caa a22 4500 467903913 CHVBK 20180406152827.0 cr unu---uuuuu 170328e20061001xx s 000 0 eng 10.1007/s10661-005-9116-2 doi (NATIONALLICENCE)springer-10.1007/s10661-005-9116-2 Prediction of Near-Surface Soil Moisture at Large Scale by Digital Terrain Modeling and Neural Networks [Elektronische Daten] [J. Contador, M. Maneta, S. Schnabel] The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained. Springer Science+Business Media, Inc., 2006 dehesa nationallicence topographic variables nationallicence forecasting soil moisture nationallicence sampling nationallicence Contador J. Department of Geography and Land Planning, University of Extremadura, Avda. Universidad s/n, 10071, Cáceres, Spain aut Maneta M. Department of Geography and Land Planning, University of Extremadura, Avda. Universidad s/n, 10071, Cáceres, Spain aut Schnabel S. Department of Geography and Land Planning, University of Extremadura, Avda. Universidad s/n, 10071, Cáceres, Spain aut Environmental Monitoring and Assessment Springer Netherlands 121/1-3(2006-10-01), 213-232 0167-6369 121:1-3<213 2006 121 10661 https://doi.org/10.1007/s10661-005-9116-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10661-005-9116-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Contador J. Department of Geography and Land Planning, University of Extremadura, Avda. Universidad s/n, 10071, Cáceres, Spain aut NATIONALLICENCE 700 1- Maneta M. Department of Geography and Land Planning, University of Extremadura, Avda. Universidad s/n, 10071, Cáceres, Spain aut NATIONALLICENCE 700 1- Schnabel S. Department of Geography and Land Planning, University of Extremadura, Avda. Universidad s/n, 10071, Cáceres, Spain aut NATIONALLICENCE 773 0- Environmental Monitoring and Assessment Springer Netherlands 121/1-3(2006-10-01), 213-232 0167-6369 121:1-3<213 2006 121 10661 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer