Journal ArticleDOI
Comparison of different approaches to the development of pedotransfer functions for water-retention curves
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TLDR
In this article, both parametric and point estimation PTFs using different approaches: multiple linear regression (MLR), extended nonlinear regression (ENR), and artificial neural network (ANN) are presented for Australian soil.About:
This article is published in Geoderma.The article was published on 1999-12-01. It has received 366 citations till now. The article focuses on the topics: Pedotransfer function & Nonlinear regression.read more
Citations
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Journal ArticleDOI
rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions
TL;DR: In this paper, the authors describe a computer program, rosetta, which implements five hierarchical pedotransfer functions (PTFs) for the estimation of water retention, and the saturated and unsaturated hydraulic conductivity.
Journal ArticleDOI
Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics
TL;DR: In this article, the authors present different equations describing hydraulic characteristics and of pedotransfer functions used to predict parameters in these equations, as well as the use of different soil properties as predictors.
Journal ArticleDOI
Responses of soil heterotrophic respiration to moisture availability: An exploration of processes and models
TL;DR: In this article, the authors provide an overview and new insights into the mechanisms involved in the respiration-moisture relationship, and provide the best estimates of average empirical relationships using published data and comparing the results for contrasting soil types.
MonographDOI
Estimating groundwater recharge
TL;DR: In this article, a critical evaluation of the theory and assumptions that underlie methods for estimating rates of groundwater recharge is provided, with detailed explanations of the methods provided - allowing readers to apply many of the techniques themselves without needing to consult additional references.
Journal ArticleDOI
The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties
José M. Soriano-Disla,Les J. Janik,Raphael A. Viscarra Rossel,Lynne M. Macdonald,Mike J. McLaughlin +4 more
TL;DR: In this article, the applicability of visible (Vis), near-infrared (NIR), and mid infrared (MIR) reflectance spectroscopy for the prediction of soil properties is discussed.
References
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Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Journal ArticleDOI
A closed-form equation for predicting the hydraulic conductivity of unsaturated soils
van Genuchten,M. Th. +1 more
TL;DR: Van Genuchten et al. as mentioned in this paper proposed a closed-form analytical expression for predicting the hydraulic conductivity of unsaturated soils based on the Mualem theory, which can be used to predict the unsaturated hydraulic flow and mass transport in unsaturated zone.
Proceedings Article
Information Theory and an Extention of the Maximum Likelihood Principle
TL;DR: The classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion to provide answers to many practical problems of statistical model fitting.
Book ChapterDOI
Information Theory and an Extension of the Maximum Likelihood Principle
TL;DR: In this paper, it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion.
Related Papers (5)
A closed-form equation for predicting the hydraulic conductivity of unsaturated soils
van Genuchten,M. Th. +1 more