III. Publication III. c 2004 Authors

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1 III Publication III J-P. Kärnä, J. Pulliainen, K. Luojus, N. Patrikainen, M. Hallikainen, S. Metsämäki, and M. Huttunen Mapping of snow covered area using combined SAR and optical data. In: Proceedings of the 4th International Symposium on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Applications. Innsbruck, Austria November c 2004 Authors

2 Mapping of Snow Covered Area using combined SAR and optical data Juha-Petri Kärnä (1) Jouni Pulliainen (1) Kari Luojus (1) Niina Patrikainen (1) Martti Hallikainen (1) Sari Metsämäki (2) Markus Huttunen (2) (1) Helsinki University of Technology, Laboratory of Space Technology P.O. Box 3000, TKK FINLAND (2) Finnish Environment Institute P.O. Box 140, 00251, Helsinki FINLAND Abstract A Bayesian inversion method for deriving Snow Covered Area (SCA) using combined SAR and optical data is introduced. SCA estimation is done for over 2000 sub drainage areas covering the whole Northern Finland using RADARSAT and NOAA AVHRR images. The applied inversion method used takes also into account the statistical accuracies of the two data sources, optical and SAR images. Keywords: remote sensing of snow, RADARSAT, SAR, SCA, AVHRR 1. INTRODUCTION Hydrological processes in boreal forest zone are highly affected by the seasonal snow cover. Thus, hydrological models operationally used for run-off and river discharge forecasting employ spatially distributed information on physical snow pack characteristics, and on the extent of snow. In Finland, the most important period is the spring melt season and the snow parameters essential for forecasts include the fraction of snow-covered area (SCA) and snow liquid water content (snow wetness). This information is required in a spatial scale from a few to several kilometers corresponding to sizes of sub-basins. The Finnish forecasting system applies snow information interpolated from weather stations and snow gauging network. However, its spatial accuracy is relatively poor. Moreover, measurements on some important parameters, such as snow liquid water content, are not carried out operationally. Space-borne observations can be used to overcome these problems. The operational system already applies SCA estimates derived from optical satellite images during the spring melt period, but they are only available under non-cloudy conditions. Space-borne SAR provides information that can be used for the mapping of SCA regardless of cloud cover. SAR measurements can be also used to retrieve information on snow wetness. 2. DATA AND TEST SITE Investigations are carried out using both SAR and optical data. The SAR data consists of five RADARSAT wide swath images (HH-polarisation) from the spring 2004 (April 18th, April 28th, May 5th, May 12th, and May 26th) covering the whole Northern Finland (Fig. 1). Also several ERS-2 SAR images from years were used to develop the technique. The area used in the study is the whole Northern Finland containing over 2000 sub drainage areas (Fig. 1) covering an area of 130,000 km 2. The area contains mostly relatively sparse conifereous forest (Scots pine, Norway spruce), bogs and some fjeldts. Since the area is so large the incident angle of the RADARSAT varies from 25 degrees to 45 degrees. The SAR images were geocoded with DEM and ground control points. The backscattering coefficients are classified into six classes (open areas, 0 50, , , , 201 m 3 /ha) based on the forest stem volume map for each sub drainage area. The optical data used is NOAA AVHRR images from spring AVHRR channel 1 ( nm) is used. Reflectance values are averaged to each sub drainage area. The date May 5th was selected for detailed investigation since the melting was then in full progress and AVHRR data from the May 6th was available. The SCA estimates produced from AVHRR data from the spring 2004 of Fig. 2 shows the problems of the optical data

3 Figure 1: The coverage of the RADARSAT images (left), and 2037 sub drainage areas of the Northern Finland shown in different shades of gray (right). Note that some sub drainage areas are outside Finland. due to cloud cover. Some parts of the area were covered by clouds in every image during the melting. Note that the days that image is missing were totally cloudy. Also WSFS hydrological model [6] data were available consisting simulated SCA values for the whole area in every single day. 3. METHOD A Bayesian inversion method for deriving SCA using combined SAR and optical data is used. The inversion method used takes also into account the statistical accuracies of the two data sources, optical and SAR images Models The backscattering model used is the semi-empirical HUT backscattering model [4], which describes the average backscattering coefficient of forested terrain as a function of forest stem volume and SCA. The backscattering equation can be given as a function of forest stem volume V, scalar variable a, and SCA: σ o (V, a, θ, SCA) = [ SCA σsnow o + (1 SCA) ground] σo t(v, a, θ) 2 + σcanopy o (V, a, θ), (1) where σsnow o is the backscattering coefficient of snow covered ground and σo ground is that of snow-free ground surface. The model requires the reference values for backscattering coefficient for the 100 % wet snow cover and totally snowfree conditions. The first two RADARSAT images were used as a reference for snow covered ground σsnow o and the two last images were used as the reference for snow free ground σground o. The effect of forest canopy is approximated by the following equation derived originally from ERS-1/2 observations [3] and adjusted for RADARSAT data: σ o canopy(v, a, θ) = a cosθ(1 t 2 ), (2) where the θ is the incident angle of the measurement and a is scalar variable reflecting the moisture content and freezing status of the forest. The square of forest transmissivity t 2 is calculated by: t(v, a, θ) 2 3 av = exp( ). (3) cosθ The behaviour of the backscattering coefficient σ o during the melting period is sketched in Fig. 3. The backscattering coefficient has a relatively high value during dry snow conditions, but decreases rapidly when the melting starts, having

4 ! $%! #"! (*)!+&,.- '/ 0!0! & "' Figure 2: SCA derived from the AVHRR data during melting season White denotes "no data" due to clouds. Images are produced by Finnish Environment Institute. its minimum when the snow is wet. When the melting advances and the under laying ground begins to show up. Hence the backscattering increases proportionally to SCA decrease. An analogous model for optical reflectance [2] is used: ρ(sca) = (1 t 2 ) ρ forest + t 2 [SCA ρ snow + (1 SCA) ρ ground ], (4) where ρ forest, ρ snow, and ρ ground are the reflectances of forest canopy, wet snow and snow-free ground, respectively. The following values were used for them: ρ forest = 2.8%, ρ snow = 68%, and ρ ground = 7.0%. The value t is empirically determined apparent forest transmissivity for each sub-drainage area ranging in practice from 0.26 to Inversion method The inversion method is based on the statistical inversion approach [1]. It is performed by minimizing the following equation for each sub drainage area: min J(χ, SCA) = min SCA,a SCA,a N w 1 (σmodel o (i) σo SAR (i))2 + w 2 (ρ model ρ AV HRR ) 2, (5) i=1 where σmodel o (i) is the calculated and σo SAR (i) is the measured backscattering coefficient for the forest stem class i and N is the number of forest stem classes. Similarily, ρ model is the calculated and ρ AV HRR the measured reflectance of the sub drainage area. Weighting factors w 1 and w 2 represent the accuracy of the corresponding values inversely proportional to variance of the modelling error. To define the weighting factor w 2, (4) can written as and by using the rules for variance we get SCA = 1/t2 ρ(sca) + (1 1/t 2 ) ρ forest ρ ground ρ snow ρ ground, (6) V ar(sca) = ( 1 t 2 1 ρ snow ρ ground ) 2 V ar(ρ(sca))

5 Figure 3: The behavior of the backscattering coefficient during melting period. Similarily we get equation for the variance of the σ o : V ar(ρ(sca)) = t 4 (ρ snow ρ ground ) 2 V ar(sca). (7) Now we can write the equation for the weighting factor w 1 : and a similar equation for w 2 : w 1 = w 2 = V ar(σ o (SCA)) = t 4 (σ o snow σ o ground) 2 V ar(sca). (8) 1 2V ar(σ o (SCA)) = 1 2t 4 (σsnow o σo ground )2 V ar(sca), (9) 1 2V ar(ρ(sca)) = 1 2t 4 (ρ snow ρ ground ) 2 V ar(sca). (10) The previously published results indicate that the SCA mapping accuracy of C-band SAR is around 35 %-units while the accuracy of optical images is around 15 %-units [5]. So we can use these values as V ar(sca) = for SAR and V ar(sca) = for AVHRR data. With the knowledge of the SCA mapping error we can estimate the accuracy of the inversion results. The SCA estimation was done for over 2000 sub-drainage areas in Northern Finland using the minimisation algorithm to find the minimum of the (5). Fig. 5 depicts the algorithm, although the actual computation was done with more effective minimisation algorithm. 4. RESULTS The SCA estimation was first performed using AVHRR and SAR data alone, and finally using both data simultanously. Fig. 4 depicts visually the results. In the first image only the AVHRR data is used. SCA is not estimated for cloudy areas which are shown as white. In the second image only the RADARSAT data is used. Naturally, the clouds do not disturb and the SCA for the whole area can be estimated, only some areas outside Finland are not estimated due to missing forest data. The southern parts are also white, because the RADARSAT images do not cover them. The third image is a result of using combined SAR and AVHRR data to estimate SCA for whole area using (5). As optical images are not always available due to cloud cover, SAR data can be applied for those cases as an important aid. To illustrate the difference of the SCA estimates derived from the AVHRR and RADARSAT data the Fig. 5 shows the scatter plot of the two SCA estimates. The future work contains accuracy assessments of the SCA mapping methods and detailed analysis of the differences between AVHRR and SAR derived SCA mapping results.

6 Figure 4: SCA derived from NOAA AVHRR data on May 5th, 2004 (left), SCA derived from SAR data on May 6th, 2004 (center) and SCA derived using both AHHRR and SAR data (right). White denotes "no data". Satellite observations - Modelled observables no d>min yes SCA update end Backscattering model Reflectance model Figure 5: Flow chart of the minimisation process (left). SCA derived from NOAA AVHRR data on May 5th, 2004 versus SCA derived from SAR data on May 6th, 2004 (right). Correlation coefficient is 0.663, RMSE=0.197

7 References [1] M. Lehtinen. On statistical inversion theory. Pitman Research Notes in Mathematics Series, (167):46 57, [2] S. Metsämäki, M. Huttunen, and S. Anttila. The operative remote sensing of snow covered area in a service of hydrological modelling in Finland. In Proceedings of the 23rd EARSel Symposium on Remote Sensing, Ghent, Belgium, June [3] J. Pulliainen, M. Engdahl, and M. Hallikainen. Feasibility of multi-temporal interferometric SAR data for stand-level estimation of boreal forest stem volume. Remote Sensing of Environment, (85): , [4] J. Pulliainen, J. Koskinen, and M. Hallikainen. Compensation of forest canopy effects in the estimation of snow covered area from SAR data. In IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS 2001), Sydney, Australia, July [5] J. Pulliainen, S. Metsämäki, K. Luojus, M. Hallikainen, S. Anttila, J.-P. Kärnä, M. Huttunen, S. Rasmus, J. Koskinen, and T. Grönholm. Feasibility of Envisat data for the estimation of snow pack characteristics and areal fraction of snow in boreal forests. In 2004 Envisat ERS Symposium, Salzburg, Austria, September [6] B. Vehviläinen. The watershed simulation and forecasting system in the National Board of Waters and Environment. Publications of the Water and Environment Research Institute No. 17, National Board of Waters and the Environment, Helsinki, 1994.