THE USE OF MERIS SPECTROMETER DATA IN SEASONAL SNOW MAPPING

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1 THE USE OF MERIS SPECTROMETER DATA IN SEASONAL SNOW MAPPING Miia Eskelinen, Sari Metsämäki The Finnish Environment Institute Geoinformatics and Land use division P.O.Box 140, FI Helsinki, Finland ABSTRACT The objective of this work is to evaluate the use of the Medium Resolution Imaging Spectrometer (MERIS) data for seasonal snow cover monitoring specifically in the boreal forest belt. For this purpose, we tuned an existing method for fractional snow cover mapping in order to produce snow maps from MERIS imagery. The method was originally developed at the Finnish Environment Institute (SYKE), where it is successfully used to provide frequent Snow Covered Area (SCA) maps from Terra/MODIS data. The possibility to switch between MERIS and successive multi spectral optical sensors could ensure the snow service continuity and sufficient data supply for snow monitoring and even enable improved accuracy in snow map production. We found that MERIS visible channels suit well for SCAmapping. 1. INTRODUCTION Snow accumulation and melting is an essential part of the hydrologic cycle in the boreal zone. There is a need to monitor the melting process by regular mapping of snow covered areas during the melting period. Earth observation provides a spatially and temporally effective means to obtain information on the snow cover extent in addition to the traditional in situ weather station and snow gauging network. In order to obtain the fraction of Snow Covered Area (SCA), a reflectance model based method SCAmod [1] was developed and implemented into operational use at the Finnish Environment Institute (SYKE) in It was designed to best perform for boreal forest areas [2]. High accuracy is usually required for hydrological applications, particularly when models are applied at regional scales across medium and small size drainage basins. Typically, snow patchiness is included as a model parameter. Since 2003, data provided by SCAmod have been successfully assimilated to the Finnish nationwide operational hydrological modelling system improving the performance of run off and river discharge forecasts provided by the models [3]. This kind of permanent activity requires sustainable availability of Earth observation data. This is a motivation for experiments on snow mapping capacity of different sensors. In SCAmod, the reflectance from a target area is expressed as a function of SCA. Average effective forest canopy transmissivity (a priori information, generated from EO data) for each unit area and generally applicable average reflectance values for wet snow, snow free ground and dense forest canopy are applied as model parameters [1]. This approach enables the employment of the method for large boreal areas with a tolerable effort. Employment of transmissivity is beneficial as it allows SCA estimation in various kinds of areas, both forested and non forested. The method is applicable to a variety of optical sensors; switching between sensors requires only tuning the values of the three contributing reflectances. Also the transmissivity is slightly sensor dependent and should therefore be calculated for each sensor. In this study, we describe the principles of the SCAmod modification for MERIS data. This work requires acquisition of spectra for relevant reflectance contributors. This is the primary function of Analytical Spectral Devices (ASD) Field Spec Pro JR spectroradiometer measurements described in this paper. The success of the method modification is demonstrated with MERIS derived snow cover maps. 2. STUDY AREA AND DATA SETS Northern Finland represents both boreal forests and tundra. The landscape is relatively flat and the forest evolves from a consistent closed canopy in the south, to a patchy mosaic of open canopy forest approaching the northern tree line. The landscape consists of multilayer vegetation covered with a thick seasonal snow pack. In Figure 1, a typical coniferous forest located in Northern Finland is depicted. Finland has a comprehensive in situ measurement network for snow parameters (160 snow courses and 700 weather stations). The boreal zone is characterized by seasonal snow cover, which has a significant influence on the interactive Earth's surface and atmosphere system. For this reason, seasonal snow cover is a sensitive climate change indicator also in regional scale. In Finland, snow pack is also an important temporary fresh water Proc. Envisat Symposium 2007, Montreux, Switzerland April 2007 (ESA SP-636, July 2007)

2 reservoir and therefore an excellent source of energy for hydropower plants. Snowmelt can, in contrast, lead up to uncontrolled flooding in certain areas. In order to calculate MERIS snow maps, 10 MERIS Level 1B Full Resolution (FR) quarter scenes were acquired over Finland under the melting periods 2004 to The MERIS data were pre processed with tools made by the Technical Research Centre of Finland (VTT) embedded to SYKE's operational data processing and archiving system [2]. Pre processing includes georectification and atmospheric correction using the Simplified Method for Atmospheric Corrections of satellite measurements (SMAC) [4]. Cloud masking was carried out using an algorithm developed in SYKE. The algorithm is mostly based on difference of near infrared channels 15 (900nm) and 10 (753.75nm). The images were geo rectified into a degrees grid in WGS 84 geographical coordinate system. As SCAmod tuning requires determination of model parameters (the three reflectance contributors), these have to be derived either from actual reflectance measurement at ground level or by extracting those from MERIS reflectance data. In this study, both approaches were used. The valid values for wet snow, dry snow and snow free ground reflectances were derived using measurements carried out with Analytical Spectral Devices (ASD) Field Spec Pro JR spectroradiometer. The valid value for forest canopy reflectance was obtained by averaging MERISreflectances from representative pixels. ASD measurements were conducted in the snowmelting season from March to April 2005 in several locations and dates. Average reflectance spectra were obtained for 2151 wavelength channels ranging from 350 nm to 2500 nm. The measurement procedure followed each time a similar pattern with respect to setup, illumination and observation geometry. The measured radiance spectra were directly pre processed to surface reflectances using the Spectralon optical standard reflectance panel with a pre determined reflectance spectrum (see Figure 2.). Figure 1. Typical coniferous forest in Northern Finland. Figure 2. ASD instrument is operated with a laptop from a temperature stabilized portable box. The system includes an optical 5 m long cable and fore optics that is attach to a tripod. 3. METHODOLOGY FOR SNOW COVERED AREA ESTIMATION The method for SCA estimation is based on an empirical model, which describes the observed reflectance as a function of SCA, average forest transmissivity and three major reflectance contributors being model parameters [1]: λ, obs ( SCA) = (1 t + t 2 λ 2 λ ) λ, forest [ SCA + (1 SCA) ] λ, snow λ, ground where ρ λ,snow, ρ λ,ground and ρ λ,forest are the generally applicable reflectances for wet snow, snow free ground and dense coniferous forest canopy at wavelength λ, respectively. ρ λ,obs (SCA) stands for the observed reflectance from a calculation unit area with the current snow cover fraction. t λ stands for effective transmissivity within the unit area. It describes how much of the upwelling radiance is originated from the forest floor and open areas together, and is strongly correlated with canopy closure. For operational large scale mapping, employment of transmissivity is beneficial as it allows SCA estimation in various kinds of areas, with forest coverage ranging from 0% to 100%. Furthermore, auxiliary information on forests is not needed: the effective transmissivity for each calculation unit area is estimated from satellite borne reflectance data. This requires reflectance data providing a high contrast between forest canopy and ground not obscured by trees. The required contrast is well obtained at short wavelengths of MERIS (or other optical instruments), particularly at full dry snow cover conditions (SCA=100%). Then, t λ 2 is obtained from (1): (1)

3 t 2 λ = λ, obs ( SCA λ, drysnow = 100%) where ρ λ,obs (SCA=100%) is the observed reflectance at full dry snow cover conditions and ρ λ,drysnow is the generally applicable dry snow reflectance. Employing dry snow here is advantageous as smaller reflectance variance is gained due to the smaller grain size variation [5]; thus leading to better accuracy for transmissivity. After the effective transmissivity t λ is determined, the SCA is obtained by inverting (1), as follows: (2) 1 1 ( ) (1 ) 2 λ, obs SCA + 2 λ, forest λ, ground tλ tλ SCA=. (3) ρ ρ λ, snow λ, forest λ, ground λ, forest The final SCA represents the average snow cover fraction (0 100%) for the calculation unit area. The method is adaptable to any optical sensor providing measurements at wavelengths sensitive to snow. MERIS fulfils well this requirement with its fine spectral resolution. In this study, we tuned the SCAmod method for MERIS data in order to calculate a set of SCA maps for Northern Finland. In this work, a three step procedure must be carried through: 1) the MERIS channel most suitable for snow/forest/bare ground discrimination must be chosen 2) the average reflectances for wet snow, forest canopy and snow free ground must be determined for the chosen channel 3) transmissivity information must be generated. 4. DETERMINATION OF REFLECTANCES APPLIED IN THE SCA ESTIMATION When applying SCAmod for MERIS data, choosing a suitable wavelength is crucial to the successful SCAestimation. The applied wavelength region for reflectance may vary from blue to near infrared, depending on sensor. With MERIS data, we chose the band 2 (442.5 nm). In these wavelengths, the effect of snow grain size to the reflectance is at its minimum [5], which is beneficial for accuracy as one general snow reflectance is applied throughout the study area. In addition, the chlorophyll absorption peak occurring in this wavelength region enables snow mapping also at the very end of the melting season, as the appearance of seasonal green vegetation disturbs the observed reflectance only slightly. Valid value for forest canopy reflectance at band 2 was empirically derived from the MERIS dataset by, averaging a number of representative pixel values. The results are shown in Table 1. Reflectance for snow free (2) ground was measured with ASD, see Table 2. Since the snow reflectance is clearly unstable compared to forest canopy and snow free ground (varies with snow grain size and metamorphosis as well as depth of snow pack), the main emphasis was put on determination of the average value and the standard deviation of the snow reflectances. The standard deviation is important in future statistical accuracy assessment of the method. As a result, several ASD spectra were gained. The results are shown in Figures 3 5. These spectra represent snow under different weather conditions and under different stages of metamorphosis and therefore give a relatively good overview on the variability of wet snow reflectance. From ASD spectra, mean and standard deviation of reflectances corresponding to MERIS channels were extracted, see Tables 3 and 4. Table 1. MERIS derived reflectances for forest canopy: mean and standard deviation ±5 nm ±5 nm ±5 nm ±5 nm ±5 nm Table 2. ASD derived reflectances for snow free ground: mean and standard deviation ±5 nm ±5 nm ±5 nm ±5 nm ±5 nm Table 3. ASD derived reflectances for dry snow: mean and standard deviation ±5 nm ±5 nm ±5 nm ±5 nm ±5 nm Table 4. ASD derived reflectances for wet snow: mean and standard deviation ±5 nm ±5 nm ±5 nm ±5 nm ±5 nm

4 5. APPLYING THE METHOD TO MERIS DATA A transmissivity map over the entire Finland was generated using reflectance data from six MERIS full resolution ¼ scenes acquired at full dry snow cover conditions during the winters 2005 and 2006 (see Table 5). Daily temperature and snow depth observations made at Finnish weather stations were used to verify the propriety of the data. The clear sky pixels from different dates were averaged to build a reflectance dataset without gaps. To this dataset, eq. 2 was applied to gain a transmissivity map. The map is shown in Figure 6. Figure 3.Dry snow reflectance spectra derived with ASD instrument. Table 5. MERIS Level 1B Full Resolution (FR) datasets used for calculating the transmissivity over Finland and neighboring areas. MERIS L1B FR Areal coverage 13 Mar 2006 Finland 22 Mar 2005 Finland 15 Mar 2005 Northern Finland 14 Mar 2005 Northern Finland Figure 4. Wet snow reflectance spectra derived with ASD instrument. Figure 6. MERIS derived two way transmissivity map in degrees grid (WGS 84 geographical coordinate system) over Finland and neighbouring areas. The transmissivity values range from opaque (t 2 =0) to transparent (t 2 =1). The transmissivity map and the average reflectances were used to estimate SCAs for four MERIS quarter scenes acquired at different grades of snowmelt over Northern Finland (see Table 6). In Figure 7, the final SCA maps are presented for degrees grid. The resolution downgrading is carried out in order to diminish the effect of reflectance variance caused by varying MERIS footprints over the terrain. Table 6. MERIS Level 1B Full Resolution (FR) datasets used for calculating fractional Snow Covered Area (SCA) over Northern Finland and neighbouring areas. Figure 5. Dry and wet snow mean spectra for various snow depths, measured with ASD instrument. MERIS L1B FR Areal coverage 05 May 2006 Northern Finland 27 Apr 2006 Northern Finland 28 Apr 2004 Northern Finland 02 Apr 2004 Northern Finland

5 a) b) Figure 7. MERIS derived SCA maps for a) April , b) April , c) May and d) April Different colors indicate the fraction of snow covered area (0 100%) for each degrees calculation unit area. c) d) 6. FEASIBLITY OF MERIS IN THE SCA ESTIMATION The aim of this study was to evaluate the feasibility of the MERIS data in fractional snow cover mapping in large boreal areas using SCAmod method. The results indicate that MERIS is well suited in fractional SCAestimation. Since the comprehensive validation falls out of the scope of this study, we relied on comparing the MERIS derived snow maps with MODIS derived ones, also produced with SCAmod by SYKE. Good correlation was gained. The in situ validation for MODIS snow map was performed earlier, indicating a typical error of 15% (SCA units). Results from this study suggest that using SCAmod method, MERIS data are useful in monitoring the melting process, at least at regional scale. The drawback with MERIS is a lack of proper channels for cloud detection; particularly over dry snow covered areas MERIS cannot distinguish between clouds and snow. Our future work will focus on utilizing the multispectral properties of MERIS, aiming at still improved accuracy of SCA estimates. An important task is also the collection of an elaborate spectral library to be utilized in the SCAmod method. This work was already started in spring 2007, when an extensive ASDmeasurement campaign in Northern Finland was conducted. As this work is still in progress, the measurements were not available for this study.

6 7. CONCLUSIONS The results indicate that MERIS performs well over the seasonal snow cover, suggesting that MERIS and successive optical sensors have suitable characteristics and potential for fractional snow mapping. However, the cloud discrimination capacity of future MERIS like sensor is yet to be improved for operational snow mapping purposes. ACKNOWLEDGEMENT This work has been supported by the European Space Agency. MERIS data were provided by ESA within Category 1 project 3590 and Envisat AO project REFERENCES [1] Metsämäki, S., Anttila, S., Vepsäläinen, J. & Huttunen, M. (2005).A Feasible Method for Fractional Snow Cover Mapping in Boreal Zone Based on a Reflectance Model, Remote Sensing of Environment Vol. 95 (1): [2] Anttila, S., Metsämäki, S., Pulliainen, J. & Luojus, K From EO data to snow covered area end products using automated processing system. IEEE 2005 International Geoscience and Remote Sensing Symposium (IGARSS). Harmony Between Man and Nature. Seoul, Korea [3] Metsämäki, S., Huttunen, M. & Anttila, S The operative remote sensing of snow covered area in a service of hydrological modeling in Finland. Remote Sensing in Transition (ed. Goossens, R.) Millpress Rotterdam, ISBN [4] Rahman, H., Dedieu, G SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. International Journal of Remote Sensing 15(1) pp [5] Warren, S. G. (1982). Optical properties of snow. Reviews of Geophysics and Space Physics, 20(1), 2 52.