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  • Rural Digital Europe
  • 2014-2023
  • Other research products
  • CemOA
  • Repositori Institucional URV

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Callau, AA; Perez-Albert, Y; Martinez, JV;

    Peri-urban forests often have extensive greenway networks that allow for outdoor recreation. However, information associated with these greenways often does not include their degree of naturalness, which is usually reduced to descriptions of the flora and fauna and often overlooks the factors that reduce naturalness. Therefore, in some cases, the naturalness of these greenways is lower than expected. Quantifying their naturalness would be helpful, especially for hikers interested in appreciating and enjoying nature. Additionally, this information would help outdoor recreation managers to design trails or decide which ones to promote as "greenways". The objectives of this study are (1) to design a method to calculate and map the naturalness of greenways using two approaches, one based on perceptual fieldwork and the other on geographic information systems (GIS); (2) to apply the designed method to a specific greenway; and (3) to compare both methodological approaches. The results show that, for the greenway studied, the naturalness scores obtained are low in all three types of analyses used. Around 70% of the greenway sections in the GIS visibility analysis and 80% in the GIS proximity analysis have a low naturalness index. In comparison, this value is reduced to 40% with the fieldwork analysis. Although the results of the GIS approach (proximity and visibility) generate naturalness indices and spatial patterns that are very similar, they differ significantly from those derived from the fieldwork analysis. The discussion of the results suggests that the three methodologies used are valid for analyzing the degree of naturalness of the trails. However, if used together, it could add flexibility to the type of variables incorporated in the analysis.

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    Authors: Martí E; Sierra J; Mari M; Ortiz C; +4 Authors
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    Authors: Zaragozi, Benito; Trilles, Sergio; Gutierrez, Aaron;

    The article uses passive mobile data to analyse the complex mobilities that occur in a coastal region characterised by seasonal patterns of tourism activity. A large volume of data generated by mobile phone users has been selected and processed to subsequently display the information in the form of visualisations that are useful for transport and tourism research, policy, and practice. More specifically, the analysis consisted of four steps: (1) a dataset containing records for four days-two on summer days and two in winter-was selected, (2) these were aggregated spatially, temporally, and differentiating trips by local residents, national tourists, and international tourists, (3) origin-destination matrices were built, and (4) graph-based visualisations were created to provide evidence on the nature of the mobilities affecting the study area. The results of our work provide new evidence of how the analysis of passive mobile data can be useful to study the effects of tourism seasonality in local mobility patterns.

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    Authors: Gutierrez, Aaron; Domenech, Antoni;

    The article analyses the socio-spatial logic behind the accumulation of foreclosed housing in the hands of large private landlords in the neighbourhoods of all the Catalan cities with over 100,000 inhabitants. Spatial regression and clustering techniques are applied to identify the determinants of the concentration patterns of 10,725 housing units in these cities. The socioeconomic variables, such as income level, percentage of foreign population, level of studies or percentage of unemployed residents, are identified as key explanatory factors of clustering of foreclosures in working-class neighbourhoods. A high presence of previously mortgaged homes is a variable especially relevant in the case of working-class neighbourhoods, but it has no incidence in the case of the medium-high class neighbourhoods. Our findings provide a detailed urban geography of the housing accumulated by banks which, at the same time, correspond to areas in which the vulture funds are focusing their business in the present and in the forthcoming years. New evidences of the spatial logic of the housing crisis and detailed information for the understanding of the new scenarios that have emerged during the post-crisis phase are revealed.

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    Authors: Gao, Q.; Zribi, M.; Escorihuela, M.J.; Baghdadi, N.; +1 Authors

    The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical-vertical) and VH (vertical-horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
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    CemOA
    2018
    Data sources: CemOA
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      CemOA
      2018
      Data sources: CemOA
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    Authors: Lavanholi, R.; Oliveira, F.C.; de Camargo, A.P.; Frizzone, J.A.; +3 Authors

    Emitter clogging is a major problem in microirrigation systems, which may result from the isolated or combined effects of physical, chemical, and biological agents. Clogging caused by suspended solid particles is the most common plugging form of emitters. Water quality and emitter geometry are key aspects in clogging processes. Any suitable test procedure to assess the sensitivity of drippers to clogging should take into account the predominant factors that influence clogging and must reproduce the field conditions. This research set out to assess the performance and suitability of a laboratory clogging test procedure in order to validate a methodology and to provide scientific results that may support the standardization of a clogging test method. The evaluated methodology has been used by the IRSTEA laboratory since 1974 (Platform of Research and experiment on Science and Technology for Irrigation - PReSTI, formerly LERMI) and its contents are currently being discussed by the ISO TC23/SC18 committee. The aim is to define a standardized testing protocol to evaluate the sensitivity of emitters to clogging due to solid particles. Replications analyzing the clogging resistance of four models of emitting pipes were carried out in a laboratory. The clogging test procedure enabled an accurate assessment of the combinations of concentration and size of particles that caused clogging in each model of dripper. However, a significant variability in degree of clogging was identified when the results of replications for each model of dripper were compared. Several requirements, concerns, and improvements related to the clogging test protocol were discussed.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
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    CemOA
    2018
    Data sources: CemOA
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      CemOA
      2018
      Data sources: CemOA
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    Authors: Koech, R.; Molle, B.; Pires de Camargo, A.; Dimaiolo, P.; +5 Authors
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    CemOA
    2015
    Data sources: CemOA
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      CemOA
      2015
      Data sources: CemOA
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    Authors: Gejadze, I.; Malaterre, P.;

    Estimating river discharge from in situ and/or remote sensing data is a key issue for evaluation of water balance at local and global scales and for water management. Variational data assimilation (DA) is a powerful approach used in operational weather and ocean forecasting, which can also be used in this context. A distinctive feature of the river discharge estimation problem is the likely presence of significant uncertainty in principal parameters of a hydraulic model, such as bathymetry and friction, which have to be included into the control vector alongside the discharge. However, the conventional variational DA method being used for solving such extended problems often fails. This happens because the control vector iterates (i.e., approximations arising in the course of minimization) result into hydraulic states not supported by the model. In this paper, we suggest a novel version of the variational DA method specially designed for solving estimation-under-uncertainty problems, which is based on the ideas of iterative regularization. The method is implemented with SIC2, which is a full Saint-Venant based 1D-network model. The SIC2 software is widely used by research, consultant and industrial communities for modeling river, irrigation canal, and drainage network behavior. The adjoint model required for variational DA is obtained by means of automatic differentiation. This is likely to be the first stable consistent adjoint of the 1D-network model of a commercial status in existence. The DA problems considered in this paper are offtake/tributary estimation under uncertainty in the cross-device parameters and inflow discharge estimation under uncertainty in the bathymetry defining parameters and the friction coefficient. Numerical tests have been designed to understand identifiability of discharge given uncertainty in bathymetry and friction. The developed methodology, and software seems useful in the context of the future Surface Water and Ocean Topography satellite mission.

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    CemOA
    2017
    Data sources: CemOA
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      CemOA
      2017
      Data sources: CemOA
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    Authors: Fiz, Ignacio; Cuesta, Rosa; Subias, Eva; Martin, Pere Manel;

    This article presents the first results obtained from the use of high-resolution images from the SAR-X sensor of the PAZ satellite platform. These are in result of the application of various radar image-treatment techniques, with which we wanted to carry out a non-invasive exploration of areas of the archaeological site of Clunia (Burgos, Spain). These areas were analyzed and contrasted with other sources from high-resolution multispectral images (TripleSat), or from digital surface models obtained from Laser Imaging Detection and Ranging (LiDAR) data from the National Plan for Aerial Orthophotography (PNOA), and treated with image enhancement functions (Relief Visualization Tools (RVT)). Moreover, they were compared with multispectral images created from the Infrared Red Blue (IRRB) data contained in the same LiDAR points.

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    Authors: Vaudour, E.; Gomez, C.; Loiseau, T.; Baghdadi, N.; +4 Authors

    The spatial assessment of soil organic carbon (SOC) is a major environmental challenge, notably for evaluating soil carbon stocks. Recent works have shown the capability of Sentinel-2 optical data to predict SOC content over temperate agroecosystems characterized by annual crops, using a single acquisition date. Considering a Sentinel-2 time series, this work intends to analyze the impact of acquisition date, and related weather and soil surface conditions on the prediction performance of topsoil SOC content (plough layer). A Sentinel-2 time-series was gathered, comprised of the dates corresponding to both the maximum of bare soil coverage and minimum of cloud coverage. Cross-validated partial least squares regression (PLSR) models were constructed between soil reflectance image spectra, and SOC content analyzed from 329 top soil samples collected over the study area. Cross-validation R2 ranged from 0.005 to 0.58, root mean square error from 5.86 to 3.02 g·kg-1 and residual prediction deviation values from 1.0 to 1.5 (without unit), according to date. The main factors influencing these differences were soil roughness, in conjunction with soil moisture, and the cloud and cloud shadow cover of the entire tile. The best performing dates were spring dates characterized by both lowest soil surface roughness and moisture content. Normalized difference vegetation index (NDVI) values below 0.35 did not influence prediction performance. This consolidates the previous results obtained during single date acquisitions and offers wider perspectives for the further use of Sentinel-2 into multidate mosaics for digital soil mapping.

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    2019
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      CemOA
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    Authors: Callau, AA; Perez-Albert, Y; Martinez, JV;

    Peri-urban forests often have extensive greenway networks that allow for outdoor recreation. However, information associated with these greenways often does not include their degree of naturalness, which is usually reduced to descriptions of the flora and fauna and often overlooks the factors that reduce naturalness. Therefore, in some cases, the naturalness of these greenways is lower than expected. Quantifying their naturalness would be helpful, especially for hikers interested in appreciating and enjoying nature. Additionally, this information would help outdoor recreation managers to design trails or decide which ones to promote as "greenways". The objectives of this study are (1) to design a method to calculate and map the naturalness of greenways using two approaches, one based on perceptual fieldwork and the other on geographic information systems (GIS); (2) to apply the designed method to a specific greenway; and (3) to compare both methodological approaches. The results show that, for the greenway studied, the naturalness scores obtained are low in all three types of analyses used. Around 70% of the greenway sections in the GIS visibility analysis and 80% in the GIS proximity analysis have a low naturalness index. In comparison, this value is reduced to 40% with the fieldwork analysis. Although the results of the GIS approach (proximity and visibility) generate naturalness indices and spatial patterns that are very similar, they differ significantly from those derived from the fieldwork analysis. The discussion of the results suggests that the three methodologies used are valid for analyzing the degree of naturalness of the trails. However, if used together, it could add flexibility to the type of variables incorporated in the analysis.

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    Authors: Martí E; Sierra J; Mari M; Ortiz C; +4 Authors
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    Authors: Zaragozi, Benito; Trilles, Sergio; Gutierrez, Aaron;

    The article uses passive mobile data to analyse the complex mobilities that occur in a coastal region characterised by seasonal patterns of tourism activity. A large volume of data generated by mobile phone users has been selected and processed to subsequently display the information in the form of visualisations that are useful for transport and tourism research, policy, and practice. More specifically, the analysis consisted of four steps: (1) a dataset containing records for four days-two on summer days and two in winter-was selected, (2) these were aggregated spatially, temporally, and differentiating trips by local residents, national tourists, and international tourists, (3) origin-destination matrices were built, and (4) graph-based visualisations were created to provide evidence on the nature of the mobilities affecting the study area. The results of our work provide new evidence of how the analysis of passive mobile data can be useful to study the effects of tourism seasonality in local mobility patterns.

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    Authors: Gutierrez, Aaron; Domenech, Antoni;

    The article analyses the socio-spatial logic behind the accumulation of foreclosed housing in the hands of large private landlords in the neighbourhoods of all the Catalan cities with over 100,000 inhabitants. Spatial regression and clustering techniques are applied to identify the determinants of the concentration patterns of 10,725 housing units in these cities. The socioeconomic variables, such as income level, percentage of foreign population, level of studies or percentage of unemployed residents, are identified as key explanatory factors of clustering of foreclosures in working-class neighbourhoods. A high presence of previously mortgaged homes is a variable especially relevant in the case of working-class neighbourhoods, but it has no incidence in the case of the medium-high class neighbourhoods. Our findings provide a detailed urban geography of the housing accumulated by banks which, at the same time, correspond to areas in which the vulture funds are focusing their business in the present and in the forthcoming years. New evidences of the spatial logic of the housing crisis and detailed information for the understanding of the new scenarios that have emerged during the post-crisis phase are revealed.

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    Authors: Gao, Q.; Zribi, M.; Escorihuela, M.J.; Baghdadi, N.; +1 Authors

    The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical-vertical) and VH (vertical-horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.

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    CemOA
    2018
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      CemOA
      2018
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    Authors: Lavanholi, R.; Oliveira, F.C.; de Camargo, A.P.; Frizzone, J.A.; +3 Authors

    Emitter clogging is a major problem in microirrigation systems, which may result from the isolated or combined effects of physical, chemical, and biological agents. Clogging caused by suspended solid particles is the most common plugging form of emitters. Water quality and emitter geometry are key aspects in clogging processes. Any suitable test procedure to assess the sensitivity of drippers to clogging should take into account the predominant factors that influence clogging and must reproduce the field conditions. This research set out to assess the performance and suitability of a laboratory clogging test procedure in order to validate a methodology and to provide scientific results that may support the standardization of a clogging test method. The evaluated methodology has been used by the IRSTEA laboratory since 1974 (Platform of Research and experiment on Science and Technology for Irrigation - PReSTI, formerly LERMI) and its contents are currently being discussed by the ISO TC23/SC18 committee. The aim is to define a standardized testing protocol to evaluate the sensitivity of emitters to clogging due to solid particles. Replications analyzing the clogging resistance of four models of emitting pipes were carried out in a laboratory. The clogging test procedure enabled an accurate assessment of the combinations of concentration and size of particles that caused clogging in each model of dripper. However, a significant variability in degree of clogging was identified when the results of replications for each model of dripper were compared. Several requirements, concerns, and improvements related to the clogging test protocol were discussed.

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    CemOA
    2018
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      CemOA
      2018
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    Authors: Koech, R.; Molle, B.; Pires de Camargo, A.; Dimaiolo, P.; +5 Authors
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    CemOA
    2015
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      CemOA
      2015
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    Authors: Gejadze, I.; Malaterre, P.;

    Estimating river discharge from in situ and/or remote sensing data is a key issue for evaluation of water balance at local and global scales and for water management. Variational data assimilation (DA) is a powerful approach used in operational weather and ocean forecasting, which can also be used in this context. A distinctive feature of the river discharge estimation problem is the likely presence of significant uncertainty in principal parameters of a hydraulic model, such as bathymetry and friction, which have to be included into the control vector alongside the discharge. However, the conventional variational DA method being used for solving such extended problems often fails. This happens because the control vector iterates (i.e., approximations arising in the course of minimization) result into hydraulic states not supported by the model. In this paper, we suggest a novel version of the variational DA method specially designed for solving estimation-under-uncertainty problems, which is based on the ideas of iterative regularization. The method is implemented with SIC2, which is a full Saint-Venant based 1D-network model. The SIC2 software is widely used by research, consultant and industrial communities for modeling river, irrigation canal, and drainage network behavior. The adjoint model required for variational DA is obtained by means of automatic differentiation. This is likely to be the first stable consistent adjoint of the 1D-network model of a commercial status in existence. The DA problems considered in this paper are offtake/tributary estimation under uncertainty in the cross-device parameters and inflow discharge estimation under uncertainty in the bathymetry defining parameters and the friction coefficient. Numerical tests have been designed to understand identifiability of discharge given uncertainty in bathymetry and friction. The developed methodology, and software seems useful in the context of the future Surface Water and Ocean Topography satellite mission.

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    CemOA
    2017
    Data sources: CemOA
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      CemOA
      2017
      Data sources: CemOA
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    Authors: Fiz, Ignacio; Cuesta, Rosa; Subias, Eva; Martin, Pere Manel;

    This article presents the first results obtained from the use of high-resolution images from the SAR-X sensor of the PAZ satellite platform. These are in result of the application of various radar image-treatment techniques, with which we wanted to carry out a non-invasive exploration of areas of the archaeological site of Clunia (Burgos, Spain). These areas were analyzed and contrasted with other sources from high-resolution multispectral images (TripleSat), or from digital surface models obtained from Laser Imaging Detection and Ranging (LiDAR) data from the National Plan for Aerial Orthophotography (PNOA), and treated with image enhancement functions (Relief Visualization Tools (RVT)). Moreover, they were compared with multispectral images created from the Infrared Red Blue (IRRB) data contained in the same LiDAR points.

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    Authors: Vaudour, E.; Gomez, C.; Loiseau, T.; Baghdadi, N.; +4 Authors

    The spatial assessment of soil organic carbon (SOC) is a major environmental challenge, notably for evaluating soil carbon stocks. Recent works have shown the capability of Sentinel-2 optical data to predict SOC content over temperate agroecosystems characterized by annual crops, using a single acquisition date. Considering a Sentinel-2 time series, this work intends to analyze the impact of acquisition date, and related weather and soil surface conditions on the prediction performance of topsoil SOC content (plough layer). A Sentinel-2 time-series was gathered, comprised of the dates corresponding to both the maximum of bare soil coverage and minimum of cloud coverage. Cross-validated partial least squares regression (PLSR) models were constructed between soil reflectance image spectra, and SOC content analyzed from 329 top soil samples collected over the study area. Cross-validation R2 ranged from 0.005 to 0.58, root mean square error from 5.86 to 3.02 g·kg-1 and residual prediction deviation values from 1.0 to 1.5 (without unit), according to date. The main factors influencing these differences were soil roughness, in conjunction with soil moisture, and the cloud and cloud shadow cover of the entire tile. The best performing dates were spring dates characterized by both lowest soil surface roughness and moisture content. Normalized difference vegetation index (NDVI) values below 0.35 did not influence prediction performance. This consolidates the previous results obtained during single date acquisitions and offers wider perspectives for the further use of Sentinel-2 into multidate mosaics for digital soil mapping.

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    CemOA
    2019
    Data sources: CemOA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      CemOA
      2019
      Data sources: CemOA