dataesr/scientific-paragraphs-categorization huggingface dataset:
In Fig. A1 , we show the misfit maps as the difference between observed and modelled surface ice speeds (only for the early half of the months as the second halves usually show a similar range of mismatch). The periods showing the largest misfit correspond to the transition between different surface conditions. This is especially true in June, July, and September, when the surface changes from snow-covered to melting ice or vice versa. Note that the November data set seems more likely to be corrupted by poor satellite imagery than by the surface conditions. The uncertainty in the observed velocity for these periods often exceeds 30 m yr -1 , which represents a significant relative uncertainty in the upper part of the domain where speed is below 100 m yr -1. However, the surface velocity is captured relatively accurately in the first 50-70 km from the margin, and the inversions give a good match between the adjacent time steps, giving us confidence in the interpretation of the basal fields in these areas.
Acknowledgements. Elmer/Ice computations and ice-velocity time series presented in this paper were performed using the https: //gricad. univ-grenoble-alpes. fr (last access: 4 October 2021) infrastructure, which is supported by Grenoble research communities. We thank  Neil Humphrey  for the inclinometry data funded by the  NSF   Office of Polar Programs-Arctic Natural Sciences awards 1203451  and  0909495 ,  SKB ,  NWMO ,  Posiva Oy , and  NAGRA. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-19-CE01-0011-01 ) and the  CNES MaISON  research project. Review statement. This paper was edited by  Huw Horgan  and reviewed by two anonymous referees.
Code and data availability. All data sets used here are publicly available. Elmer/Ice code is available here: http://elmerice. elmerfem. org/ (Elmer/Ice, 2020). BedMachine data are available from Morlighem et al. (2017). GIMP data are available here: https://doi. org/10. 5067/NV34YUIXLP9W (Howat et al. , 2015). Velocity time series are published at Zenodo (https://doi. org/10. 5281/zenodo. 5535532; Derkacheva et al. ,  2021a). Model results for 24 time steps are published at Zenodo (https://doi. org/10. 5281/zenodo. 5535624, Derkacheva et al. ,  2021b).
Coral colonies 3D reconstruction For each colony, a 3D model was reconstructed using the photogrammetry software Agisoft Metashape Professional (version 1. 5. 0 build 7618) following five steps: (i) estimating image quality as function of the sharpness, exposure, focus, resolution and field depth of the images; (ii) aligning the cameras and generating a sparse point cloud calculated by the software (Fig. 2A ); (iii) scaling the sparse cloud using the scale bar; (iv) building a dense point cloud, with depth information for each camera and densification algorithms (Fig. 2B ); (v) building a 3D mesh, the points of dense cloud are connected to create triangles and define a shape (a polyhedral object). Mesh texture was processed, although this step it is not compulsory to perform the measures and 3D analyses (Fig.
Measurement of 2D and 3D metrics For each colony 3D model, the planar area (i. e. 2D projected area) was calculated with the geographic information system (GIS) software Global Mapper (version 19. 0), using spatial analysis tools from an orthographic projection of the 3D models (Fig. 3A, B ). Then, the maximum diameter (henceforth called diameter) was computed using the open source GIS software QGIS (version 3. 4. 6 Madeira) applying the minimum enclosing circles tool (Fig. 3C ). These parameters were calculated to obtain commons metrics at the colony and reefscape scale. Also, quantitative measures of the colony's external surface (Fig. 2F  represented by a light blue line) and volume (V c in Fig. 2F ) were computed from the colony 3D models using the open source system for processing and editing 3D models, MeshLab (version 2016.
Three-dimensional analyses: Shelter volume assessment and description Three-dimensional analysis for shelter volume estimation (in dm 3 ) was performed using the 3D computer graphic program Autodesk-3ds Max2020. For each coral colony model, the process followed four steps: (i) creating a geode composed of 960 faces enveloping the colony (ii) shrinking the geode to the shape of the coral colony with the basis defined by the planar projection bounds (Fig. 2H ), thus obtaining an "enclosing shape" (Fig. 2G ); (iii) computing the "enclosing shape" volume (V e ) using MeshLab software; (iv) calculating the shelter volume (S) as the difference between the "enclosing shape" volume and the colony volume (V c ). The shelter volume (S = V e -V c ) represents the empty space within the enclosing shape and the coral colony volume (Fig.
Third, predictor intervals (the uncertainty for a single specific outcome) and confidence intervals, both at 95%, were computed by bootstrap method. In addition, leave-one-out cross validations (LOOCV) were performed to test the fitness of the predictive models. Data exploration and analyses were conducted with R sofware (R Core Team, 2019). To perform linear models, 'car'  (Fox and Weisberg, 2019)  and 'multicomp'  (Hothorn et al. , 2008)  packages were used. Figures were produced using 'ggplot2 ′  (Wickman, 2016)  and 'ggpubr'  (Kassambara, 2019)  packages.
Measurement of 2D metrics On the orthomosaics, each coral colony was manually delineated as a polygon in QGIS and classified by growth form (Fig. 4 ). Some growth forms were not included in our dataset (i. e. foliaceous, helmet-shaped, encrusting forms) and were excluded from further analyses. The surface of each polygon was calculated with the QGIS command: area ($geometry) in the field calculator tool. The maximum diameter was obtained for each polygon using the procedure presented for the coral colonies.
Application of shelter predictive models and calculation of descriptors at the reefscape scale Planar area and diameter of each polygon delineated on the reefscape orthomosaics were then used to feed the predictive model and compute the corresponding shelter volume of each coral colony; a function in R code was developed to automatize this calculation (see in data availability). The overall shelter capacity (i. e. volume of shelter calculated for the entire reefscape) was obtained by summing the shelter volume estimate for all polygons; this analysis was also performed automatically using a code created in R programming language (R Core Team, 2019). In addition, we investigated the distribution of shelter volumes by growth form in a reefscape by adapting the Shannon index to shelter provider colonies as follows: Shannon shelter index, SSI = - ∑ p i log(p i ) where pi = relative shelter volume of a given growth form.
Shelter quantification and predictive models Our training database comprised 3D models of 120 colonies: 52 branching, 26 massive, 25 tabular and 17 columnar. Taxonomically, the four growth forms were Acropora spp. and Pocillopora spp. for branching colonies, mainly Favia stelligera for columnar colonies, Porites spp. for massive colonies and Acropora hyacinthus for tabular colonies (Table  S1 ). While all growth forms were present at the three sites, most of the largest tabular colonies were found only in New Caledonia. We here present shelter predictive models based on diameter and planar area (Fig. 5 -right) and corresponding equations (Table  1 ); the predictive model based on colony surface is available in Supplementary materials (Table  S2 ). The LOESS smooth regressions of shelter volume versus each of the three metrics showed linear relationships until approximately 60 cm in diameter, 2,500 cm 2 in planar area (Fig.
Discussion We built predictive models of shelter volume provided by reef building corals from 2D metrics for four major growth forms. Training data for these metrics were obtained entirely from 3D models, reconstructed by photogrammetry of in situ coral colonies growing on fore reef slopes and shallow reef flats. The main outcome of this study was the ability to predict shelter volume, a 3D metric, from proxies like colony diameter, planar area or surface, which are 2D metrics. The accuracy of predictions was highest for planar area (87. 5%), followed by surface (87. 2%) and colony diameter (83. 1%). These proxies will make shelter volume estimation largely accessible and will be useful for managers and stakeholders in setting measurable targets for reef conservation adapted to local conditions.
Consequently, the planar areas of colonies and the shelter estimations were probably underestimated, particularly at Europa. Despite our knowledge of this possible bias, we were unable to investigate it. This points to two of our study's limitations, which needs to be considered in case of further correlation analyses with associated biodiversity. In addition, in order to improve predictions, morphologies of coral colonies growing under extreme hydrodynamic conditions (very high or very low tidal or wave-induced currents) and temperatures (extremely high or low temperatures) should be included in the database. Indeed, these two factors influence coral growth  (Lenihan et al. , 2015; Pu, 2016)  and may thus influence the shelter provided by coral colonies. In the present study, however, we observed that relationships between diameter (or planar area or surface) and shelter were consistent regardless of study site (Fig.
S5 ). Overall, the training database could be enriched with new measurements, encompassing other coral growth forms, to improve the robustness of the predictive models. Relationships between shelter volume and the colony diameter, planar area or surface correspond to the allometric growth of reef building corals: for all growth forms (branching, columnar, massive and tabular), the shelter volume is scaled to diameter to the power of 3 and to planar area to the power of ~1. 5. These allometric scaling rules indicate that shelter provision by the principal growth forms of reef building corals follow the same principles of biological design of multicellular organisms  (West et al. , 2002). Our results are consistent with the findings of  Dornelas et al. (2017) , demonstrating that reef building corals have allometric rather than isometric growth rates.
, 2017a; Agudo-Adriani et al. , 2019). The major conclusion of the study is that planar area and diameter of coral colonies are satisfactory proxies for estimating shelter volume. Since planar area is an accessible and commonly used metric in coral reef monitoring and diameter can be inferred from commonly surveys methods such as Line Intercept Method (e. g. Zawadaet al. , 2019a Zawadaet al. , , 2019b)) , shelter volume estimators have important potential applications for conservation purposes. Indeed, shelter volume quantification is feasible, especially with automated computation with a simple function in R code and could be used to estimate the shelter capacity of reefscapes in spatial and temporal surveys. Further analyses are needed to evaluate the 2D-3D relationships for other coral growth forms.
Acknowledgements The authors are thankful to  Sophie Bureau ,  Christophe Peignon ,  Mahé Dumas  and  Bertrand Bourgeois  for their help in data collection,  Claude Payri ,  Corina Iovan ,  Pascal Dumas  and  Veronique Perrin  ( IRD Nouméa, New Caledonia ) for logistic support and the captain and sailor of the research vessel Archamia, Philippe and Sam, for their help in fieldwork. We further thank the  CORCOPA program  funded through the  European Best2. 0 program  for invitation to collaborate, Ocean Inovation Tour and the crew of the sailing vessel Antsiva, Nicolas and  Anne Tisné ,  Odilon and Jonathan , for logistic support, and the  Terres Australes et Antarctiques Françaises (TAAF)  for research permits for the Europa mission. We also thank  Jane Ballard  for English revision of the manuscript and  Arnaud Vandecasteele  (Geolab) and  Alain Juif  (Creocean) for QGIS analyses advice and the inputs of  Julio A.
Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year.
Nevertheless, this method still requires in situ data over the target area in order to refine the source classification model. Therefore, for continuous and yearly mapping of irrigated areas with fewer in situ measurements, an operational methodology capable of overcoming the limitation of terrain data collection is required. The proposed methodology should be reproducible and applicable for several years. In the context of operational mapping of irrigated areas, this study proposes an operational semi-supervised framework for mapping irrigated areas at a plot scale that overcomes the limitation of the availability of in situ data. The proposed methodology is based on a pre-step of selecting irrigated and non-irrigated plots to be served as training data for building a classification model.
Field Campaigns In the study site, irrigation mainly occurs for spring and summer crops, which are generally sowed in April and May and harvested in September and October. For this reason, four field campaigns in the years 2017, 2018, 2019, and 2020 were conducted over the study site in June and July of each year to collect irrigation information. In the field campaigns, each plot was registered as either irrigated, if irrigation was in progress during the campaign date or irrigation materials exist on the plot with summer crop cycle, or non-irrigated if neither irrigation nor irrigation materials has been observed. Table  1  presents the number of terrain samples collected for each year. In 2020, a large database of 686 plots was registered. However, a total of 92, 127, and 116 plots were registered for the years 2017, 2018, and 2019 respectively.
The years 2017 and 2018 had the least percentage of non-irrigated plots from the total number of examined plots (approximately 28%). On the other hand, 49% and 42% of the collected plots were nonirrigated in the database for 2019 and 2020, respectively. 1 ). For the four years together, the average area of all the visited plots is 8. 0 ha. The area of the in situ plots for the four years varies between one ha and 48 ha. Seventy percent of the plots have a surface area between 1 and 10 ha, 21% of the plots have a surface area between 10 and 20 ha, and 9% of the plots have a surface area greater than 20 ha.
RPG Data The graphical parcel registry (RPG) is the French official graphical declaration system used by farmers, which provides an annual geo-localized representation of the agricultural parcels and their corresponding crop type. Covering around 26 million hectares, the RPG contains more than 6 million small parcels over the entire country. For each year, the corresponding RPG data were downloaded over the study site (Figure  1 ) (https://www. data. gouv. fr/en/datasets/, accessed on 1 July 2021) and used in this study. Among all the existing agricultural classes in the RPG, only classes corresponding to agricultural activities of summer and winter crops (including irrigated grasslands) were kept whereas vines and fruit trees were discarded. It is worth mentioning that the RPG data contain only the plot limits and their crop type and do not contain any information about irrigation.
Sentinel-1 SAR Data The high-resolution Level-1 ground range detected (GRD) product of the S1 satellite provides a 10 m × 10 m pixel spacing SAR image with a 6-day revisit time. However, with several crossing orbits of the S1, four SAR images could be obtained in the period of 6 days within a study site. Thus, 578 available S1 images over the study site from different orbit acquisitions have been downloaded for the four years via the Copernicus website (https://scihub. copernicus. eu/dhus/#/home, accessed on 1 July 2021). Figure  2  shows an example of the frequency of the four SAR images acquired from the four S1 orbits for the year 2020 in both ascending 'A' and descending 'D' modes. Images in the descending mode were acquired in the morning (~05h00 UTC) whereas images in the ascending mode were acquired in the evening (~17h00 UTC).
Sentinel-2 Optical Data For each year, temporal series of S2 optical images were downloaded from Theia website (https://www. theia-land. fr/, accessed on 1 July 2021) covering the period between February and October. This period largely fits the period of the irrigation of summer crops in the four years (usually between May and October). Optical S2 images were downloaded at a frequency of one to two images each month. The images downloaded from the French land data center (Theia) were calibrated for atmospheric correction and ortho-rectified for geometric correction (level 2A). Moreover, starting from 2018, Theia started providing monthly synthesized cloud-free S2 images (level 3A), which were also downloaded when available. The S2 images were mainly used to calculate the NDVI images used later in the training dataset selection criteria and the irrigation mapping classifier.
Overview The proposed methodology, later referred to as S 2 IM (Sentinel-1/Sentinel-2 Irrigation Mapping), consists of two major steps for mapping irrigated summer crops (Figure  3 ). In the first step, the irrigated/non-irrigated training plots are selected based on multi criteria derived from both SAR and optical data. The selection criteria of the training dataset are based on threshold values for the maximum attained NDVI for the plot during the studied period (calculated from S2 data), and an irrigation possibility weight at each plot obtained using the newly derived irrigation event detection model (IEDM) at plot scale  [38, 39]. After selecting the training dataset that corresponds to the plots deemed as irrigated and nonirrigated with a high confidence degree, the second step consists of implementing S1 data (radar backscattering coefficient at plot and grid scales), S2 data (NDVI), and the selected training plots into a random forest classifier to build a classifier for mapping irrigated areas (Irrigation Classifier).
Sentinel-2 Data Using each available S2 image, the average NDVI value at plot scale was obtained by averaging the NDVI pixels within each RPG parcel. The interior buffer of -10 m was also considered on the RPG plots to eliminate border pixels and plots with area less than 0. 1 ha were also not considered. Thus, an NDVI temporal profile is obtained for each plot in each year. The NDVI was first used as input in the IEDM, then in the selection criteria of the training dataset (maximum attained NDVI value), and finally as input data for the RF classifier.
Training Dataset Selection Criteria The selection of the irrigated/non-irrigated training plots from the RPG data is based on two threshold criteria fixed from S1 and optical derived metrics. Using the S1 data, an irrigation possibility weight was calculated for each plot by applying the IEDM on the several S1 temporal series in VV and VH polarizations. In addition, using the optical NDVI temporal profile, the maximum attained NDVI value at each plot was considered as additional selection metric.
Consequently, very low cumul ipw values could be evidence of the absence of irrigation events at the plot and very high cumul ipw values could be evidence of an intensively irrigated plot. Therefore, for a non-irrigated plot to be selected in the training dataset, the value of the cumul ipw should be very low to ensure that approximately no possible irrigations are detected on the plot. On the other hand, a high value of cumul ipw is required for a plot to be selected as an irrigated training plot to be sure that the plot corresponds to an irrigated plot.
Selection Criteria of Irrigated/Non-Irrigated Plots Based on the two calculated metrics, cumul ipw and max NDVI , the irrigated and nonirrigated plots for the training phase of the RF each year are selected. For the max NDV I metric, we consider that for the plot to be selected as a non-irrigated training plot, the max NDV I value must not exceed 0. 7 whereas the max NDV I value for a plot to be selected as irrigated training plot must exceed 0. 8. For the cumul ipw metric, a plot must have a low value of cumul ipw to be considered as a non-irrigated training plot. In this study, we consider that for the plot to be selected in the non-irrigated class of the training dataset, the cumul ipw value should be less than or equal to 25. The cumulative possibility weight of 25 was considered as a very low value indicating a very low number of detected irrigation events by the IEDM at the plot during the whole crop season.
Thus, the value greater than or equal to 250 ensures that a sufficient number of irrigation events are detected on the plot. Finally, the selection of the training dataset for each class was fixed by combining both metrics (max NDV I and cumul ipw ). This means that a plot is considered as "irrigated training plot" if the max NDV I ≥ 0. 8 and the cumul ipw ≥ 250 simulatenously. In contrast, a plot is selected as "non-irrigated training plot" if the max NDV I ≤ 0. 7 and the cumul ipw ≤ 25. For each of the four years (2017, 2018, 2019, and 2020), an independent training dataset of irrigated and non-irrigated plots has been selected from the RPG data using the explained selection criteria. This means that for each year, a corresponding classification model could be obtained.
[15]  demonstrated that using statistical metrics derived from S1 SAR series at plot scale in a random forest classifier leads to a good accuracy for mapping irrigated areas. Following these studies, the RF classifier was used for mapping irrigated areas using σ 0 p , σ 0 G , and NDVI temporal series (Figure  3 ). σ 0 p , σ 0 G , and NDVI temporal series of the training dataset derived from the previously explained selection criteria (Section 3. 4) were used to train the RF classifier. It is worth mentioning that for each year, an RF classifier was developed using the training dataset corresponding to each year (2017, 2018, 2019, and 2020).
Validation and Assessment Phase To assess the accuracy of the obtained RF classifier in each year, we used the in situ terrain dataset of irrigated/non-irrigated plots (Section 2. 2) as a validation dataset. This means that, for each year, the corresponding RF classifier, built using the selected training dataset, was applied on the in situ terrain plots to predict whether each plot is irrigated or not. From the obtained confusion matrix between predicted and in situ labels, the accuracy of the RF classifiers could be assessed each year. The classifier accuracy was evaluated using several accuracy metrics including the overall accuracy (OA), the weighted F_score (F_score), the F_score of the irrigated class (F_score_Ir), and the F_score of the non-irrigated class (F_score_Nir). The OA shows the percentage of correctly classified plots to the total number of plots while the F_score presents the harmonic mean between the precision and the recall of each class.