dataesr/scientific-paragraphs-categorization huggingface dataset:
Snow cover area based on the MODIS sensor i) Evolution of the snow covered area Many studies have demonstrated the usefulness of MODIS images for snow cover mapping in mountain areas  (Gascoin et al. , 2015; Klein and Barnett, 2003; Parajka and Blöschl, 2008). The MODIS mission database provides long temporal coverage (the mission was launched in 2000, and obtains daily images), so enabled a comparison between the simulated and observed snow cover evolution for 14 snow seasons (out of the 26) simulated on an almost daily basis (comparisons were limited by cloud cover in the study area). Sub-pixel snow monitoring of the snow cover at 250-m spatial resolution was performed using MODImLab software  (Dumont et al. , 2012b; Sirguey et al. , 2009). Multispectral fusion between MOD02HKM (500 m; bands 3-7) and MOD02QKM (bands 1 and 2)  (Sirguey et al.
, 2008) , enabled this software to generate images at 250 × 250 m spatial resolution to derive various snow-ice products. We used the unmixing_wholesnow (UWS) product, as it has been shown to outperform other The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-184 Manuscript under review for journal The Cryosphere Discussion started: 27 September 2017 c Author(s) 2017. CC BY 4. 0 License. snow-ice products for assessing evolution of the SCA  (Charrois et al. , 2013). We also considered the cloudiness product in MODImLab to determine the proportion of the catchment affected by cloud cover. Generation of the UWS and cloudiness products in MODImLab software was based on the same DEM used for the snowpack simulations. This ensured a direct match between of observation and simulation pixels.
Glacier surface mass balance Glaciers located in the Mer de Glace and Argentière sub-catchments have been monitored, in a sufficient number of measurement locations for our analysis, since 1995 by the French Service National d'Observation GLACIOCLIM. During this period field data were obtained twice per year, during the maximum (end April-May) and minimum (around October) snow accumulation periods. These data enabled calculation of the SMB for summer (SSMB; annual difference between the maximum and minimum acquisitions), winter (WSMB; annual difference between the minimum of the previous year and the maximum acquisitions), and annually (ASMB; year to year differences in the minimum acquisitions) at each individual point of the network (Fig. 3 ). The observation procedure involved use of glaciological methods  (Cuffey and Paterson, 2010)  to retrieve the surface mass balance for the various time periods (SSMB, WSMB, and ASMB).
Stakes (markers over the glaciers) are set up in both accumulation and ablation areas throughout the glaciers, and so reflect the evolution of the various zones of the glaciers. The spatial distribution of the stakes is shown in Figure  3. For further information on the methods for SMB data collection, see  Réveillet et al. (2017). The observations of SMB for the various time periods at more than 65 locations encompassing different glaciers enabled assessment of the snow and ice evolution over glacierized areas, as these measurements included snow and ice ablation (SSMB) and snow accumulation (WSMB) periods. Thus, the simulated SMB for the same observation periods and locations were computed based on Crocus results. With this information, a linear regression and R 2 coefficient were computed for each sub-basin for the three periods, and these were used to measure the performance of the modeling average and standard deviation for all points within each band were calculated).
Overview of SAFRAN-Crocus performance The observation dataset used in this study enabled multilevel spatio-temporal validation of the performance of snowpack simulations at the scale of a large alpine catchment. The analysis of the results of semi-distributed and distributed simulations provided a holistic evaluation of the snow and ice dynamics in the study area. Overall, the SAFRAN-Crocus simulations have shown a good capability on reproducing the temporal evolution and spatial variability of snow and ice during the study period. The simulations were evaluated using snow depth data from five Météo-France stations. Their ability to reproduce a bulk variable such as snow depth suggests that the main simulation processes were satisfactory, especially those related to the various components of the energy and mass balance.
, in prep). Such effects may be significant for elevations where the snow completely melts during summer and do not The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-184 Manuscript under review for journal The Cryosphere Discussion started: 27 September 2017 c Author(s) 2017. CC BY 4. 0 License. insulate ice from the atmosphere during late melt season; this includes the low elevation areas of glaciers, where high SSMB errors were found. At the annual time scale, glacier differences between the observed and simulated SMB at high elevations during winter and at low elevations during summer were reduced because the SMB underestimates for winter (note these were negative/positive at high/low elevations) were compensated for by more accurate simulations during summer, and vice versa. Regardless of these errors, SAFRAN-Crocus was able to replicate the interannual evolution of the SMB.
Limitations of the evaluations performed Although the observation dataset enabled comprehensive evaluation of the simulations, it had limitations. First, the discrepancy in spatial scale between the SAFRAN meteorological analysis and the snow depth observations, and the low number of stations, limited the interpretation of results in terms of the simulated snow depth. Differences in the temporal evolution of snow depth between observation and simulations were in part associated with the unresolved sub-massif spatial variability in the level of precipitation, as previously described  (Durand et al. , 2009a; Lafaysse et al. , 2013; Vionnet et al. , 2016). In situ observations are also subject to local effects associated with the topographic control at each site, including exposure to dominant winds, which markedly affects the snow depth dynamics.
Distributed vs. semi-distributed approaches In this study we performed distributed and semi-distributed snowpack simulations using the same model and evaluation setup (including ice initialization, meteorological forcing, projection on the same grid, observation databases). Thus, both approaches were affected by the same methodological limitations. The simulation results were consistent with the observed SCA evolution using both approaches. However, better The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-184 Manuscript under review for journal The Cryosphere Discussion started: 27 September 2017 c Author(s) 2017. CC BY 4. 0 License. results were obtained from the distributed simulations, especially during late summer. The energy balance was more accurately simulated in the distributed approach, as it accounted for terrain shadowing effects on incoming solar radiation.
In regard to the spatial scale of snowpack simulations over extended areas, the semidistributed and distributed simulations were compared using the same observation datasets, including: (i) the temporal evolution of the snow-covered area based on data from the MODIS sensor; (ii) measurements of surface mass balance of glaciers within the upper Arve catchment; and (iii) observational data on the annual evolution of the equilibrium-line altitude for the various glaciers considered. Both simulation methods accurately reproduced the evolution of the SCA during accumulation events, as they relied on the same meteorological forcing data. For the winter to early spring period, when the study area is almost completely covered by snow, there was little difference between the two approaches. However, for the melt period the distributed simulations better reproduced the observations.
The simulations for low elevations and elevations > 2700 m. a. s. l. underestimated (negative underestimation in low elevations and positive in high) the observed SMB. Nevertheless, the results of both simulations were in close agreement with observations at mid-elevation areas, and adequately reproduced the observed annual SMB at all elevations. Overall, the distributed simulations yielded better results. Based on comparison with ELA data obtained from various satellites at the end of summer, the SAFRAN-Crocus accurately reproduced the inter-annual variability of the snowpack over glaciated areas. However, differences between observations and simulations were evident, particularly for the smallest glacierized areas, where the spatial resolution of the simulations did not enable the high spatial variability of the The Cryosphere Discuss.
, https://doi. org/10. 5194/tc-2017-184 Manuscript under review for journal The Cryosphere Discussion started: 27 September 2017 c Author(s) 2017. CC BY 4. 0 License. topography to be included. In addition, based on the ELA evaluation, the distributed 838 approach was slightly better at reproducing the snowpack dynamics. 839 Overall, the results of this study demonstrated that distributed simulations were better at 840 reproducing snowpack dynamics in the alpine terrain of our study area. Distributed 841 simulations take account of the specific topographic characteristics of each pixel and 842 also the effects of terrain shadowing by surrounding areas. Inclusion of these two effects 843 over long time periods led to better results being obtained using the distributed 844 approach. Distributed simulations will facilitate incorporation of the latest snowpack 845 modeling advances, including assimilation of satellite data and the use of higher spatial 846 resolution meteorological forcing models.
The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-184 Manuscript under review for journal The Cryosphere Discussion started: 27 September 2017 c Author(s) 2017. CC BY 4. 0 License. (Landsat, SPOT, and ASTER). This enabled assessment of the use of distributed 139 simulations for analysis of snow and ice dynamics. The simulations were based on data 140 for the upper Arve catchment (French Alps) for the 26 years from 1989 to 2015. 141 The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-184 Manuscript under review for journal The Cryosphere Discussion started: 27 September 2017 c Author(s) 2017. CC BY 4. 0 License.
introduced a representation to depict the suitability of an observation dataset to evaluate model performance. To evaluate the simulations in this study we used four datasets based on: in situ snow depth from Météo-France stations; the snow covered area (SCA) from MODIS images; the punctual glacier surface mass balance (SMB); and the glacier equilibrium-line altitude (ELA) from Landsat/SPOT/ASTER. Based on the radar charts presented by Hanzer et al. (2016), shown in their Figure 5, it was possible to fully evaluate the simulations using the four observation datasets available for this study. The analyses presented below enabled us to draw conclusions about the impact of the methods used on the various spatiotemporal scales considered, also enabling an overall evaluation of the simulation platform.
A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT 15 Detailed mass balance calculations on Serifos skarns were also performed by  Salemink (1985). The main result is that these skarns might be mainly issued from host-rock replacement (i. e. exoskarns) through percolation of metasomatic fluids deriving in part from the leaching of the granodiorite at the post-magmatic phase. Particularly, a large zone of "bleaching" within the granodiorite has been identified and is supposed to supply the major part of the Fe required to form the skarns. Oxygen Isotope data are compatible with such interpretation (SM 2). Later, such a bleached zone has been recognized by  Tschegg and Grasemann, (2009)  within the granodiorite in the hanging wall of detachment near Agios Sostis (Figure  1b, c ). The extensive leaching of mafic components by pervasive fluid flow caused this bleaching of the granodiorite, with a much higher content of SiO 2 , compensated by lower Al 2 O 3 and CaO.
Acknowledgments This work has received funding from the  European Research Council (ERC)  under the seventh  Framework Programme  of the  European Union  ( ERC Advanced Grant , grant agreement No  290864 , RHEOLITH) and from the  Institut Universitaire de France. It is a contribution of the  Labex VOLTAIRE. We are grateful to  S. Janiec  and  J. G. Badin  ( ISTO ) for the preparation of thin section,  A. Menant ,  A. Beaudoin V. Roche  and  V. Laurent  for many discussions on the Aegean domain and their constructive comments. F. Rossetti  and anonymous reviewers have greatly improved the former manuscript versions as the editorial work of  R. Govers.
Dosimeters were collocated also at some XVPCA sites for comparison with reference measurements (6 samples for O 3 at Vic (VIC), Montcada (MON) and Montseny (MSY); and 10 samples for NO 2 at Santa María de Palau Tordera (SMPT), Palau Reial (PLR), Tona (TON), Manlleu (MAN), and Granollers (GRA)). Correction factors were obtained from the comparison between dosimeter and reference data (Dosimeter O 3 = 1. 01 • Reference O 3 + 17. 43, R 2 = 0. 97; and Dosimeter NO 2 = 1. 27 • Reference NO 2 + 1. 14, R 2 = 0. 90, in all cases in µg m -3 ) and then applied to the whole dosimeter dataset (Fig. S1  in the Supplement). NH 3 passive samplers (CEH ALPHA;  Tang et al. , 2001)  were also used in 30 specific points.
Data availability Hourly measurements of gaseous pollutants at the 48 monitoring sites belonging to the regional air quality network used in this paper can be downloaded from the regional air quality monitoring network (XVPCA) at http://dtes. gencat. cat/icqa. Table  S1  in the Supplement gives details of these monitoring sites. The meteorological data from the stations used can be requested free of charge from smc@meteo. cat. Table  S3  in the Supplement gives details of these monitoring sites. In an Excel file included in the Supplement, data from diffusion tubes for NO 2 and O 3 are reported. Finally, data from measurements with tethered balloons can be requested by mail from the corresponding authors of this paper. The Supplement related to this article is available online at doi:10. 5194/acp-17-2817-2017-supplement.
Acknowledgements. The present work was supported by the  Spanish Ministry of Economy and Competitiveness  and  FEDER  funds under the project  HOUSE  ( CGL2016-78594-R ), by the  Generalitat de Catalunya  ( AGAUR 2015 SGR33  and the DGQA). Part of this research was supported by the  Korean Ministry of the Environment  through "The  Eco-Innovation  project". The participation of  University of Marseille  and  University of Birmingham  was partially supported by two TNA actions projects carried out under the  ACTRIS2  project (grant agreement No. 654109 ) financed by the  European Union 's  Horizon 2020 research and innovation program. The support of the  CUD of Zaragoza  (project  CUD 2013-18 ) is also acknowledged. We are very thankful to the  Generalitat de Catalunya  for supplying the air quality data from the XVPCA stations, to ME-TEOCAT (the  Meteorological Office of Catalonia ) for providing meteorological data and to the  IES J.
For remote-sensing applications such as spectra classification or identification, atmospheric correction constitutes a very important pre-processing step, especially in complex urban environments where a lot of phenomenons alter the shape of the signal. The objective of this article is to compare the efficiency of two atmo-spheric correction algorithms, COCHISE (atmospheric COrrection Code for Hyperspectral Images of remote-sensing SEnsors) and an empirical method, on hyperspectral data and for classification applications. Classification is carried out on several simulated spaceborne data sets with different spatial resolutions (from 1. 6 to 9. 6 m). Four classifiers are considered in the study: a k-means, a Support Vector Machine (SVM), and a sun/ shadow version of each of them, which processes sunlit and shadowed pixels separately.
ATREM  (Goetz et al. 1997) f o r'Atmospheric REMoval program' and ACORN  (Miller 2002) f o r'Atmosphere CORrection Now' assume a flat homogeneous ground hypothesis with a Lambertian surface. FLAASH  (Cooley et al. 2002) f o r'Fast Line-of-sight Atmospheric AnalySis of Hypercubes' and COCHISE  (Miesch et al. 2005) f o r'atmospheric COrrection Code for Hyperspectral Images of remote-sensing SEnsors' consider as for them a heterogeneous background. The main drawback of these four methods is that they perform atmospheric correction without accounting for the three-dimensional structure of the ground, which may introduce artefacts due to slopes and shadows. SIERRA  (Lenot, Achard, and Poutier 2003) f o r 'Spectral reflectance Image Extraction from Radiance with Relief and Atmospheric correction' bypasses the slope issue by using a digital elevation model (DEM) but only ATCOR4  (Richter and Schlapfer 2002) f o r'Atmospheric and Topographic CORrection' and ICARE  (Lacherade et al.
G and F environment functions stand for the probability that an energy reaching the target through this effect results from the neighbour ðu; vÞ. These functions are computed using a Monte Carlo algorithm  (Miesch et al. 2000). Atmospheric parameters involved in Equations (  2 ), (4), and (5), that is, R atm , τ " dir , τ " dif , I dir , I dif and S, are computed using the radiative transfer code MODTRAN, for 'MODerate resolution atmospheric TRANsmission'  (Berk, Bernstein, and Robertson 1989) , knowing some other parameters such as aerosol type and abundance, molecular atmospheric profile, water vapour content, and illumination conditions. The inversion process in order to obtain the reflectance ρðx; yÞ is based on an iterative algorithm. At first, the reflectance associated to the environment of the target is considered equal to the reflectance of the target: ρ ð1Þ ¼ πðR tot À R atm Þ ðτ " dir À τ " dif ÞÂðI dir þ I dif ÞÀSðR tot À R atm Þ : (7) The next reflectance value is then computed as follows: ρ ðtþ1Þ ¼ π R tot À R atm À τ " dif I ðtÞ 8 > > > > > > > > > > < > > > > > > > > > > : (9) Usually, two iterations of the algorithm are enough to reach convergence.
Working data and simulation The data used in this study have been acquired on an area covering both the downtown and the suburb of Toulouse, France (cf. Figure  5 ), during the Umbra airborne campaign  (Adeline et al. 2013a ) which occurred on 24 October 2012. Two sensors of the Hyspex product line have been used simultaneously in order to cover a wavelength interval starting from 400 to 2500 nm. The first one is a VNIR-1600 sensor covering the 400 À 1000 nm interval with a spectral resolution of 3:7 nm and 160 bands. The second one is a SWIR-320m-e sensor covering the 1000 À 2500 nm interval with a spectral resolution of 6 nm and 256 bands. During this flight, both sensors were placed at an altitude of 2392 m, which implies a spatial resolution of 80 cm for the visible and near infrared (VNIR) sensor and 160 cm for the SWIR sensor.
In order to work on a single data set, the IGN (French national geographic institute) undertook the coregistration between the VNIR and SWIR data sets. To do so, the VNIR image has been downsampled in order for its spatial resolution to be the same as in the SWIR image. In this study, we focus on spaceborne hyperspectral data. Thus, a simulation protocol of HYPXIM  (Briottet et al. 2011 ) images from Hyspex data has been established. HYPXIM is a space mission aiming to commission a satellite which would carry a high-resolution hyperspectral sensor covering a wavelength interval starting from 400 to 2500 nm with a spectral resolution of 10 nm (accurate enough for urban material characterization) and a swath of 16 km. Its planned spatial resolution is 8. The simulation protocol includes four steps (cf.
Ground truth description In order to measure the accuracy of the classifiers' results, a ground truth has been built manually from the data set with the most accurate spatial resolution (cf. Figure  7 ). Five common urban classes are considered: asphalt, gravel, tile, vegetation, and water. The training sets used for the supervised classifiers are composed of 2% of the samples included in the ground truth. The classifiers' efficiency is evaluated by measuring the average and overall accuracies of the results over the rest of the samples. The overall accuracy (OA) represents the general proportion of well-classified pixels, whereas the average accuracy (AA) represents the proportion of well-classified pixels by class, which is more pertinent when the classes do not have the same number of elements.
Conclusion In this article, we compared the efficiency of two atmospheric correction algorithms, the COCHISE method and a classification-orientated empirical method, for the classification of urban hyperspectral data. This comparison have been conducted using a Hyspex data set acquired over Toulouse, France, in 2012 on four hyperspectral data sets simulated at various spatial resolutions, with the objective to evaluate the impact of spatial resolution on classification results. Four classifiers have been considered in the study: a k-means, an SVM and a sun/shadow classification algorithm using either a k-means or an SVM as a first step. In the end, the empirical atmospheric correction method, which takes shadows into account, appears more accurate than COCHISE, the flat ground method, on high spatial resolution data.
Future work will consider the addition of the ICARE icare_XC  (Ceamanos et al. 2016 ) atmospheric correction algorithm to the comparison process. Such a method, which uses a digital elevation model as an a priori, may perform even better than the empirical method on high spatial resolution data sets due to the consideration of 3D data on the surface. Furthermore, it would be interesting to compare these algorithms on several other data sets simulated from missions such as Pleiades, WorldView 3, and Sentinel-2. At last, the fusion of panchromatic high-resolution data with hyperspectral data such as HYPXIM or SHALOM is also a promising way. Indeed, the recent work of  Loncan et al. (2015)  allowed to compare several panchromatic/hyperspectral fusion methods on urban data and select the most efficient one in order to generate a hypercube with a ground sample distance equal to 2 m.
Acknowledgements. We thank the referee,  Mark Marley , for comments that significantly improved the paper. This work is partly supported by the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013 Grant Agreement No. 247060-PEPS  and grant No. 320478-TOFU ). D. S. A. acknowledges support from the  NASA   Astrobiology Program through the Nexus for Exoplanet System Science. J. M. acknowledges the support of a  Met Office Academic Partnership secondment. The calculations for this paper were performed on the DiRAC Complexity machine, jointly funded by  STFC  and the  Large Facilities Capital Fund of BIS , and the  University of Exeter Super-computer , a DiRAC Facility jointly funded by  STFC , the  Large Facilities Capital Fund of BIS  and the  University of Exeter.
Several in situ data sets, collected by the MOOSE, HyMeX and MerMex programmes, are used in the present study and are briefly described below. More details are available in  Estournel et al. (2016b). First, the Lion meteorological buoy provides hourly measurements of the air temperature and humidity at 2 m above the sea surface, the wind speed at 10 m and the sea surface temperature. A mooring named LION, part of the MOOSE network, and located only 5 km away from the Lion buoy, is also available. It provides vertical profiles of temperature and salinity, sampled over 21 levels for temperature and 15 for salinity, between 150 and 2300 m deep  (Houpert et al. , 2016). In addition, several oceanographic cruises were carried out in the NWMS during 2012-2013  (Waldman et al. , 2016b). In August 2012, the annual cruise organized by MOOSE for the monitoring of the NWMS provided 69 vertical surfaceto-bottom profiles of temperature and salinity.