dataesr/scientific-paragraphs-categorization huggingface dataset:
Remotely Sensed Products In this work, we designed an approach to identify desert PICS distributed world-wide based on computational considerations, relying essentially on space-borne observations of their surface reflectance. The identification procedure and the determination of PICS optical properties relied on complementary informations from the PARASOL and MODIS instruments. The benefit of PARASOL is mostly related to its acquisition of multi-directional data, which provides a much more detailed description of the directional signature of the surface than MODIS does. MODIS, however, provides higher spatial and temporal monitoring over a longer time period. To evaluate the directional effects of the surface, we used one typical year (2008) of PARASOL observations. Given that (i) the study focuses on very stable targets, with negligible year-to-year variations and that (ii) the temporal variation of the directional signatures are even smaller than those of the overall reflectance, the choice of only one year of data for PARASOL has a negligible impact on the conclusions.
Acknowledgements: The authors are grateful to LSCE's computer team ( François Marabelle  and  Julien Bruna  in particular) for the computing support and resources, to  Julien Waddle  ( NOVELTIS ) for his invaluable help in using Gdal, to  Pascal Maugis  ( LSCE ) for his guidance in using QGIS, and to  Sébastien Labarre (IPGP)  for sharing the source code of the Hapke models. The GSMP3 DIAS dataset is archived and provided under the framework of the Data Integration and Analysis System (DIAS) funded by  Ministry of Education, Culture, Sports, Science and Technology (MEXT).
Here, we propose an upscaling approach to retrieve daily SM with a spatial resolution of 1 km in the Babao River basin, using the BLR method to upscale WSN multi-point observations considering topographic complexity. This was achieved using remote sensing information (e. g. , ASTER aspect and elevation and MODIS LST). The WSN measurements, MODIS LST, and topography data are presented in Section 2; the upscaling approach and the statistical metrics used to evaluate the upscaled SM are explained in Section 3; and results are given in Section 4. Section 5 discusses the findings and the main conclusions are drawn in Section 6.
Validation Given that the number of available ground-measurements is not sufficient to separate them between training and validation datasets, 10-fold cross-validation methods  [40] [41] [42]  were used to evaluate the upscaled SM by comparing them against the in situ measurements from the sixteen WSN sites. In addition, the upscaled SM estimates were also evaluated by comparing them against the SM data measured by two AMSs. For more details about the cross-validation method, please refer to  Cawley et al. (2003)   [40].
g. , from WSN and AMS) in the time-series and θ In situ t are the in situ measurements from WSN and AMS on date t. θ In situ and θ upscale are the mean values of the corresponding time series. In addition to the error evaluation, it is necessary to assess the consistency between datasets at each grid using slope values calculated using a linear regression relationship between θ upscale and θ In situ from Equation  (11). The correlation coefficient r, calculated using Equation  (12) , is also used to assess the agreement between the estimates and the in situ measurements. θ In situ = slope•θ upscale + intercept (11) r = ∑ N t=1 θ upscale t -θ upscale θ WSN t -θ in situ ∑ N t=1 θ upscale t -θ upscale 2 • ∑ N t=1 θ WSN t -θ in situ 2 (12).
In this study, the procedure will be applied to the standard Landsat 8 data collected during the 26 October 2013 Mt. Etna eruptive episode and the results compared with those obtained using different satellites systems. The paper is organized as follows: Section 1 outlines the 26 October 2013 Etna eruption and Section 2 describes the PEM procedure applied to the standard Landsat-8 products. In Section 3 the results obtained are compared with the VCTH retrieved from the PEM procedure applied to the estimations realized using the spinning enhanced visible and infrared imager (SEVIRI) and the moderate resolution imaging spectroradiometer (MODIS). In Sections 4 and 5 the discussion and the conclusions are presented.
This hampers the correct deployment of the method described in  [21]. To avoid this inconvenience, we propose the following 5 step procedure: I. The dataset is rotated, so that the columns of the image matrix are aligned to the nominal azimuth direction of the satellite reported in the ancillary data files. II. A correlator to perform pixel (or sub-pixel) offset measurements is used (e. g. ,  [30] ). If one considers the OLI image as a matrix made of lines and columns, offsets among lines are the EP offsets (O e ), while offsets among columns are the P2E offsets (O p2e ). In the offset results, there could exist a ramp resulting from band mis-registration; the ramp, if found, is removed. III. The direction of the plume is measured with respect to the azimuth direction, with the convention depicted in Figure  2.
In addition, different sensors acquire data from different positions, introducing a bias in the eventual validation campaign. Here the VCTH of the Etna 26 October 2013 eruption, used for the cross-comparison with Landsat results, are estimated by using geostationary (SEVIRI) and polar (MODIS) satellite sensors. Here the VCTH of the Etna 26 October 2013 eruption, used for the cross-comparison with Landsat results, are estimated by using geostationary (SEVIRI) and polar (MODIS) satellite sensors.
SEVIRI VCTH estimation The SEVIRI instrument on board METEOSAT Second Generation (MSG) geostationary satellites is a 12 channel VIS-TIR multi-channel imager, which operates from 15 minutes repeat cycle on entire hemisphere (Full Disk) to 5 minutes over Europe (Rapid Scan). The sub-satellite point spatial resolution is 3 × 3 km 2 , and the pixel dimension in the Etnean area is about 4. 3 × 3. 3 km. Exploiting the high data frequency of the SEVIRI images, the volcanic cloud speed can be retrieved by following the volcanic cloud centre of mass. By making a basic assumption that the estimated centre of mass speed is the whole volcanic cloud speed, VCTH can be obtained by comparing this value with the wind speed profile collected in the same time and position  [26]. Also in this case the wind profile derive from NCEP/NCAR considering a box with 2.
SEVIRI VCTH Estimation The SEVIRI instrument on board METEOSAT Second Generation (MSG) geostationary satellites is a 12 channel VIS-TIR multi-channel imager, which operates from 15 min repeat cycle on entire hemisphere (Full Disk) to 5 min over Europe (Rapid Scan). The sub-satellite point spatial resolution is 3 × 3 km 2 , and the pixel dimension in the Etnean area is about 4. 3 × 3. 3 km. Exploiting the high data frequency of the SEVIRI images, the volcanic cloud speed can be retrieved by following the volcanic cloud centre of mass. By making a basic assumption that the estimated centre of mass speed is the whole volcanic cloud speed, VCTH can be obtained by comparing this value with the wind speed profile collected in the same time and position  [26]. Also in this case the wind profile derive from NCEP/NCAR considering a box with 2.
This important distinction has to be taken into account when comparing results from different sensors. The PEM method works best for volcanic clouds made of ash particles and optically thick material. The accuracy of the PEM extracted from Landsat 8 has been already assessed against ground cameras in de Michele et al. (2016) for the Holuraun fissural eruption (Iceland). They reported an accuracy of 300 m. Landsat 8 acquires data at a high spatial resolution (15/30 m grid) at the cost of a medium revisit time (every 14 days). Instead, SEVIRI has the advantage of acquiring data every half an hour everywhere in the globe. This is done at the cost of medium spatial resolution (1 km grid). Statistics performed over VCTH retrieved by Landsat 8 (PEM), SEVIRI and MODIS data on a common area show very good agreement with a maximum height value of 8 (with 1 km precision).
The advantages are manifold. First, it takes advantage of the freely available Landsat 8 data archives. Second, the method exploits the already-orthorectified Landsat 8 dataset, which is a standard Landsat 8 product, readily available on Landsat 8 archives. Third, the method could be adapted to the Copernicus Sentinel 2 data, a staggered sensor for which the products available to general public are orthorectified (similar to Landsat 8). The synergetic use of multiple sensors could improve the revisit time over a given volcanic eruption. It represents a step towards the routine monitoring of volcanic plumes height from space, at high spatial resolution. The VCTH retrievals obtained from the geostationary SEVIRI and the polar MODIS satellite instruments indicate a good agreement with the Landsat VCTH product.
This makes it extremely difficult to design and conduct large-scale investigations of variation and change. We need better understanding of the cultural taxonomy of the Upper Palaeolithic not just to enable comparison with results from other fields, but also so that we can formulate better archaeological questions. That said, Scerri is right to observe that our failure to create more robust datasets has led to the devaluation of archaeology within interdisciplinary studies of prehistory: our present shortcomings have repercussions for the status of our field as a whole. Rather than rejecting cultural taxonomy, we would prefer to see renewed attention and debate well beyond this forum. We also hope to see rapid changes in archaeological practice to provide more shared data and methodological descriptions that can inform this debate.
Acknowledgements We are very grateful to the respondants,  Ben Marwick ,  Eleanor Scerri  and  John Shea , for their perceptive commentary. N. R. thanks the  European Union 's  Horizon 2020 research and innovation programme  under  Marie Skłodowska-Curie  grant agreement 747400; F. R. thanks the  Independent Research Fund Denmark  for grant  6107-00059B  and the  European Research Council  ( Consolidator Grant  agreement  817564  under the  Horizon 2020 research and innovation programme ).
INTRODUCTION AND GOALS OF THIS WHITE PAPER As pointed out by  Wright and Nichols (2019) : "A high priority vision for future coastal science should be to enhance resilience of coastal communities by anticipating and mitigating hazards to human health, safety and welfare and reducing economic harm to coastal industries such as tourism, fisheries and shipping. " Accomplishing this objective will require anticipating future hazards to human and ecosystem health, safety and welfare. To adapt to accelerating coastal change, we must predict future conditions with increasing accuracy and precision, providing improved predictions on global, regional, and local spatial scales and on decadal, annual, and event time scales. Meeting future challenges requires collaborations across the broadest range of disciplines and coastal landscapes to integrate and assess observational data and model output.
To be effective and trusted, models and data must involve objective testing to ensure accuracy and coupling across multiple disciplines. An essential element of effective future coastal forecasting systems, in addition to observations and models, must be advanced data management, dissemination and visualization systems that enable modelers to readily access observational data for assimilation and objective model validation. Forecasting systems should provide planners, emergency managers, and the general public with clear and readily understandable information to assess impending threats. Some of this is now technically possible by rapidly evolving high-performance computing technology and the Internet of Things (IoT), which connects an ever-growing number of smart devices for real time monitoring and tracking of many physical events.
, 2003 -2004; Meur-Férec and Morel, 2004; Meur-Férec, 2006). Coastal observatories that apply a systemic approach to evaluating and monitoring the four components of vulnerability will be able to provide stakeholders with additional tools and methodologies to adapt to coastal risks. Enhanced long-term nearshore observations of physical and social processes will improve societies' understanding of and adaptation to high-impact, low-frequency natural hazards and climate change. Assessment of the reliability and accuracy of predictive models requires continuous objective monitoring of what is actually happening and how those "happenings" impact resilience. Models, sensor arrays, data management systems, communication protocols, and collaboration strategies should be considered together and not "stove piped" into isolated subsystems.
The result of this effort is a web-based software application known as the Social Media Tracking and Analysis System that can access Twitter messages worldwide. It is important to understand the variety of ways that social media are used by individuals during disaster situations and leverage this understanding to better develop and target interventions during emergencies. By optimizing methods for analyzing social media data in the context of emergency management, it is possible to identify approaches most capable of yielding actionable insights in emergency situations. In particular, the interest is in drawing on multiple aspects of Twitter data, analyzing the qualitative content of each tweet, the social networks that each tweet diffuses through, and the particular spaces and places where these tweets are created.
In some cases, these planning guides support activities such as restricting development in areas prone to coastal erosion, moving structures away from the coast, and discouraging the construction of shore protection. Government organizations including ocean and meteorological agencies (e. g. , Australian Bureau of Meteorology, METEO France, and NOAA), local universities, businesses, and citizens have provided discoverable data from networked sensors that are included as big data resources which can support decision makers. These data come in various forms including historical archives from national data centers, in situ data from the neighbor's weather station to ocean observing systems, handheld to satellite imagery, and numerical model output. Users need to sift through data from local, national, and globally available datasets that can help address environmental issues, ranging from recurrent flooding to sea level rise.
There is considerable value in having strong relationships between researchers working on AI, such as through a local university or research agency, and local first responders. In this way, research can be linked to response and expertise transferred to state and local programs. Large datasets culled from data centers, ocean observing systems, model centers, and imagery libraries can easily grow to a size beyond the analytical capability of common software tools and single researchers. To fully utilize the potential of big data requires dedicated resources and expertise. Margaret Davidson, the former director of today's NOAA Office for Coastal Management, recommended the establishment of a collaborative virtual community focusing on data intensive computing to integrate physical and social sciences and improve coastal resilience -a facility that she called a "collaboratorium.
Both projects required the accomplishment of multidisciplinary and integrated tasks completed by university, government, and industry participants that were spread throughout the United States and Canada. Typical components used to share information and integrate results in a collaborative virtual environment were distributed databases, open source models, webinars, and other types of collaborative software. Important software applications were components of the COMT cyberinfrastructure that were designed to promote data sharing of seminal datasets. In its recent report on community resilience pertaining specifically to the Gulf (of Mexico) Research Program (GRP), the National Academies of Sciences,  Engineering, and Medicine, (2019, p. 83)  recommends: "The GRP should create a resilience learning collaborative for stakeholders to exchange information, approaches, challenges, and successes in their respective and collective work to advance community resilience in the Gulf region.
Whereas the monitoring of coastal hazards is benefiting from new sensors and data centers, methodologies and tools that allow the assessment of trends, and the management of individual and social representations of risk are still limited. To address the need for a coherent coastal vulnerability evaluation framework for the benefits of both academics and stakeholders, the OSIRISC project (2017-2019) offers a methodology dedicated to the interdisciplinary observation of the coastal risks of erosion and flooding. OSIRISC focuses specifically on: (1) The elaboration of indicators describing these components. (2) The integration of such indicators into a web-based interface with high spatial resolution. (3) An iterative testing process bringing together coastal risk experts (researchers, stakeholders, land use managers) at the French regional level, in Brittany.
VISION FOR FUTURE SUPPORT OF COASTAL RESILIENCE INITIATIVES The global coastal science community should develop a vision for 2029 for observations and systems that promote and enhance coastal resilience and adaptation and meld the following enhancements into a framework for delivering new outcomes: ( that bring together the latest advances in computing, in data science, in observational science, in social science, environmental science, engineering and policy to address particular end-user use-cases with the goal of undertaking the research and development required to deliver new types of information, tools and understandings. (9) An objective evaluation of models at all locations following events to identify issues that may compromise predictions. Comparisons should be done by a collaborative group that collects the observations independent of operational groups that produce the model results.
Develop a website that connects coastal resilience resources to facilitate progress in a field that is already developing globally. The website should feature digital scholarship developed by natural and social scientists, especially results from grand challenges such as the coupling of inundation and social "urban planning" models. This website will provide a foundation for progress and the development of fully integrated partnerships that include government, university, and industry members. There should be provision for limited objective experiments that allow partnership assessment and evaluation of approaches that best achieve integrated environmental, engineering, and community resilience results at the local level. These experiments would apply data that includes historical information, in situ observations, imagery, and numerical models.
RECOMMENDATIONS Observing systems, coupled with models and information dissemination subsystems, can provide the environmental intelligence to allow coastal communities to adapt to long-term coastal degradation and short-term catastrophic events. Many of the required components already exist or are in development. The main need is for networks to connect observers, modelers, managers and first responders so that solutions can flow across disciplinary and stakeholder boundaries. This already includes connecting observing and modeling communities. For new developments in understanding and predicting to enhance the resilience of coastal communities in the future, more attention needs be devoted to education of, and outreach to, the affected communities and their leaders. Some challenges that still need to be addressed include the following: • New observing systems, including satellites, sensors, and collection mechanisms, such as community driven data collection, should be developed using the frameworks from the IoT, to allow for large scale collection and analysis of data including fundamental baseline data and maps.
The constraining issues are not primarily technical but organizational. Coastal resilience is important to all countries at some level and so the task is to take best practice from organizations around the world and deliver outcomes that enhance our ability to maintain and sustain our coastal communities. Coastal communities are at the forefront of climate change impacts including short term events and longerterm trends. Community resilience requires new approaches to the collection of observing data and new methods for data dissemination. The challenge is to develop collaborations and projects that bring all of the parties together to ensure that coastal communities can implement resilient strategies. Regionally specific coastal resilience seminars and workshops can identify optimal areas to plan transdisciplinary experiments.
The algorithm for calculating emission clumps developed in this study is inspired by research on mapping urban area and socio-demographic activities  (Li and Zhou, 2017; Elvidge et al. , 1997; Zhou et al. , 2015; Su et al. , 2015; Doll and Pachauri et al. , 2010; Letu et al. , 2010). The corresponding algorithms can be grouped in classification-based or threshold-based. Classification-based algorithms use dataset such as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) to train a machine-learning model to classify urban and non-urban areas  (Cao et al. , 2009; Huang et al. , 2016). Threshold-based algorithms classify urban grid cells where some continuous variables (e. g. nighttime lights) are above a given threshold  (Elvidge et al. , 1997; Liu and Leung, 2015; Li et al.
, 2015; Liu et al. , 2015). In threshold-based methods, given the high spatial heterogeneity of urbanization and urban forms, efforts have been devoted to find local optimal thresholds, such as the "light-picking" approach to find a local nighttime background light surrounding a target grid cell  (Elvidge et al. , 1997) , or determining local thresholds by matching local/site-based surveys and land-use/land-cover (LULC) datasets  (Zhou et al. , 2014). The problem of characterizing CO 2 emission clumps posed here consists in delineating all areas that have a potential to generate detectable atmospheric XCO 2 plumes. "Detectable" means here that the concentration within a plume formed by a clump should be large enough compared to the surrounding background in XCO 2 images of typical spatial resolution of ≈1 km.
The magnitude of a minimum detectable XCO 2 enhancement in a plume (relative to the surrounding background) depend on individual XCO 2 sounding precision. Such sounding precision should be of similar order of magnitude worldwide, although the solar zenith angle, aerosol loads, surface albedo etc. will affect it  (Buchwitz, et al. , 2013). In this context, contrary to the algorithms used for mapping urban area, common global minimum emission thresholds for land grid cells forming a clump are relevant. This study aims to provide a global dataset of fossil fuel CO 2 emission clumps for high-resolution atmospheric inversions that will use XCO 2 imager data. Such a dataset can be used for OSSE studies to compare different imagery observation concepts for constraining fossil fuel CO 2 emissions at clump scale over the whole globe.
We propose an approach that combines a threshold-based and an image-processing algorithm. Section 2 describes the high-spatial resolution global emission map upon which clumps are calculated, and the algorithm to delineate the clumps worldwide. The spatial distribution and extent of the resulting clumps throughout the globe are described in Sect. 3 and are compared with clumps diagnosed by applying the same algorithm to other emission maps. Section 4 discusses the sensitivity of the resulting clumps to the precision of XCO 2 measurements and future applications of this global dataset. Section 5 describes the data availability. Conclusions are drawn in Sect. 6.