Reference health
Classification of Soil Horizons Based on Visnir and Swir Hyperespectral Images and Machine Learning Models
26/26 checkable references clean · checked 2026-08-27
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
29 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 26 checked references that resolve
resolves10.1016/j.rse.2016.03.025
Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon
resolves10.1016/j.catena.2022.106858
Soil erosion susceptibility mapping using ensemble machine learning models: A case study of upper Congo river sub-basin
resolves10.1111/ejss.12715
Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification
resolves10.1016/j.catena.2020.104485
Prediction of soil texture classes through different wavelength regions of reflectance spectroscopy at various soil depths
resolves10.1590/0103-9016-2013-0365
Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification
resolves10.5935/1806-6690.20150054
VIS-NIR-SWIR spectroscopy in soil evaluation along a toposequence in Piracicaba
resolves10.1016/j.catena.2020.104610
Digital photography as a tool for microscale mapping of soil organic carbon and iron oxides
resolves10.1016/j.geoderma.2019.113885
Parent material distribution mapping from tropical soils data via machine learning and portable X-ray fluorescence (pXRF) spectrometry in Brazil
resolves10.1177/0967033518821965
How qualitative spectral information can improve soil profile classification?
resolves10.1007/s10064-022-02967-7
Novel approach for soil classification using machine learning methods
resolves10.1016/j.geoderma.2011.07.015
Relationships between particle size distribution and VNIR reflectance spectra are weaker for soils formed from bedrock compared to transported parent materials
resolves10.1016/j.geoderma.2017.11.032
High resolution measurement of soil organic carbon and total nitrogen with laboratory imaging spectroscopy
resolves10.1016/j.geoderma.2019.113982
Distribution mapping of soil profile carbon and nitrogen with laboratory imaging spectroscopy
resolves10.1080/05704928.2013.811081
The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties
resolves10.1016/j.geoderma.2005.03.007
Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties
resolves10.1016/j.geoderma.2009.12.025
Using data mining to model and interpret soil diffuse reflectance spectra
resolves10.1021/acs.est.7b00889
Novel Proximal Sensing for Monitoring Soil Organic C Stocks and Condition
resolves10.1016/j.geoderma.2018.03.029
Identification of soil profile classes using depth-weighted visible–near-infrared spectral reflectance
resolves10.1016/j.geoderma.2021.114961
Spectral signatures of soil horizons and soil orders – An exploratory study of 270 soil profiles
resolves10.1016/j.palaeo.2017.07.007
Assessing the utility of visible-to-shortwave infrared reflectance spectroscopy for analysis of soil weathering intensity and paleoclimate reconstruction
resolves10.1007/s12517-022-10847-3
Spatial prediction of soil particle size distribution in arid agricultural lands in central Iran
The 29 references without a DOI — listed, not checked
no DOI — not checkedMapa Geol�gico e de Recursos Minerais do Estado do Paran�
no DOI — not checkedref2
no DOI — not checkedStrategies for the development of spectral models for soil organic matter estimation
no DOI — not checkedref11
no DOI — not checkedVIS-NIR-SWIR hyperspectroscopy combined with data mining and machine learning for classification of predicted chemometrics of green lettuce
no DOI — not checkedPrediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy
no DOI — not checkedref14
no DOI — not checkedUsing pXRF and VIS-NIR for characterizing diagnostic horizons of fine-textured podzolic soils in subtropical forests
no DOI — not checkedAssessment of soil suitability using Machine Learning in arid and semi-arid regions
no DOI — not checkedTowards a dynamic soil survey: Identifying and delineating soil horizons in-situ using deep learning
no DOI — not checkedref19
no DOI — not checkedAVHYAS: A free and open source QGIS plugin for advanced hyperspectral image analysis
no DOI — not checkedHighresolution and three-dimensional mapping of soil texture of China
no DOI — not checkedEstimation of soil organic matter content based on CARS algorithm coupled with random forest
no DOI — not checkedPrediction of soil organic carbon in soil profiles based on visible-near-infrared hyperspectral imaging spectroscopy
no DOI — not checkedLeaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops
no DOI — not checkedref27
no DOI — not checkedMapping particle size and soil organic matter in tropical soil based on hyperspectral imaging and non-imaging sensors
no DOI — not checkedref30
no DOI — not checkedRapid determination of soil horizons and suborders based on VIS-NIR-SWIR spectroscopy and machine learning models
no DOI — not checkedScikit-learn: Machine Learning in Python
no DOI — not checkedDetection of soil organic matter using hyperspectral imaging sensor combined with multivariate regression modeling procedures
no DOI — not checkedref36
no DOI — not checkedref37
no DOI — not checkedref41
no DOI — not checkedref42
no DOI — not checkedRapid determination of soil class based on visible-near infrared, mid-infrared spectroscopy and data fusion
no DOI — not checkedIntegrating hyperspectral imaging with machine learning techniques for the high-resolution mapping of soil nitrogen fractions in soil profiles
no DOI — not checkedEstimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-08-27 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.2139/ssrn.4830196"><img src="https://citestamp.com/citestamped/10.2139/ssrn.4830196/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.2139/ssrn.4830196)