4.5 Article

Accounting for analytical and proximal soil sensing errors in digital soil mapping

Related references

Note: Only part of the references are listed.
Article Soil Science

Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in theCantabrianregion (Spain)

Chantal Mechtildis Johanna Hendriks et al.

Summary: Mechanistic soil models were used to develop a mechanistic model for digital soil mapping that predicted SOM stocks. The applicability of the mechanistic approach needs to be explored for different soil properties and regions. Theoretically, mechanistic models can replace the statistical relationships in digital soil mapping.

EUROPEAN JOURNAL OF SOIL SCIENCE (2021)

Review Soil Science

Perspectives on validation in digital soil mapping of continuous attributes-A review

Kristin Piikki et al.

Summary: The systematic mapping of validation methods used in digital soil mapping (DSM) indicated that while most publications include map validation, essential information such as sampling design and sample support is often lacking in the method descriptions, making interpretation of validation metrics difficult and compromising their usefulness.

SOIL USE AND MANAGEMENT (2021)

Article Soil Science

Density of soil observations in digital soil mapping: A study in the Mayenne region, France

Thomas Loiseau et al.

Summary: The study found that with increasing density of observations, ordinary kriging (OK) may perform as well or even better than quantile random forest (QRF), depending on particle-size distribution. For silt prediction, OK was systematically better than QRF. However, the prediction intervals were much larger for OK than for QRF, and OK did not seem to estimate uncertainty correctly.

GEODERMA REGIONAL (2021)

Article Soil Science

Uncertainty assessment of soil available water capacity using error propagation: A test in Languedoc-Roussillon

Quentin Styc et al.

Summary: The study aimed to develop a method for mapping Soil Available Water Capacity (SAWC) that could predict SAWC values at different maximum rooting depths and their uncertainties. By considering the correlations between soil layer errors, the accuracy of SAWC predictions and uncertainty estimates was improved.

GEODERMA (2021)

Article Soil Science

SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty

Laura Poggio et al.

Summary: SoilGrids generates global maps of soil properties using advanced technology and machine learning methods, incorporating soil observations and environmental factors to predict and analyze various soil properties worldwide. The research shows a good predictive performance for global soil properties, but also reveals the need for more soil observation data, especially in high-latitude regions.
Review Geosciences, Multidisciplinary

Machine learning for digital soil mapping: Applications, challenges and suggested solutions

Alexandre M. J-C Wadoux et al.

EARTH-SCIENCE REVIEWS (2020)

Article Soil Science

Sampling design optimization for soil mapping with random forest

Alexandre M. J-C. Wadoux et al.

GEODERMA (2019)

Article Computer Science, Interdisciplinary Applications

Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation

Hanna Meyer et al.

ENVIRONMENTAL MODELLING & SOFTWARE (2018)

Article Soil Science

Accounting for non-stationary variance in geostatistical mapping of soil properties

Alexandre M. J-C. Wadoux et al.

GEODERMA (2018)

Article Environmental Sciences

The cost-efficiency and reliability of two methods for soil organic C accounting

Raphael A. Viscarra Rossel et al.

LAND DEGRADATION & DEVELOPMENT (2018)

Article Environmental Sciences

An assessment of the variation of soil properties with landscape attributes in the highlands of Cameroon

Bertin Takoutsing et al.

LAND DEGRADATION & DEVELOPMENT (2018)

Article Environmental Sciences

Accounting for the measurement error of spectroscopically inferred soil carbon data for improved precision of spatial predictions

P. D. S. N. Somarathna et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2018)

Article Soil Science

Digital soil mapping across the globe

Dominique Arrouays et al.

GEODERMA REGIONAL (2017)

Article Automation & Control Systems

Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties

Andrew M. Sila et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2016)

Article Geosciences, Multidisciplinary

A global spectral library to characterize the world's soil

R. A. Viscarra Rossel et al.

EARTH-SCIENCE REVIEWS (2016)

Article Soil Science

Digital soil mapping: A brief history and some lessons

Budiman Minasny et al.

GEODERMA (2016)

Article Agriculture, Multidisciplinary

Land health surveillance and response: A framework for evidence-informed land management

Keith D. Shepherd et al.

AGRICULTURAL SYSTEMS (2015)

Article Soil Science

Validation of digital soil maps at different spatial supports

T. F. A. Bishop et al.

GEODERMA (2015)

Article Multidisciplinary Sciences

Taking account of uncertainties in digital land suitability assessment

Brendan P. Malone et al.

PEERJ (2015)

Article Environmental Sciences

Mapping of soil organic carbon stocks for spatially explicit assessments of climate change mitigation potential

Tor-Gunnar Vagen et al.

ENVIRONMENTAL RESEARCH LETTERS (2013)

Article Multidisciplinary Sciences

High-Resolution Global Maps of 21st-Century Forest Cover Change

M. C. Hansen et al.

SCIENCE (2013)

Article Soil Science

An error budget for different sources of error in digital soil mapping

M. A. Nelson et al.

EUROPEAN JOURNAL OF SOIL SCIENCE (2011)

Article Soil Science

Sampling for validation of digital soil maps

D. J. Brus et al.

EUROPEAN JOURNAL OF SOIL SCIENCE (2011)

Article Statistics & Probability

Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data

Xin Gao et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2010)

Article Soil Science

Prediction of Soil Fertility Properties from a Globally Distributed Soil Mid-Infrared Spectral Library

Thomas Terhoeven-Urselmans et al.

SOIL SCIENCE SOCIETY OF AMERICA JOURNAL (2010)

Article Computer Science, Artificial Intelligence

Machine learning approaches for estimation of prediction interval for the model output

Durga L. Shrestha et al.

NEURAL NETWORKS (2006)

Article Computer Science, Artificial Intelligence

Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy

HC Peng et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2005)

Article Soil Science

Model-based analysis using REML for inference from systematically sampled data on soil

RM Lark et al.

EUROPEAN JOURNAL OF SOIL SCIENCE (2004)

Article Geography, Physical

The shuttle radar topography mission - a new class of digital elevation models acquired by spaceborne radar

B Rabus et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2003)

Review Soil Science

On digital soil mapping

AB McBratney et al.

GEODERMA (2003)

Article Soil Science

Geostatistical modelling of uncertainty in soil science

P Goovaerts

GEODERMA (2001)