dataesr/scientific-paragraphs-categorization huggingface dataset:
| Allometry analysis by genus and ancestral state reconstruction As shown in previous studies of Cetacea (e. g. ,  Lanzetti et al. , 2022) , skull allometry can vary significantly across taxa. Therefore, we first tested differences in allometry among genera and obtained taxon-specific regression parameters. We again used the "procD. lm" functions, adding genus as a covariate, to reconstruct the separate allometric regressions, and tested pairwise differences between the slopes using "pairwise" in the package "RRPP" v. 1. 1. 2 (Collyer & Adams, 2021). We then used these extracted regression parameters (slopes and intercepts) to estimate the ancestral allometries and reconstruct the polarities of any heterochronic shifts  (Alberch et al. , 1979)  following  Morris et al. (2019)  and  Lanzetti et al.
(2022). Using the phylogeny from  McGowen et al. (2020) , we calculated ancestral slope and intercepts parameters for the nodes with the "fastAnc" function and mapped the character changes on the tree with the "contMap" function, both implemented in "phytools" v. 1. 0 (Revell, 2012). The package "emmeans" v. 1. 7. 2  (Lenth, 2022)  was used to assess significant changes in the allometric slopes between ancestral nodes and extant taxa. Finally, to better visualize the changes in ontogeny in the ancestral nodes within Mysticeti, we generated ancestral shapes for the prenatal and postnatal stages separately by conducting a phylogeny-corrected PCA (phyloPCA) in "geomorph. " We first calculated the mean shape for each taxon for the prenatal (early fetal and late fetal/neonate) and postnatal (juvenile and adult) stages and then used them to conduct two separate phyloPCAs.
| Conserved allometric patterns in baleen whales Based on the distribution of shape variation in the PCA plots, as well as the consistent clustering by growth stages, we expected to find distinct allometric patterns for T A B L E 1 Disparity in skull shape among Mysticeti genera, in raw data and allometry residuals. each taxon, but only significant differences in growth between lineages with distinct feeding modes and skull morphologies. Large and small Balaenoptera species and Megaptera display similar growth trends, while the other taxa with more divergent morphologies (Eschrichtius, Balaena, and Caperea) are characterized by a slightly higher slope (Supporting Information: Figure  S5  and Table  S8 ). There were no significant pairwise absolute or angular differences between slopes in the mysticete data set, potentially reflecting the limited sample sizes available for most taxa.
Raw data To assess which heterochronic processes underlie mysticete cranial evolution of the clade, we estimated the ancestral slope and intercept values for all the taxa in the data set. We found an inverse relationship between the slopes and intercepts values, with increases in slope accompanied by a reduction in intercept value and vice versa (Supporting Information: Figure  S6a, b ). The toothed whales used as outgroup appear to have a significantly lower growth rate than Mysticeti, but the difference is small overall and may reflect the smaller size of the odontocete taxa (Supporting Information: Figure  S7  and Tables  S8  and S9 ). Looking specifically among baleen whales, we found a consistent pattern in line with our hypothesis (Figure  3 ). Skim feeders Balaena and Caperea have slightly accelerated growth compared to their ancestors (nodes 1 and 2), with differences between the nodes and Balaena also being significant.
ACKNOWLEDGMENTS Without natural history collections and curators, this study would not have been possible. In particular, the authors would like to thank the curators and staff at the following museum and CT facilities for allowing us to collect the data needed for this project:  Mike Van Patten  and  John Hansen  at  San Diego State University, San Diego, CA, USA (SDSU) ;  Phil Unitt  and  Tom Deméré  of the  San Diego Natural History Museum, San Diego, CA, USA (SDNHM) ;  Daniel Klingberg  and  Morten Tange Olsen of the Natural History Museum of Denmark in Copenhagen, Denmark (ZMUC) ;  Chiara Villa  at the  Laboratory of Biological Anthropology of the University of Copenhagen, Copenhagen, Denmark ;  Matt Colbert  and  Jessie Maisano  at the  University of Texas High-Resolution X-ray CT Facility in Austin, TX, USA (UTCT) ;  Yuko Tajima ,  Tadasu Yamada ,  Naoki Kohno ,  Kumiko Matsui , and all the graduate students at the  National Museum of Science in Tsukuba, Japan (NSMT) ; and  Daisuke Hasegawa  and the rest of the staff at  Nippon Veterinary  and  Life Science University in Tokyo, Japan.
The authors would also like to thank  Brett Clark  from the  Natural History Museum, London, UK (NHMUK)  for helping with CT scanning of specimens from the  NHMUK. For sharing CT data of specimens from the  Smithsonian Institution in Washington, DC, USA (USNM) , the authors thank  Michael McGowen  and  Janine Hinton. For sharing the data of specimens at the  Museum of Zoology at the University of Cambridge (UMZC) , the authors thank  Robert Asher. The authors also thank  Emily Buchholtz  from  Wellesley College in Wellesley, MA, USA ,  Eric G. Ekdale  from  SDSU ,  Ellen Coombs  from  USNM , and  Giacomo Franci  for providing CT and surface data of specimens. This manuscript was uploaded as a preprint on BiorXIV (https://doi. org/10. 1101/2022. 11. 29. 518348 ). This project was funded by the  Marie Skłodowska-Curie Individual fellowship  awarded by the  European Commission  to A.
DATA AVAILABILITY STATEMENT The surface files will be uploaded to the open-access repository Phenome10K (https://www. phenome10k. org/) after publication. Landmarks, classifiers, and other data necessary for analyses, as well as all the code used, are uploaded to GitHub (https://github. com/AgneseLan/baleenallometry) and Zenodo (https://doi. org/10. 5281/zenodo. 7963462).
Acknowledgements This work has been funded by  SNCF Réseau-CNRS-Université Pierre et Marie Curie - Paris6-Sorbonne Université  research contracts  C13/411 ,  C15/0781 ,  SNCF Réseau-CNRS-Sorbonne Université  research contract  C21/1995  and the convention  ANRT -CIFRE 2021/0789. The geophysical equipment was provided by the  METIS laboratory at Sorbonne Université. The authors thank  SNCF Réseau local  teams for there great help and support during field works. Seismic data processing has been performed thanks to open-source software packages: SWIP (github. com/SWIPdev/SWIP/releases), Seismic Unix (github. com/ JohnWStockwellJr/SeisUnix) and GEOPSY (geopsy. org). The authors thank  A-M. Tang ,  J. -M. Terpereau ,  P. Leroux ,  A. Robinet  and  S. Nebieridze  for their advices and support during the study, as well as valuable discussion for the results interpretations.
Abstract. Very high-resolution ( ∼ 10-100 m) surface soil moisture (SM) observations are important for applications in agriculture, among other purposes. This is the original goal of the S 2 MP (Sentinel-1/Sentinel-2-Derived Soil Moisture Product) algorithm, which was designed to retrieve surface SM at the agricultural plot scale by simultaneously using Sentinel-1 (S1) backscatter coefficients and Sentinel-2 (S2) NDVI (Normalized Difference Vegetation Index) as inputs to a neural network trained with Water Cloud Model simulations. However, for many applications, including hydrology and climate impact assessment at regional level, large maps with a high resolution (HR) of around 1 km are already a significant improvement with respect to most of the publicly available SM datasets, which have resolutions of about 25 km.
In this study, the S 2 MP algorithm was adapted to work at 1 km resolution and extended from croplands to herbaceous vegetation types. A target resolution of 1 km also allows the evaluation of the interest in using NDVI derived from Sentinel-3 (S3) instead of S2. Two sets of SM maps at 1 km resolution were produced with S 2 MP over six regions of ∼ 10 4 km 2 in Spain, Tunisia, North America, Australia, and the southwest and southeast regions of France for the whole year of 2019. The first set was derived from the combination of S1 and S2 data (S1 + S2 maps), while the second one was derived from the combination of S1 and S3 (S1 + S3 maps). S1 + S2 and S1 + S3 SM maps were compared to each other, to those of the 1 km resolution Copernicus Global Land Service (CGLS) SM and Soil Water In-dex (SWI) datasets, and to those of the Soil Moisture Active Passive (SMAP) + S1 product.
1 m 3 m -3. The S1 + S3 maps show a lower SDD with respect to CGLS maps (≤ 0. 06 m 3 m -3 ) than with respect to the SMAP + S1 maps (≤ 0. 1 m 3 m -3 ) for all the six domains. Finally, all the HR datasets (S1 + S2, S1 + S3, CGLS, and SMAP + S1) were also compared to in situ measurements from five networks across five countries, along with coarseresolution (CR) SM products from SMAP, SMOS, and the European Space Agency Climate Change Initiative (CCI). While all the CR and HR products show different bias and SDD, the HR products show lower correlations than the CR ones with respect to in situ measurements. The discrepancies in between the different HR datasets, except for the Published by Copernicus Publications on behalf of the European Geosciences Union.
, 2021). The evaluation of climate models and the assessment of climate change impacts at a regional level would also benefit from a higher resolution than that of the current generation of coarse-resolution sensors. In addition, other applications in hydrology, agriculture, and risk assessment require even higher resolutions of ∼ 1 km  (Massari et al. , 2021). Downscaling the coarse-scale-resolution data by merging them with higher-resolution data is a possibility for the achievement of high-resolution SM datasets. For example, high-resolution SM estimates can be derived from visible/infrared  (Merlin et al. , 2012)  or synthetic-aperture radar (SAR;  Tomer et al. , 2016; Das et al. , 2019)  measurements. SAR observations alone have also been tested to estimate SM using different frequencies and instruments such as RADARSAT, ALOS-L, or TerraSAR-X.
Three operational high-resolution (HR) SM datasets at 1 km resolution using Sentinel-1 (S1) exist, such as the S1 SM and Soil Water Index (SWI) products from the Copernicus Global Land Service (CGLS;  Bauer-Marschallinger et al. , 2018 , 2019)  and the SMAP + S1 downscaled product  (Das et al. , 2020). SM estimates at a very high resolution (10 m scale) at some locations in Europe (and in Lebanon and Morocco) over croplands are also distributed by the French continental surfaces data center (THEIA; https://www. theia-land. fr, last access: November 2021). In contrast to the CGLS datasets, the THEIA SM dataset is obtained using synergistic S1 and Sentinel-2 (S2) measurements as inputs to the Sentinel-1/Sentinel-2-Derived Soil Moisture Product (S 2 MP) algorithm  (El Hajj et al. , 2017). This dataset has been evaluated against in situ measurements in comparison to SMAP, SMOS, and ASCAT coarse-resolution (CR) datasets  (El Hajj et al.
, 2018)  and with respect to the CGLS S1 SM dataset  (Bazzi et al. , 2019) , both in the south of France. The S 2 MP SM estimates not only showed the lowest unbiased root mean squared errors with respect to in situ measurements but also a moderate correlation, lower than that obtained for SMAP and ASCAT datasets  (El Hajj et al. , 2018). In this region, the S 2 MP SM showed better performances with respect to in situ measurements than the CGLS SM for the classical metrics  (Bazzi et al. , 2019). Taking into account the importance of having accurate HR large-scale SM datasets, in this study the S 2 MP algorithm was extended to provide SM estimates over both croplands and herbaceous vegetation at 1 km resolution, which also allowed the use of the Sentinel-3 (S3) NDVI (Normalized Difference Vegetation Index) instead of S2.
Two sets of SM maps at 1 km resolution were produced with the S 2 MP algorithm over six domains of ∼ 10 4 km 2 in Tunisia, North America, Spain, Australia, and the southwest and southeast of France (Fig. 1a ). One set is based on the combination of S1 and S2 measurements (S1 + S2 maps), while the other is based on the combination of S1 and S3 measurements (S1 + S3 maps). The S 2 MP S1 + S2 and S1 + S3 maps were compared to those provided by the SMAP + S1 product and those from the CGLS SM and SWI datasets from January to December 2019. The comparison was carried out on a per-pixel basis, and the results were analyzed according to pixel homogeneity for areas covered by croplands and herbaceous vegetation. In addition, the HR time series were evaluated against in situ measurements along with those of the coarser-resolution SM datasets from SMAP, SMOS, and ESA CCI.
The paper is structured as follows. Section 2 presents the different remotely sensed and ground-based data that are used in this study. Section 3 describes the methodology used to estimate SM over croplands and herbaceous regions using S1 + S3. Section 4 shows the S1 + S3 S 2 MP maps and time series. They are also compared to the S1 + S2 maps in addition to other HR datasets, coarse-resolution datasets, and in situ measurements. Section 5 discusses the interest of the S 2 MP algorithm modifications and the remaining challenges for large-scale HR SM mapping. Section 6 draws the conclusions of the study.
The S1 images (10 m × 10 m) were acquired in the interferometric wide (IW) swath imagining mode, with VV (vertical transmit, vertical receive) and VH (vertical transmit, horizontal receive) polarizations, and the S1 Toolbox (S1TBX) developed by the ESA was used to calibrate the images. This calibration aims to convert digital number values from S1 images into backscattering coefficients (σ • ) in a linear unit and orthorectifying the images using the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) at 30 m spatial resolution. A database of S1 images, available from January to December 2019, was created for each region of study. The databases contain S1 images acquired both in ascending (afternoon) and descending (morning) modes.
SMAP products SMAP provides passive measurements of 1. 4 GHz brightness temperatures in vertical and horizontal polarizations at a fixed incidence angle of 40 • , with a resolution of ∼ 45 km  (Entekhabi et al. , 2014). SMAP ascending and descending orbits cross the Equator at 18:00 and 06:00 LT, respectively, and the maximum revisit period is 3 d. Several HR and CR SM datasets from SMAP were used for the evaluation of the S 2 MP maps. i. The SMAP L3 V6 SM product (hereafter SMAPL3). It is a daily gridded composite of the SMAP L2 V5 SM files  (O'Neill et al. , 2018 (O'Neill et al. , , 2019b)). Only SM estimates derived from L1C brightness temperatures  (Chan et al. , 2018)  using the single-channel algorithm V-polarization  (Entekhabi et al. , 2010)  were considered. SMAP L3 data are sampled at 36 km resolution.
SMOS The SMOS mission  (Kerr et al. , 2010)  carries a passive interferometric radiometer operating at the L band (21 cm, 1. 4 GHz), with a spatial resolution of 25-50, depending on the position on the field of view (43 km on average). The following CR SM datasets from SMOS were used in this study: i. The CATDS SMOS L3 V7 SM product (hereafter SMOSL3), which is a multi-orbit SM product, provided by the Centre Aval de Traitement des Données (CATDS), with a grid resolution of 25 km  (Al Bitar et al. , 2017). The SM retrieval process is based on the algorithm used for the SMOS L2 product  (Kerr et al. , 2012)  but simultaneously using three orbits within a 1week period to better constrain the SM and optical depth estimations. ii. The ESA SMOS Near-Real Time (NRT) Neural Network (NN) V2 SM product (hereafter SMOSNRT), provided on the icosahedral equal area grid (ISEA4H9), with 15 km resolution  (Rodríguez-Fernández et al.
3. 3. The different in situ stations can be located using Fig. 1a  and b. 3 Methods 3. 1 Building S 2 MP maps using NDVI derived from Sentinel-3 The S 2 MP algorithm  (El Hajj et al. , 2017)  was originally designed to estimate surface SM at the scale of agricultural plots (10 m resolution) using synergistically data derived from the S1 radar signal and the S2 optical images as input to a neural network. The neural network was first trained using a synthetic database gathering (i) SAR C-band backscatter coefficients in the VV polarization, (ii) incidence angles (from 20 to 45 • ), and (iii) NDVI as input and SM examples as a target. This synthetic database was built using a Water Cloud Model  (Baghdadi et al. , 2017)  combined with an Integral Equation Model  (Baghdadi et al. , 2006 (Baghdadi et al.
, , 2011) )  that was specially modified and optimized for this application. Then, the S 2 MP algorithm was applied to a real database gathering the SAR backscatter coefficient in VV polarization from S1, the incidence angle of the SAR acquisitions, and the NDVI derived from optical images taken by S2 as follows. First, the NDVI was computed at 10 m spatial resolution (native resolution of S2) using the atmospherically and orthorectified S2 images. To overcome the cloud cover issue present in optical images, a gap-filling procedure was performed using the linear interpolation to obtain two cloudfree NDVI images per month (1st and 15th of each month). To derive S2 NDVI data at the S1 acquisition dates, a linear interpolation was performed for each S2 pixel, using the NDVI values corresponding to the closest S2 images acquired before and after the S1 date.
Then, the neural network was only applied over the 1 km 2 pixels that are partly or entirely covered by croplands and/or herbaceous vegetation. The objective was to assess the impact of using a lower spatial resolution NDVI as input to the S 2 MP algorithm. Hereafter, S 2 MP S1S2 and S 2 MP S1S3 will refer to the S 2 MP SM datasets derived based on the synergistic use of S1 + S2, and S1 + S3 measurements, respectively. S 2 MP S1S2 and S 2 MP S1S3 were produced from January to December 2019. For both S 2 MP S1S2 and S 2 MP S1S3 , it is important to highlight again that there is no SM estimate available over 1 km 2 pixels not covered at all by croplands and herbaceous vegetation. However, as long as there is a fraction of croplands and/or herbaceous vegetation (whatever the amount) within the pixel, SM values are provided.
Dataset comparisons Comparisons between datasets and evaluations against in situ measurements were done from January to December 2019. In a first step, the S 2 MP S1S2 and S 2 MP S1S3 maps were compared on a per-pixel basis for each region in terms of a Pearson correlation (R), bias, and standard deviation of the difference (SDD; also referred to as the unbiased root mean square of the difference by some authors). The metrics for which the P value exceeded the threshold of 5 % (interval of confidence of 95 %) were discarded. In a second step, S 2 MP S1S3 values were compared to the three HR datasets described in Sect. 2, namely CoperSSM, CoperSWI, and SMAPS1. This analysis was also performed by computing R, bias, and SDD on a per-pixel basis for each region (all the HR datasets are sampled on the same 1 km regular grid).
In addition, the metrics were analyzed as a function of croplands and herbaceous vegetation coverage over 1 km 2 pixels. The metrics for which the P value exceeded the threshold of 5 % were discarded. In a third step, all the different CR and HR datasets were evaluated against the in situ measurements available in the six regions of study. To perform the analysis under optimal conditions, only morning orbits from SMOS (ascending overpasses) and SMAP (descending overpasses) within this time period were considered. During the night and early in the morning, the soil is in thermal balance, meaning that the vegetation temperature is equal to the soil temperature. During the afternoon, the balance is lost, and the vegetation temperature is closer to the air temperature, leading to satellite estimates of lower quality.
This is often reflected by lower performances against in situ measurements for the afternoon SM estimates compared to those of the morning  (Leroux et al. , 2014). For each ground station, the closest time series from each remotely sensed dataset was compared to the in situ measurements by computing R, bias, and SDD. Only samples for which the difference in acquisition time with the in situ measurements does not exceed 1 h were taken into account to compute those statistical metrics. Metrics for which the corresponding P value exceeded the threshold value of https://doi. org/10. 5194/hess-27-1221-2023 Hydrol. Earth Syst. Sci. , 27, 1221-1242, 2023 5 % were discarded. This implies that only in situ locations where all the comparisons between the remotely sensed and in situ time series showing significant metrics were considered for the assessment (Table  1 ).
This concretely means 13 in situ time series for the Spanish region, 3 in the southwest of France, 0 in the southeast of France, 10 for the Australian region, 15 in North America, and 2 in Tunisia. Then, remotely sensed time series of anomalies in a 35 d time window were also compared to those of the in situ measurements in terms of R. They were derived as follows: SM a t = (SM t -µ t ) /σ t µ t = 1/N × t 2 n=t 1 SM n σ t = 1/(N -1) × t 2 n=t 1 (SM n -µ) 2 , (2) where SM a t and SM t are the SM and anomalies values at time t, respectively. N is the number of observations from t minus 17 d (t 1 ) to t plus 17 d (t 2 ). Finally, the HR datasets (S 2 MP S1S2 , S 2 MP S1S3 , Cop-erSSM, CoperSWI, and SMAPS1) were evaluated against in situ measurements (R, bias, and SDD with P values below 5 %) after aggregation at 25 km resolution (same grid as that of CCISM) in order to compare their performances to the CR data at a comparable resolution.
S 2 MP S1S3 comparison to S 2 MP S1S2 Figure  4  shows R, bias and SDD between S 2 MP S1S2 and S 2 MP S1S3 for the six study regions. A very good agreement between the two datasets was found in all the regions with R ≥ 0. 9, bias ≤ 0. 04 m 3 m -3 (S 2 MP S1S3 minus S 2 MP S1S2 ), and SDD ≤ 0. 03 m 3 m -3 for most of the areas. However, some differences in terms of bias can be seen between the two datasets in the northwest of the Spanish region (Fig. 4c ), in the areas with significant forests cover in the region southwest of France (Fig. 4i ), and in narrow areas of the Tunisian region (Fig. 4f ). The somewhat higher differences in the Spanish region and in the region southwest of France are seen over pixels covered by forests, with a small fraction of croplands and herbaceous vegetation (see the Spanish region in Fig.
1b  and c ). The somewhat larger differences in some narrow areas of Tunisia are due to heterogeneous land cover around several river basins with rolling topography, sparse forests, and grasslands. As discussed above, these small differences were expected due to the differences seen between S3 and S2 NDVI (Sect. 4. 1) and the different way of aggregating the S1 backscatter coefficients (Sect. 3. 1). However, taking into account the overall very good agreement between S 2 MP S1S2 and S 2 MP S1S3 , for the sake of simplicity and clarity, in Sect. 4. 3 only S 2 MP S1S3 is compared to the other HR datasets.
General comparison of S 2 MP S1S3 against the HR SM datasets Figures 5-7 present the comparison of S 2 MP S1S3 against CoperSSM, CoperSWI, and SMAPS1 over the six study regions in terms of bias, SDD, and R, respectively. Some diagonal structures can be seen in the maps comparing S 2 MP S1S3 to CoperSSM in Spain and in the southwest of France. These artifacts, most pronounced in the correlation maps and also present in the bias and SDD maps, come from the CoperSSM data, as previously discussed by  Bazzi et al. (2019). Indeed, the artifacts are seen on the sub-swaths of the S1 product, showing a big difference between the SM estimations in the CoperSSM at the same SM estimation date. Bazzi et al. (2019)  showed that the difference in the SM estimation at both sides of the sub-swath at a given date of the CoperSSM map can reach 0.