SELECTION OF SAMPLING DENSITY BASED ON DATA FROM AREAS ALREADY MAPPED FOR TRAINING DECISION TREE MODELS IN DIGITAL SOIL MAPPING
01/Jul/2015
In order to study sampling techniques useful for digital soil mapping (DSM), we evaluated the effect of changes in sampling density, based on data from areas already mapped by traditional methods, in regard to the accuracy of decision trees models for generating soil maps using DSM. In two watersheds in northwestern Rio Grande do Sul, Brazil, 1:50,000 scale conventional soils maps were used as reference maps. From the ASTER – GDEM Global Digital Elevation Model and the hydrographic network, maps […]
Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy
01/Dec/2014
Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before […]
Estimation of soil properties by means of diffuse spectroradiometry
01/Aug/2013
Several advances have been made in the field of diffuse spectroradiometry in the last decades. In agriculture, the search for quantification methods of physical and chemical properties of the production environment, based on soil reflectance, has been constantly researched. The aim of this study was to evaluate the possibility of estimating the silt, sand, clay, sum of bases, total iron and organic matter content in soil samples based on the reflected energy. The samples were collected in a 2500 ha […]
