Sébastien Rapinel

Julie Betbeder

Julien Denize

Élodie Fabre

Éric Pottier

Laurence Hubert-Moy

SAR analysis of wetland ecosystems: Effects of band frequency, polarization mode and acquisition dates

Keywords: ANOVA, Conservation, RADARSAT-2, TerraSAR-X, Random forest, Vegetation

The availability of high spatial resolution synthetic aperture radar (SAR) sensors with a wide range of acquisition modes has increased greatly over the past decade and contributed significantly to the study of wetland ecosystems. However, the relative influence of acquisition configurations (i.e. band frequency, polarization mode, number of acquisition dates) in wetland analysis remains poorly explored. This article investigated the relative influence of X-/C-band frequency, dual-/quad-polarization and single-/multiacquisition features on discrimination of vegetation types in 632 ha Ramsar-protected temperate riverine marshes (Mont-Saint-Michel Bay, France). Three SAR datasets (i.e. quad-pol/C-band, dual-pol/C-band and dual-pol/X-band) were generated from five pairs of TerraSAR-X and RADARSAT-2 images. First, a set of 25 SAR features, including backscattering coefficients and polarimetric parameters, was extracted from the SAR datasets. Second, correlation between each pair of images was calculated using the polarimetric parameter Shannon entropy to select the most similar pairs in the time series. Third, the importance of each SAR feature and modeling accuracy were calculated using a conditional random forest model for each of the three datasets. Finally, analysis of variance was performed to assess the impact of band frequency, polarization mode and number of acquisition dates on the classification of vegetation types. The results highlighted that although the time-shift of each pair of TerraSAR-X and RADARSAT-2 images was short (3-11 days), only three pairs were sufficiently similar, highlighting the high variability in wetland ecosystems. The polarimetric parameter Shannon entropy was the most discriminating feature, regardless of the frequency or polarization. Most variance in the model accuracy was explained by the number of acquisition dates (68%), followed by the frequency (23%), while polarization explained little. This article will help select the most suitable SAR sensor acquisition modes for wetland conservation.

Introduction

Wetland conservation is critically important to the planet, since wetlands provide hydrological, biogeochemical and ecological functions [START_REF] Maltby | The Wetlands Handbook[END_REF]. However, most of these ecosystems are currently damaged or threatened, especially due to human modifications and agricultural intensification [START_REF] Kingsford | Wetlands: Conservation's Poor Cousins[END_REF]. Managing and monitoring these highly dynamic and fine-grained patterned wetland ecosystems remain challenging due to limited field observations in time and space (S. [START_REF] Rapinel | Mapping Potential, Existing and Efficient Wetlands Using Free Remote Sensing Data[END_REF] and the limitations of optical remote sensing images due to cloud cover [START_REF] Gallant | The Challenges of Remote Monitoring of Wetlands[END_REF]. In this context, many studies have recently highlighted the advantage of very high spatial resolution (1-10 m) SAR satellite data, such as ALOS-2, RADARSAT-2, TerraSAR-X or Sentinel-1 images, for monitoring and characterizing wetlands [START_REF] Adeli | Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review[END_REF][START_REF] Mahdavi | Remote Sensing for Wetland Classification: A Comprehensive Review[END_REF][START_REF] Wohlfart | TerraSAR-X and Wetlands: A Review[END_REF]). However, managers and scientists who use SAR data to study wetland ecosystems could be confused by the wide range of possible configurations of these data, such as polarization (dual-or quad-pol), frequency (C-, X-or L-band) and the number of acquisition dates, especially since the configuration influences the ability to discriminate different vegetation types [START_REF] Mahdavi | Remote Sensing for Wetland Classification: A Comprehensive Review[END_REF]. In this context, [START_REF] Adeli | Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review[END_REF] recently highlighted the absence of consensus on the most appropriate SAR configuration to characterize wetlands and the need for future research. [START_REF] Lanoé | Diagnostics Phyto-Écologique et Paysager Des Marais de La Basse Vallée Du Couesnon et de Leur Rôle Fonctionnel Potentiel En Terme d'accueil de l'avifaune[END_REF], (d) field photograph of a hygrophilic grassland with Cardamine pratensis plant species (white flowers) and (e) field photograph of a pond surrounded by Eleocharis palustris plant species (long-flooded grassland). SAR analysis of wetland ecosystems is particularly challenging given (i) the highly hydrodynamic nature of wetlands, with a flooded area that can change by several ha in a few days [START_REF] Gallant | The Challenges of Remote Monitoring of Wetlands[END_REF][START_REF] Rapinel | Daily Monitoring of Shallow and Fine-Grained Water Patterns in Wet Grasslands Combining Aerial LiDAR Data and In Situ Piezometric Measurements[END_REF], and (ii) the strong impact of water on the SAR impulse response of wetland vegetation [START_REF] Cazals | Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images[END_REF][START_REF] Mahdianpari | An Assessment of Simulated Compact Polarimetric SAR Data for Wetland Classification Using Random Forest Algorithm[END_REF]. Unfortunately, multi-sensor SAR data studies rarely considered the high spatiotemporal dynamics of wetland ecosystems. For example, RADARSAT-2 (quad-pol/C-band), Sentinel-1 (dual-pol/C-band) and ALOS-2 (dual-pol/L-band) data acquired with time-shifts of 1-19 days were compared to discriminate peatland vegetation [START_REF] Amani | Separability Analysis of Wetlands in Canada Using Multi-Source SAR Data[END_REF]. Elsewhere, two time series, TerraSAR-X (dual-pol/X-band) and RADARSAT-2 (quad-pol/Cband), were acquired with 4-month time-shifts to map boreal wetlands (Mohammadimanesh, Salehi, Mahdianpari, Motagh, et al. 2018). In another study, one ALOS-1 (quad-pol/L-band) and one Sentinel-1 (dualpol/C-band) image with a 7-year time-shift were compared to delineate a wetland marsh [START_REF] Simioni | Integration of Multi-Sensor Analysis and Decision Tree for Evaluation of Dual and Quad-Pol SAR in L-and C-Bands Applied for Marsh Delineation[END_REF]. Thus, new comparative analyses of band fre-quency, polarization mode and number of acquisition dates with SAR data acquired under similar hydrological and phenological conditions are needed.

The ability of SAR data to characterize wetlands also relies on the features used [START_REF] Mahdavi | Remote Sensing for Wetland Classification: A Comprehensive Review[END_REF]. In this perspective, the relative influence of SAR features (i.e. polarimetric parameters and backscattering coefficients) on discrimination of wetland vegetation has been investigated extensively, although the conclusions differ depending on the features considered, the conditions under which SAR data were acquired and wetland type. For example, for quadpol C-band RADARSAT-2 data, some studies have demonstrated the advantage of polarimetric parameters from Cloude-Pottier decomposition over those from Freeman-Durden decomposition and backscattering coefficients to discriminate herbaceous [START_REF] Morandeira | Mapping Plant Functional Types in Floodplain Wetlands: An Analysis of C-Band Polarimetric SAR Data from RADARSAT-2[END_REF] or boreal (B. [START_REF] Brisco | Evaluation of C-Band Polarization Diversity and Polarimetry for Wetland Mapping[END_REF]) vegetation types. Conversely, other studies using also quadpol RADARSAT-2 data have highlighted the advantage of polarimetric parameters from Freeman-Durden decomposition over those from Cloude-Pottier decom-position to discriminate vegetation types in várzea forests (de Almeida Furtado, Silva, and de Moraes Novo 2016) or boreal wetlands [START_REF] Mahdianpari | Fisher Linear Discriminant Analysis of Coherency Matrix for Wetland Classification Using PolSAR Imagery[END_REF][START_REF] Mahdianpari | An Assessment of Simulated Compact Polarimetric SAR Data for Wetland Classification Using Random Forest Algorithm[END_REF]. In another study, the advantage of backscattering coefficients over polarimetric parameters from Freeman-Durden or Cloude-Pottier decomposition derived from L-band ALOS-2 and C-band RADARSAT-2 was highlighted for the same boreal wetland site [START_REF] Amani | Separability Analysis of Wetlands in Canada Using Multi-Source SAR Data[END_REF]Mohammadimanesh, Salehi, Mah-dianpari, Motagh, et al. 2018). In addition, while polarimetric parameters are frequently used in the quadpol configuration, they are rarely used in the dual-pol configuration, for example for X-band TerraSAR-X or C-band Sentinel-1 data [START_REF] Amani | Separability Analysis of Wetlands in Canada Using Multi-Source SAR Data[END_REF][START_REF] Betbeder | TerraSAR-X Dual-Pol Time-Series for Mapping of Wetland Vegetation[END_REF]. Thus, the relative advantages of backscattering coefficients and polarimetric parameters for discriminating wetland vegetation types depending on the frequency and polarization of the SAR data remained an open issue and needed to be investigated more thoroughly.

Woods

Salix alba

Broadleaf tree 6.0 -20.0 < 1

Populus alba

Mesophilic grasslands

Festuca arundinacea Herbaceous 0.2 -0.5 < 1

Arrhenatherum elatius

Meso-hygrophilic grasslands

Anthoxanthum odoratum Herbaceous 0.1 -0.3 2 -3 Bromus racemosus

Hygrophilic grasslands

Oenanthe fistulosa Herbaceous 0.1 -0.2 3 -6 Cardamine pratensis

Long-flooded grasslands

Glyceria fluitans Herbaceous 0.1 -0.2 6 -8 Eleocharis palustris

Ponds

Myriophyllum spicatum Standing water 0.0 -0.1 12

Lemna minor

The influence of SAR data-acquisition configurations (i.e. band frequency, polarization mode, number of acquisition dates) on discrimination of wetland vegetation has been studied extensively in recent years [START_REF] Mahdavi | A Comparison between Different Synthetic Aperture Radar (SAR) Sensors for Wetland Classification[END_REF][START_REF] Wohlfart | TerraSAR-X and Wetlands: A Review[END_REF]. Regarding band frequency, the advantage of the X-band over the C-band [START_REF] Maleki | Analysis of Multi-Frequency and Multi-Polarization SAR Data for Wetland Mapping in Hamoun-e-Hirmand Wetland[END_REF]Mohammadimanesh, Salehi, Mahdianpari, Brian Brisco, andMotagh 2018) andS-band (van Beijma, Comber, and[START_REF] Van Beijma | Random Forest Classification of Salt Marsh Vegetation Habitats Using Quad-Polarimetric Airborne SAR, Elevation and Optical RS Data[END_REF] for discriminating wetland vegetation types has been described, as has the advantage of the Lband over the C-band for mapping emergent [START_REF] Simioni | Integration of Multi-Sensor Analysis and Decision Tree for Evaluation of Dual and Quad-Pol SAR in L-and C-Bands Applied for Marsh Delineation[END_REF] or woody [START_REF] Amani | Separability Analysis of Wetlands in Canada Using Multi-Source SAR Data[END_REF][START_REF] Mahdianpari | Random Forest Wetland Classification Using ALOS-2 L-Band, RADARSAT-2 C-Band, and TerraSAR-X Imagery[END_REF] vegetations. The literature also describes the advantage of quad-polarized data over dual-polarized data to discriminate woody (de Almeida Furtado, Silva, and de Moraes Novo 2016; Mahdianpari, Salehi, Mohammadimanesh, and Motagh 2017) [START_REF] Almeida Furtado | Dual-Season and Full-Polarimetric C Band SAR Assessment for Vegetation Mapping in the Amazon Várzea Wetlands[END_REF], Mahdianpari et al., 2017a) or boreal (Mahdianpari, Salehi, and Mohammadimanesh 2017) wet-land vegetation types. In addition, there is clear evidence that using multi-date data instead of singledate data for classifications increases the accuracy of wetland vegetation mapping significantly [START_REF] Banks | Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests[END_REF][START_REF] Mahdianpari | Fisher Linear Discriminant Analysis of Coherency Matrix for Wetland Classification Using PolSAR Imagery[END_REF][START_REF] Schuster | Grassland Habitat Mapping by Intra-Annual Time Series Analysis -Comparison of RapidEye and TerraSAR-X Satellite Data[END_REF], especially in spring images [START_REF] Betbeder | TerraSAR-X Dual-Pol Time-Series for Mapping of Wetland Vegetation[END_REF][START_REF] Jahncke | Mapping Wetlands in Nova Scotia with Multi-Beam RADARSAT-2 Polarimetric SAR, Optical Satellite Imagery, and Lidar Data[END_REF]). However, the relative influence of each acquisition configuration on discrimination of vegetation types in wetland ecosystems has never been addressed. This issue is crucial because the choice of which SAR data to acquire to study wetland ecosystems is a compromise among band frequency, polarization mode and number of acquisition dates.

This study investigated the relative influence of three SAR acquisition configurations (i.e. bandfrequency, polarization mode and number of acquisition dates) on discrimination of wetland vegetation types. To this end, we investigated three questions:

(i) How similar are the acquisition parameters of SAR data collected by different sensors in wetland ecosystems? (ii) Are the most discriminating SAR features of wetland vegetation the same regardless of the polarization or frequency used? (iii) Is the influence of band frequency, polarization mode and number of acquisition dates the same regardless of vegetation type?

To answer these questions, 25 features were generated from RADARSAT-2 and TerraSAR-X images acquired over temperate riverine marshes. Random forest (RF) models were then used to discriminate seven wetland vegetation types, and analysis of variance (ANOVA) of classification accuracy scores was performed. The strengths and weaknesses of the method developed are discussed.

2 Materials and methods

Study site and reference data

The study focuses on 632 ha of wet grassland marshes in the Couesnon River floodplain, near Mont-Saint-Michel Bay, France (48.52°N, 1.53°W). The climate is temperate and humid (annual rainfall 1000 mm) throughout the year. The site lies within the boundaries of environmentally protected areas (i.e. European Union Natura 2000 (1992/43/EEC) and international Ramsar). The topography is flat, with elevation 6-7 m above sea level. Grasslands are managed exclusively for mowing or alternate grazing/mowing cycles. A field vegetation map was created in 2008 at 1:2,500 scale from phytosociological surveys [START_REF] Lanoé | Diagnostics Phyto-Écologique et Paysager Des Marais de La Basse Vallée Du Couesnon et de Leur Rôle Fonctionnel Potentiel En Terme d'accueil de l'avifaune[END_REF]. It highlights the dominance of four grassland vegetation types (Fig. 1) patterned by a gradient of flooding that ranges from 0 to 8 months/year [START_REF] Marechal | One Year Wetland Survey Investigations from a Quad-Pol Radarsat-2 Time Series SAR Images[END_REF] . The site contains smaller areas of three other vegetation types: ponds, woods and crops (Table 1).

Reference plots were extracted from a vector map of field vegetation. Additional field observations in 2013 (Fig. 1d-e) confirmed that the vegetation pattern in the study area had remained stable from 2008 to 2013. To avoid mixed pixels, each of the 182 reference plots was selected in GIS within 3 × 3 SAR-pixel windows contained strictly within a polygon of a given vegetation type.

Satellite data

The initial satellite dataset consisted of five pairs (i.e. "time-steps") of dual-pol RADARSAT-2 and dual-pol TerraSAR-X images acquired in 2013 from late winter to mid-summer (Table 2). To minimize bias due to environmental dynamics such as flooding and grassland mowing, we selected dates for each pair of acquisitions that were close in time. Based on the literature, we chose an incidence angle>30°to maximize vegetation penetration and water detection [START_REF] Mahdavi | A Comparison between Different Synthetic Aperture Radar (SAR) Sensors for Wetland Classification[END_REF]. All SAR satellite data were acquired in single look complex mode (i.e. Single Look Complex for RADARSAT-2 and Single Look Slant Range Complex for TerraSAR-X). Specifically, RADARSAT-2 images were acquired in Fine Quad polarization mode (HH, HV, VH and VV polarization channels); with an incidence angles of 30-47°and an approximate pixel size of 4×8 m. TerraSAR-X images were acquired in High-Resolution SpotLight mode (HH and VV polarization channels), with an incidence angle of 36-37°a nd a pixel size of 1.8×1.0 m. .

Generation of three SAR datasets

From the full SAR images acquired, the objective was to generate three different datasets corresponding to three equivalent acquisition configurations (quadpol/C-band-Band, dual-pol/C-band-Band and dualpol/X-band-Band) to be able to compare the band frequencies and channel polarizations of RADARSAT-2 and TerraSAR-X.

After radiometrically calibrating all images, RADARSAT-2 images were converted from quad-to dual-polarization, and TerraSAR-X images were resampled at the spatial resolution of RADARSAT-2 images:

(i) The quad-pol/C-band dataset was produced directly from RADARSAT-2 images. (ii) The dual-pol/C-band dataset was produced by extracting the HH and VV polarization channels, then deriving for each pixel of the RADARSAT-2 image the corresponding 2 × 2 covariance matrix C 2 . (iii) The dual-pol/X-band dataset was produced by applying a multilook of three looks in azimuth and five in the range direction to the 2 × 2 covariance matrix C 2 of each pixel of the TerraSAR-X image. As a result, this dataset had a spatial resolution similar to that of the other two datasets.

Extracting SAR features

Next, the most frequently used SAR features [START_REF] Adeli | Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review[END_REF], including backscattering coefficients and polarimetric parameters (Table 3), were extracted from the three configuration datasets defined previously.

Quad-pol dataset

Backscattering coefficients (σ°HH, σ°VV and σ°HV) were extracted from the quad-pol/C-band dataset and then despeckled using the refined Lee filter (Lee and Eric Pottier 2009) with a sliding window of 7 × 7 pixels (sigma 0.8, target 3 × 3 pixels). Next, the images were geocoded using Shuttle Radar Topography Mission (SRTM) data with a pixel spacing of 7 m (nearest neighbor resampling) to correct topographic deformations (geometric correction accuracy < 1 pixel) and were then projected into the Lambert-RGF93/IGN-69 system. Three intensity ratios (HH/VV, VV/HV and HH/HV) were calculated from the images and converted into decibels (dB) using the following equation:

σ • (db) = 10.log 10 (σ) •
Quad-polarimetric parameters were also generated from this dataset, as follows:

(i) A 3 × 3 coherency matrix (T 3 ) and a 3 × 3 covariance matrix (C 3 ) were extracted from each pixel of the SAR image described by its each scattering matrix (S). (ii) Speckle filtering was applied to the coherency (T 3 ) and covariance (C 3 ) matrices using a refined Lee filter with a sliding window of 7 × 7 pixels. (iii) Geometric correction was applied to the coherency (T 3 ) and covariance (C 3 ) matrices using SRTM data with a pixel spacing of 7 m (nearest neighbor resampling). (iv) First T 11 |S HH + S V V | 2 , as well as second

T 22 |S HH -S V V | 2 and third T 33 |S HV | 2 el-

Dual-pol datasets

Backscattering coefficients (σ°HH and σ°VV) and the ratio HH/VV were generated from the dual-pol/Cband and dual-pol/X-band datasets using the method described previously for the quad-pol dataset. Dualpolarimetric parameters were then extracted from the two datasets as follows:

(i) From each pixel of the SAR image described by its scattering elements, the corresponding 2 × 2 covariance matrix (C 2 ) was extracted. (ii) Speckle filtering was applied to the covariance matrix (C 2 ) using a refined Lee filter with a sliding window of 7 × 7 pixels. (iii) Geometric correction was applied to the covariance matrix (C 2 ) using SRTM data with a pixel spacing of 7 m (nearest neighbor resampling).

Selection of similar pairs of images

To ensure that the pairs of images were sufficiently similar to compare given the high temporal variability in wetland hydrodynamics, a Pearson correlation coefficient (r) was calculated between the SE of each pair of images in each dual-pol dataset. SE was selected because it is highly sensitive to the presence of open water, which varies greatly in wetland ecosystems [START_REF] Marechal | One Year Wetland Survey Investigations from a Quad-Pol Radarsat-2 Time Series SAR Images[END_REF]. Based on the conventional approach and visual analysis (Schober, Boer, and Schwarte 2018), image pairs with r < 0.7 were considered as moderate or weak correlation and were excluded from subsequent analysis.

Ranking the importance of SAR features for discriminating wetland vegetation types

The next step identified the SAR features and dates that discriminated wetland vegetation types the most for each of the three SAR datasets. First, all pixel values of SAR features were standardized by setting the overall mean of each feature to 0 and the standard deviation to 0.25, since the wide range of pixel values among SAR features and dates could have caused certain features to dominate the classification. Then, for each SAR dataset, the values of SAR features were extracted for each reference plot, and a specific conditional RF model [START_REF] Strobl | Conditional Variable Importance for Random Forests[END_REF], calibrated and validated by 5-fold crossvalidation repeated two times [START_REF] Kuhn | Applied Predictive Modeling[END_REF], was fitted. Compared to the commonly used RF model [START_REF] Breiman | Random Forests[END_REF], the conditional RF algorithm is designed to avoid ranking bias of correlated variables [START_REF] Strobl | Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution[END_REF]), as may be the case with SAR features. To this end, the conditional RF model was first calibrated using conditional inference trees constructed on subsampling without replacement [START_REF] Strobl | Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution[END_REF], then SAR features were ranked by conditional permutation [START_REF] Debeer | Conditional Permutation Importance Revisited[END_REF]. A cumulative mean decrease in accuracy was calculated for each SAR dataset for each feature and each date. Features or dates with a high mean decrease in accuracy influenced the RF modeling of wetland vegetation types the most.

ANOVA of model output

To quantify the percentage of the variance in model output (F1 scores) explained by polarization, frequency and the number of acquisition dates, ten F1 scores were derived per model (i.e. 5 folds × 2 replicates) of the RF model for each SAR dataset. To quantify the influence of the number of acquisition dates on model accuracy, the conditional RF model was replicated for each of the three datasets with the 2 most important dates and then the most important date. As a result, for each dataset, the model was iteratively run with 3 dates, 2 dates and 1 date, respectively. An independent three-way ANOVA was performed for each vegetation type to estimate model accuracy based on its specific F1 scores as a function of polarization, frequency and number of acquisition dates. Similarly, another independent ANOVA was performed for all vegetation types based on global F1 scores. While some vegetation types violated the assumption of normality or heteroscedasticity, a three-way permutation ANOVA was performed (Wheeler, M. Torchiano, and M. M. Torchiano 2016) because this method is not restricted by distributional assumptions and accommodates differences among within-group distributions [START_REF] Anderson | Permutational Multivariate Analysis of Variance (PERMANOVA)[END_REF]. Permutation ANOVA was performed using type III sum of squares since sample sizes differed (i.e. more dual-pol than quad-pol occurrences) [START_REF] Shaw | ANOVA for Unbalanced Data: An Overview[END_REF]. The percentage of variance in the F1 score explained by each factor (polarization, frequency and number of acquisition dates) in the ANOVA was calculated using the index eta 2 , which is the ratio of a factor's sum of squares to the total sum of squares [START_REF] Cohen | Statistical Power Analysis for the Behavioral Sciences[END_REF].

3 Results

Similar pairs of SAR images

Comparison of the pairs of SE images from the dualpol/C-band and dual-pol/X-band datasets yielded r < 0.7 for time-steps 2 and 4, which were excluded from subsequent analysis (Fig. 2). In detail, time-step 1 (winter), with a time-shift of 2 days, revealed a large and similar area of marsh flooding on February 14 and 16 (r = 0.87). Conversely, time-step 2 (winter), despite having a time-shift of only 3 days, revealed more marsh flooding on March 13 than on March 10, which yielded its low correlation (r = 0.32). For time-step 3 (spring), the 2-day time-shift revealed a similar decrease in the flood area (r = 0.83). For time-step 4 (spring), the longer time-shift (11 days) revealed much less open water area on May 4 than on April 23, which explained its low correlation (r = 0.27). Conversely, time-step 5 (summer) also had a time-shift of 11 days, but its flooded area was similar (r = 0.70). Based on these results, three pairs of SAR images (time-steps 1, 3 and 5) were retained for subsequent analysis.

Importance of SAR features for discriminating wetland vegetation types

For the quad-pol dataset, most discriminating of the 25 SAR features were the polarimetric parameters SE (mean decrease in accuracy = 0.024) and SE i (0.016), followed by HV backscattering coefficient (0.014) (Fig. 3). Volume scattering, HH and VV backscattering coefficients, diagonal elements of the coherency matrix and SPAN were moderately discriminating (mean decrease in accuracy = 0.005-0.013). The least discriminating features were the co-polarization and cross-polarization ratios; the parameters derived from Cloude-Pottier decomposition; and Yam_hlx, Yam_odd, Yam_dbl, FD_odd, FD_dbl and SE p (mean decrease in accuracy < 0.003). Time-step 5 was the most important date (mean decrease in accuracy = 0.061) while time-steps 3 and 1 were less contributing (0.047 and 0.043, respectively).

For the dual-pol datasets, the most discriminating of the nine parameters were SE and SE i (mean decrease in accuracy = 0.042 and 0.029, respectively, in the C-band and 0.027 and 0.021, respectively, in the X-band) (Fig. 3). Backscattering coefficients, diagonal elements of the covariance matrix and SPAN were moderately discriminating (mean decrease in accuracy = 0.007-0.022 in the C-band and 0.009-0.021 in the Xband). The co-polarization ratio was one of the least discriminating parameters in both C-band and X-band (mean decrease in accuracy = 0.004 and 0.003 respectively). In the C-band, time-step 5 was the most important date (mean decrease in accuracy = 0.057) while time-steps 3 and 1 were similarly less discriminating (0.048 and 0.047, respectively). Conversely, in the Xband, time-step 3 was the most important date (mean decrease in accuracy = 0.056), while time-steps 5 and 1 were similarly less discriminating (0.040 and 0.038 respectively).

Influence of polarization, frequency and number of dates on discrimination of wetland vegetation types

Generally, results of the 3-factor permutation ANOVA indicated that both frequency and number of acquisition dates as well as their interactions influenced overall accuracy (F1 score) of the RF model significantly (p < 0.001) (Table 4). Conversely, polarization had little influence on overall accuracy (p < 0.01), since the F1 scores had similar median values in dual-pol (0.59 ± 0.09) and quad-pol (0.60 ± 0.11) modes (Table 4 and Fig. 4). Interactions between polarization and number of acquisition dates were non-significant (p > 0.05) when considering all wetland vegetation types. Conversely, for individual vegetation types, polarization influenced accuracy significantly (p < 0.001), especially for discriminating woods, mesophilic grasslands, hygrophilic grasslands and long-flooded grasslands. More specifically, for woods, polarization and its interaction with the number of acquisition dates, explained 6.5% and 8.5% of the variance in model accuracy, respectively. Indeed, although the median F1 score was similar in quad-pol mode (0.86 ± 0.02) or dual-pol mode (0.86 ± 0.01) when two acquisition dates were used, it was much lower in dual-pol mode (0.85 ± 0.02) than in quad-pol mode (0.92 ± 0.02) when three acquisition dates were used (Fig. 5). For mesophilic grasslands, polarization and its interaction with the number of acquisition dates explained 2.9% and 9.4% of the variance in model accuracy, respectively, with higher F1 scores of 0.07 in dual-pol mode with three dates and even up to 0.16 with two dates (Fig. 5). For mesohygrophilic grasslands, polarization explained 4.8% of the variance in model accuracy, with higher F1 scores in dual-pol mode (0.45 ± 0.12) than in quad-pol mode (0.37 ± 0.10). For long-flooded grasslands, polarization also explained a part of the variance in model accuracy (7.3%, p < 0.001), with higher F1 scores in quad-pol mode (0.83 ± 0.27) than in dual-pol mode (0.71 ± 0.20) (Fig. 4). The interaction between polarization and number of acquisition dates explained 5.6% of the variation in model accuracy for long-flooded grasslands (p < 0.001), since the difference in F1 scores between quad-pol and dual-pol modes was larger (0.85 ± 0.02 and 0.65 ± 0.07, respectively) when two acquisition dates were used (Fig. 5). A minor (<4%) but signif-icant (p < 0.05) influence of polarization and its interactions with the number of acquisition dates was observed for crops and hygrophilic grasslands (Table 4).

Frequency explained 13.3% (p < 0.001) of the variance in model accuracy for discriminating wetland vegetation types, since median F1 scores were slightly higher and with a lower variance in the X-band than the C-band (Fig. 4). The interaction between frequency and number of acquisition dates explained 10.1% (p < 0.001) of the variance in overall model accuracy, since the advantage of the X-band over the C-band for the F1 score increased as the number of acquisition dates decreased (Fig. 5). Analysis of individual vegetation types indicated that frequency also influenced discrimination of each vegetation type significantly. For crops, frequency explained for 10.1% of the variance in overall model accuracy, with higher F1 scores in X-band frequency (0.59 ± 0.10) than in C-band frequency (0.52 ± 0.11) (Fig. 4). For woods, mesophilic grasslands and hygrophilic grasslands, interactions between frequency and the number of acquisition dates explained for 6.4%, 19.4% and 7.7% of the variance in overall model accuracy respectively. Whereas for woods, F1 scores were higher in C-band frequency than in X-band frequency except when two acquisition dates were used, conversely, for mesophilic and hygrophilic grasslands, F1 scores were higher in X-band frequency than in C-band frequency except when 2 acquisition dates were used (Fig. 5). For meso-hygrophilic grasslands, frequency and its interactions with the number of acquisition dates explained for 32.7% and 8% (p < 0.001) of the variance in model accuracy respectively, since F1 scores were globally higher in the X-band (0.49 ± 0.06) than in the C-bands (0.35 ± 0.11) (Fig. 4). Though, F1 scores were similar in frequency (0.53 ± 0.03 in X-band, and 0.52 ± 0.04 in C-band) when three acquisition dates were used (Fig. 5). For long-flooded grasslands, frequency and its interactions with the number of acquisition dates explained for 17.3% and 28% (p < 0.001) of the variance in model accuracy respectively. Although F1 scores were similar regardless of the frequency when using two or three acquisition dates, scores differed greatly when using only one acquisition date, with clear discrimination in the X-band (0.72 ± 0.02) but poor discrimination in the C-band (0.26 ± 0.07). Interactions of frequency with the number of acquisition dates also influenced slightly (6.2%) but less significantly (p < 0.05) discrimination of ponds (Table 4).

The number of acquisition dates explained more than half of the variance in model accuracy (68.4%, p < 0.001) (Table 4), with the median F1 score equal to 0.45 ± 0.07 with one date, 0.59 ± 0.02 with two dates and 0.69 ± 0.03 with three dates (Fig. 4). Moreover, the number of acquisition dates significantly explained (p < 0.001) for variance in model accuracy for each vegetation class except for ponds (Table 4). In details, analysis of individual vegetation types indicated that discrimination of crops (46%, p < 0.001), woods (48%, p < 0.001), and hygrophilic grasslands (64.8%, p < 0.001) depended greatly on the number of acquisition dates, with the F1 score increasing significantly from one to two acquisition dates for crops (0.50 ± 0.12 and 0.68 ± 0.07, respectively), woods (0.85 ± 0.15 and 0.91 ± 0.03, respectively) and hygrophilic grasslands (0.23 ± 0.07 and 0.45 ± 0.07, respectively); however, addition of a third acquisition date did not increase the F1 score significantly (Fig. 4). For mesophilic grasslands, the number of acquisition dates explained 58% (p < 0.001) of the variance in model accuracy, with the F1 score increasing as the number of acquisition dates increased (0.26 ± 0.11 for 1 date, 0.35 ± 0.13 for 2 dates and 0.61 ± 0.09 for 3 dates) (Fig. 4). To a lesser extent, the number of acquisition dates also influenced discrimination of meso-hygrophilic grasslands, and long-flooded grasslands (27.1-37.2%, p < 0.001) (Table 4).

Discussion

4.1 How similar are the acquisition parameters of SAR data collected by different sensors in wetland ecosystems?

We acquired five pairs of RADARSAT-2 and TerraSAR-X images with time-shifts of 2-11 days because it had been identified as acceptable [START_REF] Amani | Separability Analysis of Wetlands in Canada Using Multi-Source SAR Data[END_REF]Mohammadimanesh, Salehi, Mahdianpari, Motagh, et al. 2018;[START_REF] Simioni | Integration of Multi-Sensor Analysis and Decision Tree for Evaluation of Dual and Quad-Pol SAR in L-and C-Bands Applied for Marsh Delineation[END_REF]). After the correlation analysis, however, we excluded two pairs (timesteps 2 and 4) that, despite having short time-shifts (3 and 11 days, respectively), showed large differences in flooded area and thus in the values of SAR features (Fig. 2). Without this verification step, all five timesteps would have been retained, and comparisons of the frequency and polarization would have been biased by the large differences in hydrological conditions of the wetland ecosystems.

Our results confirm that wetland hydrodynamics vary throughout the year [START_REF] Betbeder | TerraSAR-X Dual-Pol Time-Series for Mapping of Wetland Vegetation[END_REF]: high variability occurred in late winter (time-series 2, r = 0.32) and spring (timeseries 4, r = 0.27), with time-shifts of 3 and 11 days, respectively, and relative stability (time-series 5, r = 0.70) was observed in summer, with a time-shift of 11 days. Verifying image pairs is thus especially important during this dynamic late winter/spring period since it is the most discriminating for wetland vegetation [START_REF] Jahncke | Mapping Wetlands in Nova Scotia with Multi-Beam RADARSAT-2 Polarimetric SAR, Optical Satellite Imagery, and Lidar Data[END_REF]. This study highlights the advantage of SE features over backscattering coefficients or other polarimetric parameters for discriminating wetland vegetation, regardless of polarization (dual-or quad-pol) or frequency (C-or X-band). This result agrees with previous studies of this site performed using TerraSAR-X images [START_REF] Betbeder | TerraSAR-X Dual-Pol Time-Series for Mapping of Wetland Vegetation[END_REF][START_REF] Betbeder | Multitemporal Classification of TerraSAR-X Data for Wetland Vegetation Mapping[END_REF]. Conversely, [START_REF] Amani | Separability Analysis of Wetlands in Canada Using Multi-Source SAR Data[END_REF] found that SE was not highly discriminating for boreal wetlands using RADARSAT-2, ALOS-2 and Sentinel-1 images. This difference may be due to the type of wetland examined: in this study, the Couesnon marshes consisted basically of four types of wet meadows patterned by flood duration (Table 1), which SE clearly discriminated [START_REF] Marechal | One Year Wetland Survey Investigations from a Quad-Pol Radarsat-2 Time Series SAR Images[END_REF]. In quad-pol mode, SE was the most discriminating, even when including the parameters derived from polarimetric decompositions (Fig. 3).

To a lesser extent, our results indicate the importance of Freeman-Durden and Yamagashi decomposition parameters, especially those related to volume scattering and, conversely, the low influence of parameters resulting from Cloude-Pottier decomposition, which agrees with some studies (de Almeida Furtado, Silva, and de Moraes Novo 2016; [START_REF] Mahdianpari | Fisher Linear Discriminant Analysis of Coherency Matrix for Wetland Classification Using PolSAR Imagery[END_REF][START_REF] Mahdianpari | Random Forest Wetland Classification Using ALOS-2 L-Band, RADARSAT-2 C-Band, and TerraSAR-X Imagery[END_REF] but contra-dicts others [START_REF] Amani | Separability Analysis of Wetlands in Canada Using Multi-Source SAR Data[END_REF]Mohammadimanesh, Salehi, Mahdianpari, Motagh, et al. 2018;[START_REF] Morandeira | Mapping Plant Functional Types in Floodplain Wetlands: An Analysis of C-Band Polarimetric SAR Data from RADARSAT-2[END_REF].

In this study, we selected and tested 25 polarimetric SAR features among those commonly used in the literature for the study of wetlands [START_REF] Adeli | Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review[END_REF], although other polarimetric SAR features were occasionally investigated, such as those derived from Kennaugh [START_REF] Mahdianpari | An Assessment of Simulated Compact Polarimetric SAR Data for Wetland Classification Using Random Forest Algorithm[END_REF], Pauli (B. [START_REF] Brisco | Evaluation of C-Band Polarization Diversity and Polarimetry for Wetland Mapping[END_REF], Touzi [START_REF] Amani | Separability Analysis of Wetlands in Canada Using Multi-Source SAR Data[END_REF][START_REF] Franklin | Object-Based Wetland Characterization Using Radarsat-2 Quad-Polarimetric SAR Data, Landsat-8 OLI Imagery, and Airborne Lidar-Derived Geomorphometric Variables[END_REF][START_REF] Millard | Quantifying the Relative Contributions of Vegetation and Soil Moisture Conditions to Polarimetric C-Band SAR Response in a Temperate Peatland[END_REF] or Van Zyl [START_REF] Amani | Separability Analysis of Wetlands in Canada Using Multi-Source SAR Data[END_REF][START_REF] Mahdianpari | Fisher Linear Discriminant Analysis of Coherency Matrix for Wetland Classification Using PolSAR Imagery[END_REF] decompositions. While several studies recently highlighted the contribution of circular polarization features derived from compact polarization data (e.g. upcoming RADARSAT Constellation Mission) to discriminate wetland vegetation [START_REF] Banks | Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests[END_REF][START_REF] Mohammadimanesh | Full and Simulated Compact Polarimetry SAR Responses to Canadian Wetlands: Separability Analysis and Classification[END_REF], we did not test this specific feature as the Radarsat-2 and TerraSAR-X data used were acquired in 2013 in dual or quad polarization.

Although we highlighted the advantage of SE, the importance of SAR features for discriminating wetland vegetation types differs among studies, since their importance depends greatly on the type of vegetation, reference data, type of model and the configuration used to acquire SAR data [START_REF] Mohammadimanesh | Multi-Temporal, Multi-Frequency, and Multi-Polarization Coherence and SAR Backscatter Analysis of Wetlands[END_REF]. Given this context, we recommend, as in the present study,comparing as many SAR features as possible and then retaining only the most discriminating ones to improve model performance and transferability [START_REF] Maxwell | Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review[END_REF]. However, variable selection is a complex process, notably when many correlated variables such as SAR features are being considered [START_REF] Gregorutti | Correlation and Variable Importance in Random Forests[END_REF]. Despite the availability of a wide range of methods to select the most contributing variables -e.g. backward selection, dimension reduction space, specific internal metric, correlation analysis -there is no consensus on which one is the best. In this study, we addressed the question of the collinearity of the variables using a conditional permutation process [START_REF] Debeer | Conditional Permutation Importance Revisited[END_REF]. However, further researches are still needed to develop a robust method for variable selection. In this perspective, [START_REF] Meyer | Importance of Spatial Predictor Variable Selection in Machine Learning Applications-Moving from Data Reproduction to Spatial Prediction[END_REF] recently highlighted the importance of spatial predictor variable selection to improve machine learning model performance and transferability. This approach is attractive and deserves to be deeper investigated.

In this study, we used a variant of the random forest model, which is one of the most widely used models for mapping wetlands [START_REF] Mahdavi | Remote Sensing for Wetland Classification: A Comprehensive Review[END_REF]). However, recent researches have highlighted the value of deep learning models to improve feature extraction [START_REF] Sharifzadeh | Ship Classification in SAR Images Using a New Hybrid CNN-MLP Classifier[END_REF][START_REF] Tirandaz | PolSAR Image Segmentation Based on Feature Extraction and Data Compression Using Weighted Neighborhood Filter Bank and Hidden Markov Random Field-Expectation Maximization[END_REF], change detection [START_REF] Samadi | Change Detection in SAR Images Using Deep Belief Network: A New Training Approach Based on Morphological Images[END_REF], or wetland mapping [START_REF] Delancey | Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada[END_REF][START_REF] Martins | Deep Neural Network for Complex Open-Water Wetland Mapping Using High-Resolution WorldView-3 and Airborne LiDAR Data[END_REF]. Thus, the monitoring of wetland vegetations using deep learning modeling of SAR images appears to be a promising perspective.

Is the influence of frequency, polarization and number of acquisition dates on model accuracy regardless of vegetation type?

Our results indicate that polarization does not influence model accuracy, which does not agree with those of several studies that highlighted the advantage quadpol mode over dual-pol mode for discriminating wetland vegetation [START_REF] Amani | Separability Analysis of Wetlands in Canada Using Multi-Source SAR Data[END_REF][START_REF] Almeida Furtado | Dual-Season and Full-Polarimetric C Band SAR Assessment for Vegetation Mapping in the Amazon Várzea Wetlands[END_REF][START_REF] Mahdavi | A Comparison between Different Synthetic Aperture Radar (SAR) Sensors for Wetland Classification[END_REF]. This difference can be explained by the high importance of SE for discriminating vegetation, which was calculated in both quad-pol and in dual-pol modes. Nonetheless, the advantage of quad-pol mode over dual-pol mode has been observed when identifying woods (with a height > 1 m, Table 1), which are thus characterized well by volume-based polarimetric parameters [START_REF] Freeman | A Three-Component Scattering Model for Polarimetric SAR Data[END_REF][START_REF] Yamaguchi | Four-Component Scattering Model for Polarimetric SAR Image Decomposition[END_REF], and identifying long-flooded grasslands with double-bounce scattering. Furthermore, our results show that the dual-pol configuration better discriminates herbaceous vegetations compared to the quad-pol configuration, although with a higher stan-dard deviation (Fig. 5). A possible explanation for this is that the dual-pol configuration includes two frequencies (X-band and C-band) exhibiting contrasting responses of herbaceous vegetations [START_REF] Mahdianpari | Random Forest Wetland Classification Using ALOS-2 L-Band, RADARSAT-2 C-Band, and TerraSAR-X Imagery[END_REF][START_REF] Maleki | Analysis of Multi-Frequency and Multi-Polarization SAR Data for Wetland Mapping in Hamoun-e-Hirmand Wetland[END_REF][START_REF] Mohammadimanesh | Multi-Temporal, Multi-Frequency, and Multi-Polarization Coherence and SAR Backscatter Analysis of Wetlands[END_REF], whereas the quad-pol configuration includes only one C-band frequency.

The most appropriate SAR frequency to characterize wetlands varies according to the physiognomy of the existing vegetation. In accordance with previous studies [START_REF] Mahdianpari | Random Forest Wetland Classification Using ALOS-2 L-Band, RADARSAT-2 C-Band, and TerraSAR-X Imagery[END_REF][START_REF] Maleki | Analysis of Multi-Frequency and Multi-Polarization SAR Data for Wetland Mapping in Hamoun-e-Hirmand Wetland[END_REF]Mohammadimanesh, Salehi, Mahdianpari, Motagh, et al. 2018), our results indicate that model accuracy is higher in the X-band frequency than in the C-band frequency for herbaceous vegetations, especially when only one date is available. However, the findings show that C-band is slightly more relevant than X-band to discriminate woody vegetations and ponds. In this study, L-band frequency SAR images, such as ALOS-2 data, were unfortunately unavailable during the period investigated, while several studies have highlighted the added value of the L-band over the C-band in discriminating woody vegetation such as swamps [START_REF] Amani | Separability Analysis of Wetlands in Canada Using Multi-Source SAR Data[END_REF][START_REF] Mahdianpari | Random Forest Wetland Classification Using ALOS-2 L-Band, RADARSAT-2 C-Band, and TerraSAR-X Imagery[END_REF] or emergent vegetation such as marshes [START_REF] Simioni | Integration of Multi-Sensor Analysis and Decision Tree for Evaluation of Dual and Quad-Pol SAR in L-and C-Bands Applied for Marsh Delineation[END_REF]. In summary, X-band frequency is the most discriminating for herbaceous vegetation, L-band or alternatively C-band for woody vegetation, and C-band for ponds. However, it should keep in mind that frequency and its interactions with the number of acquisition dates explain only for less than one-third of the variance in model accuracy (Table 4). Indeed, our study confirms that classification accuracy increases as the number of acquisition dates increases [START_REF] Banks | Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests[END_REF][START_REF] Mahdianpari | Fisher Linear Discriminant Analysis of Coherency Matrix for Wetland Classification Using PolSAR Imagery[END_REF][START_REF] Schuster | Grassland Habitat Mapping by Intra-Annual Time Series Analysis -Comparison of RapidEye and TerraSAR-X Satellite Data[END_REF]. The number of acquisition dates needed to achieve acceptable accuracy (F1 score > 0.8) ranges from one for woods and ponds, to two for long-flooded grasslands, to more than three for certain grassland vegetation types or crops (data not shown). Indeed, accurately discriminating the four grassland types requires characterizing flood duration using at least five acquisition dates [START_REF] Betbeder | TerraSAR-X Dual-Pol Time-Series for Mapping of Wetland Vegetation[END_REF]. More generally, our results emphasize that the number of acquisition dates explains more than half of the variance in the discrimination accuracy of wetland vegetation. Our results also validate the hypothesis of [START_REF] Schuster | Grassland Habitat Mapping by Intra-Annual Time Series Analysis -Comparison of RapidEye and TerraSAR-X Satellite Data[END_REF] that temporal resolution is more important than sensor characteristics. Nevertheless, a slight decrease in the F1 score was observed when the third acquisition was added for woods in X-band and ponds in C-band frequency (Fig. 5). This may be explained by the fact that the third acquisition date is the least important variable and therefore the one that introduces noise and complexity to the conditional RF model, decreasing the accuracy for some vegetation classes [START_REF] Maxwell | Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review[END_REF]. In addition, a decrease in model accuracy was also reported in Xband between 1 and 2 acquisition dates for mesophilic, meso-hygrophilic and long-flooded grasslands. In fact, whereas time-step 5, which was acquired during the dry season, was the second most important variable for all the vegetation classes in X-band frequency, it did not discriminate between grassland classes, since these classes share the same phenological and hydrological characteristics at this period of the year. The relationships between vegetation condition, SAR signal response and modelling processes are complex and require future interdisciplinary research between ecology, remote sensing and modelling sciences.

Conclusion

Five pairs of RADARSAT-2 and TerraSAR-X images and ANOVA were used to investigate the relative influence of polarization, frequency and number of acquisition dates on the accuracy of a conditional RF model used to discriminate seven vegetation types in riverine marshes. Our results suggest that (i) the strong hydrological dynamics of wetlands require verifying the similarity of SAR image pairs before comparing acquisition configurations, (ii) SE is the most discriminating SAR feature regardless of polarization mode (dual-/quadpol) or frequency (X-/C-band) and (iii) the number of acquisition dates explains more than half of the variance in model accuracy. In other words, this study indicates that wetland vegetation types could be discriminated sufficiently well using SAR images and SE generated over a dense SAR time-series, regardless of the frequency or polarization.

Figure 1 :

 1 Figure 1: Study site presentation: (a) location in northwestern Europe, (b) location within the Couesnon valley included in the Ramsar-protected natural zone of Mont-Saint-Michel Bay, (c) location of reference plots and field map of vegetation in the marshes of the lower Couesnon valley (Lanoé, 2008), (d) field photograph of a hygrophilic grassland with Cardamine pratensis plant species (white flowers) and (e) field photograph of a pond surrounded by Eleocharis palustris plant species (long-flooded grassland).

Figure 2 :

 2 Figure 2: Comparison (using Pearson's correlation coefficient) of the similarity of five pairs of Shannon entropy images from the dual-pol/X-band and dual-pol/C-band datasets. Black areas (lower values) indicate flooded areas, while white areas (higher values) indicate woods or crops.).

  (iv) First C 11 |S HH | 2 and second C 22 |S V V | 2 elements of the diagonal covariance matrix (C 2 ) were extracted. (v) SE, SE i , SE p and SPAN were calculated (Lee and Eric Pottier 2009).

Figure 3 :

 3 Figure 3: Mean decrease in accuracy cumulated per time-step of SAR features (see Table3for definitions) for wetland vegetation discrimination calculated using the random forest model for each of three SAR datasets. Backscattering coefficients are in bold font, while polarimetric parameters are in regular font. Dots indicate the cumulative mean decrease in accuracy.

Figure 4 :

 4 Figure 4: Variability in accuracy of the random forest model (F1 score) overall and for each wetland vegetation type as a function of polarization, frequency and number of acquisition dates of SAR datasets. Whiskers represent the 5th and 95th percentiles.

4. 2

 2 Are the most discriminating SAR features of wetland vegetation the same regardless of the polarization or frequency used?

Figure 5 :

 5 Figure 5: Influence of the three acquisition configurations (polarization, frequency, number of acquisition dates) and their interactions on the accuracy of the random forest model (median and standard deviation of the F1 score) overall and for each wetland vegetation type. Lines that are nearly parallel indicate weak interactions.

Table 1 :

 1 Description of vegetation types at the study site.

	Vegetation type	Typical plant species	Physiognomy Vegetation	Months
				height (m)	flooded per
					year
	Crops	Wheat Maize	Bare soil Cereal	0.0 -2.0	< 1

Table 2 :

 2 Characteristics of the five pairs of SAR images acquired for the study from TerraSAR-X (TSX) and RADARSAT-2 (RST-2) sensors.

	Time-	Sensor	Acquisition date Time-	Spatial	Acquisition
	step			shift	resolution	angle
					(azimuth
					× ground)
	1	TSX ST-2	Feb. 14, 2013 Feb. 16, 2013	2 days	1.8×1.0 m 36-37°R 4.7×6.4 m 46-48°2
		TSX ST-2	March 10, 2013 March 13, 2013	3 days	1.8×1.0 m 36-37°R 4.8×8.1 m 34-36°3
		TSX ST-2	April 15, 2013 April 13, 2013	2 days	1.8×1.0 m 36-37°R 4.8×7.4 m 37-39°4
		TSX ST-2	May 4, 2013 April 23, 2013	11 days	1.8×1.0 m 36-37°R 5.5×9.0 m 30-31°5
		TSX ST-2	July 20, 2013 July 31, 2013	11 days	1.8×1.0 m 36-37°R 4.9×7.6 m 37-39°2

Table 3 :

 3 Acronyms and description of the 25 SAR features used in this study per polarization mode.

	Acronym	SAR feature description	Quad-pol	Dual-pol
	HH	Horizontal transmit and horizontal receive	√	√
	VV	Vertical transmit and vertical receive	√	√
	HV	Horizontal transmit and vertical receive	√
	HH/VV	Co-polarization ratio	√	√
	HH/HV	Co-polarization ratio	√
	VV/HV	Co-polarization ratio	√
	T 11	First diagonal element from the coherency matrix	√
	T 22	Second diagonal element from the coherency matrix	√
	T 33	Third diagonal element from the coherency matrix	√
	C 11	First diagonal element from the covariance matrix		√
	C 22	Second diagonal element from the covariance matrix		√
	CP_alpha Alpha angle from Cloude-Pottier decomposition	√
	CP_aniso Anisotropy from Cloude-Pottier decomposition	√
	CP_ent	Entropy from Cloude-Pottier decomposition	√
	FD_vol	Volume scattering from Freeman-Durden decomposition	√
	FD_dbl	Double-bounce scattering from Freeman-Durden decomposition √
	FD_odd	Single-bounce scattering from Freeman-Durden decomposition	√
	Yam_vol	Volume scattering from Yamaguchi decomposition	√
	Yam_dbl	Double-bounce scattering from Yamaguchi decomposition	√
	Yam_odd Single-bounce scattering from Yamaguchi decomposition	√
	Yam_hlx	Helix scattering from Yamaguchi decomposition	√
	SE	Sum of the Shannon entropy intensity and polarization	√	√
	Se i	Shannon entropy intensity	√	√
	Se p	Shannon entropy polarization	√	√
	SPAN	Total scattered power	√	√

Table 3

 3

	for definitions) for wetland veg-
	etation discrimination calculated
	using the random forest model
	for each of three SAR datasets.
	Backscattering coefficients are in
	bold font, while polarimetric pa-
	rameters are in regular font. Dots
	indicate the cumulative mean de-
	crease in accuracy.

Table 4 :

 4 Results of a 3-factor permutation ANOVA that quantified the percentage of variance (expressed as eta 2 ) in the accuracy of the random forest model (F1 score) of each vegetation type explained by polarization (POL), frequency (FREQ), number of acquisition dates (NOD) and their interactions.

	Vegetation	POL FREQ NOD POL	FREQ
	type			×	×
				NOD	NOD
	All types	1.4** 13.3*** 68.4*** 0.2ns 10.1***
	Crops	2.0*	10.1*** 52.7*** 3.5** 0.7ns
	Woods	6.5*** 1.4*	48.8*** 8.5*** 6.4***
	Mesophilic	2.9*** 2.2*** 58.0*** 9.4*** 19.4***
	grasslands
	Meso-	4.8*** 32.7*** 27.1*** 1.9*	8.0***
	hygrophilic
	grasslands
	Hygrophilic	0.7ns 0.1ns 64.8*** 2.8** 7.7***
	grasslands
	Long-	7.3*** 17.3*** 37.2*** 5.6*** 28.0***
	flooded
	grasslands
	Ponds	2.2ns 1.4ns 0.8ns 1.0ns 6.2*
	Asterisks indicate a significant result (*** p < 0.001,

** p < 0.01, * p < 0.05), while "ns" indicates a non-significant result (p ≥ 0.05).

Acknowledgements

This research was funded by the French Ministry of Higher Education, Research and Innovation as well as the French National Space Agency (CNES). The authors gratefully thank Elven Lanoé and Cendrine Mony (UMR ECOBIO) for providing the GIS vegetation map, as well as the Zone Atelier Armorique (CNRS) program for funding the additional fieldwork. RADARSAT-2 and TerraSAR-X data were acquired from the Canadian and German space agencies (DLR), respectively, and funded by the VIGISAT project of the Bretagne Télédétection (GIS BRETEL) consortium.


Aurélien Bénel
email: aurelien.benel@utt.fr

Christophe Lejeune
email: christophe.lejeune@ulb.ac.be

Humanities 2.0: document, interpretation and intersubjectivity in the digital age *

Keywords: Web 2.0, Collaborative methods, Digital humanities, Hermeneutics, Intersubjectivity, Epistemology

With their focus on documents, interpretation and intersubjectivity, Web 2.0 technologies have surprising analogies with philosophical hermeneutics, the theory of texts interpretation. Philosophical hermeneutics was generalised from biblical hermeneutics by Dilthey in the 19th century, and chosen as an alternative to positivism as a foundation for the epistemology and methodology of the humanities and social sciences. This article explores how Web 2.0 technologies might better meet the needs of social and human sciences than traditional information technologies that are historically bound with logical positivism. Illustrations are provided from archaeology and sociology, two social and human sciences which were early adopters of punched cards and computers.

Introduction

The title of this article, "Humanities 2.0", is not an attempt to create a new buzzword, but to prompt thinking about the links that could exist between "Web 2.0", the new participatory trend on the Web, and the social and human sciences.

The link is not straightforward when looking at the most used "tags" on web 2.0 sites such as "wedding", "travel" and "friends" on Flickr, "hot", "rock", and "dance" on Dailymotion, or "webdesign", "software" and "web2.0" on Del.icio.us. Even when used for culture and science as in Wikipedia, participatory technologies are often criticised by researchers [START_REF] Lacour | Portrait de l'intellectuel en DJ : Wikipédia face à l'expertise scientifique[END_REF].

However, when designing Digital Humanities applications, there is a choice between two major trends on the Web: "Web 2.0" and the "Semantic Web". Our claim is that Web 2.0 is more suited to the humanities and social sciences, because its technologies have analogies with philosophical hermeneutics, the theory of interpretation inspired by biblical hermeneutics, and proposed by Dilthey (19th century) as the epistemology and methodology for humanities and the social sciences.

We will focus on documents, interpretation and intersubjectivity. For each of these dimensions, we first investigate how Web 2.0 technologies differ from traditional information technology. We then reflect on how they are related to hermeneutics. Subsequently we illustrate how they could be of use in archaeology and sociology, two social and human sciences which were early adopters of punched cards and computers.

Document-driven inquiries

Web 2.0 & documents

Whereas computers were first created to compute numbers, and whereas corporate information technology was mainly intended to manage and share data, "Web 2.0" applications are designed to let users share their own digital documents, for example: videos (YouTube, Dailymotion), photographs (Flickr), slides (Slideshare) and texts (blogs, wikis).

Nevertheless, sharing digital documents instead of "data" or "facts" is not really new. For example, in 1993, one of the first French websites was the Universal Bibliophiles' Association (ABU), in which public-domain texts were uploaded by voluntary transcribers [START_REF] Cubaud | ABU : une bibliothèque numérique et son public[END_REF]. Several similar projects even existed prior to the Web: ArXiv, an open-access repository for "preprints" in physics, was created in 1991 using a mailing list and an FTP server; Project Gutenberg was started in 1971 to share literary and historical texts in the public domain via a mainframe on one of the few nodes of what would later become the Internet.

Given the recent widespread adoption of document self-publishing technologies, a few projects presage what they would bring to digital humanities. One of the best examples is NietzscheSource (formerly HyperNietzsche), which offers not only online facsimiles of primary sources such as manuscripts, page proofs and letters, but also secondary material as transcriptions, translations, "genetic paths" (revision layers)... [START_REF] Paolo | Nietzsche on New Paths: The HyperNietzsche Project and Open Scholarship on the Web[END_REF].

Hermeneutics & documents

For Dilthey, documents are the basis of the scientific study of human and society. At the end of the 19th century, as a reaction to Comte's positivism, he stated that such studies were possible, despite the singularity of the human subjects, because those subjects' live were fixed in structured "sets" (e.g. texts) that can be understood by others (Ricoeur, 1986, p.92-93). He adopted as his epistemology a philosophical hermeneutics generalised from biblical hermeneutics (the theory and method of the Bible interpretation).

That the Bible is the exemplar of the hermeneutic text can be seen in the name "Old (or New) Testament". In Latin, "testamentum" means that something has been attested or testified. It is a "proof" of something else, not in the manner of a mathematical proof but more in that of a juridical one: a testimony to be kept, a document to be brought as evidence. Indeed, the Gospels were written by witnesses because Jesus, as Socrates, was an orator, not a writer (Ricoeur, 1986, p.138-140). Moreover, those texts are a first interpretative layer, a first confession of faith of the early Christian community (Ricoeur, 1969, p.378), just as the whole Bible ties together narrations and confessions of faith (Ricoeur, 1986, p.134-137). Therefore, the Scriptures, which tell of people who interpret signs, have themselves to be interpreted as signs.

In a philosophical hermeneutics also, contrary to a referential semantics, meaning is mediated by texts. It is worth noting that "testimonia" has a similar etymology to "testamentum" and is used in philology and history for manuscripts and stone inscriptions. There has been lately a renewal of interest in these questions because, after decades of success of models of social structures and strongly generalised theories, social researchers now stress the importance of empirically grounded interpretation whilst recognising that human and social phenomena cannot be directly addressed. To gain a material status, to be preserved and to be used as proofs, phenomena are "translated" [START_REF] Callon | Some Elements of a Sociology of Translation: Domestication of the Scallops and the Fishermen of St Brieuc Bay[END_REF] into a series of documents.

Illustration in archaeology

To speak of the centrality of documents in archaeology may be controversial since the discipline differentiates itself from history by its study of artefacts rather than texts. Museums and collections are full of physical artefacts, but we must consider the particular status of scientific archives in archaeology. For example, at the French school of archaeology in Athens, every map and photograph produced in the course of funded initiatives must be stored in the institutional archive, even if access to the document may be restricted. Although most of these intermediary documents remain unpublished, there is a requirement to keep evidence through space and time. In fact, the actual artefacts and sites are often distant or difficult to access, and need nevertheless to be shown to peers or studied in libraries or laboratories. Moreover, ancient artefacts are frequently destroyed or at least altered, not only by natural processes, vandals or real estate developers, but also by the very act of excavation which destroys their context. For these reasons it is important that the context is carefully recorded in documents. In a sense, the only way to bring reproducibility to an archaeological dig is in fact to virtually "dig" these documents which were produced about it [START_REF] Iacovella | Assistance à l'interprétation dans les bibliothèques numériques pour les sciences historiques[END_REF]. Intermediate documents cannot be considered "data" for several reasons. Firstly, measurements, maps and even photographs are the results of selective perception, which springs from the concerns, hypotheses and methods of their author. Therefore they have to be "documented" with situational clues (often called "metadata") so that the reader can situate them in their original history. Secondly, archaeological artefacts have sometimes been studied over several centuries. In such cases, their inventory identifiers have often changed through time, whereas visual documents can still be identified with them. In a way, these documents appear to be the only perennial references. Thirdly, some researchers think that there are no epistemological differences in archaeology between what are usually called "primary sources" (ancient texts), "secondary sources" (commentaries) and "data" produced in the present. If we assume that artefacts do not have inherent attributes, but only through documents (ancient or modern), all of these documents can be considered "sources" [START_REF] Desfarges | L'archéologie, système d'information scientifique' in Aplicaciones Informaticás en Arqueologia : Teorias y sistemas[END_REF].

In recent years, we have developed and experimented with prototype software on various kinds of archaeological sources, including series and monographs from a research centre, annual excavation chronicles from a particular country, 19th century reports from the excavation of a necropolis, and photographs of vases from a particular city depicting a particular subject. Figure 1 shows how a photograph from a scientific archive might be documented in our software platform1 .

Illustration in sociology

One might be tempted to argue that sociology is concerned with phenomena (such as representation, belief or experience), not with documents. However, such social phenomena cannot be addressed without recording (as audio, or video, for example) of interaction and everyday life in natural settings, or by the social scientist's interviewing of selected actors. Thus, the nature of sociological documents may vary. For instance, in exploratory research on civil society, Gautier Pirotte gathered a corpus composed of annual reports and public statements of non-governmental organisations, and press articles about them [START_REF] Pirotte | La notion de société civile[END_REF]. Traditionally, social scientists not only collect documents, but participate in their production: Sébastien Brunet, Pierre Delvenne and Catherine Fallon carried out several group interviews with neighbourhood committees about electromagnetic risks (mainly related to mobile phones); the transcribed discussions (see Figure 2) are combined with the text of legislation to comprise the research corpus [START_REF] Brunet | La perception du risque en situation de haute incertitude scientifique : le cas des champs électromagnétiques en Belgique[END_REF]. In each study, the researchers assembled a corpus in order to test research hypotheses. The leading criterion is thus the relevance of the documents to the research topic, not the documents' format. Social phenomena are thus not directly recorded as data, they are "translated" into intermediate documents. This translation is the subject of a classic debate: given that the conduct of a biographical interview or field observation varies according to the assumptions, hypotheses and theoretical orientation of the researcher, only precise (though reasonable) methodological principles can warrant this intermediate document as an accurate surrogate for the social phenomenon being investigated [START_REF] Bourdieu | Practical Reason: On the Theory of Action[END_REF].

Interpretation Web 2.0 & interpretation

Computer science has a very close relationship with artificial intelligence, especially through the figure of Alan Turing, a parent common to both. The "Turing machine" (1936) prefigured the computer by being the first abstract programmable device which was proved to be capable of computing whatever is computable, whilst the "Turing test" (1950) was one of the first attempts to define "machine intelligence". It might then be thought quite surprising to find that the Web has evolved into a "Web 2.0", where every user can contribute to content, "harnessing collective intelligence" [START_REF] Tim | What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software[END_REF], rather than into a "Semantic Web" with "content that is meaningful to computers" [START_REF] Berners-Lee | The Semantic Web[END_REF] 2 .

In fact, by implementing "folksonomic tagging" (Del.icio.us, Flickr, Connotea...) and "trackbacks" (blogs), Web 2.0 site designers have chosen human interpretation rather than automatic deduction. Both of these features can be seen as "hypertext" features as envisioned in 1965 by its inventor, Theodor Nelson. Indeed, as a philosophy and sociology graduate, he wanted to provide "literary machines" to cope with "text issues". And, one of the main features was "transclusion", letting the reader quote and virtually combine existing documents into new documents [START_REF] Holm | Xanalogical Structure Needed Now More Than Ever[END_REF]. By tagging shared resources, Web 2.0 users accomplish similar combining and aggregating operations.

Hermeneutics & interpretation

One of the major concepts of hermeneutics is represented by the phrase "liber et speculum", which means both that "the mystery contained in the text is made explicit in our experience" and that "we understand ourselves in the mirror of the words" (Ricoeur, 1969, p.377). In that sense, it concerns every great text of humanity from the Odyssey to the Qur'an, because of their renewed currency. However, Paul Ricoeur wondered why the hermeneutic method was first created explicitly for the Bible, and in particular for the Christian Bible (Ricoeur, 1969, p.373-380). First, the canon intentionally kept four gospels and also four creation stories (Gen.1, Gen.2, Es.40-55, Jn.1) with consequent contradictions from a strictly logical perspective. Secondly, some passages say that others have to be read "allegorically" (e.g. Paul in his letter to the Galatians says that Abraham's wives descent can be read as an allegory of two alliances). Lastly, the "New Testament" constantly refers to and interprets the "Old Testament". To conclude, the awareness of the hermeneutic problem in the Bible can be explained by the inner plurality of the text. Furthermore, Paul Ricoeur notes that any attempt to avoid this plurality has been declared heretic (e.g. Marcionism in the 2nd century, which rejected the Old Testament and kept only one Gospel).

In philosophical hermeneutics, the idea of "liber et speculum" is called "understanding" by Dilthey. For him, this subjective interpretation would have the same role for human sciences as that of "explanation" for natural sciences. However, the explanation, which strength is taken as a function of the number of convergent clues aligned by the scientist [START_REF] Latour | Science in Action: How to Follow Scientists and Engineers Through Society[END_REF], can also be seen as a complementary mode for the interpretation needed in human sciences [START_REF] Ricoeur | Du texte à l'action : Essais d'herméneutique II[END_REF].

As stated by [START_REF] Weber | Economy and Society: An Outline of Interpretive Sociology[END_REF], questions of subjectivity are misleading, or misunderstood. For years, social scientists have seen subjectivity as something risky, something that must be avoided, hidden or destroyed. Rather, the researcher must be aware of her position in the social world and of the (implicit or explicit) commitments of her methodological and epistemological framework. These determine the researcher's subjectivity. If not taken into account, this subjectivity can yield unexpected results (by filtering materials, for instance). In this regard, subjectivity is seen as an obstacle. This is why Pierre Bourdieu, quoting Gaston Bachelard, warns social scientists against their own implicit common-sense assumptions [START_REF] Bourdieu | Craft of Sociology: Epistemological Preliminaries[END_REF].

The subjectivity in this context is that of the researcher;that of actors is not of concern [START_REF] Lejeune | Du mode de définition de deux programmes de recherche en sociologie et en ethnométhodologie[END_REF] In fact, the actor's common sense (subjectivity) is a fruitful source for social inquiry: it constitutes the material of the social sciences. Without the common sense of actors, no social inquiry would be possible. Moreover, understanding requires empathy of the researcher. In other words, the researcher should be able to share the actor's cultural background [START_REF] Schütz | Phenomenology of the Social World[END_REF]. Common humanity makes this possible: the researcher uses his own ordinary actor's subjectivity as a tool to fully understand the life-world of other actors. The researcher's subjectivity is thus also a resource (as a tool) to access the actor's subjectivity (as the receptacle of the social phenomenon being investigated).

Finally, the researchers' subjectivity is made up of selected assumptions, questions and theoretical commitments. This observation can be taken as an objection to the conception of subjectivity as an impediment. That is what Max Weber, considered as the founder of comprehensive social science, does when he argues that being an anarchist is not an obstacle for a Professor of Law, but rather offers a fresh perspective likely to rejuvenate legal theory [START_REF] Weber | Methodology of the social sciences[END_REF]. Subjectivity thus renders one's interpretation rich and, as we will show, is a condition for scientific debate.

As we noted above, understanding recovers a cultural background and awareness of one's historical involvement (Gadamer's Wirkungsgeschichtliches Bewusstsein). Thus understanding demands consideration of the historical context of the phenomenon, the context of the analysis, and the difference between these two subjectivities. Because of this alterity, subjectivity does not imply a lack of scientific rigour; it is a warrant of it.

Illustration in archaeology

The interpretative status of archaeology was greatly debated in the sixties and seventies among proponents of a "new archaeology" and defenders of a "humanistic archaeology". A nice illustration of this debate is provided by Anne-Marie Guimier-Sorbets' thesis on the analysis and formalisation of geometric ornaments in Graeco-Roman mosaics for automatic processing. Philippe Bruneau, who was on her examining board, wrote a subsequent article which spawned an unprecedented polemic.

Discussing the status of "descriptors" in the so-called "archaeological databanks", he argued, first, that descriptors such as "foreground" or "background" are usually chosen in order to be universal, independent of era and geography; and that that itself should be quite surprising in a historical science.

Secondly, he asked, what is the validity of "foreground" and "background" in a case like Greek frets, where every black fret on white has a complementary white fret on black? Anne-Marie Guimier-Sorbets answered: "by convention, the fret to be analysed is the outer one of the mosaic. The other complementary part is analysed as background". Philippe Bruneau noted that it was a shame to decide "by convention" that the whole description would be from the border to the centre whereas mosaics were built from the central panel to the border. Lastly, the very term "descriptor", connoting agency, would lead one to think that it is not the archaeologist but the device that describes an artefact. And to forget the archaeologist as the describer leads one to forget that the first to describe and classify the artefact was indeed the ancient user himself [START_REF] Bruneau | Quatre propos sur l'archéologie nouvelle[END_REF].

It is worth noting that recent work by archaeologists such as Roland Etienne adopts an historiographical perspective. In historiography (the history of history), there is not a single historical meaning, but rather a succession of actualised meanings. Moreover, history is seen as the result of confrontation between past and present, between that which organised life and thought in those days and that which makes it possible to think about them in the present (de Certeau, 1975).

Our prototype software allows the researcher to build freely her own analytic framework, depending on her hypotheses, and to revise it accordingly. For example, Figure 3 shows the qualitative analysis developed by masters-level students in history of art3 of the scenes depicted on photographs of vases.

Illustration in sociology

As we discussed above, interpretation raises issues of subjectivity. Let us illustrate this with the previously mentioned sociological example of electromagnetic risk. Even in sociological enquiries so driven by textual documents, a temptation remains to discount the researcher's subjectivity, it being seen as an obstacle. Analytical techniques such as quantitative content analysis [START_REF] Berelson | Content Analysis in Communication Research[END_REF] seek to achieve such an exclusion. Teams of content analysts are provided with the textual material and a set of possible annotations. They are asked to literally code the material using these standard annotations. Only annotations that reach a high level of agreement (calculated as inter-coder reliability) are retained [START_REF] Krippendorff | Content Analysis: An Introduction to Its Methodology[END_REF]. The subjectivity of a sole researcher is thus overcome4 . More frequently, the researcher's subjectivity is manifested as an epistemological position toward the actor's subjectivity. The researcher might assume that actors are irrational with regard to risk. Occurring before (and independently of) examination of intermediate documents in which actors precisely express their points of view, this prejudice is part of the researcher's subjectivity. It acts as a filter, and is thus an obstacle to interpretation.

As an alternate position, more and more social scientists assume that intermediate documents are evidence of actor's subjectivity (consisting of beliefs or conceptions, for example, about electricity pylons). As an illustration of subjectivity-as-paradigmatic-commitment, the researcher could then assume that actors reason about their interests; she thus traces the development of natural theories of counter-intuitive effects and "self-fed phenomena" (see Figure 4 and Lejeune, 2008 for details). Notice that this position -consistent with the parallel programme of the anthropology of science and technology [START_REF] Latour | Science in Action: How to Follow Scientists and Engineers Through Society[END_REF] -takes the opposite view to a presumption of an actor's irrationality. Moreover, in sociology, there are as many possible interpretations as there are paradigms. The issue of subjectivity is addressed differently according to the paradigm chosen. For instance, when Bourdieu studied artistic activity, he focussed on social positions, and on strategies aimed at preserving those positions. In his brilliant analysis of jazz musicians, Becker adopts a different perspective -exhibiting how interactions take place locally, under local rules [START_REF] Becker | Outsiders. Studies in the Sociology of Deviance[END_REF]. In each case, researchers are taking care not to project their subjectivity onto the documents they collect, but their interpretations nonetheless differ in the paradigm from which they originate. Whatever the field, a number of analyses can be (and are) carried out, producing concurrent interpretations. Even within a given paradigm, selected researchers do not interpret the documents the same way. This contributes to the richness of the interpretative work in scientific work.

Intersubjectivity Web 2.0 & intersubjectivity

Since the advent of time-sharing mainframe computers, information technology has allowed thousands of users to work together on a digital medium. However, the novelty of Web 2.0 is the massive usage of multi-user systems of loosely organised volunteers. Whereas traditional information technologies focus on consensual (or imposed) data structures (database schemas, ontologies) and work processes (workflows, business processes), Web 2.0 technologies bring the dissensus among users to the fore by showing the thread of reader comments on a blog post, the "cloud" of tags assigned by different readers to documents, or the "discussion" tab on a wiki page.

The intersubjective technologies of Web 2.0 can be seen as scaled-up versions of earlier technologies prototyped in digital libraries, such as shared comments and "landmarks" for collaborative filtering [START_REF] Röscheisen | Beyond browsing: Shared comments, soaps, trails, and on-line communities[END_REF] or scholars' "viewpoints" to improve indexing [START_REF] Bénel | Indexation "sémantique" de documents archéologiques[END_REF][START_REF] Nanard | Cumulating and sharing end users knowledge to improve video indexing in a video digital library[END_REF].

Hermeneutics & intersubjectivity

The theory of the multiple meanings of texts ("multiplex intellectus") is also a crucial point of biblical hermeneutics (Ricoeur,. Origen of Alexandria (3rd century) and later Christian exegetes distinguished: l a "literal" meaning, to be learnt (because the words were written by other people of another time), l an "allegorical" meaning, to be believed (because the words can be seen as signs from another time for today), l a "moral" meaning, to act in accordance with ("moral" means "related to manners"), l an "anagogical" or "mystical" meaning, to tend to ("anagogical" means "raising", and "mystical" means "kept silent").

Those meanings are also present in the Jewish tradition as "Pshat", "Remez", "Drash" and "Sod". In a mystical tale by Moshe Cordovero (16th century), each of these meanings (abbreviated to "P", "R", "D", "S") is a level of the same "PaRDeS" (garden). It is worth noting that in this tale, every meaning is explored by a different rabbi.

In philosophical hermeneutics, the alterity between the social world and the analyst is completed with a second alterity between her viewpoint and that of her colleagues. After subjectivity, intersubjectivity becomes a test of the analysis. Indeed, as stated by sociologists of science, confrontation between conflicting viewpoints is core to the dynamics of the scientific field [START_REF] Bourdieu | Le champ scientifique[END_REF][START_REF] King | The Sociology of Science[END_REF]. However, a critical challenge remains in broadening intersubjectivity beyond paradigms, methods, and disciplinary borders.

An archaeological illustration

Even the early promoters of "archaeological "databanks" wondered about the possible effect of differences between experts' viewpoints. Jean-Claude Gardin, although he advocated a "logicist program" in humanities, had to admit that it was impossible to think about archaeological databases without wondering about the acceptance of these data by the whole archaeological community [START_REF] Gardin | Les bases de données dans les sciences de l'antiquité : l'ajustement nécessaire des fins aux moyens[END_REF]. René Ginouvès and Anne-Marie Guimier-Sorbets went further by advising database designers to incorporate intersubjectivity, rather than hoping to achieve a hypothetical consensus which would certify the objectivity of the data [START_REF] Ginouvès | La constitution des données en archéologie classique[END_REF]. However, no practical solution was given on how to implement such intersubjective databases.

One of the most important features of our prototype software prototype is enabling the comparison of different analytical frameworks as different viewpoints on the same corpus of documents. Recent archaeological projects attest that complex artefacts are studied from different disciplines, and result in bibliographies which rarely intersect. For example, Bruno Helly noted that gravestones and funerary monuments are independently studied with reference to their sculpture, paintings, architecture, inscriptions, and also the origin and cut of the stone. He stated that using a digital medium to contrast disciplinary viewpoints would be an apt way to understand such a complex object as a whole [START_REF] Iacovella | Assistance à l'interprétation dans les bibliothèques numériques pour les sciences historiques[END_REF].

A sociological illustration

Numerous sociologists address the same phenomenon, but they rarely share their material (Rigot, 2006). Moreover, given that sociology is divided into paradigms and schools, scientific debates occur only inside each paradigm. The fact that sociologists belonging to different paradigms study similar phenomena does not necessarily generate debate, given that "you adhere to a paradigm, you do not demonstrate its validity." (Rigaux, 1992, p.21).

However, debate is not absent. In the eighties, the anthropology of science happened to restate the social constructionist paradigm. The social constructivists, for whom science results from social interaction, challenged the realistic view that scientific facts reflect the world out there. The debate resulted in a refined social constructivism. According to this (new) perspective, the power of scientific discovery results from multiple contributions. As a consequence, facts are powerful because they are constructed, not despite that. The controversy, and thus, the conflict between schools of social scientists contributed to a richer perspective [START_REF] Simmel | Conflict And The Web Of Group Affiliations[END_REF].

Similar controversies can occur within our software platform. As with the anthropology of science, these debates contribute to the formulation of more powerful interpretations. For instance, the sociologist Gautier Pirotte used our tools in order to study French versions of international NGO annual reports that mention the notion of civil society (see Figure 6). He compared two concurrent theories: the "economy of worth" [START_REF] Boltanski | On Justification: Economies of Worth[END_REF] and his own theory of civil society [START_REF] Pirotte | La notion de société civile[END_REF]. Another example came from within the same paradigm: in research on the perception of electromagnetic risk, members of a team in political science developed two concurrent viewpoints, on the basis of the same documents, in this case transcribed focus groups [START_REF] Brunet | La perception du risque en situation de haute incertitude scientifique : le cas des champs électromagnétiques en Belgique[END_REF].

Conclusion

In this paper we explored how Web 2.0 technologies could be considered as a resurgence of hermeneutics, while traditional information technology can be seen as the offspring of logical positivism. We illustrated how philosophical hermeneutics intersects the main methodological debates within archaeology and sociology. For these disciplines, we saw how software can be designed and prototyped on notions such as document-driven enquiry, interpretation and intersubjectivity, in order to provide a convenient medium for researchers to carry out their analyses. We hope that the success and maturity of Web 2.0 will foster digital humanities, and humanities themselves, in an unprecedented way.

Figure 1 :

 1 Figure 1: Documented photograph from a scientific archive showing an inscribed block

Figure 2 :

 2 Figure 2: Frequent, repeated and specific tokens from a focus group on electromagnetic risks

Figure 3 :

 3 Figure 3: Qualitative analysis of the scenes depicted on vases

Figure 4 :

 4 Figure 4: Analysis of "self-fed phenomena" in electromagnetic risk perception

Figure

5

  illustrates this by showing two analyses, executed by different students, of photographs of geometric vases.

Figure 5 :

 5 Figure 5: Comparison of two analyses of the same geometric vases

Figure 6 :

 6 Figure 6: Alternate analyses of NGO reports in terms of economy of worth and civil society

Porphyry, Cassandre and Argos are available as open-source software.

The Semantic Web advocates automatic deductions based on the description of resources with formal and standardized vocabularies (named "ontologies"). Imposing no such formal constraints on resources descriptors, Web 2.0 proposes an alternate strategy.

Marion Lagarde, Elodie Lacrampe and Clélia Robinet, under the direction of Jean-Marc Luce and Pascale Jacquet (CRATA Laboratory, University of Toulouse II, France).

Such a quantitative content analysis assesses an epistemological framework similar to the Semantic Web's positivist assumptions.