dataesr/scientific-paragraphs-categorization huggingface dataset:
Geometric corrections (i. e. , image orthorectification and geo-referencing) were performed using the GeoCorr software provided also by the sensors' manufacturer. Atmospheric and topographic corrections were made using ATCOR-4 software  [60]. Images from CASI and SASI were combined into a single image hypercube (372-2442 nm) with final pixel size of 2. 5 m. The Sentinel-2 MSI image was corrected for atmospheric and topographic effects using the Sen2Corr toolbox provided by the European Space Agency. Spectral bands with nominal pixel size of 60 m and wide spectral bands were discarded, so that only the B2 to B7, B8a, B11, and B12 bands were retained in the final multispectral image cube. Mean canopy hemispherical-conical reflectance function and its standard deviation were extracted from the CASI-SASI and Sentinel-2 MSI images for a region of interest (100 × 100 m) over the spruce forest study site in the vicinity of the flux tower, where field samples for needle optical properties and LAI measurements were obtained.
Reconstruction of 3D Spruce Tree Representation The TLS point clouds supplied the explicit information about the position and orientation of dominant woody structures (trunk and branches) as well as about the foliage distribution and density of individual scanned trees. Woody and foliage elements were separated first using an intensity threshold that was determined for each tree scan manually based on visualization of laser return intensity histograms. The TLS data was itself insufficient for deriving the angular distribution of spruce needle shoots (considered here as the elementary units). Therefore, this information was taken from an external study on Norway spruce tree architecture  [54, 55]  Figure  2. Reconstruction of a 3D spruce representation from the TLS point cloud data was executed in the three stages documented in Figure  3:  1.
Translation and Scaling of Foliage Point Cloud A reconstructed 3D wooden skeleton was built in a local coordinate system with its origin at the centre of the trunk base and neglecting original coordinate system of point cloud. A translation matrix between the two was established to ensure the same coordinate system for the foliage TLS point cloud as for the wooden skeleton. The reconstructed wooden skeleton and foliage point cloud were subsequently scaled to match site-specific tree dimensions. For this purpose, a scaling factor, f s , was defined as the ratio between the required tree height and the maximum z-coordinate of the reconstructed wooden skeleton. In our case study, the TLS data had been obtained from trees older than the spruces at our primary research site. Despite the difference in tree age of almost 60 years between the Černá hora and Bílý Kříž study sites, the discrepancy in the mean canopy height is not so very pronounced, as both forests are growing in mountainous environments and are exposed to similar environmental stress impacts.
Calculation of Shoot Position The number of shoots is given by the user-defined tree LAI. The total number of shoots within a reconstructed tree crown is computed as: n shoots = 2 • LAI • S proj S shoot , (1) where n shoots is the total number of shoots within a crown, S shoot is total area of needles in one shoot (e. g. , as calculated in the Blender software, www. blender. org), and S proj is the circular projection area of the tree crown represented by the extracted and scaled foliage point cloud. The diameter d of the circular projection area S proj is calculated as: d = min(max x -min x , max y -min y ), (2) where max x ,max y , min x , and min y are the maximum and minimum coordinates of the foliage points in the x and y dimensions. Calculation of the shoot positions uses a grid of voxels.
The CASI-SASI spectral bands that are strongly influenced by atmospheric water vapour absorption (i. e. , 893-1002 nm, 1107-1167 nm, 1302-1527 nm, and 1737-2052 nm) were removed from the RMSE and d computation. Further, we extracted and cross-compared multi-directional reflectance distributions of the simulated canopy scenarios. The main criteria to evaluate the optimum scenario are (i) overall agreement with real observations extracted from airborne hyperspectral and Sentinel-2 image data and (ii) computation requirements.
Rigorous validation of the reconstructed 3D trees would be difficult and tedious, as it would require that the evaluated structural parameters of the reference trees be measured independently from their TLS scans. Moreover, the original TLS data were spatially scaled to different dimensions and the density of needle shoots was altered according to the user-defined value of tree crown LAI. Therefore, the distribution of shoots and the overall habitus of reconstructed trees were inspected only through visual comparison with the original TLS point clouds (see Figure  3a ) and photographs of modelled trees. Later, we evaluated the reconstruction of 3D trees in the context of the forest stand canopy reflectance simulated in DART and compared to the real airborne and space-borne observations.
The largest reflectance variability among the simulated scenarios and differences from RS data were obtained for the NIR spectral region. When compared to the airborne data, the NIR reflectance around 850 nm of scenario A, simple turbid, was as much as 0. 1 larger. This difference increased to 0. 15 for sunlit crown spectra. A similar NIR reflectance overestimation was observed also for scenario D, needle facet. NIR reflectance of scenarios B, needle turbid, and C, plane facet, fell within the standard deviation interval of the RS observations. Finally, all scenarios simulated very similar reflectance for the SWIR region. Nevertheless, all scenarios systematically overestimated the RS observations around 1650 nm by 0. 04 and around 2200 nm by 0. 02. Scatterplots, similarity indicator d, and RMSE between the DART-simulated scenarios and RS observations (airborne and Sentinel-2 MSI) were plotted and computed only for wavelengths corresponding to those Sentinel-2 MSI spectral bands not affected by atmospheric water absorption, (i.
A. Navigational Solution Block Model A model of the Navigational Solution Block is required to perform a sensitivity analysis. Therefore, a representative model has to be chosen to achieve realistic results. Even though an actual GPS receiver software would be the best option to perform the analysis, details of GPS receiver software structure are usually not available due to sensitivity of this information. This limitation led to the choice of an open-source software model. The chosen model is GoGPS, a software package designed to improve the positioning accuracy of GPS devices  [14, 15]. The GoGPS model was originally developed in MatLab  [16]. It takes the information contained in the digitized GPS RF signal as inputs and returns the messages in the format of National Marine Electronics Association (NMEA) that are produced by many commercial receivers.
Subsequently, the model has been adapted to MatLab Simulink  [17]  making easier the access to all the variables present in the computations. The Simulink model is divided in four major blocks corresponding to the natural steps for obtaining a positioning solution from GPS signals described below: Fig. 2 : GoGPS model diagram  [14]  Input block of the model takes all the ephemeris and timing data that the receiver needs in order to calculate its position. More information about these variables can be found at Global Positioning System standard  [5]. Atmospheric corrections block is subdivided into tropospheric and ionospheric corrections parts. Klobuchar model is used for the ionosphere modelling, reducing its induced error by 50%  [18]. Saastamoinen algorithm  [19]  was used to recreate the tropospheric error.
B. Simulation procedure It can be seen from the above description of the software blocks, some assumptions have been made. First, the ephemeris data values used as input are decimal values. Binary conversion is not made in the model, hence all the possible errors related to this transformation are not taken into account. Second, signal group delay is calculated from assumption of working with L1 signal of GPS constellation. The output variable of the sensitivity analysis is the distance from the centre of the Earth to the GPS receiver. During the analysis the ephemeris input variables are changed one by one and variation in the output variable is logged. Only one satellite signal will be corrupted at a time. A possible simultaneous variation of multiple variables or multiple satellite signals corruption was not considered in this study.
III. Results The results of the sensitivity analysis using the GoGPS model  [14]  are obtained by Spirent software SimGen  [20]. This software is able to control the simulated GNSS RF signals generated by the simulator for the most complicated GPS-based scenarios with inclusion of the satellite constellation related effects, e. g. satellite movement. Spirent's SimGen software offers an opportunity to perform Receiver Autonomous Integrity Monitoring (RAIM) that ensures the GPS signal integrity by monitoring and analysing GPS measurements using redundant GPS signals  [21]. Since four satellites are needed to calculate the receiver position, for RAIM implementation at least five are needed to perform the comparison and detect a corrupted signal. This technology may not be always available as five or more healthy satellites must be available.
The Sentinel-2 satellite mission, developed by the European Space Agency (ESA) for the Copernicus program of the European Union, provides repetitive multi-spectral observations of all Earth land surfaces at a high resolution. The Level 2A product is a basic product requested by many Sentinel-2 users: it provides surface reflectance after atmospheric correction, with a cloud and cloud shadow mask. The cloud/shadow mask is a key element to enable an automatic processing of Sentinel-2 data, and therefore, its performances must be accurately validated. To validate the Sentinel-2 operational Level 2A cloud mask, a software program named Active Learning Cloud Detection (ALCD) was developed, to produce reference cloud masks. Active learning methods allow reducing the number of necessary training samples by iteratively selecting them where the confidence of the classifier is low in the previous iterations.
The detection of clouds and cloud shadows is one of the first issues encountered when processing optical satellite images of land surfaces. The difficulty lies in the large diversity of cloud types and Earth surface landscapes  [4]. It is already frequent to confuse bright landscapes with clouds, but moreover, it is especially difficult to detect semi-transparent clouds for which the observed reflectances contain a mixture of cloud and land signals. The detection of cloud shadows is also complex, as a similar low reflectance range can also be frequently observed on targets that are not obscured by clouds. This leads to confusions with water pixels, burnt areas, or topographic shadows. In the case of semi-transparent clouds, shadow detection is even more challenging  [5]. Until 2008, when Landsat 8 data started to become free and easily accessible, images with a decametric resolution were expensive, and as a result, users ordered mostly cloud-free images from the image providers.
These methods are explained in the next section. The Centre National d'Etudes Spatiales (CNES) and the Centre d'Etudes Spatiales de la Biosphère (CESBIO), on the one hand, and the German Aerospace Centre (DLR), on the other hand, separately developed Level-2A processors containing cloud and shadow detection methods applicable to Sentinel-2 time series of images. CNES developed the Multi-sensor Atmospheric Correction and Cloud Screening software (MACCS) (  [4, 11] ), while DLR developed the atmospheric Correction software (ATCOR) (  [12] ). In 2015, the institutes decided to merge their efforts to develop the MACCS-ATCOR Joint Algorithm (MAJA). MAJA is built on the structure of MACCS, whose methods are extended with a few complementary elements from ATCOR. Sen2Cor and FMask are mono-temporal methods that use only the image to process and determine the cloud cover, while MAJA methods exploit multi-temporal information.
Although some cloud detection methods based on machine learning algorithms are starting to appear (  [13, 14] ), the three methods we selected are rule-based methods that apply thresholds to selected features. In order to compare the results provided by the three processors quantitatively, reference cloud masks are necessary. To our knowledge, there is no in situ reference database that provides the cloud cover on a regular basis, with a good coverage at a decametric resolution. The validation of cloud masks classically relies on manually-classified images (  [13, 15] ) or polygons selected in a large number of images (  [16, 17] ). Such a validation dataset has already been generated for Sentinel-2 (  [16] ). A human operator selected and labeled polygons within a set of images, but looking at the selected polygons, we found that the authors had avoided selecting pixels near the cloud limits, where part of the difficulty and subjectivity of identification lies.
Sample Selection Along with the features creation, ALCD generates an empty vector data file per class. The user is then invited to populate each file using a GIS software program (we used QGIS  [32] ). The user can either select the pixels on a "true color" red-green-blue color composite image made with Channels 4-3-2 of Sentinel-2 or on a display of Band 10 to see the potential cirrus clouds. After the first iteration, the user can also view the classification generated at the previous iteration, as well as a confidence map described in the next paragraphs. This information is used to select and label samples where the classification looks wrong or where the confidence is low. Before producing a large quantity of cloud masks, we studied whether it was more efficient to label points or polygons.
with an Existing Dataset Hollstein et al. created a publicly-available database  [16]. It consists of manually-classified Sentinel-2A images with polygons. Their classes were the same as the ALCD ones: cloud, cirrus, snow/ice, shadow, water, and clear sky. A direct comparison is therefore possible. As only one of the images they classified was part of our 32 scenes, we decided to classify 6 additional scenes with ALCD that are part of their dataset. We selected images for which a clear image was available within a month of the desired date. This dataset was not used as the reference image for the validation of operational cloud masks in Section 5, because a large part of the images were acquired very early in Sentinel-2's life when the acquisitions of Sentinel-2 were not steady yet and some auxiliary files were sometimes incorrect.
The comparison of masks showed a very good agreement, but we noticed on one image that the "shadow" class from the Hollstein dataset also included terrain shadows. On another image, a few polygons with the "cirrus" class were not thick enough to be classified as clouds according to our criteria defined above. Therefore, on those two images, along with the unchanged original reference cloud masks from Hollstein et al. , a corrected version of the dataset was produced, to validate the ALCD output more accurately. The results of the correctly-classified pixels are given in Table  3. It has to be noted that the cloud masks provided by Hollstein et al. do not contain the edges of the clouds where the distinction between valid and invalid pixels is difficult and somewhat subjective. This contributed to the very good agreement between both datasets as some of the most difficult pixels to classify were not included in the analysis.
Conclusions This article presents the results of the validation of Sentinel-2 cloud masks delivered by MAJA, FMask, and Sen2Cor. The validation was conducted with reference cloud masks generated with a new tool based on a supervised active learning procedure. ALCD was designed to allow generating reference cloud masks over whole Sentinel-2 images and minimizing the time spent by the operator. A trained operator was able to generate a good and complete reference cloud mask in less than two hours. The ALCD method was validated using three methods. First, a visual evaluation of the produced cloud masks gave satisfactory classification maps. Second, a k-fold cross-validation was computed on the 32 scenes, leading to a global mean overall accuracy of 98. 9%. Third, a comparison with an existing dataset indicated that the ALCD tool was capable of producing masks with a quality similar to manual classification via polygons, with an accuracy 98.
3% on the original dataset and 99. 7% on the corrected one. However, the fact that the ALCD method required the existence of a cloud-free image prevented us from choosing reference scenes acquired in permanently cloudy sites such as Guyana or Congo. As a result, we were not able to provide validation in such situations that were not conducive to a multi-temporal methods such as MAJA. The ALCD tool could also be used to prepare reference cloud masks for other multi-temporal sensors such as Landsat, and its methodology could also be used to build other types of reference masks, such as water, snow, or forests for instance. ALCD is currently being used at CNES to prepare water and snow masks. Thirty two reference cloud masks were generated on 10 different sites in various biomes selected around the world.
These 32 scenes have been made available for free download  [37] , and the source code of ALCD is available on an online source repository  [38]. These cloud masks were used to compare the performance of three cloud masking methods used to provide Level 2A products operationally, namely MAJA, Sen2Cor, and FMask. FMask and MAJA gave very similar results, with a small advantage for MAJA. The accuracy of Sen2Cor was on average 6% lower than that of the two other methods when considering dilated cloud masks and 3% lower with non-dilated cloud masks. These results show that the multi-temporal cloud mask enabled MAJA to perform better than the other methods, but the difference from the well-tuned FMask was still quite low. The advantages of the multi-temporal cloud masks seem to be counter balanced by the processing at a lower resolution needed to speed-up the complex processing due to the use of multi-temporal methods.
Classified Pixels in the Polygons with the Original Dataset with the Modified Dataset 29RPQ 17 Nov. 2016 27 Apr. 2016 19,371 99. 8% 99. 8% 32TMR 23 Mar. 2016 26 Mar. 2016 11,558 99. 9% 99. 9% 32TNR 08 Nov. 2016 29 Oct. 2016 8547 99. 2% 99. 2% 33UUU 17 Aug. 2016 27 Aug. 2016 3372 98. 0% 98. 0% 35VLJ 31 Aug. 2016 13 Sep. 2016 5996 93. 5% 99. 7% 37PGQ 06 Dec. 2015 16 Nov. 2015 15,691 96. 0% 100. 0% 49JGL 26 Jan. 2016 05 Feb. 2016 5961 100. 0% 100. 0% Mean 98. 3% 99. 7% 4. 3. 4. Conclusions on ALCD Validation.
g. in Greenland  (Fettweis et al. , 2013 (Fettweis et al. , , 2017)) , Svalbard  (Lang et al. , 2015) , Adélie Land (Antarctic coastal area;  Gallée et al. , 2013; Amory et al. , 2015) , and Dome C (Antarctic plateau;  Gallée et al. , 2015). We compare MAR-simulated SMB with the state-of-the-art RCM RACMO2  (van Wessem et al. , 2018). We use available SMB observational datasets to show that MAR and RACMO2 perform similarly well in simulating the SMB spatial gradients. In addition, we identify significant processes that still need to be included or improved in both RCMs. In Sect. 2, we describe MAR and its specific set-up for Antarctica, together with RACMO2, the forcing fields, observational datasets, and methods designed for model evaluation. In Sect. 3, we show that both RCMs share com-mon biases against observed SMB, resulting from drifting snow transport fluxes.
We used MAR version 3. 6. 4, simply called MAR hereafter. In this version the physical settings are the same as in MAR version 3. 5. 2 used for Greenland  (Fettweis et al. , 2017) , except for the adaptations detailed below. Grid. Projection is the standard Antarctic polar stereographic method (EPSG:3031). The horizontal resolution is 35 km, an intermediate resolution that results from a computation time compromise in order to run the model with multiple reanalyses and global climate model forcings over the 20th and the 21st centuries. The vertical discretisation is composed of 23 hybrid levels from ∼ 2 m to ∼ 17 000 m above the ground. Boundaries. The topography is derived from the Bedmap2 surface elevation dataset  (Fretwell et al. , 2013). Because the Antarctic domain is about 4 times larger than the Greenland domain, the circulation has to be more strongly constrained.
This is why we use a boundary relaxation of temper-ature and wind in the upper atmosphere starting from 400 hPa (∼ 6000 m above the ground) to 50 hPa (upper level), as in van de Berg and  Medley (2016) , whereas relaxation starts from 200 hPa in  Fettweis et al. (2017). Parameterisations. a. The surface snow density ρ s (kg m -3 ) is computed as a function of 10 m wind speed ws 10 (m s -1 ) and surface temperature T s (K): ρ s = 149. 2 + 6. 84 ws 10 + 0. 48 T s , with minimum-maximum values of 200-400 kg m -3. This parameterisation was defined so that the simulated density of the first 50 cm of snow fits observations collected over the AIS (see Fig. S1 , with the snow density database detailed in Table  S1  in the Supplement). b. The aerodynamic roughness length z 0 is computed as a function of the air temperature, as proposed in  Amory et al.
SMB observations and sectors of strong SMB gradients We use SMB observations of the GLACIOCLIM-SAMBA dataset detailed in  Favier et al. (2013)  and updated by  Wang et al. (2016). This dataset is an update of the one assembled by  Vaughan et al. (1999)  following the qualitycontrol methodology defined by  Magand et al. (2007). It includes 3043 reliable SMB values averaged over more than 3 years. We add accumulation estimates from  Medley et al. (2014) , retrieved over the Amundsen Sea coast (Marie Byrd Land) with an airborne-radar method combined with ice-core glaciochemical analysis. The first-order feature of the Antarctic SMB is a strong coastal-inland gradient, with mean values ranging from typically greater than 500 kg m -2 yr -1 at the ice sheet margins to about 30 kg m -2 yr -1 in the dry interior plateau (Fig.
1a ; see also, e. g. Wang et al. , 2016). As observations only cover 5 % of MAR grid cells over the ice sheet, we divide that sparse observation dataset into 10 sectors detailed in Table  1  and shown in Fig. 2. Six of them are stake transects with a stake every ∼ 1. 5 km, which have been proven very valuable for evaluating modelled SMB  (Agosta et al. , 2012; Favier et al. , 2013; Wang et al. , 2016). The four other sectors are composed of more scattered observations covering large elevawww. the-cryosphere. net/13/281/2019/ The Cryosphere, 13, 281-296, 2019 tion ranges (Victoria Land, Dronning Maud Land, and Ross Ice Shelf-Marie Byrd Land).
We use model settings comparable to previous MAR simulations over Greenland  (Fettweis et al. , 2017)  but with a specific upper atmosphere relaxation and new surface snow density and roughness length parameterisations. We present simulations of MAR forced by ERA-Interim, JRA-55, and MERRA-2 for the satellite era  in which we can rely on reanalyses products. Remarkably, MAR forced by those three reanalyses gives similar spatial and temporal SMB patterns. We also compare MAR with the latest simulations of the RCM RACMO2 forced by  ERA-Interim (van Wessem et al. , 2018). We find no significant differences between MAR and RACMO2 SMB when integrated on the AIS and its major basins (Table  2 ). As the dominant feature of the Antarctic SMB is its strong coast-to-plateau gradient, we extract stake transects and sectors with large elevation ranges from the GLACIOCLIM-SAMBA SMB observational dataset.