Journal ArticleDOI
Modelling and mapping soil organic carbon stocks in Brazil
Lucas Carvalho Gomes,Lucas Carvalho Gomes,Raiza Moniz Faria,Eliana de Souza,Gustavo Vieira Veloso,Carlos Ernesto Gonçalves Reynaud Schaefer,Elpídio Inácio Fernandes Filho +6 more
TLDR
In this paper, the authors applied a methodological framework to optimize the prediction of soil organic carbon (SOC) stocks for the entire Brazilian territory and determine how the environmental heterogeneity of Brazil influences the SOC stocks distribution.About:
This article is published in Geoderma.The article was published on 2019-04-15. It has received 195 citations till now. The article focuses on the topics: Soil carbon & Soil organic matter.read more
Citations
More filters
Climate change 2014 - Mitigation of climate change
TL;DR: The work of the IPCC Working Group III 5th Assessment report as mentioned in this paper is a comprehensive, objective and policy neutral assessment of the current scientific knowledge on mitigating climate change, which has been extensively reviewed by experts and governments to ensure quality and comprehensiveness.
Journal ArticleDOI
SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty
Laura Poggio,Luis de Sousa,Niels H. Batjes,Gerard B. M. Heuvelink,Bas Kempen,Eloi Ribeiro,David G. Rossiter +6 more
TL;DR: SoilGrids as discussed by the authors produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods to generate the necessary models.
Journal ArticleDOI
Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review
TL;DR: The environmental covariates that have been identified as the most important by RF technique in recent years in regard to digital mapping of SOC are revealed, which may assist in selecting optimum sets of environmental covariate for mapping SOC.
Journal ArticleDOI
Machine learning and soil sciences: a review aided by machine learning tools
TL;DR: A comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (latent Dirichlet allocation) to find patterns in a large collection of text corpora finds research gaps and finds that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve knowledge and understanding of soil.
Journal ArticleDOI
Machine learning for digital soil mapping: Applications, challenges and suggested solutions
TL;DR: For future developments, ML could incorporate three core elements: plausibility, interpretability, and explainability, which will trigger soil scientists to couple model prediction with pedological explanation and understanding of the underlying soil processes.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
Very high resolution interpolated climate surfaces for global land areas.
Robert J. Hijmans,Susan E. Cameron,Susan E. Cameron,Juan L. Parra,Peter G. Jones,Andy Jarvis +5 more
TL;DR: In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).
Journal ArticleDOI
Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Related Papers (5)
SoilGrids250m: Global gridded soil information based on machine learning
Tomislav Hengl,Jorge Mendes de Jesus,Gerard B. M. Heuvelink,Maria Ruiperez Gonzalez,Milan Kilibarda,Aleksandar Blagotić,Wei Shangguan,Marvin N. Wright,Xiaoyuan Geng,Bernhard Bauer-Marschallinger,Mario Guevara,Rodrigo Vargas,R. A. MacMillan,Niels H. Batjes,Johan G. B. Leenaars,Eloi Ribeiro,Ichsani Wheeler,Stephan Mantel,Bas Kempen +18 more