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All publications of “André Carnieletto Dotto”

3 results

Satellite Spectral Data on the Quantification of Soil Particle Size from Different Geographic Regions

José Alexandre Melo Demattê, Clécia Cristina Barbosa Guimarães, Caio Troula Fongaro, Emmily Larissa Felipe Vidoy, Veridiana Maria Sayão, André Carnieletto Dotto, [...]

12/Sep/2018

ABSTRACT: The study of soils, including their physical and chemical properties, is essential for agricultural management. Soil quality must be maintained to ensure sustainable production of food and conservation of natural resources. In this context, soil mapping is important to provide spatial information, which can be performed using remote sensing (RS) techniques. Modeling through use of satellite data is uncertain regarding the amplitude of replicability of the models. The aim of this study was to develop a quantification model for […]

Assessment of Digital Elevation Model for Digital Soil Mapping in a Watershed with Gently Undulating Topography

Jean Michel Moura-Bueno, Ricardo Simão Diniz Dalmolin, Alexandre ten Caten, Luis Fernando Chimelo Ruiz, Priscila Vogelei Ramos, André Carnieletto Dotto

06/Jun/2016

ABSTRACT Terrain attributes (TAs) derived from digital elevation models (DEMs) are frequently used in digital soil mapping (DSM) as auxiliary covariates in the construction of prediction models. The DEMs and information extracted from it may be limited with regard to the spatial resolution and error magnitude, and can differ in the behavior of terrain features. The objective of this study was to evaluate the quality and limitations of free DEM data and to evaluate a topographic survey (TS) underlying the […]

Potential of Spectroradiometry to Classify Soil Clay Content

André Carnieletto Dotto, Ricardo Simão Diniz Dalmolin, Alexandre ten Caten, Jean Michel Moura-Bueno

06/Apr/2016

ABSTRACT Diffuse reflectance spectroscopy (DRS) is a fast and cheap alternative for soil clay, but needs further investigation to assess the scope of application. The purpose of the study was to develop a linear regression model to predict clay content from DRS data, to classify the soils into three textural classes, similar to those defined by a regulation of the Brazilian Ministry of Agriculture, Livestock and Food Supply. The DRS data of 412 soil samples, from the 0.0-0.5 m layer, […]