SNOW COVER MONITORING IN ALPINE REGIONS WITH COSMO-SKYMED IMAGES BY USING A MULTITEMPORAL APPROACH AND DEPOLARIZATION RATIO

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1 SNOW COVER MONITORING IN ALPINE REGIONS WITH COSMO-SKYMED IMAGES BY USING A MULTITEMPORAL APPROACH AND DEPOLARIZATION RATIO B. Ventura 1, T. Schellenberger 1, C. Notarnicola 1, M. Zebisch 1, T. Nagler 2, H. Rott 2, V. Maddalena 3, R. Ratti 3, L. Tampellini 3 1 EURAC-Institute for Applied Remote Sensing, Viale Druso 1, Bolzano, Italy 2 ENVEO GmbH, Austria 3 Compagnia Generale per lo Spazio, Via Gallarate 150, Milano, Italy

2 OUTLINE Introduction Description of the acquired COSMO-SkyMed (CSK) images and of the test areas Description of the multi-temporal approach Experimental results: Time series of the snow maps from CSK images acquired in winter and comparison with optical images Comparison of the CSK backscattering coefficients with the simulations derived from an electromagnetic model Conclusions and future steps

3 Introduction and motivation Cosmo-SkyMed (CSK ) constellation represents a great challenge to extent this previous knowledge to the X-band data for snow detection. A high resolution (up to 1 m) and an higher repetition time (8 days in standard mode with the full constellation) gives the chance to analyze the problem of detecting wet snow and derive Snow Cover Area (SCA) with a greater spatial and temporal resolution. The main objectives of the work are: To adapt the already developed multi-temporal techniques to X-band CSK images; To analyze the temporal variability of the key parameters used for the distinction of the snow from the no-snow areas; to address the depolarization ratio as an aid to detect snow cover areas in case no reference image is available. The activities are carried out in the framework of the project SNOX- snow cover and glacier monitoring in alpine areas with COSMO-SkyMed X-band data funded by the Italian Space Agency.

4 Test sites - Description Bolzano Trento The test areas in South Tyrol. The pink markers indicate the placement of the manual ground measurement stations, while the green ones of the automatic ground measurement stations

5 COSMO-SkyMed data sets Area Date Mode Polarization Look Side Pass Beam Proc. Level Ulten Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten Ping Pong VV/VH Right Ascending 10 1A-SCSB Ulten Ping Pong VV/VH Right Ascending 10 1A-SCSB Brenner Ping Pong VV/VH Right Descending 11 1A-SCSB Brenner Ping Pong VV/VH Right Descending 11 1A-SCSB Brenner Ping Pong VV/VH Right Descending 11 1A-SCSB Brenner Ping Pong VV/VH Right Descending 11 1A-SCSB Brenner Ping Pong VV/VH Right Descending 11 1A-SCSB List of the acquired COSMO-SkyMed images: in green are indicated the melting season data; in black the winter season data; in blue the summer season (snow free) images.

6 Overview of methods for snow cover area detection with SAR images AUTHOR Method +/- Koskinen et al. (1997) /Luojus et al., (2009) Nagler & Rott (2005) Storvold et al. (2005) Venkataraman et al. (2009) wet snow: difference technique (using 3 images) map of snow cover fraction wet snow: difference technique (using 2 images) dry snow: upper boundary of wet snow cover as the lower boundary of dry snow wet snow difference technique (using 2 images) dry snow mean altitude of wet snow pixel negative air temperature wet snow pixel in the surrounding of potential dry snow Application to TERRASARX images (-3 db threshold) Comparison with ASAR and ALOS-PALSAR SCA in forest zone No dry snow detection difference technique not useful for waterbodies No SCA in forestzone difference technique not useful for waterbodies misclassification occurs under cold conditions no wet snow exists pixel are classified as bare ground difference technique not useful for waterbodies Similar threshold for C and X band images No dry snow

7 Detecting snow cover area with SAR images The method derived from Nagler (1996) is based on the difference in backscattering behavior between snow covered and snow free images. Distribution for snow and no snow areas r 0 r B r A

8 Preprocessing outputs COSMO-SkyMed geocoded image, March 12 th 2011, VV COSMO-SkyMed geocoded image, March 12 th 2011, VH COSMO-SkyMed -ASI All rights reserved

9 Time Series of Ratio Values Distribution of ratio values (db) under three different conditions in areas without vegetation: : wet snow : no snow : dry snow wet snow

10 Threshold for wet snow classification Distribution of ratio R for different land cover classes Grassland Rocks Rock (db) Grassland (db) VV VH Threshold for mapping wet snow with CSK Frost (7 7) ratio-images in dependence of polarization and land cover Forest

11 Dependence of SCA on the threshold As the snow and no-snow ratio distributions partially overlap, the choice of the thresholds is a key aspect of the analysis. To study the dependence of SCA on the threshold, SCA derived with a threshold of -2.3 db based on statistical analysis was compared to SCA based on a -3.0 db threshold, commonly used for detecting snow with C-band data and TerraSAR-X data. Using a lower threshold of -3.0 db leads to a smaller SCA and snow-covered pixels, which show a higher ratio, are wrongly classified as snow-free. In contrast, when using a higher threshold, snow-free pixel which have lower ratio values than -2.3 db are no longer classified as snow. 26April % SNOW - LANDSAT NO SNOW - LANDSAT 12March % SNOW- LANDSAT NO SNOW- LANDSAT SNOW - CSK SNOW - CSK NO SNOW - CSK NO SNOW - CSK April % SNOW - CSK SNOW - LANDSAT NO SNOW - LANDSAT db Normal -3.0 db - Italics NO SNOW - CSK

12 Dependence of the threshold r 0 on the reference images The choice of the reference image has a considerable impact on the threshold and hence on the classification result. The threshold of -2.3 db is found when taking the average of the three September images or the image of Sept. 17 th as a reference. The threshold decreases to -1.7 db and -1.6 db when choosing the image of Sept. 1 st or Sept. 2 nd, respectively. The strong influence of the reference image on the threshold is also reported by Luojus al., 2009 for C-band data. et When for each reference image, these different thresholds are applied the resulting SCA area varies up to 6%. When using a fixed threshold of -3 db (applied to grassland and rocks land-use classes), the resulting SCA area varies up to 12%.

13 Depolarization factor analysis The use of the depolarization factor to discriminate snow is another important aspect to be investigated because it may improve the classification obtained with the ratio method. It is also considered as an alternative approach when no reference images are available. The depolarization factor (σ 0 VH/σ 0 VV) calculated by using the cross- and co-polarized channels of the same image describes the depolarization of effect of the snow layer on the incident waves.

14 Time series of CSK snow maps: November Snow No Snow COSMO-SkyMed November 28 th 2011 MODIS snow line November 26 th 2011

15 Time series of CSK snow maps: January Snow No Snow COSMO-SkyMed January 23 rd 2011 MODIS snow line January 21 st 2011

16 Time series of CSK snow maps: March Snow No Snow COSMO-SkyMed March 12 th 2011 LANDSAT March 6 th 20011

17 Time series of CSK snow maps: April Snow No Snow COSMO-SkyMed April 5 th 2011 LANDSAT April 7 th 20011

18 Snow only for LANDSAT MultiTemp2011, July 2011, Trento - Italy Comparison CSK and Landsat snow cover maps -3.0 db March 12 th 2011 April 5 th 2011 % NO SNOW - LANDSAT SNOW - LANDSAT NO SNOW - CSK SNOW - CSK % NO SNOW - LANDSAT SNOW - LANDSAT NO SNOW - CSK SNOW - CSK Snow for LANDSAT and CSK No Snow for LANDSAT and CSK Snow only for CSK

19 Comparison between e.m. model simulations and CSK backscattering coefficients λ (cm) = 3.1 l (cm) = ε snow = [ ] s (cm) = By using the IEM model, the main hypothesis is that we are dealing with surface scattering. This hypothesis is verified only the case of wet snow.

20 Conclusions and future steps The possibility to discriminate wet snow from snow-free areas in COSMO-SkyMed X-band images using a multi-temporal approach was studied in dependence of different key parameters. SCA increases up to 8% when a threshold of -2.3 db is applied instead of a threshold of -3.0 db. Analyzing the dependence of the threshold on the reference image showed that the threshold, and hence the classification result, strongly depends on the reference image. An average of suitable reference images is advisable in order to reduce the impact of conditions deriving from a single image. The preliminary analysis on the depolarization factor indicates that for the images analyzed in this study, the snow and no-snow distribution cannot be distinguished. The analysis will be extended to another test area where the CSK acquisitions were in the afternoon. The probability of error as a function of the change in radar intensity between two dates will be provided along with the wet snow maps. The multi-temporal approach will be extended to VH polarization. A comparison with TERRASARX images is foreseen The problem of the snow cover extension beyond wet snow will be faced.

21 Thank you for the attention! Comments/questions?