx.doi.org/10.1080/014311600210876 [16] Valor, E. and Caselles, V. (1996) Mapping Land Surface Emissivity from NDVI: Application to European, African and South American Areas. Remote Sensing of Environment, 57, 167-184. http://dx.doi.org/10.1016/0034-4257(96)00039-9 [17] Van deGriend, A.A. and Owe, M. (1993) On the Relationship between Thermal Emissivity and the Normalized Vegetation Index for Different Natural Surfaces. International Journal of Remote Sensing, 14, 1119-1131. http://dx.doi.org/10.1080/01431169308904400 [18] Sobrino, J.A., Jimenez-Munoz, J.C. and Paolini, L. (2004) Land Surface Temperature Retrieval from LANDSAT TM5. Remote Sensing of Environment, 90, 434-440.
http://dx.doi.org/10.1016/j.rse.2004.02.003