dataesr/scientific-paragraphs-categorization huggingface dataset:
As noted above, the result for the numbers for SM hours was very different from the cases for Tair and Twb. Thus, for > 50% of the days there was no statistically significant linear relationship of SM with elevation, but the proportion of those days having inverted lapse rates (9. 4%) was less than for Tair and Twb. For SM daily hours there was a clear reduction in the inverted lapse rate with time during the ski season, with average values of 22, 8. 3, 4. 7, and 2. 2 days per month per year for December, January, February, and March, respectively. Figure  4  shows the average hourly lapse rates for Tair and Twb calculated for the entire dataset. This shows clear sub-daily variability, with steeper lapse rates during the hours in the middle of the day (from 11:00 am to 15:00 pm), when lapse rates were similar to the standard ELR (-5.
Atmospheric pressure (Pat) was also strongly positively correlated with the Tair and Twb lapse rates. This variable also exhibited high collinearity with RH and WS, so seems to be redundant in explaining the temporal dynamics of lapse rates when information on RH and WS is available. However, it may be a useful indicator of temporal fluctuations in lapse rates in regions where limited meteorological information is available, as Pat tends to exhibit a relatively homogeneous behavior over large areas, including in complex topography, and where reliable gridded datasets covering long-term periods are available  (Allan, 2006). The temporal dynamics of the SM lapse rates was more complex. Thus, correlations with meteorological variables were lower than Twb because the average Tair in the ski resort led to 0 or 24 SM hours independently of the magnitude of the atmospheric variables.
9 to 2. 4 wt%  (Helz et al. , 1994; 2012). They have therefore been used widely to study stable isotopic fractionation during igneous processes (e. g. , Zn:  Chen et al. , 2013; Ga: Kato et al. , 2017; Fe: Teng et al. , 2008; Mg: Teng et al. , 2010; Sn: Badullovich et al. , 2017; Li: Tomascak et al. , 1999). To evaluate the Sr isotopic composition of BSE we report data to test the effect of igneous differentiation on a series of Kilauea Iki lava lake samples, and to evaluate the spatial and temporal heterogeneity of Sr stable isotope compositions in the terrestrial mantle using basaltic rocks from different settings (ocean island basalts [OIB], mid ocean ridge basalts [MORB], continental flood basalts [CFB], and komatiites). We also report data for a variety of chondritic meteorites for comparison with the estimated BSE Sr isotopic composition.
Acknowledgments We thank two anonymous reviewers, and the Editor,  Klaus Mezger , for their constructive comments. FM acknowledges funding from the  European Research Council  under the  H2020 framework program / ERC  grant agreement # 637503  (Pristine) and financial support of the  UnivEarthS Labex program at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ), and the  ANR  through a chaire d'excellence  Sorbonne Paris Cité. We also thank the  IPGP multidisciplinary program  PARI, and by  Paris-IdF region SESAME  Grant No. 12015908. Finally, we are grateful to  Brandon Mahan  for proof-reading the manuscript.
Concerning the strengths and limitations of multispectral and airborne LiDAR data, the fusion of such datasets can compensate for the weakness of each other. This work have investigated the integration of multispectral and airborne LiDAR data for the land cover mapping of large urban area. Different LiDAR-derived features are involoved, including height, intensity, and multiple-return features. However, there is limited knowledge relating to the integration of multispectral and LiDAR data including three feature types for the classification task. Furthermore, a little contribution has been devoted to the relative importance of input features and the impact on the classification uncertainty by using multispectral and LiDAR. The key goal of this study is to explore the potenial improvement by using both multispectral and LiDAR data and to evaluate the importance and uncertainty of input features.
Experimental results revealed that using the LiDAR-derived height features produced the lowest classification accuracy (83. 17%). The addition of intensity information increased the map accuracy by 3. 92 percentage points. The accuracy was further improved to 87. 69% with the addition multiple-return features. A SPOT-5 image produced an overall classification accuracy of 86. 51%. Combining spectral and spatial features increased the map accuracy by 6. 03 percentage points. The best result (94. 59%) was obtained by the combination of SPOT-5 and LiDAR data using all available input variables. Analysis of feature relevance demonstrated that the normalized digital surface model (nDSM) was the most beneficial feature in the classification of land cover. LiDAR-derived height features were more conducive to the classification of urban area as compared to LiDAR-derived intensity and multiple-return features.
Introduction Detailed knowledge of the land cover types and their aerial distribution are essential components for the management and conservation of the land resource and are of critical importance to a series of studies such as climate change assessment and policy purpose  [1] [2] [3]. In recent decades, satellite remote sensing has exhibited its ability to achieve the land cover information with different temporal and spatial scales in the urban area. A multispectral satellite image can collect spectral information of land surfaces, and supply extra advantages to discriminate differences between urban land cover classes  [4] [5] [6] [7]. Even though many studies have successfully employed multispectral data for the classification of urban land cover, classification accuracy is more likely to be lower using spectral signature alone in the urban environment as compared to other environments such as forest environment.
The LiDAR system plays an increasingly vital role in the urban land cover mapping due to its power to acquire the vertical structure of surface objects with high positional accuracy. Different LiDAR-derived features can be extracted from LiDAR data, including LiDAR-derived height, intensity and multiple-return features  [18, 20, 21]. LiDAR-derived features can describe the 3D topographic features of the earth surface, and a range of LiDAR-derived height features such as normalized DSM (nDSM) and height variation, has proved its help in improving map accuracy  [18, 20, 22, 23]. Intensity data as the radiometric component of LiDAR data, which is the peak backscattered laser energy from the illuminated object, can provide additional information for the land cover classification  [24, 25]. Song et al.
[26]  initially investigated the possibility of intensity data as the input features for the urban land cover mapping, and concluded that intensity data could be conducive to the land cover classification. In addition, a small number of researchers investigated the contribution of multiple-return features to the land cover classification  [27, 28]. Charaniya et al. [27]  adopted the difference between the first and last return as the auxiliary feature, leading to an accuracy improvement of 5% to 6% of roads and buildings classes. However, it has been shown that classification accuracy derived from using LiDAR data alone is limited owing to the lack of spectral information. The combination of multispectral and LiDAR data can compensate for the shortcomings of each other, and generate better classification results than those obtained from using an individual sensor, considering the merits and limitations of multispectral and LiDAR data.
Despite the fact that many studies have dedicated to combining multispectral and LiDAR data for improving classification performance, there are still some problems worth our deep concern. Firstly, the valuable information acquired from LiDAR data involves in elevation, to the author's knowledge, intensity and multiple-return features, whereas most of the research was based on one or two feature types derived from LiDAR data for classification  [18, 21, 22, [29] [30] [31] [32] [33] [34]. Therefore, there is limited knowledge concerning the integration of three feature types (i. e. , height, intensity and multiple-return) acquired from airborne LiDAR and multispectral for the classification task. Secondly, while some LiDAR-dervied features such as mean absolute deviation (MAD) from median height based on height or intensity information have been increasingly used for the classification of tree species, only a few studies have used such features to aid in the classification of urban land cover  [35].
APs were usually used to get the spatial information from the high-resolution images, whereas there is a lack of research dedicated to extracting spatial information from SPOT-5 image, and exploring its effects on the classification accuracy especially for the urban area. Lastly, another important aspect that should be noticed is that classification uncertainty as an additional accuracy measure can be employed to evaluate the spatial variation of the classification performances  [36, 37]. However, there is very little research that has explored the impacts of the integration of multispectral and LiDAR datasets on the classification uncertainty. This study is aimed at bridging these gaps. In addition to evaluating the classification accuracy, the contribution of each feature to the classification accuracy was also explored by an assessment of the relative importance of all input features for the land cover classification in this study.
The key blueobjectives of our study are presented as follows: (i) to investigate how much classification accuracy can be improved by integrating different input features provided by multispectral and LiDAR data; (ii) to quantify the relative importance of all input variables and explore the contribution of each feature to the classification accuracy; (iii) to assess the influence of different input features on the classification uncertainty. Section 2 describes the study area and presents an overview of datasets as well as the preprocessing. Section 3 reports the methodological details, including feature extraction, classification algorithm, accuracy assessment in conjunction with classification uncertainty. The detailed experimental results are summarized in Section 4. Section 5 provides the discussion and summarizes the paper with remarks and future lines of the research.
LiDAR Data Airborne laser scanning data was acquired by Optech ALTM Gemini instrument on 21 April 2009. The wavelength and frequency of the laser pulse were 1. 06 µm and 167 kHz, respectively. The returned laser pulse signals after backscattered from the targets were recorded with the mean point density of 4. 1 points/m 2 and up to four returns. The dataset contains point cloud data from a series of overlapping flight lines, each with an overlap of 20-30% between adjacent flight lines. LiDAR data provided accurate height data and contained multiple returns per laser pulse and the intensity information, which reflects the surface characteristics. The identified overlap and noisy points were removed from all LiDAR point clouds. Raster layers for both bare earth surface, digital elevation model (DEM), and the first return, digital surface model (DSM), were generated from a triangular irregular network (TIN) of the LiDAR data points with the pixel size of 10 m.
SPOT-5 Image A SPOT-5 multispectral image was acquired over the study area on 1 January 2010. The SPOT-5 data set was composed of shortwave infrared (SWIR) band (1. 58-1. 75 µm) with 20m spatial resolution, and three bands with the spatial resolution of 10m covering green (0. 50-0. 59 µm), red (0. 61-0. 68 µm), and near-infrared (0. 78-0. 89 µm). It was orthorectified and resampled to a 10 m pixel size using the software ENVI. The details of ground reference data is shown in Table  1. We have collected them by using the visual interpretation of the orthorectified SPOT-5, aerial photograph and high-spatial-resolution images from Google Earth (http://earth. google. com/). Attribute Profiles (APs), an extension of the Morphological profiles (MPs), were employed to extract spatial features from the SPOT-5 image.
LiDAR-Derived Features Given that LiDAR and multispectral can provide complementary information, the LiDAR-based features were extracted to classify land cover except spectral features. The LiDAR feature vector comprises multi-return and intensity LiDAR features, where multi-return LiDAR features can be separate into height-based, return-based LiDAR features. The resulting feature vector f LiDAR can be formulated as follow: f LiDAR = [ f elevation , f intensity , f return ] (4) (1) Height-based Features Height information in LiDAR data has demonstrated an importance for the precise description of vertical structure  [18, 35]. In this regard, height-based LiDAR features were extracted based on 3D points heights within each pixel of 10 m spatial resolution. In this study, the following height metrics were computed: nDSM, mean, mode, standard deviation, variance, CV (coefficient of variation), skewness, AAD (average absolute deviation), L-moments (L1, L2), L-moment skewness, L-moment kurtosis, MAD median (Median of the absolute deviations from the overall median), MAD mode (Median of the absolute deviations from the overall mode), canopy relief ratio, quadratic mean, cubic mean, and percentile values (1st, 10th, 25th, 50th, 75th, 95th) percentiles.
• afafbovemode: (all returns above mode height)/(total first returns). Moreover, it should be noted that all the datasets were co-georeferenced and resampled to a 10 m pixel size, which ensures the consistency of corresponding spatial position. Different feature sets can contribute to improving classification accuracy in a variety of ways. Feature combination can integrate the advantages of different feature sets and has proved to be effective in many projects of urban land cover classification. To gain a detailed understanding of what level of classification result could be obtained by using different scenarios of input features using Random Forest classifier, seven different scenarios were employed for the image classification experiments, as shown in Table  2.
Classification Based Only on SPOT-5 Image We explored the potential of spectral and spatial features derived from the SPOT-5 image for classifying urban land cover classes. The potential of spectral information for classification (Scenario 4 in Table  2 ) was firstly investigated, leading to an overall classification accuracy of 86. 51% (Figure  2 ). In contrast, the overall classification increased by 6. 03% when spatial features were concatenated to spectral measures (Scenario 5 in Table  2 ). The result of the McNemar z-score statistical test indicated Scenario 5 significantly outperformed the result achieved with dataset Scenario 4 at the 95% level. Averaged producer's and user's accuracies per land cover category with SPOT-5 image only were presented in Table  3. As we can see from the table, producer's and user's accuracies with approximate or higher than 90% were recorded for building land, water, and woodland classes by using spectral features only.
METHODOLOGY The remote sensing retrieval of the absorption vertical structure is performed with the synergy of passive and active remote sensing instruments, i. e. multiwavelength lidars and sun-photometers. The sun-photometer provides the columnar properties of the particles (e. g. [4] ), whereas lidars are capable of providing vertically resolved profiles of the backscatter and extinction coefficients, along with vertical profiles of the particle microphysical properties, mainly for the fine mode (e. g. [5] ). The Generalized Aerosol Retrieval from Radiometer and Lidar Combined data algorithm (GARRLiC  [6] ), developed in the framework of ACTRIS, goes a step further using for the first time both sun-photometer and lidar measurements in the combined retrieval of the particle microphysical properties.
CONCLUSIONS In this study we used remote sensing algorithms utilizing the lidar measurements (i. e. , lidar standalone algorithm) and the combination of lidar and sun-photometer measurements (i. e. , GARRLiC algorithm), along with airborne and surface in situ measurements to characterize the particle aerosol absorption vertical distribution. Data were gathered during three ACTRIS-2 JRA1 campaigns in Germany, Greece and Cyprus in 2015-2016. The lidar stand-alone and the GARRLiC retrievals agree well for a case study in Athens. The remote sensing and airborne in situ measurements were not statistically different in Melpitz, where absorption and extinction coefficients were low. The comparison in Cyprus shows very good agreement between GARRLiC and in situ measurements for most cases investigated.
In an unprecedented voluntary collaboration of the international tree-ring community, the COSMIC initiative gathered wood samples from the majority of the world's longest tree-ring records, spanning the decades 770-780 and 990-1000 CE. The resulting COSMIC network includes data from 44 disjunct treering width measurement series (Supplementary Note 1), with sampling locations on five continents (Fig. 1 ; Supplementary Fig. 1 ). With the individual sites being distributed between 42°S and 72°N, and ranging from around 40 to 4000 m a. s. l. , COSMIC represents most of the world's extra-tropical forest biomes. Covering four atmospheric radiocarbon zones  17  , our dataset is composed of 13 genera, including 25 coniferous and two broadleaf species (Supplementary Tables  1 2 ). Wood fibres from each tree ring, dendrochronologically cross-dated to the period 770-780 CE, or, if not reaching that far back in time, to the interval 990-1000 CE, were manually separated, chemically extracted and automatically graphitized (Methods; Supplementary Table  3 ).
Methods Sample preparation. Although the highest level of caution has been applied in each step throughout the COSMIC project, the final 14 C measurements may still contain some degree of uncertainty, because of a wider range of possible biases and errors inherent to such a community wide, voluntary collaboration (Supplementary Note 1). Once identified, the original data provider, usually an experienced tree-ring researcher either at a university, institute or laboratory, be it private or governmental, delivered the right wood sample that comprises either the 770-780 or 990-1000 interval CE. Although this sounds trivial as individual samples are usually coded after sampling and before archiving, the relevant material often originates from fieldwork campaigns that, in some cases, have been conducted Δ 14 C (‰) Fig.
3 ). In the resulting model, each hemisphere contains 11 boxes (Supplementary Fig. 3 ; Supplementary Table  4 ), and the carbon exchange between the NH and SH only takes place in the atmosphere and the ocean. Furthermore, the different distribution of land and ocean, the varying growth seasonality and seasonal variation across the tropopause were considered  39. The carbon fluxes between the boxes are balanced and were adapted using bomb peak data  40. The model can be fitted simultaneously to two datasets using a X²-fitting routine which evaluates the most likely event date and the additional production needed to reproduce the data (Supplementary Table  4 ). Data uncertainty. While the above sections, together with the Supplementary Note 1, outline much of the complexity our COSMIC project was facing in terms of data archiving, identification, preparation and processing, further challenges associated with species-specific tree physiological processes of carbon storage and re-mobilization  41  , as well as slight differences in growing season lengths  42  , and climate-induced intra-annual changes in xylogenesis and carbon allocation  43  could not be entirely neglected.
Furthermore, repeated measurements along different radii of an individual Scots pine (Pinus sylvestris) sample from the Torneträsk region in northern Sweden suggested a one-year inconsistency. The Torneträsk 14 C measurements herein used, however, precisely detected the cosmic signal in either 993 or 774 CE (depending on the material used). A similar, 1-year dating irregularity was found in an individual kauri (Agathis australis) from northern New Zealand. The kauri samples herein used, however, confirm both of the events in 993 and 774 CE. It is important to note at this stage that in the temperate regions of the SH, tree growth starts in the spring of a certain year and ends in the summer of the following year. Following Schulman's (1956) convention  46  , which assigns to each tree ring the date of the year in which tree growth started, year 774 in our paper refers to the spring-summer of the NH and November 774 until March 775 on the SH On top of biological carry-over effects and wood anatomical features, several biotic and abiotic disturbance factors, such as insect outbreaks, fungal diseases and severe climate anomalies, cannot be ignored, especially if datasets are less replicated.
Acknowledgements We thank everyone who participated in fieldwork, sample preparation, cross-dating and/ or chronology development. This study was funded by the  WSL-internal   COSMIC  project ( 5233. 00148. 001. 01 ), the  ETHZ (Laboratory of Ion Beam Physics) , the  Swiss National Science Foundation  (SNF Grant  200021L_157187/1 ), and as the  Czech Republic Grant Agency  project no. 17-22102s.
Acknowledgments M A  N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT  We are grateful to the geologists Carlos Torres,  Engelberth Montilla ,  Crisanto Silva ,  Norelis Valera , and  Cementos Andinos CA. , for generous and significant collaboration during fieldwork. Tito Barros ,  Gilson Rivas  and  John Moody , are thanked for support and permission to study the specimens housed in the  Museo de Biología de La Universidad del Zulia. Anne-Lise Charruault (ISE-M, University of Montpellier ) helped with the restauration of some specimens from Trujillo state. To the  Instituo del Patrimonio Cultural de Venezuela  gave permissions for collecting and studying the specimens. Marcelo Sánchez-Villagra  provided generous economical support and collaboration during fieldwork activities. The reviewers ( C.
Acknowledgements. This work is supported by  FAPESP  ( 2009/50528-6 ),  CAPES/ANA  ( 88887. 115872/2015-01 ), CNPq  ( 490237/2011-8  and  302218/2016-5 ), the  Brazilian Research Network on Global Climate Change FINEP/Rede Clima  (Grant  01. 13. 0353-00 ), and  National Institute for Science and Technology on Climate Change INCT-MC  ( 573797/2008-0 ). 12  Geosci. Model Dev. Discuss. , https://doi. org/10. 5194/gmd-2018-209Manuscript under review for journal  Geosci. Model Dev. Discussion started: 25 October 2018 c Author(s) 2018. CC BY 4. 0 License.
Materials and Methods Study site. The Seine River watershed, located in northern France, covers an area of 76,750 km 2 with a median slope of 2. 2° and 89. 5% of its area is less than 300 m A. S. L. It has a pluvio-oceanic regime and its annual water flow in the period 2013-2016 averaged 550 m 3 s -1 at the river outlet at Poses (Fig. 1 ). The Poses monitoring station, located at a navigation dam, is the most downstream station not subject to the dynamic influence of the tidal estuary. Low water flows (<300 m 3 s -1 ) are generally observed from March to November, while high flows (>800 m 3 s -1 ) occur in winter, from December to February (data provided by the HYDRO database, http://www. hydro. eaufrance. fr, 2018). Three major diverted reservoirs (Fig. 1a ) located in the upstream part of the basin (the Marne reservoir, the Aube reservoir and the Seine reservoir) were built to reduce high water events in winter, and to sustain the flow in late summer.