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Digital Mapping of Agricultural Soils Texture of the Brazilian Cerrado Biome

https://doi.org/10.2139/ssrn.4900174
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inferredcites →https://doi.org/10.1002/hyp.10216openalex-snapshot
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inferredcites →https://doi.org/10.1002/joc.1276openalex-snapshot
Very high resolution interpolated climate surfaces for global land areas
inferredcites →https://doi.org/10.1002/jpln.202200417openalex-snapshot
World Reference Base for Soil Resources—Its fourth edition and its history
inferredcites →https://doi.org/10.1002/widm.1301openalex-snapshot
Hyperparameters and tuning strategies for random forest
inferredcites →https://doi.org/10.1007/978-1-349-15508-8_9openalex-snapshot
The Geographic Cycle in Periglacial Regions as it is Related to Climatic Geomorphology
inferredcites →https://doi.org/10.1007/978-3-031-19949-3_5openalex-snapshot
Soils of Cerrados, the Brazilian Savannas
inferredcites →https://doi.org/10.1007/s10661-009-0988-4openalex-snapshot
Land cover mapping of the tropical savanna region in Brazil
inferredcites →https://doi.org/10.1016/bs.agron.2015.12.004openalex-snapshot
A Career Perspective on Soil Management in the Cerrado Region of Brazil
inferredcites →https://doi.org/10.1016/j.catena.2016.01.001openalex-snapshot
Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions
inferredcites →https://doi.org/10.1016/j.clay.2018.05.028openalex-snapshot
Soil weathering analysis using a portable X-ray fluorescence (PXRF) spectrometer in an Inceptisol from the Brazilian Cerrado
inferredcites →https://doi.org/10.1016/j.earscirev.2016.01.012openalex-snapshot
A global spectral library to characterize the world's soil
inferredcites →https://doi.org/10.1016/j.geoderma.2008.05.008openalex-snapshot
Soil organic carbon concentrations and stocks on Barro Colorado Island — Digital soil mapping using Random Forests analysis
inferredcites →https://doi.org/10.1016/j.geoderma.2011.01.001openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.geoderma.2011.10.010openalex-snapshot
Uncertainty in the spatial prediction of soil texture
inferredcites →https://doi.org/10.1016/j.geoderma.2017.11.009openalex-snapshot
Soil structure as an indicator of soil functions: A review
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Modelling and mapping soil organic carbon stocks in Brazil
inferredcites →https://doi.org/10.1016/j.geoderma.2019.01.025openalex-snapshot
Is it possible to map subsurface soil attributes by satellite spectral transfer models?
inferredcites →https://doi.org/10.1016/j.geoderma.2019.05.043openalex-snapshot
The Brazilian Soil Spectral Library (BSSL): A general view, application and challenges
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Soil texture prediction in tropical soils: A portable X-ray fluorescence spectrometry approach
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Identifying and mapping terrons in Denmark
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Clay content prediction using spectra data collected from the ground to space platforms in a smallholder tropical area
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Beyond prediction: methods for interpreting complex models of soil variation
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Mapping Brazilian soil mineralogy using proximal and remote sensing data
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A bibliometric analysis of soil research in Brazil 1989–2018
inferredcites →https://doi.org/10.1016/j.gloenvcha.2014.06.001openalex-snapshot
When enough should be enough: Improving the use of current agricultural lands could meet production demands and spare natural habitats in Brazil
inferredcites →https://doi.org/10.1016/j.rse.2017.06.031openalex-snapshot
Google Earth Engine: Planetary-scale geospatial analysis for everyone
inferredcites →https://doi.org/10.1016/j.rse.2018.04.047openalex-snapshot
Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images
inferredcites →https://doi.org/10.1016/j.rse.2018.09.015openalex-snapshot
Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging
inferredcites →https://doi.org/10.1016/j.rse.2018.12.031openalex-snapshot
Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017
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Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison
inferredcites →https://doi.org/10.1016/j.rse.2023.113845openalex-snapshot
Remote sensing of the Earth's soil color in space and time
inferredcites →https://doi.org/10.1016/j.trac.2010.05.006openalex-snapshot
Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy
inferredcites →https://doi.org/10.1016/s0016-7061(03)00223-4openalex-snapshot
On digital soil mapping
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1029/1999jd900060openalex-snapshot
Optimal design of surface networks for observation of soil moisture
inferredcites →https://doi.org/10.1038/s41598-020-61408-1openalex-snapshot
Bare Earth’s Surface Spectra as a Proxy for Soil Resource Monitoring
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African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning
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Predictive mapping of soil organic carbon stocks in South Australia’s agricultural zone
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Modelling of soil texture and its verification with related soil properties
inferredcites →https://doi.org/10.1080/00045605009352070openalex-snapshot
The Geographic Cycle in Periglacial Regions as it is Related to Climatic Geomorphology
inferredcites →https://doi.org/10.1080/01431161.2010.549522openalex-snapshot
Interference of salt and moisture on soil reflectance spectra
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Factors of Soil Formation
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Predicting and mapping of soil particle‐size fractions with adaptive neuro‐fuzzy inference and ant colony optimization in central I ran
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A note on knowledge discovery and machine learning in digital soil mapping
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Hands-On Machine Learning with R
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Scoping review of proximal soil sensors for grain growing
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Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions
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inferredcites →https://doi.org/10.1590/1413-70542016404023016openalex-snapshot
Iron oxides and organic matter on soil phosphorus availability
inferredcites →https://doi.org/10.1590/1678-992x-2017-0300openalex-snapshot
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inferredcites →https://doi.org/10.1590/1806-9479.2020.207669openalex-snapshot
Agribusiness participation in the economic structure of a Brazilian region: analysis of GDP and indirect taxes
inferredcites →https://doi.org/10.1590/s1676-06032011000100001openalex-snapshot
O cerrado não é um bioma
inferredcites →https://doi.org/10.18227/2177-4307.acta.v10i22.2345openalex-snapshot
MEIO FÍSICO E PATRIMONIALIZAÇÃO DE ÁREAS: ELEMENTOS PARA A CONSERVAÇÃO DO BIOMA CERRADO NO NORTE GOIANO?
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ranger : A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
inferredcites →https://doi.org/10.2136/sssaj2001.652480xopenalex-snapshot
Near‐Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties
inferredcites →https://doi.org/10.2136/sssaj2012.0275openalex-snapshot
High‐Resolution 3‐D Mapping of Soil Texture in Denmark
inferredcites →https://doi.org/10.2136/sssaj2013.07.0269openalex-snapshot
Spatiotemporal Variability of Soil Moisture as Affected by Soil Properties during Irrigation Cycles
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Potential of Soil Minerals to Sequester Soil Organic Carbon
inferredcites →https://doi.org/10.3389/fsoil.2021.770326openalex-snapshot
Topsoil Texture Regionalization for Agricultural Soils in Germany—An Iterative Approach to Advance Model Interpretation
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Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images
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Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil
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Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based
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Soil Clay Content Mapping Using a Time Series of Landsat TM Data in Semi-Arid Lands
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On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping
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Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution
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Geoambientes no Pantanal do Abobral, Mato Grosso do Sul, Brasil
inferredcites →https://doi.org/10.5039/agraria.v17i4a1386openalex-snapshot
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inferredcites →https://doi.org/10.5194/essd-14-4719-2022openalex-snapshot
Colombian soil texture: building a spatial ensemble model
inferredcites →https://doi.org/10.5194/soil-4-1-2018openalex-snapshot
Evaluation of digital soil mapping approaches with large sets of environmental covariates
inferredcites →https://doi.org/10.7312/oliv12042-003openalex-snapshot
2. Relation of Soils and Geomorphic Surfaces in the Brazilian Cerrado
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