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Covariates in sample planning optimization for digital soil fertility mapping in agricultural areas
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Quantitative analysis of land surface topography
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Improving soil property maps for precision agriculture in the presence of outliers using covariates
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Factors of soil formation. A system of quantitative pedology
inferredcites →https://doi.org/10.1016/j.aasci.2018.06.003openalex-snapshot
Assessment of spatial variability of soil properties using geostatistical approach of lateritic soil (West Bengal, India)
inferredcites →https://doi.org/10.1016/j.advwatres.2017.06.005openalex-snapshot
Sampling design optimisation for rainfall prediction using a non-stationary geostatistical model
inferredcites →https://doi.org/10.1016/j.cageo.2005.12.009openalex-snapshot
A conditioned Latin hypercube method for sampling in the presence of ancillary information
inferredcites →https://doi.org/10.1016/j.cageo.2010.04.005openalex-snapshot
An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means
inferredcites →https://doi.org/10.1016/j.catena.2013.09.006openalex-snapshot
A tutorial guide to geostatistics: Computing and modelling variograms and kriging
inferredcites →https://doi.org/10.1016/j.earscirev.2020.103359openalex-snapshot
Machine learning for digital soil mapping: Applications, challenges and suggested solutions
inferredcites →https://doi.org/10.1016/j.geoderma.2006.10.016openalex-snapshot
Optimization of sample patterns for universal kriging of environmental variables
inferredcites →https://doi.org/10.1016/j.geoderma.2009.07.005openalex-snapshot
Sampling design optimization for multivariate soil mapping
inferredcites →https://doi.org/10.1016/j.geoderma.2013.05.029openalex-snapshot
Regional scale mapping of soil properties and their uncertainty with a large number of satellite-derived covariates
inferredcites →https://doi.org/10.1016/j.geoderma.2017.06.001openalex-snapshot
Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information
inferredcites →https://doi.org/10.1016/j.geoderma.2017.12.022openalex-snapshot
How should a spatial-coverage sample design for a geostatistical soil survey be supplemented to support estimation of spatial covariance parameters?
inferredcites →https://doi.org/10.1016/j.geoderma.2018.02.030openalex-snapshot
Optimization of second-phase sampling for multivariate soil mapping purposes: Case study from a wine region, Hungary
inferredcites →https://doi.org/10.1016/j.geoderma.2018.03.026openalex-snapshot
Satellite land surface temperature and reflectance related with soil attributes
inferredcites →https://doi.org/10.1016/j.geoderma.2018.07.036openalex-snapshot
Sampling for digital soil mapping: A tutorial supported by R scripts
inferredcites →https://doi.org/10.1016/j.geoderma.2019.05.031openalex-snapshot
Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review
inferredcites →https://doi.org/10.1016/j.geoderma.2021.115531openalex-snapshot
A multiple soil properties oriented representative sampling strategy for digital soil mapping
inferredcites →https://doi.org/10.1016/j.jag.2012.07.004openalex-snapshot
Representing major soil variability at regional scale by constrained Latin Hypercube Sampling of remote sensing data
inferredcites →https://doi.org/10.1016/j.spasta.2012.11.001openalex-snapshot
Optimized multi-phase sampling for soil remediation surveys
inferredcites →https://doi.org/10.1016/j.spasta.2020.100461openalex-snapshot
Using multiple linear regression and random forests to identify spatial poverty determinants in rural China
inferredcites →https://doi.org/10.1016/s0016-7061(98)00056-1openalex-snapshot
Constrained optimisation of soil sampling for minimisation of the kriging variance
inferredcites →https://doi.org/10.1016/s0166-2481(06)31014-8openalex-snapshot
Chapter 14 Designing Spatial Coverage Samples Using the k-means Clustering Algorithm
inferredcites →https://doi.org/10.1111/ejss.12790openalex-snapshot
Pedology and digital soil mapping (DSM)
inferredcites →https://doi.org/10.1127/0941-2948/2013/0507openalex-snapshot
Köppen's climate classification map for Brazil
inferredcites →https://doi.org/10.1134/s1064229312040047openalex-snapshot
The Dokuchaev hypothesis as a basis for predictive digital soil mapping (on the 125th anniversary of its publication)
inferredcites →https://doi.org/10.3390/rs12162534openalex-snapshot
Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery
inferredcites →https://doi.org/10.5194/gmd-8-1991-2015openalex-snapshot
System for Automated Geoscientific Analyses (SAGA) v. 2.1.4
inferredcites →https://doi.org/10.5194/soil-4-1-2018openalex-snapshot
Evaluation of digital soil mapping approaches with large sets of environmental covariates
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4764373openalex-extracted
Influence of Data Interpolation on the Characterization of Spatial Covariate Importance in Digital Soil Mapping
inferred← citeshttps://doi.org/10.1007/s10661-023-12088-7openalex-snapshot
Enhancing the accuracy of digital soil mapping using the surface and subsurface soil characteristics as continuous diagnostic layers
inferred← citeshttps://doi.org/10.1007/s11119-025-10311-8openalex-snapshot
Performance of interpolation methods in digital soil mapping: the influence of data characteristics
inferred← citeshttps://doi.org/10.1007/s11119-026-10404-yopenalex-snapshot
Complementarity of proximal and orbital soil sensing for mapping on-farm soil variability
inferred← citeshttps://doi.org/10.1007/s42729-023-01470-6openalex-snapshot
Sampling Design and Spatial Modeling of Available Phosphorus in a Complex Agricultural Area in Southern Brazil
inferred← citeshttps://doi.org/10.1007/s42729-026-03528-7openalex-snapshot
Crop-field scale prediction of soil organic carbon in a complex terrain and its implications for nitrogen recommendation
inferred← citeshttps://doi.org/10.1016/b978-0-44-333376-7.00033-1openalex-snapshot
Soil-mapping applications
inferred← citeshttps://doi.org/10.1016/b978-0-44-333376-7.00044-6openalex-snapshot
Full list of references
inferred← citeshttps://doi.org/10.1016/b978-0-443-29879-0.00005-8openalex-snapshot
Applications of soil sensors
inferred← citeshttps://doi.org/10.1016/j.atech.2025.101059openalex-snapshot
Enhancing soil organic carbon prediction in coastal farmlands using multi-source remote sensing data and machine learning
inferred← citeshttps://doi.org/10.1016/j.atech.2026.102360openalex-snapshot
Enhancing soil nutrient prediction accuracy using a robust UAV-based modeling framework in dryland environments
inferred← citeshttps://doi.org/10.1016/j.catena.2024.108312openalex-snapshot
Assessing the potential of multi-source remote sensing data for cropland soil organic matter mapping in hilly and mountainous areas
inferred← citeshttps://doi.org/10.1016/j.still.2024.106087openalex-snapshot
Temporal evaluation of soil chemical quality using VNIR and XRF spectroscopies
inferred← citeshttps://doi.org/10.1016/j.still.2025.106567openalex-snapshot
Multi-spectral evaluation of total nitrogen, phosphorus and potassium content in soil using Vis-NIR spectroscopy based on a modified support vector machine with whale optimization algorithm
inferred← citeshttps://doi.org/10.1163/9789004725232_058openalex-snapshot
Complementarity of proximal soil sensing techniques for mapping clay and chemical quality index
inferred← citeshttps://doi.org/10.1590/1809-4430-eng.agric.v44e20230148/2024openalex-snapshot
CLASSIFICATION OF THE OCCURRENCE OF BROADLEAF WEEDS IN NARROW-LEAF CROPS
inferred← citeshttps://doi.org/10.1590/1809-4430-eng.agric.v46e20250063/2026openalex-snapshot
Optimizing the number of subsamples for characterization of soil attributes in management zones
inferred← citeshttps://doi.org/10.19028/jtep.012.2.242-258openalex-snapshot
Macro-Nutrient Prediction of Paddy Field Soil Using Artificial Neural Network and NIR Spectroscopy
inferred← citeshttps://doi.org/10.2139/ssrn.7309011openalex-snapshot
Best Fit Interpolator: A QGIS decision-support plugin for comparing, validating, and applying spatial interpolation methods
inferred← citeshttps://doi.org/10.2139/ssrn.7345477openalex-snapshot
Digital and precision agriculture in complex terrain: factors influencing soil nutrient mapping
inferred← citeshttps://doi.org/10.3390/agriengineering7010010openalex-snapshot
Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes
inferred← citeshttps://doi.org/10.3390/agriengineering7010021openalex-snapshot
Environmental Covariates for Sampling Optimization and Pest Prediction in Soybean Crops
inferred← citeshttps://doi.org/10.3390/agriengineering7120420openalex-snapshot
Precision Zones: An Open-Source QGIS Plugin for Management-Zone Segmentation in Precision Agriculture
inferred← citeshttps://doi.org/10.3390/agriengineering8100428openalex-snapshot
Exploratory Evaluation of UAV-Guided and Conventional Soil Sampling Strategies in a Semi-Arid Agroecosystem
inferred← citeshttps://doi.org/10.3390/agronomy15051220openalex-snapshot
Using Constrained K-Means Clustering for Soil Texture Mapping with Limited Soil Samples
inferred← citeshttps://doi.org/10.46604/aiti.2023.12683openalex-snapshot
Estimating Macronutrient Content of Paddy Soil Based on Near-Infrared Spectroscopy Technology Using Multiple Linear Regression
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