Journal ArticleDOI
On digital soil mapping
TLDR
The generic framework, which the authors call the scorpanSSPFe (soil spatial prediction function with spatially autocorrelated errors) method, is particularly relevant for those places where soil resource information is limited.About:
This article is published in Geoderma.The article was published on 2003-11-01. It has received 2527 citations till now. The article focuses on the topics: Digital soil mapping & SCORPAN.read more
Citations
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Journal ArticleDOI
Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties
TL;DR: In this article, partial least squares regression (PLSR) was used to construct calibration models which were independently validated for the prediction of various soil properties from the soil spectra, including soil pHCa,p H w, lime requirement (LR), organic carbon (OC), clay, silt, sand, cation exchange capacity, exchangeable calcium (Ca), exchangeable aluminium (Al), nitrate-nitrogen (NO3-N), available phosphorus (PCol), exchangeability potassium (K) and electrical conductivity (EC).
Posted ContentDOI
System for Automated Geoscientific Analyses (SAGA) v. 2.1.4
Olaf Conrad,Benjamin Bechtel,Michael Bock,Helge Dietrich,Elke Kerstin Fischer,Lars Gerlitz,Jan Wehberg,V. Wichmann,Jürgen Böhner +8 more
TL;DR: The wide spectrum of scientific applications of SAGA is highlighted in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.
Journal ArticleDOI
A generic framework for spatial prediction of soil variables based on regression-kriging
TL;DR: In this paper, a methodological framework for spatial prediction based on regression-kriging is described and compared with ordinary kriging and plain regression, which can adopt both continuous and categorical soil variables in a semi-automated or automated manner.
Journal ArticleDOI
Soil organic carbon storage as a key function of soils - A review of drivers and indicators at various scales
Martin Wiesmeier,Livia Urbanski,Eleanor Hobley,Birgit Lang,Margit von Lützow,Erika Marin-Spiotta,Bas van Wesemael,Eva Rabot,Mareike Ließ,Noelia Garcia-Franco,Ute Wollschläger,Hans-Jörg Vogel,Ingrid Kögel-Knabner +12 more
TL;DR: In this paper, the authors identify measurable biotic or abiotic properties that control soil organic carbon (SOC) storage at different spatial scales and could serve as indicators for an efficient quantification of SOC.
Journal ArticleDOI
Digital Soil Map of the World
Pedro A. Sanchez,Sonya Ahamed,Florence Carré,Alfred E. Hartemink,Jonathan Hempel,Jeroen Huising,Philippe Lagacherie,Alex B. McBratney,Neil McKenzie,Maria de Lourdes Mendonça-Santos,Budiman Minasny,Luca Montanarella,Peter F. Okoth,Cheryl A. Palm,Jeffrey D. Sachs,Keith D. Shepherd,Tor-Gunnar Vågen,Bernard Vanlauwe,Markus G. Walsh,Leigh A. Winowiecki,Gan-Lin Zhang +20 more
TL;DR: Increased demand and advanced techniques could lead to more refined mapping and management of soils, and conventional soil mapping delineates space mostly according to qualitative criteria and renders maps using a series of polygons, which limits resolution.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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.
Book
The Structure of Scientific Revolutions
TL;DR: The Structure of Scientific Revolutions as discussed by the authors is a seminal work in the history of science and philosophy of science, and it has been widely cited as a major source of inspiration for the present generation of scientists.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.