dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The data sets analysed in this study are available online: Russian Historical Soil Temperature Data: https://data. eol. ucar. edu/dataset/106. ARCSS078; borehole temperature measurements during the International Polar Year (IPY): http:// gtnpdatabase. org/boreholes; WISE30sec v1. 0 soil database: http://www. isric. org/explore/ wise-databases; NCSCD soil database: https://bolin. su. se/data/ncscd/tiff. php; ESA CCI SM v02. 2 product: http://esa-soilmoisture-cci. org/node/202; soil moisture from the ERA-Interim/Land product: https://www. ecmwf. int/en/forecasts//archive-datasets/reanalysisdatasets/era-interim-land; CMIP5 model outputs: https://esgf-node. ipsl. upmc. fr/search/ cmip5-ipsl/. Other data that support the findings of this study, such as the ORCHIDEE-MICT model outputs, are available from the corresponding author on reasonable request.
When isolated, the DSM provided the highest gain percentage (3. 42%), followed by the NIR (2. 58%), and RE (2. 02%). When joined, the combination of the DSM with both RE and NIR was the best contributor (4. 98%), followed by the DSM with RE (4. 80%), DSM with NIR (3. 74%), and RE with NIR (3. 22%). At the class scale, all datasets increasingly advantaged sand, gravel, reef, mud and water. The rather low effect of the DSM with NIR (3. 74%) was assumed to be linked with a statistical noise originated from redundant information in the intertidal area.
3 m spatial resolution based on the 16-band superspectral dataset  [9]. However, only five visible spectral bands were appropriate to provide benthic information given the strong light absorption by water from infrared  [10]. The green wavelength of the airborne bathymetric Light Detection And Ranging (LiDAR) helped separate the five reef states at 0. 5 m point spacing, only based on LiDAR surface and intensity predictors, trained by red-green-blue (RGB) imagery that was acquired from an unmanned aerial vehicle (UAV), called a drone  [7]. The emission of electromagnetic radiation (i. e. , active remote sensing) in the form of a laser is necessary to penetrate seawater in order Drones 2019, 3, 67 2 of 12 to surpass the light absorption by water  [11]. However, the issue of the water absorption can be addressed when surveying intertidal reefs deprived of the water column, i.
e. , at low tide. A passive airborne study has recently shown a positive contribution of the near-infrared (NIR) in the classification accuracy of live parts of honeycomb worm reefs, compared to the traditional RGB dataset  [8]. On the other hand, mapping with airborne systems is often limited by cost considerations and is thereby not applicable to most reefs worldwide. The low-cost but promising UAV imagery has been efficient to help create RGB orthomosaics of honeycomb worm reefs  [12]  and even digital surface models (DSMs) of underwater reef colonies, using a photogrammetric approach  [13]. Nevertheless, it is important to underline that both latter UAV studies were fruitful because the wind was weak, impeding optically blurring wind-waves to be generated, and the water was clear, hindering light to be significantly absorbed  [12, 13].
17 m pixel size. The largest European intertidal biogenic reefs, constructed by Sabellaria alveolota (Linnaeus, 1767) and located in the middle of Bay of Mont-Saint-Michel (BMSM, Figure  1 ), will embody the case study. The contributions of the DSM, red edge (RE), and NIR bands into this classification accuracy of five common reef classes will be evaluated, separately and jointly. Calibration and validation ground-truth will be used, the classification will result from the maximum likelihood, the classification accuracy, in the form of accuracy percentage (AP), will be quantified by the overall, producer's, and user's accuracies (OA, PA, and UA, respectively) derived from the confusion matrix, and datasets' contributions will be computed by the gain percentage (GP). Even though this experiment supposes that reef drone surveys should be implemented at low tide, it is transferable to most of the reefs, which are subject to tidal regimes.
Findings will then be discussed to support the relevance of this methodology to be applied to worldwide reefs. In this study, we propose to test the surplus value of a multispectral UAV to classify biogenic reefs at very high resolution (VHR), namely 0. 17 m pixel size. The largest European intertidal biogenic reefs, constructed by Sabellaria alveolota (Linnaeus, 1767) and located in the middle of Bay of Mont-Saint-Michel (BMSM, Figure  1 ), will embody the case study. The contributions of the DSM, red edge (RE), and NIR bands into this classification accuracy of five common reef classes will be evaluated, separately and jointly. Calibration and validation ground-truth will be used, the classification will result from the maximum likelihood, the classification accuracy, in the form of accuracy percentage (AP), will be quantified by the overall, producer's, and user's accuracies (OA, PA, and UA, respectively) derived from the confusion matrix, and datasets' contributions will be computed by the gain percentage (GP).
Combined with the common RGB imagery (Figure  1 ), the reef classification integrated RE and NIR spectral information. The Pix4Dmapper ® photogrammetric software was used to create the RE and NIR orthomosaics (Figure  2a, b ) and to produce the DSM (Figure  3 ) for the RGB dataset, on the one hand, and for the four other nadir sets, on the other hand. Due to the difference in image dimensions, the number of images and spatial resolution of the RGB and four multispectral datasets reached 1108 images at 0. 04 m, and 4 × 1114 images at 0. 17 m, respectively. Given the overlapping parameter, the xyz accuracy attained 0. 17 (9 GCPs) and 0. 62 m (11 GCPs) for the RGB and four multispectral datasets, respectively. Insofar as the objectives are to evaluate the contributions of the multispectral RE and NIR as well as the DSM bands, the multispectral spatial resolution was adopted to build a multilayer dataset of RGB, RE, NIR, and DSM at 0.
17 m pixel size. Based on the GCPs, this main dataset was georeferenced in the national RGF 93 datum (GRS 80 spheroid) projected in Lambert 93 (conformal conic). Drones 2019, 3, x FOR PEER REVIEW 6 of 12 Insofar as the objectives are to evaluate the contributions of the multispectral RE and NIR as well as the DSM bands, the multispectral spatial resolution was adopted to build a multilayer dataset of RGB, RE, NIR, and DSM at 0. 17 m pixel size. Based on the GCPs, this main dataset was georeferenced in the national RGF 93 datum (GRS 80 spheroid) projected in Lambert 93 (conformal conic).
Classification Analysis A protocol based on the classification accuracy of various datasets was designed in order to assess the added value of the RE, NIR, and DSM onto RGB. Eight datasets were created: firstly, RGB (as the benchmark), RGB+RE, RGB+NIR, RGB+RE+NIR; secondly, RGB+DSM, RGB+RE+DSM, RGB+NIR+DSM, RGB+RE+NIR+DSM. Ground-truth pixels were derived from the spectral growth of the evenly distributed georeferenced photoquadrats embodying every class (see Figure  1 ), until 2000 pixels were attained for each of the five classes. Each dataset was equally split into calibration and validation subsets, each comprising 1 000 pixels. For the sake of transferability, the widespread maximum likelihood (ML) algorithm was used to be trained with calibration subsets. Mapping results of the eight ML classifications were confronted with the validation datasets and gauged with the confusion matrix  [20].
Classification Analysis A protocol based on the classification accuracy of various datasets was designed in order to assess the added value of the RE, NIR, and DSM onto RGB. Eight datasets were created: firstly, RGB (as the benchmark), RGB+RE, RGB+NIR, RGB+RE+NIR; secondly, RGB+DSM, RGB+RE+DSM, RGB+NIR+DSM, RGB+RE+NIR+DSM. Ground-truth pixels were derived from the spectral growth of the evenly distributed georeferenced photoquadrats embodying every class (see Figure  1 ), until 2000 pixels were attained for each of the five classes. Each dataset was equally split into calibration and validation subsets, each comprising 1000 pixels. For the sake of transferability, the widespread maximum likelihood (ML) algorithm was used to be trained with calibration subsets. Mapping results of the eight ML classifications were confronted with the validation datasets and gauged with the confusion matrix  [20].
Systemic Contributions of the DSM, RE, and NIR Even if the intertidal reef mapping was satisfactory with VHR (0. 17 m) RGB drone (Table  2  showing the APs), the classification was shown to be improved by the spatial DSM derived from photogrammetry and multispectral information linked with both RE and NIR imageries. the DSM (Figure  4e ) drastically augmented the primary RGB classification (3. 42%), becoming the best single contributor. The NIR (Figure  4c ) and the RE (Figure  4b ) both gradually increased the RGB classification by 2. 58% and 2. 02%, respectively (Table  2 ). When combined, the best combination increasing the standard RGB classification was the full dataset DSM+RE+NIR (4. 98%, Figure  4h ), followed by the DSM+RE dataset (4. 80%, Figure  4f ), the DSM+NIR dataset (3. 74%, Figure  4g ), and finally the spectral couple RE+NIR (3.
22%, Figure  4d ). Contributions (in the form of GPs) were described when variables were isolated, on the one hand, and combined, on the other hand, in order to discriminate the inherent effect. Taken separately, the DSM (Figure  4e ) drastically augmented the primary RGB classification (3. 42%), becoming the best single contributor. The NIR (Figure  4c ) and the RE (Figure  4b ) both gradually increased the RGB classification by 2. 58% and 2. 02%, respectively (Table  2 ). When combined, the best combination Drones 2019, 3, 67 8 of 12 increasing the standard RGB classification was the full dataset DSM+RE+NIR (4. 98%, Figure  4h ), followed by the DSM+RE dataset (4. 80%, Figure  4f ), the DSM+NIR dataset (3. 74%, Figure  4g ), and finally the spectral couple RE+NIR (3. 22%, Figure  4d ).
Specific Contributions of the DSM, RE, and NIR The contributions (in the form of GPs) of the spatial DSM and spectral RE and NIR were also investigated at the class level (Figure  5 ). Prior to detailing the effects of DSM, RE, and NIR on the PA and UA of the five classes, gains and losses drawn from average PA+UA were examined to retrieve trends at the class scale. The highest contribution, associated with the full dataset DSM+RE+NIR, increasingly favored sand (0. 37%), gravel (0. 63%), reef (4. 97%), mud (6. 79%), and water (10. 85%) (Figure  5c, d ). The effects of both isolated RE and NIR acted on the same sorting of the five classes: sand (-0. 70 and 0. 29%, respectively), gravel (-1. 35 and 0. 15%, respectively), reef (-0. 13 and 0. 57%, respectively), mud (6. 07 and 6. 21%, respectively) and water (5.
66%), while increasing the UA of water (5. 76%) and mud (6. 84%), but decreasing the UA of gravel (-0. 05%), reef (-0. 85%), and sand (-2. 03%). NIR boosted the PA of reef (0. 06%), sand (2. 04%), mud (5. 23%), and water (6. 05%), but reduced the PA of gravel (-0. 03%), while augmenting the UA of gravel (0. 32%), reef (1. 09%), water (6. 00%), and Prior to detailing the effects of DSM, RE, and NIR on the PA and UA of the five classes, gains and losses drawn from average PA+UA were examined to retrieve trends at the class scale. The highest contribution, associated with the full dataset DSM+RE+NIR, increasingly favored sand (0. 37%), gravel (0. 63%), reef (4. 97%), mud (6. 79%), and water (10. 85%) (Figure  5c, d ). The effects of both isolated RE and NIR acted on the same sorting of the five classes: sand (-0.
Influence of the Spectral RE and NIR At the reefscape scale, the best spectral contribution, deprived of and provided with the DSM, to the RGB classification was attributed to the joint use of RE and NIR. However, the discrimination derived from the combination of the DSM+RE greatly surpassed this the DSM+NIR dataset (sooner discussed in Section 4. 1). At the class scale, the strong performance of RE+NIR, when deprived of DSM, was easily highlighted by the high gain in both the PA and UA of mud and water classes. The detection of these two classes is directly associated with the water content, which shows a significant signature into the RE and NIR bands  [10]. These spectral windows enabled to discriminate mud from water, which were confounded over the natural-colored imagery (Figure  7a ), while obviously separable over the NIR imagery (Figure  7b ).
Influence of the Spectral RE and NIR At the reefscape scale, the best spectral contribution, deprived of and provided with the DSM, to the RGB classification was attributed to the joint use of RE and NIR. However, the discrimination derived from the combination of the DSM+RE greatly surpassed this of the DSM+NIR dataset (sooner discussed in Section 4. 1). At the class scale, the strong performance of RE+NIR, when deprived of DSM, was easily highlighted by the high gain in both the PA and UA of mud and water classes. The detection of these two classes is directly associated with the water content, which shows a significant signature into the RE and NIR bands  [10]. These spectral windows enabled to discriminate mud from water, which were confounded over the natural-colored imagery (Figure  7a ), while obviously separable over the NIR imagery (Figure  7b ).
Conclusions Intertidal bioconstructions of the honeycomb worm reefs were surveyed by a 150 m-height fixed-wings (eBee+ ® ) provided with a multispectral sensor (Parrot Sequoia ® ). The RGB classification accuracy of the reefscape, examined as a mosaic of five habitats, was improved by the DSM, RE, and NIR information, both separately and jointly. Separately, the highest overall GP stemmed from the spatial asset DSM (3. 42%) derived from the photogrammetric approach, while the best spectral performance was ensured by the NIR spectral band (2. 58%), followed by the RE (2. 02%). At the class scale, the three DSM, NIR, and RE datasets coincidentally and increasingly advantaged sand, gravel, reef, mud, and water. Synergistically, the best overall GP relied on the combination of the full dataset (DSM+RE+NIR, 4.
98%), followed by the DSM with RE (4. 80%), the DSM with NIR (3. 74%), and the spectral couple RE with NIR (3. 22%). These efficient datasets also profited the same ascending sorting at the class level: sand, gravel, reef, mud, and water. The joint effect of the DSM and NIR produced an overall low gain, compared to the DSM with RE, what was hypothesized to be associated with a statistical noise originated from redundant information in the intertidal area. As short-term perspectives, the habitat classification will be very likely refined by the integration of spatially-and spectrally-derived products (such as runoff, topographic position index, rugosity index, normalized difference vegetation index, normalized difference water index, etc. ) into an object-based image analysis  [25].
x FOR PEER REVIEW 5 of 12 the four other nadir sets, on the other hand. Due to the difference in image dimensions, the number of images and spatial resolution of the RGB and four multispectral datasets reached 1 108 images at 0. 04 m, and 4 × 1 114 images at 0. 17 m, respectively. Given the overlapping parameter, the xyz accuracy attained 0. 17 (9 GCPs) and 0. 62 m (11 GCPs) for the RGB and four multispectral datasets, respectively.
Drones 2019, 3 , 3 x FOR PEER REVIEW 5 of 12 the four other nadir sets, on the other hand. Due to the difference in image dimensions, the number of images and spatial resolution of the RGB and four multispectral datasets reached 1 108 images at 0. 04 m, and 4 × 1 114 images at 0. 17 m, respectively. Given the overlapping parameter, the xyz accuracy attained 0. 17 (9 GCPs) and 0. 62 m (11 GCPs) for the RGB and four multispectral datasets, respectively.
We use the complete datasets of IASI-A, IASI-B and CrIS to give estimates for both the 2008-2017 (IASI-A) and 2013-2017 (CrIS, IASI-A and -B) intervals. We show that all instruments provide comparable emission estimates and similar interannual variability. In Sect. 2, we describe the datasets and the methods, and estimate the uncertainty of the method used in this study. In Sect. 3, we describe the results, starting with the fitted lifetime and the plume spread (σ ), which will be used to constrain the final emission fits given in Sect. 3. 2. Furthermore, Sect. 3. 3 describes the temporal variations in the emission estimates. Finally, in Sect. 4, we summarize and discuss the results. 2 Datasets and method 2. 1 Satellite products.
9 ppb, as reported by  Shephard and Cady-Pereira (2015) , but under ideal conditions reaches down to ∼ 0. 3 ppb  (Kharol et al. , 2018)  (assuming that 1 ppb is equal to a total column of 2±1×10 15 molecules cm -2 , this leads to a detection limit of 1. 8 ± 0. 9 × 10 15 molecules cm -2 ). Note, version 1. 5 of the CrIS NH 3 retrievals only contains values that have a detectable NH 3 spectral signal; thus, in regions where there is a significant fraction of the values below the detection limit, the mean values can be biased towards values at or above the detection limit (see  Shephard et al. , 2019  for more details). For this study, only daytime observations between January 2013 and December 2018 are used. Furthermore, only observations with a quality flag of 5 are used  (Shephard et al. , 2019) ; the dataset was further filtered by only allowing observations that have a signal-to-noise ratio > 2 (which in practice means that all observations with the unpolluted a priori are filtered out), degrees of freedom > 0.
IASI The IASI instruments aboard the MetOp-A and -B satellites are also in Sun-synchronous orbits and pass locations twice daily with Equator crossing times at 09:30 and 21:30 LST with a difference of about 45 min  (Clerbaux et al. , 2009). Both IASI instruments have an observational swath width of over 2000 km and have a pixel footprint of around 12 km diameter at nadir viewing angles. The footprints are larger towards the edges of the swaths, with the outermost pixels having a footprint of 20 × 39 km 2. The Artificial Neural Network for IASI (ANNI) NH 3 v2. 2 retrieval product (here called IASI-NNv2. 2, unless otherwise noted)  (Van Damme et al. , 2017)  was released in mid-2018. This product is the most recent iteration of the IASI-NH 3 products and an improvement over the previous datasets (Van  Damme et al.
, 2014a; Whitburn et al. , 2016a). The IASI-NNv2. 2 product uses a neural network to link the hyperspectral range index (HRI) with a set of parameters, representing the atmospheric state, to derive the total columns NH 3. The nearreal-time IASI-NN data products have small discontinuities in the dataset following updates on the meteorological parameters provided by EUMETSAT. The IASI-NNv2. 2 product improved this by providing a reanalysis dataset (ANNI-NH 3 -v2. 2R-I, here called IASI-NNv2. 2(R)) that allows us here to evaluate the emission estimates from year to year. Unless otherwise noted, we use the reanalysis dataset, which is available until the end of 2016. For 2017, we rely on the near-real-time dataset. As of yet, there is no validation study focused specifically on the newest data product.
NH 3 emission inventories and source locations A list of potentially observable emission sources was created through a combination of an analysis of global yearly averaged concentration fields and emission inventories. Inspection of the concentration fields revealed that a large number of these locations correlated with known industrial locations. Van Damme et al. (2014b)  showed that there are discrepancies between modeled NH 3 and IASI NH 3 observations for a few of these locations. Fertilizer production and gasification plants were common in this initial search; therefore, we compiled a list of worldwide plant locations based on internet sources (https: //globalsyngas. org/resources/world-gasification-database/, last access: 6 December 2018) and used Google Earth to determine the exact locations.
To this initial set, we added locations from commonly used and readily available point-source emission inventories. For Europe, the European Pollutant Release and Transfer Register (E-PRTR; http://prtr. ec. europa. eu/, last access: 30 May 2018) is used; for Australia, the National Pollutant Inventory (NPI; http://www. npi. gov. au/, last access: 11 February 2019); for Canada, the National Pollutant Release Inventory (NPRI; https://www. canada. ca/ en/services/environment/pollution-waste-management/ national-pollutant-release-inventory. html, last access: 11 February 2019); and for the United States, the National Emissions Inventory (NEI; https://www. epa. gov/ air-emissions-inventories/national-emissions-inventory-nei, last access: 23 July 2018). From the point-source databases, any locations with NH 3 emissions over 0.
Emission estimates will still be attempted for sources that fail to meet the criteria, such as a large number of locations in, for example, the Netherlands, China and India, where extended regions with high NH 3 concentrations exist. Several locations found in the point-source emission databases are within 15 km of one another. In such instances, the locations are merged under a single location name with the location representing multiple sources. The satellite emission estimates will also be compared to the commonly used Hemispheric Transport Atmospheric Pollution version 2 emission inventory (HTAPv2; http: //edgar. jrc. ec. europa. eu/htap_v2/, last access: 10 December 2017;  Janssens-Maenhout et al. , 2015)  and the individual point-source emission databases where available (e. g. , the 2014 NPRI, NEI, E-PRTR and the NPI inventories).
The HTAPv2 database covers the year 2010 and provides emissions with a high spatial resolution of 0. 1 • × 0. 1 •. The database consists of a combination of gridded regional emission inventories such as the Model Inter-Comparison Study (MICS) for Asia, Environmental Protection Agency (EPA) for the US and Canada, and the Netherlands Organisation for Applied Scientific Research -Monitoring Atmospheric Composition and Climate (TNO-MACC) II database for Europe. These high-spatial-resolution inventories were complemented by the Emissions Database for Global Atmospheric Research (EDGAR) v4. 3  (Crippa et al. , 2016)  to gap fill missing emissions for countries not covered by the highresolution databases.
Meteorology In this study, a wind rotation approach is used  (Pommier et al. , 2013; Fioletov et al. , 2015; Clarisse et al. , 2019) , which requires the wind fields for each satellite observation, in order to align the observations in an upwinddownwind coordinate system and thus map out the emission plume. Here, we use the wind fields (U, V ) from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim dataset (https://apps. ecmwf. int/datasets/data/interim-full-daily/levtype=sfc/, last access: February 2018;  Dee et al. , 2011)  at a resolution of 0. 75 • × 0. 75 • (∼ 60 × 80 km 2 at 45 • N) with a 6 h temporal resolution. Most of the observed NH 3 is located in the lower boundary layer  (Dammers et al. , 2017a; Y. Zhang et al. , 2018; Tevlin et al. , 2017). To represent the transport of NH 3 as well as possible, we choose to use an average of the wind fields for the first kilometer of the lower troposphere (∼ 1000-900 hPa).