dataesr/scientific-paragraphs-categorization huggingface dataset:
The development and improvement of methods to map agricultural land cover are currently major challenges, especially for radar images. This is due to the speckle noise nature of radar, leading to a less intensive use of radar rather than optical images. The European Space Agency Sentinel-1 constellation, which recently became operational, is a satellite system providing global coverage of Synthetic Aperture Radar (SAR) with a 6-days revisit period at a high spatial resolution of about 20 m. These data are valuable, as they provide spatial information on agricultural crops. The aim of this paper is to provide a better understanding of the capabilities of Sentinel-1 radar images for agricultural land cover mapping through the use of deep learning techniques. The analysis is carried out on multitemporal Sentinel-1 data over an area in Camargue, France.
The data set was processed in order to produce an intensity radar data stack from May 2017 to September 2017. We improved this radar time series dataset by exploiting temporal filtering to reduce noise, while retaining as much as possible the fine structures present in the images. We revealed that even with classical machine learning approaches (K nearest neighbors, random forest, and support vector machines), good performance classification could be achieved with F-measure/Accuracy greater than 86% and Kappa coefficient better than 0. 82. We found that the results of the two deep recurrent neural network (RNN)-based classifiers clearly outperformed the classical approaches. Finally, our analyses of the Camargue area results show that the same performance was obtained with two different RNN-based classifiers on the Rice class, which is the most dominant crop of this region, with a F-measure metric of 96%.
SAR Data Since wheat cultivation is the only winter crop presence after May and the major agricultural practices in Camargue are in summer (e. g. , from May to September), we focus our data analysis on this period. The Sentinel-1A/1B SAR dataset includes 25 acquisitions in terrain observation with progressive scan (TOPS) mode from May to September 2017 (5 months), with a revisit period of 6 days. This is dual-polarization (VV + VH) data, resulting in 50 images. Figure  2  summarizes the temporal profiles of the 11 agricultural classes per polarization. Each time series is made up of 25 points (one for each acquisition). Figure  3  provides information on the temporal dynamic of these classes by giving their average and standard deviation.
Temporal Filtering Reliable estimates of the intensity from a distributed target require that the estimated number of looks (ENL) is sufficiently large. Speckle filtering is often used to increase the ENL with loss of spatial resolution  [26]. In properly coregistered multitemporal datasets, it is possible to employ the technique of temporal filtering, which, in principle, increases radiometric resolution without degrading spatial resolution. The temporally filtered images usually show markedly diminished speckle, with little or no reduction in spatial resolution. In this paper, we improve the time series SAR Sentinel-1 dataset by exploiting a temporal filtering method developed by  [26]  to reduce noise, while retaining, as much as possible, the fine structures present in the images.
Geocoding After pre-processing and filtering, all the processed images are in the imaging geometries of the master image. In order to create a unified dataset, all image data have to be orthorectified into map coordinates. This is done by creating a simulated SAR image from a SRTM DEM 30 m, and using the simulated SAR image to coregister the two image sets (polarizations). The pixel size of the orthorectified image data is 20 m. After geocoding, all intensity images are transformed to the logarithmic dB scale, normalized to values between 0-255 (8 bits) and input into classifiers. The SAR Sentinel-1 data are processed by the TomoSAR platform, which offers SAR, interferometry and tomography processing  [27, 28]. Finally, for each pixel, a 25 × 2 matrix (25 acquisitions and two polarizations: VV + VH) is generated as input for classifiers.
Random Forest The Random Forest algorithm has demonstrated its ability to yield high quality mappings for a different varieties of crop type systems with a much faster computation when compared to other state of the art classifiers  [32] [33] [34]. The classifier relies on aggregating the results of an ensemble of simpler decision tree classifiers. In other words, it is a meta-estimator that fits a number of decision tree classifiers on various subsamples of the dataset, and uses averaging to improve the predictive accuracy and control over-fitting  [35]. To reduce the computational complexity of the algorithm and the correlation between subsamples, tree construction can be stopped when a maximum depth is reached or when the number of samples on the node is less than a minimum sample threshold.
Experimental Settings We compared RNN-based classification approaches (LSTM and GRU) with standard machine learning approaches. For the RF model, we set the number of trees at 400 and a maximum tree depth of 25. For the KNN model, we set the number of nearest neighbors at 10. For the RF model, the number of randomly selected features at each node is kept at its default value for a classification problem (i. e. , the square root of the total number of variables  [42] ). For the SVM model, we use the RBF kernel with a default gamma (i. e. , 1/number of samples  [43] ) and a parameter of complexity equal to 10 5. For RF and KNN, we use the python implementation provided by the Scikit-learn library  [42] , while for SVM we use the LibSVM implementation  [43]. For RNN-based classifiers, we define the number of hidden dimensions equal to 512 (General speaking, the choice of the number of hidden dimensions depends on the volume of the training dataset available.
If the dataset is small, we have to use a simple model in order to estimate fewer weight parameters and vice versa. For example, in the work  [20] , the number of units is set to 64 and 512 for Thau and Reunion test sites, respectively. We experimentally found that our best performance was achieved by setting 512, after trying with 64, 128, 254, 512 and 1024 ). An initial learning rate of 5 ×10 -4 , rho of 0. 9 and a decline of 5 × 10 -5 was employed. We implement the model via the Keras python library with Theano as the back end  [44]. To train the model, we used the Rmsprop strategy, which is a variant of stochastic gradient descent  [45]. The loss function being optimized is categorical cross entropy, which is the standard loss function used in all multiclass classification jobs  [46]. The model is trained for 250 epochs, with a batch size set at 64.
For the different methods, we apply cross-validation 5 times on the dataset, with a split procedure in two steps:  (1)  At the polygon-level to separate train and test instances and (  2 ) at the pixel-level to extract each instance  [47, 48]. In other words, we impose that pixels of the same object belong exclusively to the training or to the test set. In this way, the initial set of data (66,201 pixels) is reduced to 57,585 pixels, since our polygons are not the same size. In detail, the set of data (57,585 pixels) is randomly divided into 5 partitions (folds) of equal size. Then, four folds are used to train the models (about 46,068 pixels), and the fifth fold (about 11,517 pixels) is used for the test phase. The operation is repeated five times, so that each fold is like a possible test set.
Results The multitemporal Sentinel-1 data, processed as in Section 3, are used as an input for classification using classical approaches (KNN, RF, and SVM) in Section 4 and two RNN-based models (LSTM and GRU) in Section 5. The summary of the results of different classification approaches is reported in Table  2. This is the performance from cross-validation 5 times on Sentinel SAR-1 time series data, showing the average and standard deviation values of the F-measure, Accuracy, and Kappa assessment metrics from 5 repetitions. For this multi-temporal SAR Sentinel-1, all classifier performance metrics are very high, showing the quality of the dataset for agricultural classification tasks. Second, to illustrate more precise comprehension of the behavior of the different classifiers, we report a per-class F-measure comparison in Figure  6.
Thanks to the joint optimization of nonlinear input transformations along with classification, deep learning approaches provide here a valuable strategy to discriminate among the different agriculture classes. In addition, as expected, the ability of RNNs to deal with the temporal correlations characterizing the SAR Sentinel-1 data result in a gain in performance on all classes, with particular emphasis on those classes that exhibit similar temporal behaviors for longer periods. Together, these results confirm that the RNN models (both LSTM-based and GRU-based) are well suited to detect and exploit temporal dependencies, as opposed to common classification approaches that do not explicitly leverage temporal correlations. Our finding is consistent with previous reports, in which Deep Learning outperformed the classical machine learning approaches  [19] [20] [21].
Conclusions In this paper, we studied the potential of high spatial and temporal resolution Sentinel-1 remote sensing data for different agriculture land cover mapping applications and assessed the new deep learning techniques. We proposed to use two deep RNN approaches to explicitly consider the temporal correlation of Sentinel-1 data, which were applied on the Camargue region. We demonstrated that even with the classical approaches (KNN, RF and SVM), good classification performance could be achieved with Sentinel-1 SAR image time series. We experimentally demonstrated that the use of recurrent neural networks to deal with SAR Sentinel-1 time series data yields a consistent improvement in agricultural classes as compared with classical machine learning approaches. The experiments highlight the appropriateness of a specific class of deep learning models (RNNs) which explicitly consider the temporal correlation of the data in order to discriminate among agricultural classes of land cover, typically characterized by similar but complex temporal behaviors.
We observed for the Rice class the same high performance of both RNN-based LSTM and GRU classifiers, with a F-measure of 96%. We found that in the Camargue region, rice land cover is still dominant, but decreasing significantly. Future work could be focused on estimating rice production yields in order to propose strategies for sustainable agriculture development. Finally, although the number of exploitable optical images are limited due to cloud cover, the use of multi-source data including optical and SAR radar data will be an important subject of future research. Acknowledgments: This work was supported in part by  European Space Agency ,  Centre National d'Etudes Spatiales/Terre, Ocean, Surfaces Continentales, Atmosphere  and in part by the  National Research Agency  within the framework of the program " Investissements d'Avenir " for the  GEOSUD  under Project  ANR-10-EQPX-20.
Global wheat production reached 754. 8 million tons in 2017, according to the FAO database. While wheat is considered as a staple food for many populations across the globe, mapping wheat could be an effective tool to achieve the SDG2 sustainable development goal-End Hunger and Secure Food Security. In Lebanon, this crop is supported financially, and sometimes technically, by the Lebanese government. However, there is a lack of statistical databases, at both national and regional scales, as well as critical information much needed in the subsidy and compensation system. In this context, this study proposes an innovative approach, named Simple and Effective Wheat Mapping Approach (SEWMA), to map the winter wheat areas grown in the Bekaa plain, the primary wheat production area in Lebanon, in the years of 2016 and 2017.
The implementation of SEWMA approach at regional/national scale shall enable an adequate planning and managements by decision makers and governments while saving on resources and monetary values for field based statistics. Section 2 presents the study site and the cropping calendar, followed by Section 3 which describes the database and materials used. Section 4 describes the methodology of the proposed approach. The results are shown in Section 4. Section 5 presents a discussion of the important results, followed by a conclusion.
Datasets and Preprocessing Two types of datasets were essential to conduct this study: two-year field data containing ground reference plots to train and validate our approach, and corresponding Sentinel-2 imageries (each tile is of 100 × 100 km 2 ). These datasets were used to extract the NDVI temporal profile, as it was the main key to eventually classify winter wheat at the Bekaa plain of Lebanon during the two years of study (i. e. , 2016 and 2017). Figure  3  represents a flowchart summarizing the preparation work, whose output will be used as input for our proposed approach. Sensors 2018, 18, x FOR PEER REVIEW 5 of 23.
Datasets and Preprocessing Two types of datasets were essential to conduct this study: two-year field data containing ground reference plots to train and validate our approach, and corresponding Sentinel-2 imageries (each tile is of 100 × 100 km 2 ). These datasets were used to extract the NDVI temporal profile, as it was the main key to eventually classify winter wheat at the Bekaa plain of Lebanon during the two years of study (i. e. , 2016 and 2017). Figure  3  represents a flowchart summarizing the preparation work, whose output will be used as input for our proposed approach.
Satellite Data Sentinel-2 is the second generation Earth Observation (EO) satellite operated by the European Space Agency (ESA)  [26]. The launching of Sentinel-2A and Sentinel-2B was in June 2015 and March 2017 respectively, as an integral part of Europe's Copernicus program aiming at independent and continued global observation capacities  [20]. Sentinel-2 offers a fine spectral, spatial and temporal resolutions (i. e. , 13 bands ranging from 10 m to 60 m with a revisit time of five days). Datasets produced by this satellite could be downloaded free of charge from Europe's Copernicus website  [27]. Eight Sentinel-2 images were used each year (i. e. , 2016 and 2017) between January and May (Table  1 ), as these images cover the main phenological stages. The pre-processing of L1C (Top of Atmosphere or TOA reflectance) Sentinel-2 images, which includes ortho-rectification, cloud removal (using cloud mask produced by Sen2Cor/SNAP), radiometric calibration and atmospheric correction, was produced using SNAP/Sentinel-2 toolbox.
During this period, NDVI normally does not exceed 0. 3. After stem elongation and booting stage are initiated, NDVI comes closer to 1  [32]. While applying the mean shift segmentation (post cloud removal) to the study area for each year, the area of interest was clustered into homogeneous units (segments) in each year (2016 and 2017). For both years, eight NDVI images (Table  1 ) were stacked together and used as an input to the mean-shift algorithm, to produce unique homogeneous units' map (segments) for each year (i. e. , 2016 and 2017). The mean-shift segmentation  [33, 34]  is a widely used segmentation approach  [35] , firstly proposed by  [36]. It relies basically on spatial and range radii and was executed in this paper using the open source software QGIS  [37]. The segmentation parameters are: Spatial radius = 10 pixels and NDVI range radius = 0.
Ground Data Field visits were carried out between February and June of the corresponding years (i. e. , 2016 and 2017), as this period covers the most critical wheat phenological stages needed for classification. Cereals plots (i. e. , wheat, barley and triticale) as well as other cultivated plots (i. e. , spring potato and spring vegetables, fruit trees, vineyards, and alfalfa) and bare soil areas were visited and their coordinates were recorded as reference plots (Table  2 ). These plots were fragmented according to the segmentation output (produced earlier) of each year and used for training and validation processes.
In that event, after selecting the candidate segments (at the end of phase one) and the elimination of barley and triticale segments (at the end of phase two), wheat segments are then identified for both 2016 and 2017 years. It is important to note that the proposed approach was established by 2016 datasets and validated through 2017 datasets, and vice versa, using three thresholds (i. e. , (1) µ + 1σ; (2) µ + 1. 5σ; and (3) µ + 2σ) as mentioned above.
Validation Accuracy assessments were done to evaluate the classification approach presented in this study. Since the classification approach for 2016 was done by relying on 2017 ground truth data (GTD) to calibrate it, and vice versa, we had to test the overall accuracy of 2016 classification using 2016 GTD and for 2017 using 2017 GTD. For this purpose, wheat segments collected from 2016 were used to validate year 2016, and wheat segments collected in 2017 were used to validate year 2017. As to run the confusion matrix, wheat and non-wheat segments were needed. Same number as wheat segments was chosen for non-wheat segments. For 2017 classification, we had 348 wheat segments, thus an equivalent number of non-wheat segments were randomly chosen from the whole area of study so that the total number of segments to run the confusion matrix of 2017 was 696 consisting of 50% wheat segments and 50% non-wheat segments.
Strengths, Limitations and Future Directions The proposed method, Simple and Effective Wheat Mapping Approach (SEWMA), has proven to be successful in predicting wheat spatial distribution in the Bekaa plain of Lebanon for the years 2016 and 2017. SEWMA appears to have several strong points; (1) it only requires limited number of satellite imageries datasets in one single season for executing the classification, an option highly crucial in a developing country such as Lebanon; (2) it discriminates wheat from other similar winter cereals (i. e. , barley and triticale) with only few field campaigns required; (3) it produces accurate (87%) early outputs in an automated way, thus saving resources and time. In the same context, application of SEWMA is technically simple and easy to implement. However, it is site dependent and some requirements have to be met.
For instance, replicating SEWMA in different regions may be affected by climate, farming conditions, agricultural practices and crop calendar. Concerning this, avoiding field visits in a new study site may result in critical drawbacks and unsatisfactory results. Distinguishing winter wheat from barley and triticale could not be well achieved if the key phenological dates were not well known, particularly anthesis. In addition, irrigation practices were very important to deriving the conditions applied in the second phase of SEWMA. As previously mentioned (Section 5. 3), wheat plots that are not irrigated due to shortage in water, could be susceptible to elimination, in addition to barley plots, during the second phase of SEWMA. As reported in Sentinel-2 data quality report in 2018 [54], Sentinel-2A images before 15 June 2016 stem registration errors, due to three main contributors: (a) dynamic vibrations residuals mainly related to on-board oscillations; (b) static LOS calibration residuals; and (c) correlation noise and outliers.
028 ± 0. 1 × 10 m in the x direction and 0. 034 ± 0. 1 × 10 m in the y direction. It could be related to the location of our study area at the center of the Sentinel 2A tile (Figure  1 ) and far from the swath edges  [55]. For future studies, an open source software  [58, 59]  designed for mis-registration quantifications and corrections is recommended. These approaches are particularly needed when combining different sensors such as Landsat 8 and Sentinel-2 datasets. As cloudy images are always a drawback when working with optical satellites, the availability of cloud-free datasets, or further pre-processing, is always recommended. Several algorithms have been proposed (e. g. , Mean Attribute, Most Common Attribute value and k-nearest neighbor imputation) to fill the data lost by cloud removal  [60].
The decision on whether to apply those algorithms, and which to choose amongst, is highly dependent on climatic and environmental conditions, as well as the purpose of use. Furthermore, since Sentinel-2B was not launched before March, 2017, we could not benefit from its data for our study, otherwise, temporal resolution could be maximized (5-days) and more images could have been available. As other previous studies have proven, the usage of both optical and radar images would improve the classification, especially when pilot areas are covered with clouds  [18, 19]. The performance of SEWMA was tested on the Bekaa plain of Lebanon, which is a semi-arid climatic region. Hence, for future studies, including other climatic regions and enlarging the sampling data would generate better outputs. However, with a total accuracy of 87%, our proposed approach could be implemented across the Bekaa region and in similar climatic areas.
The beam size on the sample surface was 12 × 3 µm 2 in the ERDA configuration, and the beam was scanned over the sample surface to produce compositional maps. Forward scattered incident He particles were prevented from reaching the ERDA detector by placing a 15 µm Al foil in front of the detector. At the beginning of each session, the standards were analyzed in the RBS geometry to determine the RBS solid angle ( RBS ). Standards such as Al 2 O 3 , FeS 2 , SiO 2 , CaCO 3 , SnBi, Zr, ZrH, and Kapton are used to calibrate the RBS solid angle. Analysis time was 15 min for each standard, except for the Kapton (30 min). The SIMNRA program  (Mayer, 1999)  was used to model RBS and ERDA energy spectra. The average solid angle, RBS , thus determined was 73. 12 ± 9. 72 in the first session, 66. 4 ± 3.
26 in the second session and 59. 53 ± 3. 09 in the third session. The ERDA solid angle ( ERDA = 16. 66 mSr in the first session, 15. 59 ± 0. 54 mSr in the second session and 8. 47 mSr in the third session) was calibrated from a 3,700 s analysis in ERDA configuration of the Kapton standard, but also of ZrH standard in the second session  (Wang, 2004). The RBS spectrum allows the quantification of the charge on the sample during the course of the ERDA measurement. The Kapton standard was assumed to be stoichiometric C 22 H 10 O 5 N 2. The samples were analyzed in the ERDA configuration with analysis times that varied between 20 and 120 min. An area of the sample was scanned with the beam (Figure  1A ). Using the RISMIN software  (Daudin et al. , 2003) , areas can be selected within the analyzed map to avoid cracks, grain boundaries or secondary mineral phases (see Figure  1B ).
A meteorological and blowing snow dataset issued from the high-altitude experimental site of Col du Lac Blanc (2720 m altitude, Grandes Rousses mountain range, France) is presented and detailed in this paper. Emphasis is placed on data relevant to the observations and modelling of wind-induced snow transport in alpine terrain. This process strongly influences the spatial distribution of snow cover in mountainous terrain with consequences for snowpack, hydrological and avalanche hazard forecasting. In-situ data consist of wind (speed and direction), snow depth, and air temperature measurements (recorded at four automatic weather stations), a database of blowing snow occurrence and measurements of blowing snow fluxes obtained from a vertical profile of Snow Particle Counters. Observations data span the period from December 1 st to March 31 st for each winter season from 2000-2001 until 2015-2016.