Accuracy Assessment of SAR Data-Based Snow-Covered Area ... - Pdx

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 2, FEBRUARY 2006

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Accuracy Assessment of SAR Data-Based Snow-Covered Area Estimation Method Kari P. Luojus, Student Member, IEEE, Jouni T. Pulliainen, Senior Member, IEEE, Sari J. Metsämäki, and Martti T. Hallikainen, Fellow, IEEE

Abstract—Employment of satellite radar-based remote sensing data for snow monitoring during the snow melt season has been widely studied by several investigators. Several methods for the estimation of snow-covered area (SCA) fraction have been developed for different types of regions. One common deficiency with the SCA estimation methods has been the lack of statistical accuracy analyses for them. In order to incorporate SCA estimates for operational use, one vital requisite is a thorough statistical analysis of the SCA estimation accuracy. This shortcoming has been addressed for boreal forest region, as an extensive statistical accuracy analysis has been carried out for the Helsinki University of Technology (TKK)-developed SCA method. The TKK SCA method was developed for boreal forest regions, and it is studied here with 24 European Remote Sensing 2 synthetic aperture radar intensity images, on a boreal-forest-dominated test area located in northern Finland. The performance of the SCA method is investigated by using reference data acquired through hydrological modeling. The accuracy analysis is carried out for several statistical variables, and the statistical interpretation is done with respect to several affecting parameters. The accuracy analysis shows a high correlation coefficient between the SCA estimates and the reference data and root mean square error values of 0.213 for open areas and 0.179 for forested areas. In addition, the TKK method employs two reference images for the SCA estimation, and the usability of multiyear reference image utilization was analyzed and proven feasible in this study. Index Terms—Hydrological forecasting, snow-covered area (SCA), snow monitoring, spaceborne synthetic aperture radar (SAR).

I. INTRODUCTION

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ONITORING the snow melt season is an important issue for various environmental, hydrological and meteorological applications. The information of snow cover during the snow melt season can be used in predicting and preventing floods and in optimizing the hydropower industry operations. Snow-covered area (SCA) is a descriptive hydrological variable, which can directly be utilized in snow melt and river discharge forecasting. SCA is operatively monitored in Finland using optical remote sensing means [1], [2]. These have enhanced the accuracy of hydrological forecasting in Finland. However, serious limitations for optical means are caused by the dependences on solar illuManuscript received June 21, 2005; revised August 26, 2005. This work was supported in part by the European Commission EnviSnow-Project EVG1-CT2001-00052. K. P. Luojus, J. T. Pulliainen, and M. T. Hallikainen are with the Laboratory of Space Technology, Helsinki University of Technology, FIN-02015 TKK, Finland (e-mail: [email protected]; [email protected]; [email protected]). S. J. Metsämäki is with Finnish Environment Institute, Geoinformatics and Land Use Division, Helsinki, FIN-00251, Finland. Digital Object Identifier 10.1109/TGRS.2005.861414

mination and cloud-free weather conditions. Thus, for operational systems the ability to obtain accurate information in all weather conditions is an important factor. This can be accomplished using microwave-based sensors. The capabilities of spaceborne synthetic aperture radar (SAR) in snow monitoring have been widely studied [3]–[8]. The suitability of SAR for snow monitoring has been confirmed, and studies are made for snow monitoring algorithm development. The spaceborne microwave radars are found to be feasible for snow monitoring especially during the snow melt season, when the optical methods are often hindered by cloudiness and lack of solar illumination. However, current spaceborne SAR systems are also known to have several limitations on snow monitoring applications [4]–[7]. Snow monitoring is based on changes in the backscattering properties of the snow pack and ground surface. Various methods for fractional snow-covered area estimation have been developed for different geographical regions. The SAR-based Helsinki University of Technology (TKK) SCA method studied here, was developed for boreal forest zone. A statistical accuracy characterization of a SCA method for boreal forest region is presented in this study for a first time. The most of prior studies have been focused on mountainous areas [9]. Accuracy assessments for the SCA methods are needed in order to employ them with operational snow melt monitoring and hydrological forecasting systems. The accuracy characterization for the TKK SCA method is addressed in this study by using a test region dominated by boreal forest. The results of accuracy analyses can be used to integrate the TKK SCA method with the Watershed Simulation and Forecasting System (WSFS), which is operatively used in Finnish Environment Institute’s (SYKE) hydrological forecasting. The integration, when applied, should increase the overall accuracy of the WSFS. A common approach for snow melt monitoring is the comparison of SAR images taken from different temporal instances to obtain the SCA estimate for a certain instance. The TKK SCA method uses two reference images for the estimation. The performance of multiyear reference image employment is studied and proven feasible in this study. II. TEST SITE, SATELLITE DATA, AND REFERENCE DATA A. Test Site The selected test site situates in northern Finland south of Lokka reservoir (Lokka dam coordinates: latitude 67.82 N, longitude 27.75 E). The site is shown in Fig. 1.

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TABLE I LAND-USE DISTRIBUTION OF THE DRAINAGE BASINS

Fig. 1. Fourteen subdrainage basins of the test site located in northern Finland. The test site is located south of Lokka reservoir (Lokan tekojärvi in the map).

The test site represents typical northern boreal forest: relatively sparse coniferous forests, with some open areas which are typically bogs. It consists of 14 river Kemijoki subdrainage basins, having a total area of 1162 km . The selected drainage basins build up the main river discharge source beneath the Lokka reservoir. The subdrainage basin classification was chosen because the subdrainage basins are used as computational units in the WSFS model [10]. Also the available reference data are divided according to the drainage basin classification. The drainage basin classification was available in 100 m 100 m spatial resolution which determined the used resolution of other data. The results of SCA estimation were analyzed independently for each subdrainage basin to allow the accuracy analysis to be compared with the subdrainage basin characteristics. The average drainage basin size was 83 km and was dominated by boreal forest. In average the amount of open areas was 4% and forest covered 62% of the area. Open bogs covered 15% and forested bogs covered 18% of the land area. The water pixels 1% were not used in this study. The main analyses were carried out by combining the open areas and the open bogs for the analyses of open areas. The forested areas and forested bogs were also combined, thus the main analyses were carried out for

two classes: open areas and forested areas. In average the open areas contained 18.6% and forests covered 80.4% of the land area. More extensive land-use characteristics for the drainage basins are shown in Table I. The land-use classification discriminates the forested areas according to stem volume classes (0-50 m /ha; 50–100 m /ha; 100–150 m /ha; 150–200 m /ha; over 200 m /ha). The land-use classification data have been produced by National Land Survey of Finland. The data are based on the National Forest Inventory data of the Finnish Forest Research Institute. The Forest Inventory data are based on cartographic data, ground truth sampling and Landsat Thematic Mapper imagery [11]. The spatial resolution of the land-use map is 25 m 25 m, and it was processed to 100 m 100 m for the data analysis. A digital elevation model (DEM) of the test site was used in the SAR image processing. The DEM has been produced by the National Land Survey of Finland and has a spatial resolution of 25 m 25 m. The elevation resolution is 10 cm. B. Satellite Data The satellite data consisted of 24 European Remote Sensing 2 (ERS-2) C-band SAR intensity images gathered during the snow melt seasons of years 1997, 1998, 2000, 2001, and 2002. The images are listed in Table II. The images for the years 1997 and 1998 were rectified and geocoded using software developed at Technical Research Centre of Finland (VTT). The software uses the Rauste algorithm [12], which employs a DEM to correct errors caused by topography. The images from years 2000, 2001, and 2002 were processed using Gamma Ltd. Software [13]. The Gamma software applies a correlation correction method for the image rectification, which results in very high spatial accuracy for the geocoding. All ERS-2 Precision Image (PRI) images were processed using the DEM resolution of 25 m, and thereafter the images were sampled into the spatial resolution of 100 m. The total amount of test cases, meaning the amount of subdrainage basins covered by the 24 images was 283. Of the 24 images, five represented the snow conditions of a wet snow with SCA near 100% and were chosen as the reference images for the beginning of snow melt season. Seven images

LUOJUS et al.: ACCURACY ASSESSMENT OF SAR

TABLE II ERS-2 SATELLITE DATASET

represented the situation with SCA near 0% and wet ground, and those were chosen as reference images for the end of snow melt season. The other 12 images represented the situation during the snow melt season with SCA between 0% and 100%. The SCA was estimated also for reference images. The data processing was conducted using the following procedure: first the images were calibrated, rectified, and geopositioned. Second step was the calculation of average backscattering coefficients for each drainage basin and each forest stem volume class inside a single basin. The third step was the forest compensation which was conducted independently for each drainage basin and for each image. The resulting mean backscattering coefficients for open and forested areas, for each drainage basin for each image, were then used in the linear interpolation phase of the SCA method, resulting in the final SCA estimates. Although the land-use classification, shown in Table I, had separate classes for bogs and nonbogs, this discrimination was not used. Open areas and open bogs were analyzed as one category, and forests and forested bogs were combined into a forest category. C. Reference Data The SCA estimation results were compared with data from model simulations of the Watershed Simulation and Forecasting System. The operative hydrological model WSFS simulates the hydrological cycle for all the land area of Finland daily [10]. Forecasts for river discharge and water levels are made for 5500 subdrainage basins, to which the 14 basins used in this study also belong. The WSFS functions by using the precipitation and temperature information as input and calculating the water level and

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river runoff for the drainage basins. Information on snow-covered area is produced as a by-product of the simulations. The SCA functions as a state variable in the simulations, and can also be used as an input for correcting the simulations and forecasts of the WSFS model. The WSFS is calibrated using ground truth data, which include: the actual river runoff data, measured temperature, and precipitation information; snow course measurements; and daily weather station measurements. The WSFS is managed by Finnish Environment Institute’s Hydrological services division, and according to their analyses the accuracy of the WSFS SCA is 20% RMSE. The accuracy characteristics of WSFS have been determined in comparison with operatively used optical remote sensing method (based on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer data). The characteristics are obtained using multiyear observations and spatial coverage of Finland, thus creating a very large dataset with 127 000 samples. The large dataset also indicates that WSFS-based SCA estimates are nonbiased. The accuracy of the operative optical remote sensing method has been evaluated against a large ground truth measurement dataset [1], [2], and has been shown to have a RMSE of 15%-units on average. Although WSFS-based SCA data have been shown to be nonbiased for large datasets, yet for a single drainage basin, during a single snow melt season the values predicted by it may include a slight deviation. For this study the WSFS data was the only suitable reference data, since it was the only available data with a temporal and spatial coverage sufficient to quantitative analysis of the satellite dataset. The reference data acquired from the WSFS covered the studied snow melt seasons completely. The data had separate values for all the 14 drainage basins and separate values for the open areas and the forested areas. The WSFS SCA data are produced on a daily basis. III. SNOW-COVERED AREA ESTIMATION METHOD A. TKK SCA Method The Snow Covered Area (SCA) method developed at TKK is a two step procedure for obtaining the SCA estimate from SAR intensity images during the snow melt season. The method has been developed for boreal forest zone, and has been formulated by [7] and [14]. The method is based on the behavior of snow backscattering during the spring snow melt season, reported in [15]–[17]. In C-band the level of backscattering from wet snow is generally lower than that of dry snow or bare ground. The backscattering coefficient typically decreases with increasing snow wetness. When the snow melt season is progressing, the snow layer melts and bare ground is revealed from underneath. Bare ground typically has a much higher level of backscattering when compared with wet snow; so as the amount of bare ground increases, the amount of backscattering also increases. When all the snow has melted and only bare ground is visible, the backscattering level is the highest. This temporal behavior of backscattering illustrated in Fig. 2 is utilized for SCA evaluation. The TKK SCA method requires the knowledge of the forest stem volume distribution of the target area and additionally two reference images are needed. One reference image describes the

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Fig. 2. Behavior of backscattering coefficient during the snow melt season.

wet snow situation in the beginning of snow melt season and the other describes the snow-free situation at the end of the snow melt season. The reference images define the backscattering levels for the SCA estimation, thus the acquisition instances for them are critical. The reference image for the beginning of snow melt season needs to be chosen so that the snow layer is wet and the ground is completely covered by snow. The backscattering coefficient of bare ground is mostly dependent of the soil moisture and the ground vegetation and since these parameters vary temporally, the optimal time for the reference image acquisition for the end of snow melt season is when all the snow has melted and the ground is still wet. The first step of the SCA estimation method is a forest canopy compensation which is done using forest stem volume information. The second step is the employment of linear interpolation algorithm which uses the reference images to calculate the snow-covered area estimate. B. Forest Compensation Phase The radar backscattering coefficient is a sum of backscattering signatures from different contributors. Some contributions are caused by the snow and ground layers, some are caused by the backscattering from forest canopy. The contribution from forest canopy is not related to the SCA and it is therefore a source of error. This error can be minimized by using the forest compensation method, which is based on the boreal forest backscattering model [18]–[20] developed at TKK. Using the semiempirical forest model the backscattering contribution of forest canopy is calculated. The calculated forest backscattering contribution is reduced from the total observed backscattering coefficient. This forest compensated value is then used in the second step of the SCA estimation. The boreal forest backscattering model describes the average observed backscattering coefficient as function of stem volume, and it is formulated for ERS-2 intensity images as [21]

Fig. 3. Forest compensation model visualized for May 12, 1997. Contribution from forest canopy is drawn with dotted line. Ground contribution is drawn with a dashed line, and the total modeled backscattering coefficient is drawn with a solid line. The observed backscattering coefficients are drawn with circles, and the model is fitted to these values. The forest compensated value is acquired from the point where stem volume reaches 0 m /ha. Additionally the observations for snow-free situation of May 18, 2001 are shown using triangles.

where is the forest stem volume [m /ha] and is related to the volumetric vegetation water content, parameter defines the backscattering coefficient of the surface (ground or snow-covered ground), and is the angle of incidence. The first defines the forest canopy backscattering contriterm of (1) bution. The second term defines the backscattering concompensated by the two-way tribution from the surface transmittivity through the forest canopy. The values are derived in dry summer conditions where is close to 1, and for wet snow situation during the melt season the values for and need to be solved. Using (1), the backscattering contribution for forest canopy can be solved by extracting backscattering coefficients representing different stem volume classes from the satellite images by using the forest stem volume information. The solving is done by nonlinearly fitting the observed backscattering coefficients to the model, where the parameters and are the variables to be optimized. For the optimization the backscattering for each stem volume class needs to be calculated. The minimization problem is written by (2) where is the number of stem volume classes, is the mean observed backscattering coefficient for the stem volume class . is the model predicted average backscatis used if the stem tering coefficient. The weighing factor volume classes are unevenly distributed. When the minimization has been conducted and the parameters for the backscattering coefficient are known, the solved variables and can be used with (1) to calculate the backscattering coefficient without the contribution from the forest canopy, corresponding to the backscattering with stem volume of 0 m /ha. This is the forest compensated backscattering coefficient which is then used in the second step of the SCA estimation; the linear interpolation phase. The basis of forest compensation is illustrated in Fig. 3. C. Linear Interpolation Phase

(1)

The linear interpolation method has been formulated by [7] and is briefly explained here. The method is based on the as-


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sumption that the observed surface backscattering coefficient is a linear combination of the backscattering from the area covered by snow and the area of snow-free ground. If the fraction of snow-covered ground is denoted by SCA then the area of snow-free ground is (1-SCA). The observed backscattering coefficient can then be written using the backscattering levels and the fraction of snow-covered area (3) is the total surface backscattering coefficient, is the backscattering coefficient from the snow-free is the backscattering coefficient from the ground, and snow-covered ground. When this (3) is solved for SCA, it is seen that SCA can be estimated for any studied image, when backscattering levels for the snow-free ground and snow-covered ground are known where

(4) is the observed surface backscattering coefficient where is the reference (modeled value for the forests), backscattering coefficient from the snow-free ground and is the reference backscattering coefficient from the wet snow-covered ground. Thus, using the knowledge of the reference backscattering signatures, the SCA can be solved from the observed backscattering coefficient. The linear interpolation is conducted independently for open and forested areas. For forests, the forest compensated backscattering coefficients are used in the evaluation, and the open areas are calculated by using the mean backscattering coefficients from the open areas. The linear interpolation algorithm can be used in a pixelwise manner for open areas, thus resulting in an independent SCA estimate for each image pixel. Pixelwise SCA estimation is however impractical since the uncertainty produced by radar speckle. Because of speckle and since the use of forest compensation requires the backscattering information from several stem volume classes; the SCA estimation is commonly done for larger areas. This is done by averaging the backscattering signatures from the selected areas, and calculating the SCA estimates for them. In this study the method was used with averaged backscattering coefficients from the observed subdrainage basins and the drainage basins each contained several thousands of pixels. The TKK SCA method produces separately formed SCA estimates for open and forested areas, for each subdrainage basin. These are readily in a suitable format which can be used in hydrological applications, e.g., with the WSFS; Watershed Simulation and Forecasting System. IV. METHODS FOR THE ACCURACY ANALYSES The statistical accuracy analysis was assessed by comparing the estimated SCA data to the WSFS reference data. The fact that the reference data have an accuracy of 20% RMSE needs to be considered in the overall interpretation of the results. It is presumable that the accuracy acquired in this study to be slightly pessimistic, because of the addition of statistical error from the reference data. Thus, the accuracy presented here can be con-

sidered as a “lower limit” for the accuracy of the TKK method. All the analyses were conducted independently for open areas and forested areas as the SCA method handles them separately. The complete dataset consisted of 24 images, of those, five were suitable as the reference for the wet snow situation, and seven images were used as the reference for the snow-free situation. Typical backscattering coefficients for wet snow-covered areas and dB; and for snow-free ground were between and dB. The SCA estimation was also conbetween ducted for the reference images and these were also part of the accuracy analysis. The accuracy analyses were carried out by calculating the RMS error between the reference data and the SCA estimates for each reference image combination. The average accuracy of all the reference image combinations yielded an estimate for the overall accuracy of the TKK SCA method. The optimal performance for the method was acquired from the results obtained with the most suitable reference image pair. This pair was determined by comparing the different combinations and selecting the pair which yielded the lowest RMS error. In addition to the RMS error, also mean absolute errors, biases and correlation coefficients were calculated for the SCA estimates in respect to the reference data. The SCA estimation was also considered categorically; meaning that the SCA estimation accuracy was measured independently for different SCA situations. This was accomplished by dividing the SCA estimates into ten separate intervals (0% to 10%, 10% to 20%, 20% to 90% to 100%), and calculating the average error for 30% each of them. The prevailing SCA conditions were determined according to the reference data. The accuracy analysis for the forest compensation algorithm was carried out by calculating the SCA estimates without the forest compensation algorithm, and comparing those with the forest compensated estimates. The nonforest-compensated estimates were acquired by averaging the backscattering coefficients from the different forest stem volume classes by the amount of pixels. Thus, meaning that the linear interpolation was carried out using the average backscattering value for the forested areas. The feasibility of multiyear reference data utilization was studied by dividing the dataset by the acquisition time. Data were studied according to snow melt seasons, and the most promising reference image pair was determined for each year. The selection was performed according to the RMS error between the SCA estimates and the reference data. The reference image pair which yielded the lowest RMS error for the studied year was chosen as the most suitable one. This evaluation was carried out for all snow melt seasons. The effect of topography was studied by comparing the error of the SCA estimation with the amount of topographical variations on the test area. The test site and the SCA estimates were divided into 14 drainage basins and the effect of topography was calculated using this classification. The amount of topographical variation was measured by calculating the standard deviation of elevation for each drainage basin. This was done using the DEM. The average RMS error for each drainage basin was then compared with the standard deviation of elevation. Correlation coefficient between the RMS errors and the topography was also calculated.

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TABLE III OVERALL ACCURACY ANALYSIS FOR THE TKK SCA METHOD

The effect of satellite flight direction on SCA estimation was studied by dividing the original dataset according to satellite flight direction and analyzing the accuracy characteristics for either the ascending or descending node independently. There were 11 images from ascending node and 13 images from descending node. Since the two datasets were studied separately, the reference image selection was carried out for each dataset once again. The best reference image pair was again chosen by minimizing the resulting RMS errors for each dataset.

Fig. 4. Estimated SCA values plotted in comparison with reference SCA values for the cases of best reference image pair (above) and the cases of all reference image combinations (below). For all the reference image combinations, the amount of samples were 7280. Thus, the graph shows the average SCA estimate and the standard deviation of the samples.

V. RESULTS AND DISCUSSION In this chapter the results of the analyses are presented along with the relevance and background for the studied phenomena. The statistical accuracy analysis was carried out for several statistical variables and the statistical interpretation was done with respect to several affecting parameters. The main goal was the assessment of the overall accuracy. Other important factors were the analysis of the forest compensation, the analysis of multiyear reference data use and the effects of topography and satellite flight direction on the SCA estimation accuracy. A. Overall Statistical Accuracy The first step of the analysis was to determine the overall accuracy of the SCA estimation. Since there were 5 7 different reference image combinations, there were also performance variations between the different reference image pairs. Thus, an average accuracy of all the reference image combinations can be viewed as the average performance of the method. The results that were acquired with the best reference image pair can be interpreted as the accuracy when the reference image selection succeeds. There were 7280 SCA estimates for different reference image combinations for both open and forested areas and 268 estimates for the best reference image pair. The results of the analyses are shown in Table III. The results show that the differences between the average accuracy obtained with all the reference image combinations and the accuracy obtained with the best reference image pair are quite considerable. This is reasonable, since it was predicted that the reference image selection has a great overall effect on the estimation results. The higher performance obtained with best reference image pair is remarkable in all aspects of the statistical accuracy analysis. The RMSE is improved by 24% in

the case of open areas, and 14% in the case of forested areas. The improvement in mean absolute error is 34% for open areas and 23% for forested areas. The bias is improved by 47% for open areas, but slightly weakened for forested areas. The linear correlation coefficients are also clearly improved in the case of the best reference image pair. These are visualized by plotting the estimated SCA in respect with the reference SCA values in Fig. 4. In addition to the overall accuracy analysis the performance of the method was analyzed by measuring the average RMSE for different SCA situations during the snow melt seasons. It has been reported in previous studies [2] that the SCA estimation accuracy can be dependent on the prevailing SCA conditions. The estimation errors are typically smaller near the beginning and end of the snow melt season and larger in the middle of the snow melt season. The results acquired in this study are shown in Fig. 5. An interesting behavior can be seen from the illustration. The estimation accuracies differ largely between the open and forested cases. The estimation accuracy for forested areas seems to be quite independent on the prevailing SCA situation. However, the estimation accuracy for the open areas seems to vary greatly with respect to the snow conditions. The accuracy near the beginning and the end of snow melt season are better than average, and the accuracy in the middle of the snow melt season is noticeable inferior than the average accuracy. A definite reason for this behavior was not determined but when the Fig. 4 is studied, it can be seen that the estimates for open areas have quite strong bias and it appears that the transition in backscattering from wet snow to bare ground is not linear. If a nonlinear fitting was made instead of the linear interpolation, the result might be better. The reason for the different


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Fig. 5. Estimation error plotted for various SCA intervals. The solid bars show the average RMS errors for the respective intervals and the dotted line shows the average RMS error for all the cases. The results shown are for all reference image combinations.

Fig. 6. Estimation error plotted for various SCA intervals. The solid bars show the average RMS errors for the respective intervals and the dotted line shows the average RMS error for all the cases. The results shown are for the best reference image pair combination.

behavior of open and forested areas may be caused by the difference in snow melting process. On the open areas the snow pack is greatly affected by the direct heating of sun, whereas on the forested areas the snow pack is much less affected by the sun. Thus, the composition and structure of the snow pack can deform much more on the open areas, and on the forested areas the snow pack tends to be more homogenous, at a given date and in space. Also the snow wetness and the snow roughness may vary more dramatically on the open areas because of the solar heating and differential melting. The SCA estimation accuracy depending on the prevailing SCA conditions was also analyzed for the best reference image pair. The results of the analysis are shown in Fig. 6. The Figure shows another interesting aspect of the TKK SCA method. When comparing Figs. 5 and 6, it can be noted that the overall behavior is similar on both cases, with one exception; in the case of forests the estimation error for the best reference image pair is noticeably larger for the end of the snow melt season when compared to the case of all reference image combinations. This emphasizes the effect of reference image selection. Even when the reference image selection is successful, and the best reference image for the given dataset is found, there can still be a slight bias near one of the reference image situations. One way to eliminate this would be to select the reference image pair independently for open and forested areas. For the images with spatially extensive coverage, the reference images or situations should be determined independently for each drainage basin, since the beginning and the end of the snow melt season typically varies in regard to the basins geographical location.

TABLE IV SIGNIFICANCE OF FOREST COMPENSATION

canopy backscattering in order to increase the SCA estimation accuracy. The results of the statistical analysis for forest compensation are shown in Table IV. The results indicate that the forest compensation improves the SCA estimation accuracy. The improvement is seen in all statistical variables except the bias. The RMSE and the correlation coefficient show the highest improvement. The statistical significances of the improvements were tested using chi-squared test. The improvements were statistically significant with 99.9% confidence interval for all the cases, and 97.5% interval for the best reference image pair. As a conclusion, the forest compensation can be considered successful and a beneficial addition to the SCA estimation method. C. Usability of Multiyear Reference Data

B. Effect of Forest Compensation The forest compensation is a key element of the TKK SCA method. The purpose of it is to minimize the effect of forest

A key issue of the TKK SCA method is the utilization of reference images for the SCA estimation. Thus, to interpret the SCA for one image, two additional images are needed. Since one of

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TABLE V OPTIMAL REFERENCE IMAGE PAIRS FOR EACH SNOW MELT SEASON

the reference images needs to be taken at the end of the snow melt season, it is essential to be able to use historical reference data, if the SCA estimation is done in real time. In order to study this aspect a temporally extensive dataset is required. The used dataset contained images from years 1997, 1998, 2000, 2001, and 2002. Suitable reference images were available from years 1997, 2000, 2001, and 2002. Thus, the dataset was well suited for investigating the issue. The results of the multiyear reference data analysis are shown in Table V. The results show that the best reference image pairs were often not the ones acquired during the same snow melt season. It can be seen that for the majority of the cases the reference image pair which yielded the best SCA estimates was composed of two images from different snow melt seasons. In six out of ten cases, the best reference image for the beginning of snow melt season was from year 1997. In two cases, it was from 2000 and in two cases it was from 2002. The most suitable reference image for the end of the snow melt was in seven cases the image from year 2001, and in three cases it was from 1997. So it seems quite clear that the reference images do not need to be from the same snow melt season as the actual image being analyzed; thus the TKK SCA method can be implemented using multiyear reference images.

Fig. 7. Correlation between standard deviation of elevation and RMS error of SCA estimation. TABLE VI EFFECT OF SATELLITE FLIGHT DIRECTION ON SCA ESTIMATION

D. Effect of Topography The satellite images are taken from either an ascending or a descending node of the satellites orbit. In order to maximize the temporal coverage of the imaging period for each snow melt season, satellite images are usually utilized from both nodes. The images from different nodes are slightly dissimilar, since the imaging geometry is different and the topographical formations of earth portray differently when seen from different nodes. These differences in satellite images are a possible source of error for the SCA estimation and were also studied. The results show that the correlation is positive, which means that the amount of RMS error increases when the amount of topography increases. The correlation coefficients are 0.618 for open and 0.740 for forested areas. The correlation is reasonably high and can be clearly seen from Fig. 7. The correlation is clearly visible and a fitted trend line is also shown along the data. The study suggests that the accuracy of SCA estimation is dependent on the amount of topography of the target area. This information could be used to predict the accuracy of obtained SCA estimates in some degree. It is, however, important to note

that the results acquired in this study are from a region with relatively small variations on the topography, and do not necessarily apply to mountainous regions as such. E. Effect of Satellite Flight Direction The topographical variations affect the SCA estimation because the satellite images that are analyzed may be taken with different imaging geometry than the reference images. An interesting aspect was to investigate whether the accuracy of SCA estimation could be increased by conducting the SCA estimation using reference images from only the same node as the analyzed image. The comparison was done for results acquired with the best reference image combinations and are shown in Table VI. The results show that the SCA estimation accuracy with ascending and descending node separated data seems quite similar when compared with the accuracy from the original dataset. Only the results from forested areas are improved slightly. It appears that the ascending and descending node separation does not affect the estimation accuracy in a serious


Fig. 8.

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SCA estimation visualized for May 28, 1997, open areas.

way. The effect of ascending and descending node separation might be greater in areas with high topographical variations, which, however is typically not the case in Finland or the area studied here. Thus, the SCA estimation can be conducted by mixing images and reference data from both nodes. Based on these observations, it can also be assumed that the slight variation of the incidence angle of the ERS-2 PRI data does not hinder the SCA estimation either, since the variations in the incidence angle were greatly magnified in combining the images from different nodes, and these did not significantly alter the estimation accuracy. F. Visual Interpretation of the SCA Estimation A visual interpretation of the SCA estimation is presented in Fig. 8. The SCA map presents the situation for May 28, 1997 on open areas; the estimation is drawn on top of the ERS-2 SAR PRI image. This image is acquired in the middle of the snow melt season, so a noticeable variation of SCA situation within the image is seen. The variation on the intensity of backscattering can also be seen in the image background. The backscattering near the drainage basins with high SCA on the east is weaker and the backscattering in the west where SCA is closer to zero is stronger. This is the fundamental behavior of backscattering during the snow melt season, which was also explained by Fig. 2. The behavior of the SCA estimation can also be qualitatively analyzed by plotting the SCA estimates with respect to the WSFS reference data during the snow melt season. Using the visualization it is easier to see the possible false results. An example of this visualization is shown in Fig. 9. The visualization is demonstrated for six drainage basins for the snow melt season of 1997. VI. CONCLUSION The purpose of this study was to resolve the statistical accuracy of SAR-based SCA estimation. This was carried out by analyzing the performance of TKK SCA method. Information of

Fig. 9. Visualization of the SCA estimates in respect with the WSFS reference data for snow melt season 1997. The solid line shows the reference for open areas. The dashed line shows the reference for forested areas. The crosses present the open area SCA estimates and the circles present the SCA estimates for forested areas.

the statistical accuracy is required in order to integrate the SCA method for the Finnish Environment Institute’s operational Watershed Simulation and Forecasting System in future. The statistical accuracy analysis was also an effective way to demonstrate the overall usability of the TKK SCA estimation method for the monitoring of snow melt seasons. This study was carried out by comparing the SAR SCA estimates to a reference data acquired through hydrological modeling. The reference data have a known accuracy of 20% RMSE, thus the accuracy results acquired in this study might show a slightly pessimistic figure for the performance of the TKK SCA method. The results of this study show that the TKK SCA method works well in boreal forest region with a stem volume range of 0–250 m /ha. The analysis shows a fair accuracy for open areas and the results for forested areas are significantly better. The results for the effect of forest compensation on the SCA estimation show a consistent improvement in the estimation accuracy and the improvement was determined statistically significant with 99.9% confidence interval for the complete dataset, and 97.5% interval for the best reference image pair case. These results confirm the functioning and justify the use of the forest compensation algorithm. The findings of this study indicate that the SCA estimation, using the TKK SCA method, seems more accurate for the forested areas than for the open areas. Additionally the SCA estimation on forested areas seems to work well regardless of the SCA conditions, whereas the estimation accuracy on open areas seems to be dependent on the prevailing SCA conditions. This could be improved by using a nonlinear interpolation for the SCA estimation of open areas. This is an obvious possibility for future studies.

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One of the key aspects of the TKK SCA estimation method is the utilization of two reference images in the estimation process. Since the primary applications of SCA estimation require real time snow monitoring, the use of reference images from previous snow melt seasons are typically required. The results of the data analysis show that the utilization of multiyear reference data is feasible with the TKK method. The effect of topography variations on the SCA estimation was conducted by comparing the accuracy analysis results to the topography characteristics of the studied drainage basins. A noticeable correlation between the amount of topography and the RMS errors of SCA estimates was discovered and this can possibly be used to determine the accuracy for the SCA estimates in some degree, in nonmountainous regions. The flight direction of the satellite affects the SAR imaging by the variation of imaging geometry and through different portraying of topography on the SAR images. SCA estimation with ascending and descending node separated data was studied and the results show that the separation does not affect the SCA estimation in a significant way. REFERENCES [1] S. Metsämäki, J. Vepsäläinen, J. Pulliainen, and Y. Sucksdorff, “Improved linear interpolation method for the estimation of snow-covered area from optical data,” Remote Sens. Environ., vol. 82, pp. 64–78, 2002. [2] S. Metsämäki, S. Anttila, M. Huttunen, and J. Vepsäläinen, “A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model,” Remote Sens. Environ., vol. 95, pp. 77–95, 2005. [3] H. Rott, “The analysis of backscattering properties from SAR data of mountain region,” IEEE J. Oceanic Eng., vol. JOE-9, no. 5, pp. 347–355, Dec. 1984. [4] J. Koskinen, L. Kurvonen, V. Jääskeläinen, and M. Hallikainen, “Capability of radar and microwave radiometer to classify snow types in forested areas,” in Proc. IGARSS, Pasadena, CA, 1994, pp. 1283–1286. [5] J. Piesbergen, F. Holecz, and H. Haefner, “Snow cover monitoring using multitemporal ERS-1 SAR data,” in Proc. IGARSS, Florence, Italy, 1995, pp. 1750–1752. [6] T. Guneriussen, H. Johnsen, and K. Sand, “DEM corrected ERS-1 SAR data for snow monitoring,” Int. J. Remote Sens., vol. 17, no. 1, 1996. [7] J. Koskinen, J. Pulliainen, and M. Hallikainen, “The use of ERS-1 SAR data in snow melt monitoring,” IEEE Trans. Geosci. Remote Sens., vol. 35, no. 3, pp. 601–610, May 1997. [8] T. Nagler and H. Rott, “SAR tools for snowmelt modeling in the project HydAlp,” in Proc. IGARSS, vol. 3, Seattle, WA, Jul. 6–10, 1998, pp. 1521–1523. , “Retrieval of wet snow by means of multitemporal SAR data,” [9] IEEE Trans. Geosci. Remote Sens., vol. 38, no. 2, pp. 754–765, Mar. 2000. [10] B. Vehviläinen, “The watershed simulation and forecasting system in the National Board of Waters and Environment,” in Publications of the Water and Environment Research Institute. Helsinki, Finland: Nat. Board Waters Environ., 1994. [11] J. Paavilainen, T. Siltala, and A. Vertanen, “Digital land-use map, product spesification,” Nat. Board of Surv., Dept. Remote Sens., Helsinki, Finland, 1992. [12] Y. Rauste, “Methods for analysing SAR images,” Tech. Res. Centre of Finland, Espoo, VTT Res. Rep. 612, 1989. [13] U. Wegmüller, C. Werner, and T. Strozzi, “SAR interferometric and differential interferometric processing chain,” in Proc. IGARSS, vol. 2, Seattle, WA, Jul. 6–10, 1998, pp. 1106–1108. [14] J. Pulliainen, J. Koskinen, and M. Hallikainen, “Compensation of forest canopy effects in the estimation of snow covered area from SAR data,” in Proc. IGARSS, Sydney, Australia, Jul. 9–13, 2001, pp. 813–815.

[15] W. Stiles and F. Ulaby, “The active and passive microwave response to snow parameters: Part I—Wetness,” J. Geophys. Res., vol. 85, pp. 1037–1044, 1980. [16] F. Ulaby, R. Moore, and A. Fung, Microwave Remote Sensing, Active and Passive. Norwell, MA: Artech House, 1986, vol. 3. [17] T. Strozzi, A. Wiesmann, and C. Mätzler, “Active microwave signatures of snow covers at 5.3 and 35 GHz,” Radio Sci., vol. 32, pp. 479–495, 1997. [18] J. Pulliainen, K. Heiska, J. Hyyppä, and M. Hallikainen, “Backscattering properties of boreal forests at the C- and X-band,” IEEE Trans. Geosci. Remote Sens., vol. 32, no. 5, pp. 1041–1050, Sep. 1994. [19] J. Pulliainen, P. Mikkelä, J.-P. Ikonen, and M. Hallikainen, “Seasonal dynamics of C-band backscatter of boreal forests with applications to biomass and soil moisture estimation,” IEEE Trans. Geosci. Remote Sens., vol. 34, no. 3, pp. 758–770, May 1996. [20] J. Pulliainen, L. Kurvonen, and M. Hallikainen, “Multi-temporal behavior of L- and C-band SAR observations of boreal forest,” IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 927–937, Mar. 1999. [21] J. Pulliainen, M. Engdahl, and M. Hallikainen, “Feasibility of multitemporal interferometric SAR data for stand-level estimation of boreal forest stem volume,” Remote Sens. Environ., vol. 85, pp. 397–409, 2003.

Kari P. Luojus (S’04) was born in 1978 in Vantaa, Finland. He received the M.Sc. degree from the Helsinki University of Technology (TKK), Espoo, Finland, in 2004. He is currently pursuing the Ph.D. degree in technology at TKK. Since 2004, he has been a Research Scientist with the Laboratory of Space Technology, TKK. His research interests include the development of active microwave remote sensing techniques for cryospheric and hydrological applications. He has authored and coauthored 11 scientific publications on microwave remote sensing.

Jouni T. Pulliainen (S’91–M’95–SM’03) received the M.Sc., Lic.Tech., and Dr.Sci.Tech. degrees from the Helsinki University of Technology (TKK), Espoo, Finland, in 1988, 1991, and 1994, respectively. Since 2001, he has been a Professor of space technology at TKK, specializing in remote sensing. He is currently working with the Laboratory of Space Technology, TKK. His research interests include direct and inverse modeling in remote sensing, and additionally, remote sensing data assimilation and application development. Recently, his work has focused on the active and passive remote sensing of boreal forest zone, snow cover and lake/coastal waters applying both microwave and optical data. He has directed several remote sensing research projects in the Laboratory of Space Technology. He has been a Principal Investigator for several nationally funded research projects, TKK Assistant Project Manager of EC projects in the 5th framework Programme, and Project Manager for several ESA contracts. From 1993 to 1994, he was Acting Director of the Laboratory of Space Technology. He has authored over 220 scientific papers and technical reports in the field of remote sensing. Dr. Pulliainen is a member of the ESA Advisory Committee on Education (2001–present) and Co-Chair of EARSeL Interest Group on Forestry (2003present).

Sari J. Metsämäki was born in 1965 in Helsinki, Finland. She received the M.Sc. degree from the Helsinki University of Technology (TKK), Espoo, Finland, in 1991. Since then, she has acted as a Remote Sensing Scientist in the Finnish Environment Institute, Helsinki. Her major scientific interest is in optical remote sensing of snow, particularly related to hydrological modeling.


Martti T. Hallikainen (M’83–SM’85–F’93) received the M.Sc. degree in engineering and the Dr.Sci.Tech. degree from the Helsinki University of Technology (TKK), Espoo, Finland, in 1971 and 1980, respectively. Since 1987, he has been a Professor of Space Technology at TKK. In 1988, he established the TKK Laboratory of Space Technology and serves as its Director. He was a Visiting Scientist from 1993 to 1994 at the European Union’s Joint Research Centre, Institute for Remote Sensing Applications, Ispra, Italy. He was a Postdoctoral Fellow at the Remote Sensing Laboratory, University of Kansas, Lawrence, from 1981 to 1983, and was awarded an ASLA Fulbright Scholarship for graduate studies at the University of Texas, Austin, in 1974–1975. His research interests include development of microwave sensors for airborne and spaceborne remote sensing, and development of methods to retrieve the characteristics of geophysical targets using satellite and airborne measurements. Currently his team is involved in the development of the interferometric L-band radiometer MIRAS for the ESA SMOS satellite and a similar airborne instrument (HUT-2D). Concerning remote sensing applications his main topic is the cryosphere including snow, sea ice, and boreal forest. He is an author/coauthor of over 500 scientific publications. Dr. Hallikainen served as President of IEEE Geoscience and Remote Sensing Society (IEEE GRSS) in 1996 and 1997, and as Vice President in 1994 and 1995. Since 1988, he has been a member of the IEEE GRSS Administrative Committee, and from 1999 to 2001, he served as the IEEE GRSS Nominations Committee Chair and since 2002, as the Fellow Search Committee Chair. He was the General Chairman of the IGARSS’91 Symposium and Guest Editor of the Special IGARSS’91 Issue of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGARS). Since 1992, he has been an Associate Editor of TGARS. He was a member of the IEEE Periodicals Committee in 1997 and Corresponding member of the IEEE New Technology Directions Committee from 1992 to 1995. He was Secretary General of the European Association of Remote Sensing Laboratories (EARSeL) from 1989 to 1993 and Chairman of the Organizing Committee for the EARSeL 1989 General Assembly and Symposium. He has been a member of the EARSeL Council since 1985, and he was a member of the Editorial Board of the EARSeL Advances in Remote Sensing from 1992 to 1993. He has been a member of the European Space Agency’s (ESA) Earth Science Advisory Committee since 1998 and a member of the ESA SMOS Scientific Advisory Group since 2000. He was a national delegate to the ESA Earth Observation Scientific and Technical Advisory Group (EOSTAG) from 1988 to 1994, and he has served in the same capacity on the ESA Earth Observation Data Operations Scientific and Technical Advisory Group (DOSTAG) since 1995. He was Thematic Coordinator of the ESA EMAC-95 airborne campaign for Snow and Ice activities. He was a member of the ESA Multi-frequency Imaging Microwave Radiometer (MIMR) Expert Group from 1988 to 1994 and was a member of the ESA MIMR Scientific Advisory Group from 1994 to 1996. Since 1992, he has been a member of both the Advisory Committee for the European Microwave Signature Laboratory of the European Union’s Joint Research Centre and the National Liaison of the International Space University. He is currently serving as Chair of Commission F International Union of Radio Science (URSI) from 2002 to 2005 and has served as its Vice Chair from 1999 to 2002. He was a member of the URSI Long Range Planning Committee from 1996 to 1999, a member of the URSI Committee on Geosphere and Biosphere Program from 1989 to 1999, and a URSI representative to SCOR from 1999 to 2002. He has been a national official member of URSI Commission F (Wave Propagation and Remote Sensing) since 1988. He was Secretary of the Organizing Committee for the URSI Nordic Antenna Symposium in 1976, and he served as Secretary of the Finnish National Committee of URSI from 1975 to 1989. He was Vice Chair of the URSI Finnish National Committee from 1990 to 1996, and he has served as its Chair since 1997. He is Vice Chair of the Finnish National Committee of COSPAR since 2000. He is the recipient of three IEEE GRSS Awards: 1999 Distinguished Achievement Award, IGARSS’96 Interactive Paper Award, and 1994 Outstanding Service Award. He is the winner of the Microwave Prize for the best paper in the 1992 European Microwave Conference, and he received the TKK Foundation Award for excellence in research in 1990. He and his research team received the 1989 National Research Project of the Year Award from Tekniikka & Talous (Technology & Management Magazine). He received the 1984 Editorial Board Prize of Sähkö—Electricity in Finland.

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