MONITORING SNOW COVER IN ALPINE REGIONS THROUGH THE INTEGRATION OF MERIS AND AATSR ENVISAT SATELLITE OBSERVATIONS

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1 MONITORING SNOW COVER IN ALPINE REGIONS THROUGH THE INTEGRATION OF MERIS AND AATSR ENVISAT SATELLITE OBSERVATIONS Maria Lucia Tampellini (1), Pietro Alessandro Brivio (2), Paola Carrara (2), Daniele Fantoni (1) ; Stefania Gnocchi (2), Giovanna Ober (1), Monica Pepe (2), Anna Rampini (2), Raffaella Ratti (1), Francesco Rota Nodari (2), Tazio Strozzi (3) (1) Carlo Gavazzi Space S.p.A. - via Gallarate, Milan Italy ltampellini@cgspace.it (2) IREA-CNR Sez. Milano via Bassini Milan Italy rampini.a@irea.cnr.it (3) Gamma Remore Sensing - Thunstrasse 130, 3074 Muri BE, Switzerland strozzi@gamma-rs.ch ABSTRACT The ENVISAT mission with a suite of high performance sensors offers some opportunities for mapping snow cover at regional and catchment scale. The geometric resolution of MERIS data and the spectral resolution of AATSR data are suitable for these purposes. A new approach, developed in the framework of the GLASNOWMAP project (ESA-DUP2) for monitoring snow cover in Alpine regions, based on the combined use of MERIS and AATSR observations, and topographic information, is proposed. As MERIS spectral bands are not completely proper for the discrimination of snow from clouds - due to the lack of short wave infrared channels - a multisource classification scheme has been developed to combine the results obtained by the classification of MERIS data with the information on cloud distribution as derived from AATSR data; the integration is performed with the aid of snow elevation distribution as derived from the Digital Elevation Model. A supervised fuzzy statistical classifier (Wang 1990) has been chosen to perform classification of MERIS images, being particularly suited for the representation of land cover class mixture. The classifier bases estimates of the distribution of pixels in multispectral space on the concept of the probability measure of fuzzy events to produce an output of the proportions of individual components. A cloud normalized index has been defined to extract clouds from AATSR images previously registered and resampled on MERIS images. The results of MERIS and AATSR processing are integrated to produce a snow cover map masked over the cloud covered areas, taking into account also the elevation. The Alpine region is selected as test area to demonstrate the potential and limitations of the novel approach. In particular, the attention is focused on three regions of Northern Italy (Valle d Aosta, Piemonte, Lombardia). The first results obtained by the application of this new method to Earth Observation data will be presented and analysed. 1 INTRODUCTION The quantitative estimation of the extent and depth of snow cover, the stored amount of water, the state of snow metamorphism and intensity of snowmelt runoff are of prime importance to hydrologists and managers of water resources. In the recent years the increased demand for water resources has led to a conflict between human needs and the needs to sustain freshwater ecosystems. On seasonal to annual timescales, the accumulation and melting of snow dominates the hydrological cycle of many alpine and high latitude drainage basins. Over 50% of Eurasia and North America can be seasonally covered by snow [1]. It was estimated that 50% of the annual runoff from a representative basin in Sweden occurs during the spring snowmelt flood in May [2]. In Norway about 50% of the annual precipitation falls as snow, and furthermore Norway produces nearly 100% of its electricity from hydropower [3]. In the Italian Alps most of the reservoirs are characterised by a nivo-pluvial regime and receive about 40-70% of their annual contribution during April-July. About 45,000 billion kwh of hydropower is being generated per annum, which is approximately 25 % of the total energy production in Italy. Remote sensing techniques are useful for acquiring near real time snow cover data that can be used to model complex hydrological process and predict snowmelt runoff [4]. The use of satellite remote sensing data is advantageous as it provides low-cost, repetitive, synoptic and uniform observations over large areas. 2 GLASNOWMAP-IS OVERVIEW The GLASNOWMAP project proposes to define and implement an information service for monitoring of glacier and snow cover changes.

2 Test area selected for service validation includes three Regions of the Northern Italy (Val d Aosta, Piemonte and Lombardia) and four glaciers (Adamello and Lys in the Italian Alps, Gorner and Allalin in the Swiss Alps). The main goals of the project are: o To provide a new approach for glacier monitoring through the combined use of Envisat ASAR APS (Advanced SAR Alternate Polarisation) images, Landsat Thematic Mapper (TM) acquisitions and Digital Elevation Model (DEM). Glacier monitoring includes the identification of accumulation and ablation basin and delimitation of snow transient line (equilibrium line). o To provide an innovative tool for seasonal and inter-annual changes of snow cover extent at regional scale using ASAR Wide Swath (WSM), MERIS and AATSR (Advanced Along Track Scanning Radiometer), Envisat on board instruments, observations. o To quantify water availability setting up a snowmelt runoff model, through the combined use of meteorological and satellite data. GLASNOWMAP Information Service is composed by three services: o Snow Cover Monitoring Service. For this service a new methodology, based on the combined use of MERIS and AATSR acquisition, was developed. If optical images are not available, due to cloud coverage or bad light condition, it is possible to use ASAR WSM data, in order to produce wet snow map with every weather and light conditions. o Glacier Mapping Monitoring Service. Glacier monitoring is carried out through multisource integration of optical (Landsat TM) and radar data (ASAR APS). o Snow Melting Service. Snow cover extension information during the melting season are used together with meteorological data for the calibration of dynamic snow melt runoff model used to estimate river discharge. This model was validated for Aymavilles basin of Dora Baltea River. GLASNOWMAP Information Service is able to interface with: o Satellite Data Providers. To retrieve satellite data (images, ancillary data) that are used to generate the products. o Ground Measurement Stations. to retrieve the ground measured data (e.g.: meteorological data) that are used to generate the final products. o Service Manager Interface. GLASNOWMAP-IS interfaces the Service Manager through this dedicated interface. o The Service Manager is the operator in charge of the maintenance and the operations of the Service. User. The End User sends to the service its Requests related to the desired products, and the service makes available to the User the elaborated Final Products. All the activities performed by the GLASNOWMAP-IS are managed by a dedicated interface developed in IDL (Interactive Data Language) environment. This interface allows to manage all software modules of the system. In particular hereafter the methodology for the snow cover monitoring through the combined use of MERIS and AATSR acquisition, developed in the framework of the project, will be described. 3 SNOW COVER MAPPING THROUGH THE INTEGRATED USE OF MERIS AND AATSR DATA The ESA ENVISAT mission with a suite of high performance sensors offers some opportunities for mapping snow cover at regional and catchment scale. The MEdium Resolution Imaging Spectrometer (MERIS) 15 bands across the range µm are useful for snow detection but not completely proper for the discrimination of snow from clouds, due to the lack of short wave infrared channels, but its spatial resolution of m is particularly useful for the scale of investigation. The Advanced ATSR 7 spectral bands across the range 0.55 to 12 µm includes two spectral short-wave infrared bands where the reflectance of snow drops to near zero values while reflectance of most clouds remains high, allowing their discrimination, but its spatial resolution ( m) is not well suited for the study. Then a multisource classification scheme has been developed to combine the results obtained by the classification of MERIS data with the information on cloud distribution as derived from AATSR data; the integration is performed with the aid of the Digital Elevation Model. 3.1 Processing of MERIS images for Snow Cover classification. The first step of the snow cover map production is the classification of MERIS images. At first the image is geocoded using a set of Ground Control Points (GCP) with 1 order polynomial warping algorithm, with a root mean square error less than 0.5 pixels and using nearest neighbour radiometric resampling. The image is then classified by means a soft classifier based on fuzzy set theory. The supervised fuzzy statistical classifier designed by Wang [5] has been chosen being particularly suited for the representation of cover class mixture [6, 7]. The classifier bases estimates of the distribution of pixels in multispectral space on the concept of the probability measure of fuzzy events to produce an output of the proportions of individual components. The result of the

3 classification is an image for each class in which pixel value represents the degree of membership to that class. The degree of membership provided by the fuzzy-statistical classifier may be interpreted as fractional cover [5]. The classification process produces two types of map: soft classification maps and hard land-cover map. The first type is a set of raster images, one for each class: each soft map represents the degree of membership provided by the fuzzystatistical classifier, interpreted as fractional cover in percentage, hence ranging from 0 to 100. The second type is a single raster image, the hard land cover map, in which each pixel is labeled as belonging to only one class through a harderization process in which we assign to the pixel the label of the class whose degree of membership is maximum in the soft maps. The hard land-cover map is used to evaluate classification accuracy, where test set pixels are considered and compared to classification results. The percentage of agreement between manually and automatically classified pixels i.e. expected and obtained classification values - is used as a measure of the overall accuracy of classification. If the overall accuracy is less then 80%, the classification procedure is repeated after an appropriate editing of the training set. If the overall accuracy is greater the 80% two map products are derived: the fractional snow cover map, which is the soft map corresponding to the snow class the binary snow cover map, which represents the snow class in the hard land cover map 3.2 Processing of AATSR images for cloud mapping. The objective of the processing of AATSR data is to extract cloud cover information to be merged to the information extracted from MERIS data. The first step is to resample and register the AATSR image to the MERIS simultaneous overpass image. The coregistration the image provides also the geocoding since the reference MERIS image is taken already geocoded. The brightness temperatures of middle wave infrared (band 5, centred at 3.7 µm) and thermal infrared (band 6, centred at 11 µm ) are used to calculate a normalized difference index (Cloud Index - CI) for cloud recognition. This index is an integral part of the optical processing algorithm for the identification of clouds: CI AATSR band6 AATSR band5 = (1) AATSR band 6 + AATSR band5 Having its heritage with the Normalized Difference Snow Index [8] and band-rationing techniques for automatic snow/cloud classification [9], the CI is used to identify clouds in an automated-algorithm environment. The utility of the CI is based on the fact that the reflectance of clouds remains high in the middle infrared (while reflectance of snow drops to near-zero values). However this is not true for cirrus clouds, since their absorption in the mid-infrared is similar to that of snow, and then the thermal infrared is helps in the discrimination. In testing cloud-cover areas on AATSR scenes over the Alps, CI values in the range and 1 were found to well represent cloud pixels, and to separate clouds from snow. Results reveal that there is not a fixed CI threshold for clouds, but a credible threshold for cloud mapping can be established, mainly depending on the season, increasing with the mean temperatures: in fact the same value ( ) was found suitable for both November and December imagery, an higher one ( ) was found suitable for both April and May acquisition, and the highest one ( ) for June. Most likely a constant relationship between the threshold value and some statistical descriptors of the thermal infrared channel can be found; unfortunately the yearly set of images is not large enough to perform such kind of analysis. The main drawback of the use of a different source for detecting clouds from the one used for snow, lies in the fact that different spatial resolutions together with registering accuracies could negatively affect the exact superimposition of the two different kind of information. The problem is mainly connected with border conditions between clouds and underlying covers, as well as to cloud shadowed areas. The adopted solution is to be very conservative in the CI threshold determination, using the lower CI value corresponding to clouds, even if representing very few portions of the image. 3.3 Multisource procedure for snow cover mapping The input to the procedure are the Snow Cover derived from the classification of MERIS images, the Cloud Cover Map obtained by AATSR data and the digital elevation model of the area. The output snow cover product is a byte map where 0 correspond to snow free surfaces, 255 to snow surfaces and 1 to cloud cover. The procedure implemented in the Glasnowmap IS assigns the final snow cover thematic map pixel value to 1 for each pixel mapped as cloud in the Cloud Cover Map produced analysing AATSR satellite data. Moreover all snow pixel found under the elevation threshold of 300 m is assigned to snow free pixel.

4 4 RESULTS MERIS and AATSR images acquired over three regions of Northern Italy (i.e. Piemonte, Lombardia and Valle d Aosta) were used in order to generate snow cover thematic maps, exploiting methodology previously described. In Tab. 1 are listed MERIS and AATSR acquisition used in order to generate snow cover maps. Tab. 1. MERIS and AATRS acquisition used in order to generate snow cover thematic maps. Date Type of data Track Frame Orbit Mode 12 Nov 2002 MERIS Full Resolution Desc 12 Nov 2002 AATRS Desc 1 Dec 2002 MERIS Full Resolution Desc 1 Dec 2002 AATRS Desc 17 April 2003 MERIS Full Resolution Desc 17 April 2003 AATRS Desc For each region belonging to the area snow cover maps generated are masked on the base of regional boundaries. In addition for each region the statistical data (snow covered surfaces, minimum, maximum and mean elevations of snow) are computed. The accuracy of the whole mapping procedure was estimated by testing results on the AATSR images, while it is the imagery on which is possible to distinguish snow from clouds. In the Fig. 1, Fig. 2 and Fig. 3 snow cover thematic maps obtain over Valle d Aosta, Lombardia and Piemonte respectively are depicted. In these maps the snow free region is represented in black, the snow region in white and the clouds in grey. Moreover, for each map estimated accuracy is reported. Fig. 1. Snow cover thematic map generated over Valle D Aosta through the combined use of MERIS and AATSR images acquired on: 12 Nov 2002, accuracy: 0,94 (a), 1 Dec 2002, accuracy: 0,87 (b), 17 Apr 2003, accuracy: 0,91 (c). Fig. 2. Snow cover thematic map generated over Lombardia through the combined use of MERIS and AATSR images acquired on: 12 Nov 2002, accuracy: 0,80 (a), 1 Dec 2002, accuracy: 0,89 (b), 17 Apr 2003, accuracy: 0,89 (c).

5 Fig. 3. Snow cover thematic map generated over Piemonte through the combined use of MERIS and AATSR images acquired on: 12 Nov 2002 accuracy: 0,74 (a), 1 Dec 2002, accuracy: 0,86 (b), 17 Apr 2003, accuracy: 0,83 (c). In addition the snow cover regional contour is extracted from snow cover thematic maps and stored in a ArcView shape file. In the Fig. 4 Fig. 5 and Fig. 6 snow cover contours, superimposed on the digital elevation model, extracted from snow cover thematic maps obtain over Valle d Aosta, Lombardia and Piemonte respectively are depicted. Fig. 4. Snow cover contours extracted from snow cover thematic map generated over Valle d Aosta through the combined use of MERIS and AATSR images acquired on: 12 Nov 2002 (a), 1 Dec 2002 (b), 17 Apr 2003 (c). Fig. 5. Snow cover contours extracted from snow cover thematic map generated over Lombardia through the combined use of MERIS and AATSR images acquired on: 12 Nov 2002 (a), 1 Dec 2002 (b), 17 Apr 2003 (c).

6 Fig. 6. Snow cover contours extracted from snow cover thematic map generated over Piemonte through the combined use of MERIS and AATSR images acquired on: 12 Nov 2002 (a), 1 Dec 2002 (b), 17 Apr 2003 (c). All results reported in these section were obtained during the demonstration phase of GLASNOWMAP-IS. Results described are available on the GLASNOWMAP-IS web site Here the results can be downloaded or visualized using a WebGIS. The access to the user homepage, where product are stored, is protected by password. 5 REFERENCES 1. Romanov, P., G. Gutman, and I. Csiszar, Automated monitoring of snow cover over North America with multispectral satellite data, J.Appl. Meteorol., 39: , Bergstrom, S., Spring flood forecasting by conceptual models in Sweden, Proceedings, Workshop on Modelling Snow Cover Runoff, US Army Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire. pp , Winther J. G. and D. K. Hall, Satellite-derived snow coverage related to hydropower production in Norway: present and future, Int. Journal of Remote Sensing, 20 (15): , Engman E. T. and R. J. Gurney, Remote Sensing in Hydrology, Chapman and Hall, London. p. 225, Wang, F., Fuzzy Supervised Classification of Remote Sensing Images, IEEE Trans. on Geosci. Remote Sensing, Vol. 28, , Baraldi A., Binaghi E., Blonda P., Brivio P.A., Rampini A., A Detailed Comparison of Neuro-Fuzzy Estimation of Sub-pixel Land-Cover Composition from Remotely Sensed Data, IEEE Trans. on Geosc and Remote Sensing, Vol. 38 (5): pp , Binaghi E., P. A. Brivio, P. Ghezzi, A. Rampini and E. Zilioli, Investigating the behaviour of neural and fuzzystatistical classifiers in sub-pixel land cover estimations, Canadian Journal of Remote Sensing, 25 (2), , D. K. Hall, K. J. Bayr, W. Schoener, R. A. Bindschadler and J. Y. L. Chien, Consideration of the error inherent in mapping historical glacier position in Austria from the ground and space ( ), ELSEVIER, Remote Sensing of Environment 86, pp , Robinson D., K. Dewey and R. Heim, Global snow cover monitoring: an update. Bull - American Meteorological Society, 74: , 1993.