dataesr/scientific-paragraphs-categorization huggingface dataset:
4, 0. 5, 0. 6 µm) could be obtained. Thus, the working ANN uses four inputs (three reflectances in bands 0. 6, 0. 8, and 1. 6 µm, and the ρ 0. 6,c ρ 0. 8,c ratio) and two hidden layers with 10 nodes each, and yields two outputs, the reflectances ratios ρ 0. 6,c ρ 0. 4,c and ρ 0. 6,c ρ 0. 5,c , used to retrieve the 0. 4 and 0. 5 µm reflectance values. As shown in Figure  1 , SEVIRI and MODIS have similar spectral bands at 0. 6, 0. 8 and 1. 6 µm. This spectral overlap is used to train the ANN with MODIS level1B measured reflectances over several surfaces (ocean, desert, clouds, green regions) with the same Rayleigh and ozone corrections. Although these spectral bands are not exactly the same between SEVIRI and MODIS, the spectral behavior of the considered landscapes evolves slowly. This allows us to assimilate the MODIS data products to generate the SEVIRI ones.
(b) As a result, a cloud of 40 × 2 × 2 ANNs was computed. All ANNs take seven input data, and are built with two hidden layers with 10 nodes each, and lead to one cloudy flag output 0 or 1. For the daytime ANN, the seven inputs are the latitude, the three PVC bands, and the three PTB bands. For the nighttime ANN, the seven inputs are the latitude; the radiances at 3. 9 µm, 8. 7 µm, and 9. 7 µm; and the three PTB bands. The ANNs were trained with an iterative empirical process, which involves the selection of several millions of pixels in representative cloudy areas to build the training set, then a subsequent visualization of the resulting cloud mask, and the correction of potential imperfections by growing the training input datasets with additional pixels. Figure  4  demonstrates that our cloud mask corrects some discrepancies of the CLM EUMETSAT product and is able to accurately distinguish clouds from sandstorms.
Having built a reliable cloud mask, the second issue is to discriminate sandstorm events among the thermal anomalies. First, a rolling cloudfree average background of the PTB within a period of 10 days (REF_PTB) is computed as a reference for each of 96 time slots at the pixel level, using our new cloud mask. The choice of 10 days has been fixed empirically, as it seems to be the minimum number of days to have cloud-free values over the whole area. For the daytime ANN, the seven inputs are the latitude, the three PVC bands, and the three PTB bands. For the nighttime ANN, the seven inputs are the latitude; the radiances at 3. 9 µm, 8. 7 µm, and 9. 7 µm; and the three PTB bands. The ANNs were trained with an iterative empirical process, which involves the selection of several millions of pixels in representative cloudy areas to build the training set, then a subsequent visualization of the resulting cloud mask, and the correction of potential imperfections by growing the training input datasets with additional pixels.
Validation of AOD Retrieval Method To fully demonstrate that the AOD retrieval performs well, we select the AERONET AOD values from the available sites in 2016 (Dakar, Malaga, El Arenosillo, Saada, Sede_Boker, IER_Cinzana). As we have already shown, the synchronization between the AERONET and SEVIRI measurements is not perfect, especially near cloudy areas, introducing a slight bias when comparing NASCube to AERONET data. However, the comparison presented in Figure  10  for year 2016 comforts us in the quality of the AOD NASCube model, as the results are of similar quality to that used in the training dataset (see Figure  9 ).
Data Description There are two types of instruments that provide AOD values; the in-situ instruments as those implemented by the AERONET network with, a very good accuracy and hundreds of measurements per day and the satellites retrievals as Deep Blue using the MODIS experiment with large scenes. The limitations that we are facing are the limited number of geographical areas for AERONET and the reduced number of images, up to two per day for MODIS. The geostationary MSG-SEVIRI instrument.
Data Description There are two types of instruments that provide AOD values; the in-situ instruments as those implemented by the AERONET network with, a very good accuracy and hundreds of measurements per day and the satellites retrievals as Deep Blue using the MODIS experiment with large scenes. The limitations that we are facing are the limited number of geographical areas for AERONET and the reduced number of images, up to two per day for MODIS. The geostationary MSG-SEVIRI instrument provides an extraordinary tool with a global vision of the North African and Saudi Arabia deserts and images every 15 min, which allow following the evolution and trajectories of the sandstorms.
Selection of Data For The Comparison of AOD Values (NASCube, AERONET, Deep Blue) The NASCube AOD retrieved values are validated against AERONET and Deep Blue AOD values. implies: (1) Selecting over six years, from 2007 to 2012, of AERONET AOD measurements for several wavelengths (0. 675, 0. 500, 0. 440 µm), from 10 sites geolocalized in Figure  7. (2) Over the same time period, we extract the corresponding pixel over land from the Deep Blue MYD04L2 NASA PRODUCT the AOD at 0. 550 µm. (3) Time and space (±1 pixel around the AERONET site) synchronized NASCube retrievals (0. 675, 0. 500, 0. 440 µm).
We have identified some sites where the AOD values are overestimated, such as Tamanrasset, as discussed in Section 3, and the reasons for this discrepancy are presently unclear. The availability of a 10-year database opens the way to extensions along the following lines: (1) sandstorm source detection; (2) tracking of sandstorm trajectories; (3) prediction of sandstorm trajectories at short time; (4) validating the nighttime AOD retrieval using numerous lunar photometer measurements; (5) improving sandstorm detections in mountainous areas by fine tuning the REF_PTBrgb reference, which will also help predicting small AOD values (below 0. 3); and (6) building of a quantitative AOD retrieval over oceans. The NASCube database has already been beneficial to several experiments such as the AMMA campaign of 2006, the FENNEC campaigns in 2011/2012 and in the ChArMEx campaign of 2012/2015.
A significant effort was devoted to the creation of a website  [57]  able to display the whole database results since 2006. The website can be regarded as a "blackboard" platform where we can implement new applications following our day-to-day needs or external user recommendations. The user is able to select and display any color composite over the seven years to near real time and download videos with the sandstorm inlays. Color code levels ranging from white to deep orange indicate the level of sandstorms in the calendars and daily sliders. The platform also provides a useful external interface to download the composite results using command lines either with wget or curl processes  [58]. Statistics of the web access to the NASCube page  [59]  demonstrate that the older AMMA website was intensively used during 2007-2012 by French laboratories, while the currently running NASCube platform is hopefully interesting to English institutions as well.
Acknowledgments: The authors wish to thank  J. Pelon  for is support to start the project in 2006 (AMMA),  C. Flamant  for is unconditional support in 2011 (FENEC) and  C. Deroo  for her invaluable technical advice and for the web site implementation. We wish to thank  Eumetsat  and  Eumetcast  for providing access to the  MSG-SEVIRI  instrument measurements. We thank  V. E. Cachorro Revilla ,  E. Cuevas-Agullo ,  P. Goloub ,  B. N. Holben ,  A. Karnieli ,  B. Mougenot ,  J. L. Rajot , and  D. Tanré , and their staff for establishing and maintaining the AERONET sites used in this investigation.
This project was originally supported by the  African Monsoon Multidisciplinary Analysis (AMMA)  experience [38] and funded by the  French institutes Institut Pierre-Simon Laplace (IPSL) / Centre National de la Recherche Scientifique (CNRS)-Université Pierre and Marie Curie (UPMC)-Université Versailles Saint Quentin (UVSQ) ,  Aerosol Lidar  and  Infrared Pathfinder Satellite PI. The first results have been used in several experiments, as demonstrated by the website citations in the papers [7,.
ES maps constitute a very important tool to bring ES into practical application. Maps can efficiently communicate complex spatial information and people generally prefer to look at maps and to explore their content and practical applicability. Thus, ES maps are very useful for raising awareness about areas of ecosystem goods and services supply and demand, environmental education about human dependence on functioning nature and to provide information about interregional ecosystem goods and services flows. Furthermore, maps are mandatory instruments for landscape planning, environmental resource management and (spatial) land use optimisation. To fulfil the requirements of the above-mentioned applications, high quality, robust and consistent data and information on ES supply, flow and demand are needed at different spatial and temporal levels.
Benjamin Burkhard & Joachim Maes riculture, natural resources use or regional development programmes or to support decision-making. These accounts are intended to measure and monitor the extent, the condition, the services and the benefits of ecosystems to support different policies. Regularly updated and high quality geo-referenced data on capacity, use and demand of ES are essential inputs for natural capital accounts. The development of respective ES mapping approaches, models and tools has profited from the increasing popularity of the ES concept in science, especially within the last decade. However, this popularity of ES mapping studies has, together with the rapid development of computer-based mapping programmes, also led to an almost inflationary generation of various ES maps. Besides the many very promising and well-derived mapping products, maps of inferior quality have also, unfortunately, been published.
It takes more than just some data and a software package to make a good map that fulfils the criteria of being a geometrically accurate, correctly-scaled and appropriately-explained graphic representation of three-dimensional real space. Cartography, the art and science of graphically representing a geographical area usually on a map, has served humanity since its emergence by providing information on the environment, resources, risks, paths, connections and barriers. The theory, methods and practical applications of ES mapping are presented in this book, thus bringing together valuable knowledge and techniques from leading experts in the field. The different chapters can be explored to learn what is necessary to make proper and applicable ES maps. This book addresses an audience which is broader than the research community alone.
ES are becoming mainstream outside the academic world: national and regional authorities are calling for or are involved in large-scale studies to map ES for mapping their natural capital. Cities need ES maps to design, implement or maintain urban green infrastructure. Large businesses start assessing ecosystems and their services on their sites so that they can better understand possible impacts of their operations on the environment. Nature managers need to know how parks and reserves contribute to human wellbeing. Whereas, although not all of these stakeholders will suddenly start mapping ES, they may rely on consultants, students, ecologists and other researchers to help them with spatial data analysis, to understand problems related to mapping or to give practical guidance. Full Open Access to this book is provided to better reach this audience.
Vertical coordinate systems A vertical coordinate system defines the vertical position of the dataset from a reference vertical position -usually its elevation (height) or depth from the sea level (Figure  2 ). While the definition of a geographic or projected coordinate system is obligatory for all datasets, vertical coordinate systems are only needed if the vertical height of data is of relevance. Lack of, or wrongly defined, coordinate system information leads to problems of spatial data integration. (Figure  3 ). Therefore it is very important when using digital mapping tools that the used datasets are defined in an eligible coordinate system.
Marker maps Marker maps are a special form of dot maps that emerged with the advent of web mapping applications such as Google maps. Lying on top of a topographic base map, every marker or "pushpin" symbolises a feature of interest in its geographic location. With each marker being hyperlinked, the user can obtain additional object information or trigger certain actions, like booking a hotel room. The map itself acts foremost as an interface to data which is structured by its spatial location. Paper maps showing the location of entities often use different symbols for different object types referenced in a legend. Thus the selection of the currently relevant object is performed visually by the user. Contrary to this, a web map allows the user to query the objects of interest within a database first and then show the query result in the map.
Due to the omnipresent nature of mapping products in all kinds of media, the map-maker needs to be aware that many colours are connected with particular geographic phenomena (e. g. green for forests, blue for water bodies, red or black for urban areas; see for example the European CO-RINE land cover data set) 2. Applying such commonly used colour schemes is essential for an easy and correct communication of the map content (see Chapter 6. 4).
Natural breaks (Jenks) The natural breaks classification method is applied by checking the data distribution (for example in a histogram or in a graph) and placing class breaks around data 3 To avoid double-representation of data, each subsequent class must start with the next higher value than the one before ended (i. e. 0-2; 2. 1-4; 4. 1-6 etc. ) (cp. Figure  4 ). clusters. This avoids large value variations within one class and highlights differences between different classes. As with quantilebased classified maps, the map-creator and reader must be aware of the effects of this (sometimes subjective) data classification on the resulting map. Figure  4  shows examples of the different classification methods applied to the same dataset. In the example, quantiles produce the most heterogeneous map but there are only minor differences for equal intervals and jenks.
Description of main mapping software, tools and databases Computing power and data availability that support GIS analysis have evolved substantially in recent years. Several freeware GIS platforms have been developed, such as QGIS (Quantum GIS), GRASS GIS (Geographic Resources Analysis Support System GIS), SAGA (System for Automated Geoscientific Analyses), and gvSIG (Generalitat Valenciana Sistema de Información Geográfica) that provide similar functionality to the popular commercial ArcGIS software from ESRI (a list of GIS software is available here 1  ).
Services such as water regulation usually require modelling approaches that integrate meteorological databases, vegetation, soils and topographic data (Chapter 5. 5. 1), while others such as cultural identity might require a participatory mapping ap-proach  (Chapters 5. 5. 3 and 5. 6. 2). Other services such as food production might use complex agricultural models or indicator-based approaches (Chapter 5. 5. 2). However, the complex nature of ES and the inter-linkages between provisioning, regulating and cultural services have led to the use of different tools for each ecosystem service.
Future developments Several challenges lie ahead for mapping ES. These are related to the progress that is currently underway in research and monitoring, remote sensing, sensor networks, data storage, data and knowledge integration, data harmonisation and sharing, database and tool maintenance and crowdsourcing, among others. On the technical side, the accumulation of a growing quantity of data raises the challenge of effective storage and analysis of large amounts of data and is leading to an increased emphasis on machine learning, pattern recognition (in complex data or remote sensing products), and data mining.
A comprehensive review There are multiple approaches to uncover socio-cultural values of ES depending on data availability and the purpose of the valuation. In this chapter, we will focus on seven methods that are frequently used in literature. Preference assessment is a direct consultative method that assesses the individual and social importance of ES by analysing motivations, perceptions, knowledge and associated values of ES. Data is collected through free-listing exercises, ecosystem service ranking, rating, or other selection mechanisms. Techniques for weighting the preferences related to impacts on the ecosystem service of different management alternatives such as multi-criteria analysis are examples of integrated preference assessment valuation. In the same manner, but aiming at a more quantitative indicator of socio-cultural values of ES, the time use method creates hypothetical scenarios for willingness to give up time (WTT).
Quantitative data can be collected both at individual and group level and then aggregated to generalise the results from the sample to larger populations. Qualitative data allow an in-depth understanding of values and underlying motivations, but usually for a much smaller (and often non-representative) sample. Qualitative data can be collected at the individual and group level in the form of narrative arguments (mainly words, but also pictures, drawings, etc. ). Due to the heterogeneity of types of data, aggregation is often impossible and other means of synthesis have to be used (e. g. narrative methods or deliberation). In practice, quantitative and qualitative approaches can be placed along a continuum (Figure  2 ) and, in many cases, they are used in a mixed and complementary approach.
Land use/land cover modelling Land use/land cover data are key inputs to many ES mapping approaches (see Chapter  5 ) and there are various ways to link LULC with additional datasets to map ES (e. g. , the "matrix approach" see Chapter 5. 6. 4). Initial land cover data are often derived from remote sensing or habitat mapping and land use can be modelled from this baseline in a wide range of ways. Given the impacts of LULC change on ES (i. e. , through urbanisation, agricultural intensification, or ecological restoration), LULC data have obvious value in understanding how ecosystem service flows are changing over time. Three common approaches are detailed below. First, "Lowry-type" models can quantify where the location of an attribute of interest (e. g. , demographic data or recreational opportunities) is a function of the attraction and travel costs associated with different locations (using, for example, the Rural Urban Growth model (RUG) or the Ecosystem Service Mapping Tool (ESTIMAP)).
Matrix-based approaches Matrix-based approaches sit on the border between ES mapping and modelling. They combine GIS (geographical information system) and spreadsheets analysis of LULC input data to produce maps of ES supply and/or demand. At their simplest, these are just mapping techniques (see Chapter 5. 6. 4 for a more complete description): they combine GIS LULC layers and scored values for the provision of ES to provide ES provision maps across a study area. By using standardised values, ES provision may be compared between regions or, by using locally targeted ES values, more locally appropriate values can be generated. The process can be undertaken with stakeholders to allow maps of both ES supply and demand to be mapped. Additional GIS datasets can be included to improve the process-a process known as a multi-attribute lookup table-and these can be modified to reflect management scenarios.
InVEST InVEST 1 is a suite of modelling tools that provides a standard approach for application to varied contexts. InVEST includes 18 tools for assessing marine, coastal, terrestrial and freshwater ES. Each output is spatially explicit and driven by user-input spatial datasets. Most InVEST models account for both ES supply and demand, in terms of the locations of people who would benefit from 1 http://www. naturalcapitalproject. org/invest/ these services; this allows supply-demand mismatches to be assessed. InVEST models are freely available and open-source. InVEST requires quantitative skills to be run; although experience with GIS is required to map outputs, coding is not required and the models can be run independently of specialised software (using industry standard or open-source GIS platforms to visualise and prepare input and output data).
Collecting and processing the datasets required can take time and effort. Each of InVEST's tools is a separate model and can be run independently, depending only on the user's needs. The outputs can be produced in biophysical units (i. e. , tonne C km -2 ) or monetised, however the interactions between ES are not specifically modelled. InVEST has been used in a wide range of contexts including using ES metrics to assess sustainable coastal management in Belize, supporting decision-making over clean water supply in Latin America and national ecosystem planning in China. InVEST is a good example of a suite of tools that has gained ground through its use in multiple contexts. However, it does not yet assess the full range of ES and, like many biophysical models, is weaker on harder-to-assess cultural ES (see Chapter 6.
Uncertainty Uncertainty is a key aspect of model interpretation: how sure are we that the model output represents the real world phenomenon it seeks to quantify? Model validation is often used to address model uncertainty. Inter-model comparison studies also reveal differences in outputs due to different model types. Probabilistic approaches and sensitivity analysis can also be used to address scenario and input data uncertainty by exploring the influence of input parameter changes on model outputs by performing multiple runs and identifying overall patterns. It is, however, rare that the full holistic uncertainty (that addresses all these factors) is addressed. A validation statistic may be produced that says, for example, "this model explains 80% of the variation in the dataset we tested it against," but this provides no information about the confidence in this dataset (was it randomly sampled, or taken from locations easily accessible by monitoring teams?); the factors within the model that provide the modeller with confidence in the approach taken (e.
Trend analysis Ecosystems evolve over time as they are affected by and react to different human and environmental drivers of change. This evolution can result in cumulative effects for the ecosystem (e. g. the cumulative effect of soil sedimentation in wetlands) but can also allow determination of the influence of specific drivers in relation to specific ecological functions. Here lies the value of trend analysis, the contribution for understanding the past and current development pathways in order to create knowledge about the future of ecosystems. Current assessments of ES do not always favour the use of time series. This often comes from data limitations regarding the use and availability of contemporary datasets for all system components but also and more importantly, the availability of temporal datasets with an amplitude and a frequency that is relevant for the processes under study.