dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of the order of magnitude According to Fig. 5 , S 2 MP S1S3 shows a bias in the range from -0. 1 to 0. 1 m 3 m -3 with respect to the other HR products over most of the pixels within the six regions of study. However, there are areas in the domains of Spain, Tunisia, and southeast of France where S 2 MP S1S3 shows a dry bias of an absolute value larger than 0. 1 m 3 m -3. This is also particularly the case in the southwest of France, with respect to CoperSSM and CoperSWI, and in Australia and North America, with respect to SMAPS1n. For these regions and HR datasets, the bias is negative over the whole area. For all the other combinations of regions and HR products, the bias values are both positive and negative. In general, there is no clear relationship between the sign of the bias and the dominant land cover class.
However, in the case of the comparison between S 2 MP S1S3 and SMAPS1 in the region in the southwest of France, the bias distribution is split in two (Fig. 5i ). A wet bias is observed in the western part of the region, corresponding to forests areas with low fractions of croplands and herbaceous vegetation, while a dry bias is found in the eastern part, corresponding to areas dominated by croplands (Fig. 1b  and c ). In addition, the dry bias observed in the eastern part of the Tunisian region corresponds to an area of salted lakes, whose water and moisture contents can vary significantly according to climate. Figure  6  shows that the SDD values of S 2 MP S1S3 , with respect to CoperSSM and CoperSWI, are lower than 0. 06 m 3 m -3 over almost all of the pixels within the four regions for which the Copernicus datasets are available.
Higher values close to 0. 08-0. 10 m 3 m -3 between S 2 MP S1S3 and CoperSSM are found in the southwestern part of the southwest region of France. The SDD obtained between S 2 MP S1S3 and SMAPS1 are comparable to those obtained with respect to the Copernicus datasets. However, values reaching 0. 08-0. 12 m 3 m -3 are more often found, in particular, in the western part of the Spanish region and are sparse in the southwest and southeast of France. In Australia and North America (Fig. 6m  and n ), the SDD with respect to SMAPS1 is quite similar to those found in Spain, Tunisia, and France (Fig. 6c, f, i  and l ), where values can reach ∼ 0. 08-0. 12 m 3 m -3 in the southeastern and western parts of Hydrol. Earth Syst. Sci. , 27, 1221-1242, 2023 https://doi. org/10. 5194/hess-27-1221-2023 the Australia and North America regions, respectively.
In general, high R values are found in Spain (0. 54-0. 63), moderate values (0. 28-0. 63) are observed in France, and low values are found in Tunisia (0. 37-0. 38). In addition, R values obtained in Australia and North America with respect to SMAPS1 are comparable to those found in Spain. Absolute bias between S 2 MP S1S3 and the three HR datasets in Spain (0. 01 m 3 m -3 ) are lower than or similar to those found in the other regions. The strongest absolute bias is observed in North America, with respect to SMAPS1, with 0. 07 m 3 m -3. According to the SDD values, no particular trend is observed over the six regions of study, and values range from 0. 04 to 0. 09 m 3 m -3. For most of the regions and comparisons, the correlation values significantly increase (+0. 05-0. 1) over pixels that contain at least 75 % of croplands.
In overall, absolute bias and SDD val- ues remain similar, but sometimes there is a slight decrease (-0. 01 m 3 m -3 ). Taking into account pixels where herbaceous vegetation represent less than 75 % of the area, S 2 MP S1S3 is only better correlated to CoperSSM in Spain and in the southwest of France. In general, high R values are found in Spain (0. 64-0. 67), moderate values (0. 36-0. 69) are observed in France, and low values are found in Tunisia (0. 32-0. 36). In addition, R values obtained in Australia and North America with respect to SMAPS1 are comparable to those found in the southwest of France. Absolute bias between S 2 MP S1S3 and the three HR datasets in Tunisia (0. 01-0. 02 m 3 m -3 ) are lower than or similar to those found in the other regions. The strongest absolute biases are observed in the southwest of France, with 0.
09 m 3 m -3 for CoperSWI and SMAPS1, respectively. The SDD is higher with respect to SMAPS1 and can reach 0. 07, 0. 08, and 0. 10 m 3 m -3 in Tunisia, Spain, and in the southwest of France, respectively. In contrast with croplands, the correlation values do not systematically increase over pixels that contain at least 75 % of herbaceous vegetation. There is no significant change concerning the SDD, and overall, the absolute bias increases with respect to almost all of the HR datasets (+0. 01-0. 04 m 3 m -3 ). It is noteworthy that the bias is significantly higher over herbaceous vegetation than over croplands in Spain and in the southwest of France, while the SDD is quite similar, regardless of the region of study. In addition, higher R values are found over herbaceous vegetation in Spain.
Evaluation against in situ measurements Table  3  presents the evaluation of the different CR and HR SM products with respect to in situ measurements in terms of bias, SDD, and R of the original time series, as well as the Pearson correlation of the anomalies' time series (R a ). In addition, Fig. 8  shows examples of the time series of the different HR and CR datasets at six in situ stations used in this study (one for each region). The highest absolute biases with respect to in situ measurements are obtained for S 2 MP S1S2 and S 2 MP S1S3 , https://doi. org/10. 5194/hess-27-1221-2023 Hydrol. Earth Syst. Sci. , 27, 1221-1242, 2023   with -0. 06 m 3 m -3 , closely followed by CoperSWI, with 0. 05 m 3 m -3. SMOSNRT, SMOSL3, CCISM, and SMAPS1 show the lowest absolute biases, with 0. 03 m 3 m -3.
The highest SDDs with respect to in situ measurements are obtained for CoperSSM (0. 08 m 3 m -3 ) and SMOSL3 (0. 07 m 3 m -3 ). The other datasets show comparable SDDs, with 0. 05 or 0. 06 m 3 m -3. - - - CoperSSM - - - - - - - - - - - - Australia CoperSWI - - - - - - - - - - - - SMAPS1 0 - - - - - - - - - - - - North America CoperSWI - - - - - - - - - - - - SMAPS1 0 In general, higher correlation values are obtained for the CR data (0. 67-0. 77) than for the HR data (0. 53-0. 74). The lowest correlations are found for the Sentinel-only HR datasets, with 0. 53 for CoperSSM, 0. 56 for S 2 MP S1S3 , and 0. 59 for S 2 MP S1S2. Concerning the HR data obtained from merging approaches, SMAPS1 still shows a value lower than the CR datasets, with 0. 64, but CoperSWI shows the third-best value, with 0.
74, following SMAPL3E (0. 77) and SMAPL3 (0. 76). Regarding the correlation of the anomalies time series, SMAPL3E and SMAPL3 obtain the highest R a with respect to in situ measurements, with 0. 59 and 0. 58. CoperSSM, S 2 MP S1S2 , SMAPS1, and S 2 MP S1S3 show the lowest R a , with 0. 18, 0. 36, 0. 35, and 0. 37, respectively. The CR time series have a temporal revisit roughly 5 times higher than those from S 2 MP S1S2 , S 2 MP S1S3 , CoperSSM, and SMAPS1 (Fig. 8 ). In order to understand if the low tem-poral revisit of the HR data affects their performances against in situ measurements, one observation out of five was removed from the CR time series, and the metrics were recomputed (not shown). However, no significant differences in terms of R, bias, and SDD were found. The performances of the two Sentinel-only HR datasets averaged at 25 km resolution (S 2 MP * S1S2 , S 2 MP * S1S3 , and CoperSSM * ) with respect to in situ measurements are comparable to the performances obtained for the original 1 km datasets (S 2 MP S1S2 , S 2 MP S1S3 , CoperSSM, and CoperSWI * ).
In contrast, for the SMAPS1 * dataset, which is a downscaled product, the correlation increases from 0. 64 at 1 km resolution (SMAPS1) to 0. 79 at 25 km resolution, which is the highest correlation found among all the datasets. In addition, the SDD and bias slightly decrease. R a also increases from 0. 35 at 1 km resolution to 0. 44 at 25 km resolution, but it does not reach the values of R a obtained for the SMAP-only products (SMAPL3 and SMAPL3E with 0. 58-0. 59).
Discussion S 2 MP, S 2 MP S1S3 , CoperSSM, CoperSWI, and SMAPS1 are all HR datasets that were produced with different ap-Hydrol. Earth Syst. Sci. , 27, 1221 Sci. , 27, -1242 Sci. , 27, , 2023  https://doi. org/10. 5194/hess-27-1221-2023 2017 ) was only providing SM estimates over croplands at 10 m resolution, using NDVI derived from S2 optical images. In the framework of this study, the algorithm has been extended to provide SM estimates at 1 km resolution, also over herbaceous vegetation areas, and S2 was replaced by S3. However, despite the different ways of aggregating the S1 radar signal and the differences between S3 and S2 NDVI (Sect. 4. 1 with Figs. 3  and 2 ), S 2 MP S1S2 and S 2 MP S1S3 are in very good agreement over the six study regions (Sect. 4. 2 with Fig. 4 ). Thus, these results imply that it is possible to replace S2 with S3 in the S 2 MP approach without losing skill.
7. NDVI above this value corresponds to well-developed vegetation, and even if it is more common to have NDVI lower than 0. 7 with S3 than with S2 (Fig. 2 ) due to averaging effects, using S3 NDVI does not solve the problem. Indeed, in the particular cases of well-developed vegetation, the problem does not arise from the S2 of S3 NDVI itself but from the C-band SAR signal, which fails to penetrate the vegetation cover. Regarding the comparisons between the HR datasets in Sect. 4. 3, S 2 MP S1S3 shows a temporal dynamic closer to those of CoperSSM, CoperSWI, and SMAPS1 over semidry areas such as in Spain, North America, and Australia. However, the correlation drops significantly over very dry zones (Tunisia). Over semi-humid areas (France), the tem-poral dynamic between S 2 MP S1S3 and CoperSSM is in better agreement than with respect to CoperSWI and SMAPS1.
The order of magnitude (bias and SDD) between S 2 MP S1S3 and the other HR datasets is quite similar, regardless of the study region, the land cover, or the climate zone (rather dry or humid). In addition, it is noteworthy to highlight that differences in terms of bias and SDD are not systematically reduced over homogeneous pixels covered by croplands or herbaceous vegetation. This means that inherent biases exist between the algorithms, and they might persist over other land cover classes such as over forests (if the S 2 MP approach is extended to forests). At the moment, by construction, the S 2 MP algorithm starts being out of its application domain when considering pixels dominated by forest cover. This is also the case for the change detection approach used to produce CoperSSM. Indeed, the SM indices computation does not currently account for vegetation dynamics, which can lead to biases over areas covered by seasonal and dense vegetation.
In addition, for most applications, the CoperSSM indices and those from CoperSWI should be transformed into SM time series, and this will be problematic without reference SM values under forests to scale them. Therefore, an extension of the S 2 MP algorithm to forest areas would definitely be interesting to provide HR SM mapping over large regions inside and outside of Europe. In this study, most of the SM measurements used from the ground stations were representative of croplands and herbaceous regions. Hence, the relative performances of the HR datasets (S 2 MP, CoperSSM, CoperSWI, and SMAPS1) were not assessed over dense vegetation areas. In addition, slightly better results of S 2 MP S1S3 , with respect to in situ measurements, compared to those of Cop-erSSM were found, except for the bias (Table  4 ).
SM estimates using the S 2 MP algorithm were already evaluated against in situ measurements, along with other  HR and CR datasets, by El Hajj et al. (2018)  and by  Bazzi et al. (2019 ). In El Hajj et al. (2018) , the authors found that S 2 MP S1S2 shows a lower correlation with respect to in situ measurements than SMAPL3 and SMAPL3E but higher than SMOSNRT, SMOSL3, and SMAPS1. In contrast, in the current study, the S 2 MP S1S2 shows a lower correlation with in situ measurements than all the other HR and CR products. However, in El Hajj et al. (  2018 ), SM estimates from the S 2 MP algorithm were only derived over croplands, while in our study, the SM estimation was performed both over croplands and herbaceous vegetation. Moreover, their analysis was only carried out in the south of France during a different time period (from January 2016 to June 2017).
In  Bazzi et al. (2019) , the authors found that S 2 MP S1S2 is better correlated to in situ measurements than CoperSSM. According to the results of our study (Table  4 ), higher correlations are also obtained for S 2 MP S1S2. In addition, it is interesting to note that S 2 MP S1S2 and S 2 MP S1S3 show similar performances with respect to in situ measurements. Taking into account the evaluations of all the HR and CR products (Table  4 ), HR-merged datasets (SMAPS1 and CoperSWI) provide better estimations or temporal agreement with respect to in situ measurements than the HR Sentinelonly ones (S 2 MP S1S2 , S 2 MP S1S3 , and CoperSSM). However, they still show performances similar to or lower than the CR datasets (Table  4 ). This can be partly explained by the fact that the HR datasets provide SM estimates using C-band measurements, while the CR datasets used in this study are computed using L-band measurements.
Indeed, SMOS and SMAP were specifically designed to measure surface SM, which was not the case for the Sentinel satellites. Regarding the higher performances of the CR datasets over the HR ones, with respect to in situ measurements,  Bauer-Marschallinger et al. (2019)  also demonstrated that 25 km resolution SM estimates from ASCAT were better correlated to in situ measurements within the Italian Umbria region than those of the 1 km resolution CoperSSM. The results of our study are also in perfect agreement with the findings by  Ojha et al. (2021) , who showed, over several regions in France and Spain, that two merged products, SMAPS1 and SMAP + DISPATCH  (Merlin et al. , 2012) , were better correlated to in situ measurements than CoperSSM. Finally, it is noteworthy that the performances of SMAPS1 aggregated from 1 to 25 km resolution (CR) increase to values similar to those of SMAPL3.
This implies that the gain in resolution brought by merging data of different resolutions comes at the expense of introducing uncertainties in the resulting HR dataset. Obviously, this study was limited to comparisons over six regions of 10 4 km 2 within a 1-year time period, so the results cannot be straightforwardly extended to a global scale. However, the results of the study show that the use of S3 NDVI as input to the S 2 MP algorithm leads to SM estimates comparable to those obtained with S2 NDVI. However, HR SM estimates do not necessary lead to better performances than CR SM estimates, with respect to in situ measurements. In particular, retrieval algorithms only based on Sentinel measurements (S 2 MP and CoperSSM) need improvements before reaching performances comparable to those used with the HR-merged or CR datasets.
However, the S 2 MP datasets still have two advantages over CoperSSM. They currently show better performances against in situ measurements and are able to provide SM estimates outside of Europe. Hence, it would be interesting to extend the S 2 MP algorithm to all the types of land cover and to provide SM estimates over the entire globe. The objective would be to produce the first Sentinel-only SM dataset available at a global scale and to perform a deeper comparisons against SMAPS1 over large areas. However, the estimation of SM using the S 2 MP algorithm outside of Europe would remain challenging due to the inhomogeneous spatial and temporal coverage of S1  (Bauer-Marschallinger et al. , 2019).
Conclusions The goal of this study was to adapt the S 2 MP approach, originally designed to retrieve SM at 10 m resolution over agricultural fields, to a 1 km resolution, which allows the replacement of S2 by S3, and to significantly improve the NDVI temporal sampling. In addition, the approach was extended to herbaceous land cover areas and tested in six regions over four continents to assess its performances beyond previous evaluations in southern France. A very good agreement was found between the S1 + S3 and the S1 + S2 S 2 MP maps for the V regions (R ≥ 0. 9, bias ≤ 0. 04 m 3 m -3 , and SDD ≤ 0. 03 m 3 m -3 ), meaning that it is possible to replace S2 by S3 NDVI. The S 2 MP maps were then compared to those of the 1 km surface SM product provided by CGLS (CoperSSM), which is also a Sentinel-only-based dataset.
In contrast to S 2 MP, CoperSSM provides local indices of SM variations. For many applications, they have to be scaled against a reference to transform the variations to actual SM in volumetric units (m 3 m -3 ) before being used. Then, S 2 MP was also compared to two HR-merged datasets, that is (i) the SWI dataset from CGLS, combining S1 and ASCAT measurements (CoperSWI), and (ii) the SMAP + S1 dataset. As for the surface SM dataset, the SWI data had to be scaled into absolute SM values. CGLS products only provide estimates over the European continent and the Mediterranean basin. Overall, the S 2 MP dataset is better correlated to the 1 km surface SM product provided by CGLS over the four regions of study in Europe (R ∼ 0. 7-0. 8). Over almost all of the pixels within the six regions, the SDD between S 2 MP and the other HR datasets is lower than 0.
06 m 3 m -3. In addition, the bias differs significantly inside the same region and can be strongly dry or wet (±0. 1 m 3 m -3 ). The correlations between S 2 MP and the other HR datasets improve over croplands when the 1 km pixels become homogeneous, but a similar behavior was not clearly found for the other metrics (SDD and bias) or over pixels where the dominant land cover class is herbaceous vegetation. The S 2 MP datasets were also evaluated with respect to in situ measurements, along with the three other HR datasets, and with coarser-resolution datasets from SMOS, SMAP, and ESA CCI. The coarse-resolution (CR) products show higher correlations (0. 68 ≤ R ≤ 0. 77) than the HR datasets (0. 54 ≤ R ≤ 0. 74), and the HR-merged datasets showed higher correlations than the HR Sentinel-only ones.
S 2 MP S1S2 showed the highest bias with respect to in situ measurements, with -0. 07 m 3 m -3. Finally, the SDD differs according to the dataset in addition to the spatial resolution and range from 0. 05 (with S 2 MP S1S2 , for example) to 0. 08 m 3 m -3 (Coper-SWI). In general, these results show that the HR datasets only based on Sentinel (S 2 MP and CoperSSM) are not as competitive as the other HR-merged and CR datasets with respect to in situ measurements in terms of correlation and bias, but S 2 MP still presents several advantages. In contrast to the HR data from Copernicus, the S 2 MP data do not depend on auxiliary data to be scaled into volumetric units and can provide SM estimates outside of Europe. S 2 MP S1S2 also shows lower SDD, with respect to in situ measurements, than the Copernicus and the SMAP + S1 datasets.
It would be interesting to extend the S 2 MP to all the types of land cover and to provide SM maps at the globe scale. S 2 MP S1S2 would be the first global SM dataset at 1 km resolution only that is based on Sentinel measurements. It would also allow the performing of deeper comparisons against SMAP + S1 over large areas, and S 2 MP S1S2 could be used to assess the climate impact at a regional level in the future. However, a remaining challenge is to provide HR SM data with comparable spatiotemporal coverage and retrieval quality across different land cover types than those of the state-of-the-art coarse-resolution products, such as the SMOS, SMAP, and ESA CCI products.
Evaluation of the HR and CR SM time series against in situ measurements in terms of the Pearson correlation (R; R a ), bias (remotely sensed minus ground-based SM in m 3 m -3 ), and standard deviation of the difference (SDD in m 3 m -3 ). The metrics were computed by taking into account the six regions of study, and only the median values are shown here. The asterisk * indicates the HR datasets averaged at 25 km resolution. The analysis was performed from January to December 2019.
Evaluation of the HR and CR SM time series against in situ measurements in terms of Pearson correlation (R; R a ), bias (remotely sensed minus ground-based SM in (m 3 m -3 ) and standard deviation of the difference (SDD in m 3 m -3 ). The metrics were computed by taking into account the six regions of study, and only the median values are shown here. The asterisk * indicates the HR datasets averaged at 25 km resolution. The analysis was performed from January to December 2019.
Acknowledgements. This research made use of data from the  Centre Aval de Traitement des Données SMOS (CATDS)  operated for the  Centre National d'Etudes Spatiales  (CNES) by the  Institut Français de Recherche pour l'Exploitation de la Mer (IFRE-MER) in France , the  Copernicus Global Land Service (CGLS) , the  National Snow and Ice Data Center (NSIDC) , the  ESA ' s Climate Change Initiative for Soil Moisture  project, the  Centre d'Etudes Spatiales de la BIOsphère (CESBIO) , the  Institut National Agronomique de Tunisie (INAT) , and the  International Soil Moisture Network (ISMN). Financial support. This research has been supported by the  European Space Agency  (grant nos. 4000104814/11/I-NB  and  4000112226/14/I-NB ) and the  Centre National d'Etudes Spatiales  ( TOSCA SMOS-TE program ).
05-0. 05 4 (3) Calvet et al. (2007) SMOSMANIA Southeast of France 0. 05-0. 05 5 (0) Calvet et al. (2007) OZNET Australia 0-0. 05 11 (10) Smith et al. (2012); Young et al. (2008) USCRN North America 0. 05-0. 05 2 (1) Bell et al. (2013) ARM North America 0. 05-0. 05 24 (13) Cook (2016, 2018) MERGUELLIL Tunisia 0-0. 05 5 (2) Amri et al. (2011); Gorrab et al. (2015) , L2 datasets https://doi. org/10. 5194/hess-27-1221-2023 Hydrol. Earth Syst. Sci. , 27, 1221-1242, 2023.
Diffraction data were collected from the AlPO 4 -17 single crystals using phi scans from -30 to +30°or -45 to +45°, depending on the opening angle of the DAC. Data reduction was performed with CrysAlisPro 1. 171. 39. 46 (Rigaku OD, 2018). The crystal structure was refined using Shelxl-2017/1  29  with the WinGX  30  and OLEX  31  interfaces. The Squeeze method  32  was applied using the OLEX interface. Crystal structures were plotted using Vesta. 33.
RESULTS AND DISCUSSION Synchrotron X-ray diffraction studies were performed on both single crystals and powders of AlPO 4 -17 in N 2 , O 2 , and Ar. In the pressure range studied up to 6 GPa, good single-crystal XRD data were limited to AlPO 4 -17 in O 2 due to its higher solidification point of 5. 5 GPa. Solidification of N 2 and Ar occurs at much lower pressures of 2. 4 and 1. 5 GPa, respectively. Insertion of O 2 in AlPO 4 -17 was clear from Fourier difference maps (Figure  S3 ). Refining the with guest atoms on fully occupied sites based on the peaks on the Fourier difference maps yielded R1 agreement factors in the range of 5. 4-11. 5%. This type of model, Figure  1 , resulted in large atomic displacement parameters for the guest atoms as in ref 3, implying significant disorder, probably both positional and orientational, and corresponds to filling of the pores with 24 guest molecules per unit cell.