Data Integration Procedures Research Articles

The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research.

Investigating low-prevalence diseases such as multiple sclerosis is challenging because of the rather small number of individuals affected by this disease and the scattering of real-world data across numerous data sources. These obstacles impair data integration, standardization, and analysis, which negatively impact the generation of significant meaningful clinical evidence. This study aims to present a comprehensive, research question-agnostic, multistakeholder-driven end-to-end data analysis pipeline that accommodates 3 prevalent data-sharing streams: individual data sharing, core data set sharing, and federated model sharing. A demand-driven methodology is employed for standardization, followed by 3 streams of data acquisition, a data quality enhancement process, a data integration procedure, and a concluding analysis stage to fulfill real-world data-sharing requirements. This pipeline's effectiveness was demonstrated through its successful implementation in the COVID-19 and multiple sclerosis global data sharing initiative. The global data sharing initiative yielded multiple scientific publications and provided extensive worldwide guidance for the community with multiple sclerosis. The pipeline facilitated gathering pertinent data from various sources, accommodating distinct sharing streams and assimilating them into a unified data set for subsequent statistical analysis or secure data examination. This pipeline contributed to the assembly of the largest data set of people with multiple sclerosis infected with COVID-19. The proposed data analysis pipeline exemplifies the potential of global stakeholder collaboration and underlines the significance of evidence-based decision-making. It serves as a paradigm for how data sharing initiatives can propel advancements in health care, emphasizing its adaptability and capacity to address diverse research inquiries.

Analysis of the spatial extension of pineapple monocultures in northern Costa Rica using heterogeneous geographic data

Abstract. In recent years, Costa Rica has become the world's leading producer and exporter of pineapple, with more than 65.000 hectares of its territory dedicated to this crop. The development of these intensive pineapple crops is mainly concentrated in the north of the country, especially in the border area with Nicaragua. This rapid expansion in these regions has many economic, social and environmental consequences. In order to understand the local impact of this phenomenon, a first analysis of land use changes for the benefit of pineapple monocultures must be performed. For this purpose, pineapple crop delineation layers in 2000 and 2019 were provided by MOCUPP, a United Nations agency responsible for monitoring agricultural areas in Costa Rica. Landsat 5 satellite images from 1998 and 2001 were also used to generate land cover classifications. The pineapple crop delineation layers produced by MOCUPP are characterized by heterogeneous granularities and positional accuracies, complicating the analysis of land cover changes through the generation of artefacts. To address this problem, an artefact removal procedure was conducted, based on the use of minimum shape and size indicators. The assignment of land use changes to pineapple crops allowed the assessment of deforested areas between 2000 and 2019. Thus, over this period, more than 6300 hectares of forest were lost to pineapple crops, mainly in the cantons of Los Chiles and San Carlos, in the northern part of the country. Finally, this article illustrates the problems associated with the use of heterogeneous data to analyse land cover changes. To avoid these problems, the use of prior data integration procedures, such as data matching algorithms, is recommended.

Application of Defined Approaches to Assess Skin Sensitization Potency of Isothiazolinone Compounds.

Isothiazolinones (ITs) are widely used as antimicrobial preservatives in cosmetics and as additives for preservation of consumer and industrial products to control bacteria, fungi, and algae. Although they are effective biocides, they have the potential to produce skin irritation and sensitization, which poses a human health hazard. In this project, we evaluated nonanimal defined approaches (DAs) for skin sensitization that can provide point-of-departure estimates for use in quantitative risk assessment for ITs. The skin sensitization potential of six ITs was evaluated using three internationally harmonized nonanimal test methods: the direct peptide reactivity assay, KeratinoSens™, and the human cell line activation test. Results from these test methods were then applied to two versions of the Shiseido Artificial Neural Network DA. Sensitization hazard or potency predictions were compared with those of the in vivo murine local lymph node assay (LLNA). The nonanimal methods produced skin sensitization hazard and potency classifications concordant with those of the LLNA. EC3 values (the estimated concentration needed to produce a stimulation index of three, the threshold positive response) generated by the DAs had less variability than LLNA EC3 values, and confidence limits from the DAs overlapped those of the LLNA EC3 for most substances. The application of in silico models to in chemico and in vitro skin sensitization data is a promising data integration procedure for DAs to support hazard and potency classification and quantitative risk assessment.

Joint integrative analysis of multiple data sources with correlated vector outcomes

We propose a distributed quadratic inference function framework to jointly estimate regression parameters from multiple potentially heterogeneous data sources with correlated vector outcomes. The primary goal of this joint integrative analysis is to estimate covariate effects on all outcomes through a marginal regression model in a statistically and computationally efficient way. We develop a data integration procedure for statistical estimation and inference of regression parameters that is implemented in a fully distributed and parallelized computational scheme. To overcome computational and modeling challenges arising from the high-dimensional likelihood of the correlated vector outcomes, we propose to analyze each data source using Qu, Lindsay and Li’s (Biometrika 87 (2000) 823–836) quadratic inference functions and then to jointly reestimate parameters from each data source by accounting for correlation between data sources using a combined meta-estimator in a similar spirit to the generalized method of moments put forward by Hansen (Econometrica 50 (1982) 1029–1054). We show both theoretically and numerically that the proposed method yields efficiency improvements and is computationally fast. We illustrate the proposed methodology with the joint integrative analysis of the association between smoking and metabolites in a large multicohort study and provide an R package for ease of implementation.

General purpose multiply robust data integration procedures for handling nonprobability samples

AbstractIn recent years, there has been an increased interest in combining probability and nonprobability samples. Nonprobability sample are cheaper and quicker to conduct but the resulting estimators are vulnerable to bias as the participation probabilities are unknown. To adjust for the potential bias, estimation procedures based on parametric or nonparametric models have been discussed in the literature. However, the validity of the resulting estimators relies heavily on the validity of the underlying models. Also, nonparametric approaches may suffer from the curse of dimensionality and poor efficiency. We propose a data integration approach by combining multiple outcome regression models and propensity score models. The proposed approach can be used for estimating general parameters including totals, means, distribution functions, and percentiles. The resulting estimators are multiply robust in the sense that they remain consistent if all but one model are misspecified. The asymptotic properties of point and variance estimators are established. The results from a simulation study show the benefits of the proposed method in terms of bias and efficiency. Finally, we apply the proposed method using data from the Korea National Health and Nutrition Examination Survey and data from the National Health Insurance Sharing Services.

History of Land Cover Change on Santa Cruz Island, Galapagos

Islands are particularly vulnerable to the effects of land cover change due to their limited size and remoteness. This study analyzes vegetation cover change in the agricultural area of Santa Cruz (Galapagos Archipelago) between 1961 and 2018. To reconstruct multitemporal land cover change from existing land cover products, a multisource data integration procedure was followed to reduce imprecision and inconsistencies that may result from the comparison of heterogeneous datasets. The conversion of native forests and grasslands into agricultural land was the principal land cover change in the non-protected area. In 1961, about 94% of the non-protected area was still covered by native vegetation, whereas this had decreased to only 7% in 2018. Most of the agricultural expansion took place in the 1960s and 1970s, and it created an anthropogenic landscape where 67% of the area is covered by agricultural land and 26% by invasive species. Early clearance of native vegetation took place in the more accessible—less rugged—areas with deeper-than-average and well-drained soils. The first wave of settlement consisted of large and isolated farmsteads, with 19% of the farms being larger than 100 ha and specializing in diary and meat production. Over the period of 1961–1987, the number of farms doubled from less than 100 to more than 200, while the average farm size decreased from 90 to 60 ha/farmstead. Due to labor constraints in the agricultural sector, these farms opted for less labor-intensive activities such as livestock farming. New farms (popping up in the 1990s and 2000s) are generally small in size, with <5 ha per farmstead, and settled in areas with less favorable biophysical conditions and lower accessibility to markets. From the 1990s onwards, the surge of alternative income opportunities in the tourism and travel-related sector reduced pressure on the natural resources in the non-protected area.

Experimental evaluation of data integration methods for isotopic analysis of uranium with ultra-trace levels using TIMS

Isotopic analysis of ultra-trace levels of uranium (1 ng, 100 pg, 30 pg, and 5 pg) was performed using thermal ionization mass spectrometry (TIMS). The data integration procedure was optimized through experimental evaluation of three data integration methods. In Method I, the isotope ratios of uranium were calculated using pre-integrated ion signal intensities. Statistical integration of the pre-calculated uranium isotope ratios for each data set was performed with and without applying weighting for Methods III and II, respectively. Methods I and III efficiently minimized the deviation of the uranium isotope ratios caused by mass fractionation. The integration range was required to be reduced to 25–100% of the maximum intensity for data sets containing samples with small weights (i.e., 5 pg) for the best accuracy. Method III, with an integration range over 25%, was recommended for individual microparticle analysis by TIMS due to its accuracy, precision, and possibility of uncertainty estimation. The excellent agreement of the isotope ratios of uranium in microparticles (approximately 2–3 μm in diameter) with the certified values verified the applicability of the optimized procedure to the TIMS isotopic analysis of uranium for nuclear safeguards purposes.

Immediate Post-Concussion and Cognitive Testing (ImPACT): Effects of Data Integration Strategies on Classification Accuracy.

The current study aimed to evaluate varying data integration procedures and their effects on the classification accuracy of Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT). Data were collected from an independent secondary school that included students in grades 9 to 12. The study examined 300 consecutive ImPACT score reports generated by secondary school students between 2010 and 2015. To appraise ImPACT's utility as a serialized measure, standardized regression-based equations were formulated to compute reliable change index scores. Discriminant function analyses (DFAs) consisting of varying combinations of ImPACT composite scores were conducted and their accuracy was compared to that produced by the standard interpretive procedure. Varying combinations of scores produced on Verbal Memory, Visual Memory, Visual Motor Speed, Reaction Speed, and the Postconcussion Symptom Scale were included in analyses. DFAs yielded sensitivities ranging from 31% to 49%, specificities from 88% to 95%, positive predictive values (PPVs) from 61% to 83%, and negative predictive values (NPVs) from 67% to 75%. Conversely, the standard interpretive procedure yielded a sensitivity of 73%, specificity of 43%, PPV of 45%, and NPV of 72%. The standard interpretive procedure produced a higher sensitivity than the DFAs; however, its PPV did not exceed chance levels. Conversely, DFA equations produced superior PPVs; however, their sensitivity hovered around 50%, leaving a substantial proportion of individuals with concussion undetected. Cognitive composite scores did not appear to offer significant incremental utility in relation to symptom self-report. Base rate conditions and psychometric factors appeared to contribute to ImPACT's limited classification accuracy.

Ontological modeling of manufacturing resources

Standards and ontologies for manufacturing understand resources differently. Because of this heterogeneity, misunderstandings arise concerning the basic features that characterize them. The purpose of the paper is to investigate how to ontologically model resources with the goal of facilitating the development of knowledge representation models for manufacturing. By reviewing the literature, we discuss and compare three approaches for the representation of resources depending on whether they are conceived in connection to either processes, plans or goals. By addressing the advantages and shortcomings of each view, we present a unifying perspective to enable the modeling of resources in an integrated manner. In this way, the intended meanings of the used notions are harmonized and, as a result, one can facilitate multiple experts to interact e.g., via data sharing and/or data integration procedures. Differently, by keeping three separated views, there is no guarantee that data coming from different parties will share common meanings even if the same terms are used. By the end of the paper, we present a case study to show the application of our approach and to compare it with an existing ontology for manufacturing.

Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel

AbstractNowcasts, or near-real-time (NRT) forecasts, of soil moisture based on the Soil Moisture Active and Passive (SMAP) mission could provide substantial value for a range of applications including hazards monitoring and agricultural planning. To provide such a NRT forecast with high fidelity, we enhanced a time series deep learning architecture, long short-term memory (LSTM), with a novel data integration (DI) kernel to assimilate the most recent SMAP observations as soon as they become available. The kernel is adaptive in that it can accommodate irregular observational schedules. Testing over the CONUS, this NRT forecast product showcases predictions with unprecedented accuracy when evaluated against subsequent SMAP retrievals. It showed smaller error than NRT forecasts reported in the literature, especially at longer forecast latency. The comparative advantage was due to LSTM’s structural improvements, as well as its ability to utilize more input variables and more training data. The DI-LSTM was compared to the original LSTM model that runs without data integration, referred to as the projection model here. We found that the DI procedure removed the autocorrelated effects of forcing errors and errors due to processes not represented in the inputs, for example, irrigation and floodplain/lake inundation, as well as mismatches due to unseen forcing conditions. The effects of this purely data-driven DI kernel are discussed for the first time in the geosciences. Furthermore, this work presents an upper-bound estimate for the random component of the SMAP retrieval error.

Bayesian mixture regression analysis for regulation of Pluripotency in ES cells

BackgroundObserved levels of gene expression strongly depend on both activity of DNA binding transcription factors (TFs) and chromatin state through different histone modifications (HMs). In order to recover the functional relationship between local chromatin state, TF binding and observed levels of gene expression, regression methods have proven to be useful tools. They have been successfully applied to predict mRNA levels from genome-wide experimental data and they provide insight into context-dependent gene regulatory mechanisms. However, heterogeneity arising from gene-set specific regulatory interactions is often overlooked.ResultsWe show that regression models that predict gene expression by using experimentally derived ChIP-seq profiles of TFs can be significantly improved by mixture modelling. In order to find biologically relevant gene clusters, we employ a Bayesian allocation procedure which allows us to integrate additional biological information such as three-dimensional nuclear organization of chromosomes and gene function. The data integration procedure involves transforming the additional data into gene similarity values. We propose a generic similarity measure that is especially suitable for situations where the additional data are of both continuous and discrete type, and compare its performance with similar measures in the context of mixture modelling.ConclusionsWe applied the proposed method on a data from mouse embryonic stem cells (ESC). We find that including additional data results in mixture components that exhibit biologically meaningful gene clusters, and provides valuable insight into the heterogeneity of the regulatory interactions.

The kinetic direct peptide reactivity assay (kDPRA): Intra- and inter-laboratory reproducibility in a seven-laboratory ring trial

While the skin sensitization hazard of substances can be identified using non-animal methods, the classification of potency into UN GHS sub-categories 1A and 1B remains challenging. The kinetic direct peptide reactivity assay (kDPRA) is a modification of the DPRA wherein the reaction kinetics of a test substance towards a synthetic cysteine-containing peptide are evaluated. For this purpose, several concentrations of the test substance are incubated with the synthetic peptide for several incubation times. The reaction is stopped by addition of monobromobimane, which forms a fluorescent complex with the free cysteine of the model peptide. The relative remaining non-depleted amount of peptide is determined. Kinetic rate constants are derived from the depletion vs concentration and time matrix and used to distinguish between UN GHS sub-category 1A sensitizers and test substances in sub-category 1B/not classified test substances. In this study, we present a ring trial of the kDPRA with 24 blind-coded test substances in seven laboratories. The intra- and inter-laboratory reproducibility were 96% and 88%, respectively (both for differentiating GHS Cat 1A sensitizers from GHS Cat 1B/not classified). Following an independent peer review, the kDPRA was considered to be acceptable for the identification of GHS Cat 1A skin sensitizers. Besides GHS Cat 1A identification, the kDPRA can be used as part of a defined approach(es) with a quantitative data integration procedure for skin sensitization potency assessment. For this aim, next to reproducibility of classification, the quantitative reproducibility and variability of the rate constants were quantified in this study.

Improvement of Forest Canopy Density Model Using Remote Sensing Data Integration

Forest Canopy Density Mapper is a method based on spectral indexes integration in forest canopy density classification. In this paper, a data integration procedure is used to improve the result. In this respect, SFIM method and spectral response algorithm is utilized without a bad effect on the spectral and radiometric properties of bands. In the following, Landsat images of Hyrcanian forests in the north of Iran were used to implement the conventional and improved methods. Also, the ground measurements including grass-land, thin forest, semi-dense forest and forest is utilized for evaluation. The result shows that the forest canopy density model is inefficient in the thin and semi-dense forests. Alternatively, the results in the dense forest and grass land is reliable. Additionally, the improvement of the proposed method in these two areas is clearly seen. It seems that a high resolution image should be used to improve the accuracy of the forest density classification in the semi-dense and thin forests

Spectrum: fast density-aware spectral clustering for single and multi-omic data.

MotivationClustering patient omic data is integral to developing precision medicine because it allows the identification of disease subtypes. A current major challenge is the integration multi-omic data to identify a shared structure and reduce noise. Cluster analysis is also increasingly applied on single-omic data, for example, in single cell RNA-seq analysis for clustering the transcriptomes of individual cells. This technology has clinical implications. Our motivation was therefore to develop a flexible and effective spectral clustering tool for both single and multi-omic data.ResultsWe present Spectrum, a new spectral clustering method for complex omic data. Spectrum uses a self-tuning density-aware kernel we developed that enhances the similarity between points that share common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to reduce noise and reveal underlying structures. Spectrum contains a new method for finding the optimal number of clusters (K) involving eigenvector distribution analysis. Spectrum can automatically find K for both Gaussian and non-Gaussian structures. We demonstrate across 21 real expression datasets that Spectrum gives improved runtimes and better clustering results relative to other methods.Availability and implementationSpectrum is available as an R software package from CRAN https://cran.r-project.org/web/packages/Spectrum/index.html.Supplementary information Supplementary data are available at Bioinformatics online.

A novel integrative procedure for identifying and integrating three-dimensions of objectively measured free-living sedentary behaviour

BackgroundThe widely accepted definition of sedentary behaviour [SB] refers to any waking behaviour characterized by an energy expenditure ≤1.5 metabolic equivalents [METs] while in a sitting or reclining posture. At present, there is no single field-based device which objectively measures sleep, posture and activity intensity simultaneously. The aim of this study was to develop a novel integrative procedure [INT] to combine information from two validated activity monitors on sleep, activity intensity and posture, the three key dimensions of SB.MethodsParticipants in this analysis were initially recruited from a series of three studies conducted between December 2014 and June 2016 at the University of Leeds. Sixty-three female participants aged 37.1 (13.6) years with a body mass index of 29.6 (4.7) kg/m2 were continuously monitored for 5–7 days with the SenseWear Armband [SWA] (sleep and activity intensity) and the activPAL [AP] (posture). Data from both activity monitors were analysed separately and integrated resulting in three measures of sedentary time. Differences in Sedentary time between the three measurement methods were assessed as well as how well the three measures correlated.ResultsThe three measures of sedentary time were positively correlated, with the weakest relationship between SEDSWA (awake and <1.5 METs) and SEDAP (awake and sitting/lying posture) [r(61) = .37,p = .003], followed by SEDSWA and SEDINT (awake, <1.5 METs and sitting/lying posture) [r(61) = .58,p < .001], and the strongest relationship was between SEDAP and SEDINT [r(61) = .91,p < .001]. There was a significant difference between the three measures of sedentary time [F(1.18,73.15) = 104.70,p < .001]. Post-hoc tests revealed all three methods differed significantly from each other [p < .001]. SEDSWA resulted in the most sedentary time 11.74 (1.60) hours/day, followed by SEDAP 10.16 (1.75) hours/day, and SEDINT 9.10 (1.67) hours/day. Weekday and weekend day sedentary time did not differ for any of the measurement methods [p = .04–.25].ConclusionInformation from two validated activity monitors was combined to obtain an objective measure of free-living SB based on posture and activity intensity during waking hours. The amount of sedentary time accumulated varied according to the definition of SB and its measurement. The novel data integration and processing procedures presented in this paper represents an opportunity to investigate whether different components of SB are differentially related to health end points.

Gene Prioritization by Compressive Data Fusion and Chaining.

Data integration procedures combine heterogeneous data sets into predictive models, but they are limited to data explicitly related to the target object type, such as genes. Collage is a new data fusion approach to gene prioritization. It considers data sets of various association levels with the prediction task, utilizes collective matrix factorization to compress the data, and chaining to relate different object types contained in a data compendium. Collage prioritizes genes based on their similarity to several seed genes. We tested Collage by prioritizing bacterial response genes in Dictyostelium as a novel model system for prokaryote-eukaryote interactions. Using 4 seed genes and 14 data sets, only one of which was directly related to the bacterial response, Collage proposed 8 candidate genes that were readily validated as necessary for the response of Dictyostelium to Gram-negative bacteria. These findings establish Collage as a method for inferring biological knowledge from the integration of heterogeneous and coarsely related data sets.

A Generic Data Harmonization Process for Cross-linked Research and Network Interaction. Construction and Application for the Lung Cancer Phenotype Database of the German Center for Lung Research.

Joint data analysis is a key requirement in medical research networks. Data are available in heterogeneous formats at each network partner and their harmonization is often rather complex. The objective of our paper is to provide a generic approach for the harmonization process in research networks. We applied the process when harmonizing data from three sites for the Lung Cancer Phenotype Database within the German Center for Lung Research. We developed a spreadsheet-based solution as tool to support the harmonization process for lung cancer data and a data integration procedure based on Talend Open Studio. The harmonization process consists of eight steps describing a systematic approach for defining and reviewing source data elements and standardizing common data elements. The steps for defining common data elements and harmonizing them with local data definitions are repeated until consensus is reached. Application of this process for building the phenotype database led to a common basic data set on lung cancer with 285 structured parameters. The Lung Cancer Phenotype Database was realized as an i2b2 research data warehouse. Data harmonization is a challenging task requiring informatics skills as well as domain knowledge. Our approach facilitates data harmonization by providing guidance through a uniform process that can be applied in a wide range of projects.

Enabling heterogenous multi-scale database for emergency service functions through geoinformation technologies

Abstract. Geographical Information Science (GIS) is now graduated from traditional desktop system to Internet system. Internet GIS is emerging as one of the most promising technologies for addressing Emergency Management. Web services with different privileges are playing an important role in dissemination of the emergency services to the decision makers. Spatial database is one of the most important components in the successful implementation of Emergency Management. It contains spatial data in the form of raster, vector, linked with non-spatial information. Comprehensive data is required to handle emergency situation in different phases. These database elements comprise core data, hazard specific data, corresponding attribute data, and live data coming from the remote locations. Core data sets are minimum required data including base, thematic, infrastructure layers to handle disasters. Disaster specific information is required to handle a particular disaster situation like flood, cyclone, forest fire, earth quake, land slide, drought. In addition to this Emergency Management require many types of data with spatial and temporal attributes that should be made available to the key players in the right format at right time. The vector database needs to be complemented with required resolution satellite imagery for visualisation and analysis in disaster management. Therefore, the database is interconnected and comprehensive to meet the requirement of an Emergency Management. This kind of integrated, comprehensive and structured database with appropriate information is required to obtain right information at right time for the right people. However, building spatial database for Emergency Management is a challenging task because of the key issues such as availability of data, sharing policies, compatible geospatial standards, data interoperability etc. Therefore, to facilitate using, sharing, and integrating the spatial data, there is a need to define standards to build emergency database systems. These include aspects such as i) data integration procedures namely standard coding scheme, schema, meta data format, spatial format ii) database organisation mechanism covering data management, catalogues, data models iii) database dissemination through a suitable environment, as a standard service for effective service dissemination. National Database for Emergency Management (NDEM) is such a comprehensive database for addressing disasters in India at the national level. This paper explains standards for integrating, organising the multi-scale and multi-source data with effective emergency response using customized user interfaces for NDEM. It presents standard procedure for building comprehensive emergency information systems for enabling emergency specific functions through geospatial technologies.

Quantifying the effects of data integration algorithms on the outcomes of a subsurface–land surface processes model

Trans-disciplinary hydrologic models oriented toward practical questions must be accompanied by accurate parameterization techniques. This paper describes the effects of different choices in the integration of various data sources on outcomes of the model Process-based Adaptive Watershed Simulator coupled with the Community Land Model (PAWS + CLM). Using our Hierarchical Stochastic Selection method, the represented land use percentages are much closer to the raw dataset, and lead to a 26% difference in carbon flux from that of the traditional dominant classes method. River bed elevations extracted using a novel algorithm agree well with the groundwater table and significantly increase baseflow contribution to streams relative to a coarse-DEM-based model. The inclusion of additional information in the soil pedotransfer functions drastically shifts ET, Net Primary Production and recharge. These results indicate that judicious treatment of input data has strong impacts on hydrologic and ecosystem fluxes. We emphasize the need to report details of data integration procedures.

Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT2 images and hydrological modeling

• The surface soil moisture content (SMC) dynamic of mountain grasslands is analyzed. • RADARSAT SAR, hydrological model outputs and ground observations are compared. • We evaluate RADARSAT with respect to a hydrological model in estimating SMC. • RADARSAT has a good ability to reproduce SMC patterns for alpine grasslands. • The main physical controls on SMC patterns are soil, land cover and topography. This paper analyzes the spatial patterns of surface soil moisture of alpine meadows and pastures in the Matsch/Mazia Valley in the Italian Alps by comparing estimations from three different sources of information: (I) RADARSAT 2 synthetic aperture radar (SAR) images; (II) simulations by using the GEOtop hydrological model and (III) ground observations, derived from a network of fixed stations and field campaigns with mobile devices. The aim of this paper is to assess the added value of RADARSAT 2 products with respect to a distributed hydrological model in capturing soil moisture patterns in mountain areas, which is a challenging environment with a high degree of spatial variability. Moreover, the physical controls of the observed soil moisture patterns are analyzed by using the hydrological model. Results show that the model, once calibrated for soil and vegetation parameters, predicts the plot-scale temporal dynamic in station locations and the spatial averages with sufficient accuracy. However, the model output shows lower spatial variability with respect to the ground surveys, with a limited capability of reproducing moist areas in irrigated meadows. Differences arise due to difficulties in knowing soil model parameters and irrigation amounts with accurate spatial detail. RADARSAT 2 soil moisture maps well reproduce the spatial ground surveys, as well as over-irrigated meadows. However, SAR products are limited to slopes with a favorable viewing angle, to bare soil or to grassland areas. Moreover, the signal penetration depth is restricted to the soil surface layer. The major control on RADARSAT 2 patterns is land use. Irrigated meadows in the bottom of the valley have moister conditions, with respect to pastures along the upper hillslopes. In this case, model simulations suggest that differences in soil type could have a relevant impact on soil moisture estimation. A secondary control is topography, with increased moisture in convergent locations with a high topographic wetness index. Results suggest that the capability of RADARSAT 2 products to reproduce small-scale (20 m pixels size) surface soil moisture patterns in mountain grassland areas could complement the ability of the hydrological model to predict variations of soil moisture continuously in space and time. Therefore, RADARSAT 2 products can give useful information to improve spatial parameterization and validation of distributed hydrological models in mountain grassland areas, also in the perspective of implementing data integration procedures for operational soil moisture monitoring.