| Abstract | The estimation of topsoil water content is of primary interest in the framework of precision farming, but, in general, such assessment is costly and complicated by several interfering factors which do not allow an accurate prediction. Proximal sensing can provide suitable technological facilities to support researchers and technicians in this task. GPR and EMI sensors are valuable instruments as they can provide very informative covariates to be used for improving soil water content estimation. In the present work, it was explored the single (EMI or GPR) and the combined (EMI + GPR) contribution of these proximal data sources. Furthermore, geostatistical (Ordinary Kriging and Kriging with external drift) and linear mixed effects models were applied to compare their respective predictive capabilities. As a result, GPR demonstrated to be more effective in estimating topsoil water content with respect to EMI but, combining both the information, an improvement in the prediction accuracy was observed. Moreover, adding more covariates in the models (GPR outcomes or GPR + EMI outcomes) allowed filtering out the structured spatial component of soil water content. Finally, the statistical approaches proved to behave very similarly, with a slight better performance of Kriging with external drift. |
| Text | 424059 2019 10.1016/j.geoderma.2019.01.030 Scopus 2 s2.0 85062069662 ISI Web of Science WOS WOS 000463304300027 Kriging with external drift KED Linear mixed effects models LMM Ground penetrating radar GPR Electromagnetic induction EMI Principal component analysis PCA Contribution of EMI and GPR proximal sensing data in soil water content assessment by using linear mixed effects models and geostatistical approaches Barca E.; De Benedetto D.; Stellacci A.M. Water Research Institute IRSA , National Research Council CNR , Bari, , , Italy; Water Research Institute IRSA , National Research Council CNR , Bari, , , Italy; Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, CREA AA, Via Celso Ulpiani 5, Bari, 70125, , Italy; Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro , via Amendola 165/A, Bari, 70126, , Italy The estimation of topsoil water content is of primary interest in the framework of precision farming, but, in general, such assessment is costly and complicated by several interfering factors which do not allow an accurate prediction. Proximal sensing can provide suitable technological facilities to support researchers and technicians in this task. GPR and EMI sensors are valuable instruments as they can provide very informative covariates to be used for improving soil water content estimation. In the present work, it was explored the single EMI or GPR and the combined EMI GPR contribution of these proximal data sources. Furthermore, geostatistical Ordinary Kriging and Kriging with external drift and linear mixed effects models were applied to compare their respective predictive capabilities. As a result, GPR demonstrated to be more effective in estimating topsoil water content with respect to EMI but, combining both the information, an improvement in the prediction accuracy was observed. Moreover, adding more covariates in the models GPR outcomes or GPR EMI outcomes allowed filtering out the structured spatial component of soil water content. Finally, the statistical approaches proved to behave very similarly, with a slight better performance of Kriging with external drift. 343 Published version http //www.scopus.com/record/display.url eid=2 s2.0 85062069662 origin=inward 03.pdf 03.pdf Articolo in rivista Elsevier. 0016 7061 Geoderma Amst. Geoderma Amst. Geoderma Amst. Geoderma. Amst. Geoderma Lausanne Amst. Geoderma New York Amst. Geoderma Oxford Amst. Geoderma Shannon Amst. Geoderma Tokyo Amst. Geoderma London Amst. emanuele.barca BARCA EMANUELE |
