dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene expression The transcriptomes of the sugarcane variety SP80-3280 from the roots, shoots and stalks were mapped on HP600 and CENP-C (NCBI SRR7274987), and the set of transcripts was used for the transcription analyses. The reads from the sugarcane transcriptomes were mapped to the reference genes with the Bowtie2 software 2. 2. 5  (Langmead and Salzberg, 2012)  with default parameters; low-quality reads and unmapped reads were filtered out (samtools -b -F 4); bam files were sorted (samtools sort); and only mapped reads to the genes were extracted from the bam files (samtools fastq) and recorded in a FASTQ format file. A haplotype was considered to be expressed only when the transcript reads were mapped with 100% similarity. SNPs not found in the dataset were searched in the SP80-3280 transcriptomes from  Vettore et al.
ACKNOWLEDGMENTS This study was supported by the  São Paulo Research Foundation (FAPESP)  ( 2008/52197-4 ) and  Coordination for the Improvement of Higher Education Personnel (CAPES, Computational Biology Program). The first author was supported by a  FAPESP Ph. D. fellowship from  FAPESP  ( 2010/50119-6 ). The third and fourth authors were supported by  PD fellowships  ( MM 2014/11482-9  and  CC-S 2015/16399-5 ). AP received a research fellowship from the  National Council for Scientific and Technological Development (CNPq).
Long-term climate records of soil moisture are of increased importance to climate researchers. In this study, we aim to evaluate the quality of three different fusion approaches that combine soil moisture retrieval from multiple satellite sensors. The arrival of L-band missions has led to an increased focus on the integration of L-band-based soil moisture retrievals in climate records, emphasizing the need to improve our understanding based on its added value within a multi-sensor framework. The three evaluated approaches were developed on 10-year passive microwave data (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) from two different satellite sensors, i. e. , SMOS (2010SMOS ( -2013) )  and AMSR-E (2003AMSR-E ( -2011)), and are based on a neural network (NN), regressions (REG), and the Land Parameter Retrieval Model (LPRM).
The ability of the different approaches to best match AMSR-E and SMOS in their overlapping period was tested using an inter-comparison exercise between the SMOS and AMSR-E datasets, while the skill of the individual soil moisture products, based on anomalies, was evaluated using two verification techniques; first, a data assimilation technique that links precipitation information to the quality of soil moisture (expressed as the R value ), and secondly the triple collocation analysis (TCA). ASCAT soil moisture was included in the skill evaluation, representing the active microwave-based counterpart of soil moisture retrievals. Besides a semi-global analysis, explicit focus was placed on two regions that have strong land-atmosphere coupling, the Sahel (SA) and the central Great Plains (CGP) of North America.
The NN approach gives the highest correlation coefficient between SMOS and AMSR-E, closely followed by LPRM and REG, while the absolute error is approximately the same for all three approaches. The R value and TCA show the strength of using different satellite sources and the impact of different merging approaches on the skill to correctly capture soil moisture anomalies. The highest performance is found for AMSR-E over sparse vegetation, for SMOS over moderate vegetation, and for ASCAT over dense vegetation cover. While the two SMOS datasets (L3 and LPRM) show a similar performance, the three AMSR-E datasets do not. The good performance for AMSR-E over spare vegetation is mainly perceived for AMSR-E LPRM, benefiting from the physically based model, while AMSR-E NN shows improved skill in densely vegetated areas, making optimal use of the SMOS L3 training dataset.
Introduction Long-term, consistent soil moisture records are essential to improving our understanding of the impact of climate change, as already demonstrated in many research studies (e. g. , see  [1]  for a comprehensive overview). In 2010, the importance of soil moisture was recognized by the Global Climate Observation System (GCOS), which labeled this important variable an Essential Climate Variable (ECV)  [2]. Direct measurements of soil moisture can be done with in situ sensor networks, but the current coverage is sparse and unequally distributed worldwide. In addition, soil moisture from in situ sensors generally has a small spatial support, which makes the use of in situ networks unsuitable for providing reliable global soil moisture climate records. Problems with continuity, sensor characteristics, and non-uniform data processing make consistency a significant issue; however, initiatives like the International Soil Moisture Network (ISMN)  [3, 4]  aim at providing this.
4 GHz). Theoretically, this is the optimal frequency for surface soil moisture sensing due to its increased sensing depth (approximately the first 5 cm instead of the first 2 cm from C-and X-band) and decreased sensitivity to vegetation as compared to the higher frequencies. This gave way to the development of L-band specific algorithms  [13] [14] [15] [16] [17]. In 2010, ESA initiated a project with the goal of developing a consistent long-term dataset of soil moisture derived from a wide range of active and passive microwave sensors (http://www. esasoilmoisture-cci. org)  [18]. The CCI soil moisture dataset (ESA-CCI-SM) is currently the most popular ESA CCI product, with over 4000 registered users. The ESA-CCI-SM consists of a merger between active and passive soil moisture retrievals, following the baseline methodology of Liu et al.
[19, 20] , which was recently further advanced  [21]. This merging of active and passive is based on the complementary nature of the two data sources, with passive soil moisture retrievals performing better over sparsely vegetated areas, and active sensors performing better over more densely vegetated areas  [22] [23] [24]. The specific contribution of L-band based retrievals within such a multi-sensor approach is currently not well understood. Building a consistent long-term soil moisture dataset is not straightforward due to large differences in satellite sensor characteristics and retrieval algorithms. Therefore, several alternative fusion approaches that aim to construct coherent time series from instruments that were only working at the same time for a limited time period are currently being explored  [25] [26] [27].
With the arrival of L-band missions, these fusion approaches have an increased focus on the integration of L-band-based soil moisture retrievals. However, it is still unclear how the use of different fusion approaches will affect the quality of single-sensor soil moisture datasets and how L-band inclusion in a multi-sensor approach adds value. This study aims to provide insight into these questions by evaluating three different fusion approaches that have been applied to satellite observations from both AMSR-E (2003-2011) and SMOS (2010-onwards). The first evaluated fusion approach is based on a global neural network approach (NN)  [26, 28] , which uses AMSR-E brightness temperature observations from several frequencies as input and uses the SMOS Level 3 soil moisture product from CATDS (Centre Aval de Traitement de Données SMOS) as a reference dataset for the training phase in the common ASMSR-e and SMOS period.
Once trained, the NN is applied to the whole AMSR-E data record for 2003-2010. The second fusion approach also uses SMOS L3 soil moisture as the training dataset for AMSR-E observations, and the training is based on local regressions (REG)  [14, 25]  and applying regression coefficients to the AMSR-E period retrospectively. The third approach uses a refined version of the Land Parameter Retrieval Model (LPRM)  [27, 29] , which is the current baseline algorithm for passive microwave component in the ESA-CCI-SM. The LPRM was further developed for the application to SMOS L-band observations  [27, 30]. To put the results in perspective considering an existing multi-sensor approach like the ESA-CCI-SM, soil moisture retrievals based on observations from the Advanced Scatterometer (ASCAT)  [31]  are also included in the evaluation.
The ASCAT soil moisture reflects the active counterpart and is retrieved using the Change Detection Algorithm (CDA)  [7]. This will provide users from datasets like the ESA-CCI-SM the opportunity to better understand the pros and cons of these different sensors and merging strategies. A first indication of the quality of these fusion approaches was provided by Rodriguez-Fernandez et al. [26] , in whose study the three different AMSR-E datasets (NN, REG and LPRM) were compared against in situ data and modeled data from NASA's Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) [32] and from the European Centre for Medium-Range Weather Forecasts its ERA-Interim/Land  [33]. Van der Schalie et al. [17]  presented an extensive comparison between the two SMOS datasets (L3 and LPRM) against in situ measurements from the ISMN.
However, as discussed earlier, the use of ground observations has its shortcomings. Models, on the other hand, have global coverage but contain unique error characteristics and the results are therefore difficult to interpret. The evaluations, as done in these paper, are also based on the bulk statistics of soil moisture time series, which could be heavily influenced by structural differences in seasonal cycles, and do not necessarily provide information on the ability of a dataset to capture individual soil moisture anomalies  [34]. In order to overcome these problems, several methods have been developed that are better able to quantify the skill of soil moisture datasets. One of these is a large-scale precipitation-based validation technique that provides a robust representation of (anomaly) correlation-based skills in satellite soil moisture retrieval through data assimilation (expressed as the R value )  [35, 36].
Another method is the Triple Collocation Analysis (TCA)  [24, [37] [38] [39] , which uses three collocated soil moisture datasets in order to estimate the root mean square error (rmse) of the random error component of the actual estimate, in the absence of a dataset that can be used as the absolute truth at the global scale and provided some conditions are satisfied. As these statistical methods also have their limitations (for example, correlated errors in soil moisture datasets could lead to uncertainties in TCA results), it is important to interpret them together instead of using a single metric. In this study, the different fusion approaches are evaluated, focusing on the ability of the different approaches to best match AMSR-E and SMOS in their overlapping period and defining the skill of the individual datasets in the next steps.
The four steps are: (1) A comparison exercise between the AMSR-E and SMOS fusion approaches, i. e. , LPRM/NN/REG, to check the consistency between the datasets in their overlapping period. The skills of the SMOS, AMSR-E and ASCAT soil moisture anomalies were determined through (2) the R value technique with precipitation data from the Tropical Rainfall Measuring Mission (TRMM), and (3) TCA based on combining soil moisture from a passive (AMSR-E/SMOS), active (ASCAT), and modeled source (Modern-Era Retrospective analysis for Research and Applications, MERRA). In a final exercise, (4) explicit focus is placed on two regions that have strong land-atmosphere coupling, the Sahel and the central Great Plains of North America  [40] [41] [42] , which means soil moisture plays an important role here from a climate perspective as soil moisture variability directly affects the energy fluxes and weather patterns over these regions.
Secondly, the multi-frequency observations from AMSR-E give a unique ability to switch between frequencies when the frequency is disturbed by artificial noise, which is often named Radio Frequency Interference (RFI)  [43] [44] [45] , and to simultaneously retrieve the soil temperature from higher frequencies (e. g. , from 36. 5 GHz observations)  [36, 46]. Three different soil moisture datasets derived from AMSR-E observations are used in this study, the AMSR-E NN, AMSR-E REG and AMSR-E LPRM. Only observations from the descending overpasses of AMSR-E (01. 30 AM) are used, as it generally results in higher-quality retrievals  [22, 45] , which is caused by the increased thermodynamic equilibrium conditions of the soil and canopy at night, leading to more reliable temperature estimates  [36].
AMSR-E NN Soil Moisture The first AMSR-E soil moisture dataset evaluated in this study was developed by Rodriguez-Fernandez et al. [26]  using a Neural Network approach (hereafter, this dataset will be called AMSR-E NN). Soil moisture from AMSR-E observations is retrieved using a global non-linear relationship linking AMSR-E brightness temperatures to SMOS L3 soil moisture. This relationship was obtained using a neural network. A large number of input data configurations and NN architecture were studied. The optimal input configuration was suing ASMR-E brightness temperatures at 6. 9 GHz (H/V), 10. 7 GHz (H/V), 23. 8 GHz (H) and 36. 5 GHz (H/V), with the first two frequencies giving information on soil moisture and the last two frequencies giving additional information on the soil temperature, which improve the NN performance to retrieve soil moisture by ~2%.
This was evaluated by comparing the AMSR-REG soil moisture product against the reference (i. e. , SMOS L3 soil moisture product) over the calibration period. A reasonable agreement was obtained with global mean correlation of 0. 60 and rmse = 0. 057 m 3 m -3 as shown by Al-Yaari et al. [25]. These regression coefficients where applied to AMSR-E brightness temperature datasets for the period of 2003 to 2011, to retrieve the AMSR-E REG soil moisture dataset. The accuracy of AMSR-REG soil moisture retrievals was assessed by comparing them with an old version of AMSR-E soil moisture retrievals derived from LPRM against the global MERRA-Land soil moisture simulations and ground-based measurements over 2003-2009, showing a with respect to in situ observations and higher (lower) correlations (ubrmse) with MERRA-land over the boreal regions.
AMSR-E LPRM Soil Moisture The third AMSR-E dataset that is evaluated in this study is developed by  [27] , and uses the physically based Land Parameter Retrieval Model  [29] , which was further developed to specifically allow for the direct integration with SMOS LPRM soil moisture data (see section below). The internal parameterization, e. g. , surface roughness and single scattering albedo, of the LPRM model as used for AMSR-E-based soil moisture retrievals was revisited using SMOS LPRM as a training dataset. The final dataset also includes a linear scaling to SMOS LPRM, bringing the datasets within the same absolute range without changing their individual temporal dynamics. [46]  established a linear relation for temperature from 36. 5 GHz observations, which is used for the temperature input in the LPRM model.
ASCAT Soil Moisture The ASCAT soil moisture data product is used as an independent remotely sensed surface soil moisture dataset, presented as degree of saturation. Backscatter observations derived from the Advanced Scatterometer (ASCAT) instrument are used to retrieve soil moisture. ASCAT is a C-band (5. 3 GHz) real aperture radar on board of two satellites that are part of the Meteorological Operational satellite program (MetOp-A and MetOp-B), which is a joint program by ESA and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The MetOp-A satellite was launched in October 2006, with MetOp-B increasing the coverage from September 2012 onwards. The ASCAT soil moisture dataset used in this study was developed by the Technical University of Vienna and is based on the commonly used Change Detection Algorithm (CDA)  [7, 31].
In the CDA, reference backscatters for a vegetated land surface under dry and saturated conditions are used to derive the degree of saturation. The signature of the backscatter over multiple incidence angles is used to correct for inter-annual variations in vegetation cover. The retrievals were done for both ascending (9. 30 p. m. ) and descending (9. 30 a. m. ) overpasses; when both overpasses are available their average was taken  [55]. This gives the ASCAT soil moisture dataset a revisit time of one to two days. Data used are between January 2007 and May 2015.
MERRA Soil Moisture Modelled soil moisture from the Modern-Era Retrospective analysis for Research and Applications (MERRA) is used as part of the statistical evaluation strategy (i. e. , Triple colocation Analysis (TCA), see Section 3. 3). MERRA is an atmospheric reanalysis developed by NASA, which uses the Goddard Earth Observing System model, version 5 (GEOS-5)  [56] , supported by the Data Assimilation System (DAS)  [57]. More specifically, data from the MERRA-Land [32] reanalysis were used, which is specifically developed to improve the output of the model over land and is the recommended dataset for use in hydrological studies over land compared to the regular MERRA model. These improvements are obtained by two changes over the baseline MERRA model, which is the use of a more accurate precipitation forcing and by using an improved Catchment land surface model.
The soil moisture from MERRA-Land as used in this study is the top layer, representing the first 2 cm of the soil. Data is provided in a 1 / 2 • (latitude) by 2 / 3 • (longitude) grid, and regridded using bilinear interpolation to a quarter degree grid. For use in the TCA, it is important to note that the data assimilation system of MERRA-Land does not include satellite-retrieved soil moisture, nor passive or active microwave observations. During the evaluation and writing, the latest version of MERRA (MERRA-2) was not yet available; however, Dorigo et al. [24]  used several product combinations and showed that the error estimates are only marginally influenced by the choice of the third (independent) dataset.
TRMM 3B42 Precipitation The R value evaluation requires a precipitation dataset as input. In line with earlier studies, a dataset based on observations from the Tropical Rainfall Measuring Mission (TRMM) is used (e. g. ,  [35, 36] ). TRMM was developed in collaboration between NASA and JAXA, with the aim to study precipitation behavior over the tropical regions, and was launched late 1997. Onboard, TRMM carries several rain measuring instruments, the Precipitation Radar, TRMM Microwave Imager and Visible and Infrared Scanner. More specifically, the TRMM 3B42 (v. 07) Merged HQ/Infrared Precipitation  [58]  is used, which provides precipitation rate and precipitation-error estimates between a latitude of 50 • N-50 • S. Precipitation estimates from multiple microwave and infrared sensors are used, which are calibrated to TRMM-based precipitation estimates, and afterwards a rain-gauge-based correction was applied.
Comparison of AMSR-E and SMOS Soil Moisture The SMOS and AMSR-E retrievals from the three data fusion approaches NN, REG and LPRM are cross-compared during the overlapping period between July 2010 and October 2011. This is done to get a better understanding of the ability of the different fusion approaches to match soil moisture retrievals from the different satellites. The Pearson correlation and unbiased root mean square difference (ubrmsd, in m 3 m -3 ) between the AMSR-E and SMOS datasets were determined, together with their standard deviation (σ) for individual pixels and then averaged for the different NDVI bins. When running statistics for the Pearson correlation, the Fisher z-transformation is applied. These analyses were done over the range of 50 • N-50 • S to keep the same coverage as for the TRMM precipitation dataset as used in the R value evaluation.
Precipitation-Based Data Assimilation Technique The R value data assimilation technique is based on the connection between precipitation and the subsequent changes in soil moisture. The technique uses the contrasting quality of two separate precipitation datasets to evaluate the degree to which analysis increments, derived via the sequential assimilation of soil moisture into a simple water balance model, accurately compensate for known rainfall errors  [35, 36, 59 ]. More specifically, soil moisture anomalies derived from satellite observations are assimilated into an antecedent precipitation index (API), which is based on the low quality precipitation dataset, using a Kalman filter. The anomaly analysis increments produced in this process can then directly be compared to the rainfall anomaly difference between the high and low quality precipitation datasets, which is expressed as the R value and based on the Pearson's correlation.
Values between 0 and 0. 7 are typical for the R value technique, however they should be seen as a relative performance metric (e. g. , for comparing multiple soil moisture datasets) as the value depends on the quality of the two precipitation datasets. The R value technique was adapted to run on an anomaly basis and validated as a robust method to determine the temporal performance of satellite soil moisture retrievals by Crow et al. [35] , to which we refer for the complete mathematical framework of the technique as used in this study. One modification was introduced following Parinussa et al. [45, 60] , who applied an artificial deterioration to the gauge-corrected TRMM 3B42 precipitation product in order to create the low quality precipitation dataset, instead of relying on the near-real-time (3B40RT) precipitation product.
This is advantageous for the R value technique as it creates a contrast between the two precipitation datasets that is uniform in space. More information on how the artificial noise is applied can be found in Crow et al. [61]. There are well-known limitations  [35, 36, 45, 60]  to the precipitation-based R value verification technique over extremely arid climate regimes, as this technique requires a sufficient number of precipitation events. This requirement cannot be met under extremely arid conditions, as indicated in Figures  2 3 4 by "No sensitivity-not enough precipitation". In line with this limitation of the R value technique is the well-known limitation of remotely sensed soil moisture under dense vegetation canopies, as the emission from the soil (moisture) becomes entirely blocked by the overlying vegetation.
This limitation in regions with a very dense vegetation cover was indicated in Figures  2 3 4 by "No sensitivity-soil moisture loses skills". Although the exact same spatial cover is taken for all different soil moisture datasets, due to the short overlap time the temporal coverage differs for the SMOS and AMSR-E datasets. By taking multiple years (2007-2011 for AMSR-E, 2010-2015 for SMOS and 2007-2015 for ASCAT) over a large spatial cover, the impact of using different time periods is expected to be minimal for the R value results. For datasets that used the same sensor, only the mutual observations in both space and time were used.
Triple Collocation Analysis (TCA) The triple collocation analysis (TCA) is a statistical verification technique that has been commonly used for the validation of remotely sensed soil moisture anomalies  [24, 26, 37, 39, 45]. The TCA estimates the root mean square error (rmse) of the random error component of the actual estimate based on three linearly related (soil moisture) datasets that have independent error structures. Results from the TCA were validated against ground observations  [41] , while Dorigo et al. [24]  demonstrated that rmse estimates are only marginally influenced by the choice of the third product (generally surface soil moisture from a reanalysis model). The conventional product combination in large scale surface soil moisture evaluation studies is soil moisture from an active and passive microwave source, complemented with the top layer of a reanalysis model.