Frontiers in Soil Science | Pedometrics section | New and Recent Articles https://www.frontiersin.org/journals/soil-science/sections/pedometrics RSS Feed for Pedometrics section in the Frontiers in Soil Science journal | New and Recent Articles en-us Frontiers Feed Generator,version:1 2026-05-14T01:12:05.873+00:00 60 https://www.frontiersin.org/articles/10.3389/fsoil.2026.1811516 https://www.frontiersin.org/articles/10.3389/fsoil.2026.1811516 <![CDATA[Integrating deep learning with digital soil core sensing for subsurface soil image segmentation]]> 2026-05-13T00:00:00Z Original Research Perseverança MungofaDaniel RooneyStephen FarringtonWoody WallaceNicolas GuriesArnold SchumannSabine Grunwald https://www.frontiersin.org/articles/10.3389/fsoil.2026.1770292 https://www.frontiersin.org/articles/10.3389/fsoil.2026.1770292 <![CDATA[Critical edaphic and altitudinal factors influencing cation exchange capacity in coffee-growing soils of northeastern Peru: implications for sustainable fertility management]]> 2026-05-05T00:00:00Z Original Research Henry Díaz-ChuquizutaLuis Fernando Manrique GonzalesMartín Sánchez OjanastaJuan Pablo Cuevas-GiménezCarlos Carbajal-LlosaNéstor Cuellar-CondoriBoris MartínezGeomar Vallejos-Torres https://www.frontiersin.org/articles/10.3389/fsoil.2026.1780422 https://www.frontiersin.org/articles/10.3389/fsoil.2026.1780422 <![CDATA[Soil total nitrogen prediction using sentinel-2 simulated bands and machine learning: a laboratory spectroscopy study in Hemerocallis citrina Baroni fields]]> 2026-04-13T00:00:00Z Original Research Ping LiXuran LiPeng HeJingshu WangChenbo YangZelong YaoRutian BiLishuai XuHongfen ZhuFan Yang https://www.frontiersin.org/articles/10.3389/fsoil.2026.1745154 https://www.frontiersin.org/articles/10.3389/fsoil.2026.1745154 <![CDATA[Soil organic carbon content mapping along the coast of northern Peru: an ensemble machine learning approach]]> 2026-03-26T00:00:00Z Original Research Wilian Salazar-CoronelCarlos Carbajal-LlosaRodolfo Chuchon-Remon 0.7%) in areas with greater vegetation density, within a semi-arid context where SOC levels are generally low. In contrast, lower areas exhibited reduced SOC contents (< 0.6%). The uncertainty analysis indicated that SOC predictions had high to moderate confidence (PIR < 0.2) in the middle-and upper zones of the basin, and moderate confidence (0.1–0.2) in the lower areas. The results suggest that machine learning and ensemble methods improve SOC prediction, benefiting the sustainable management of soil fertility and quality in arid and semi-arid ecosystems of northern Peru.]]> https://www.frontiersin.org/articles/10.3389/fsoil.2026.1760011 https://www.frontiersin.org/articles/10.3389/fsoil.2026.1760011 <![CDATA[Soil spectroscopy improves mid infrared soil property prediction through optimized preprocessing and variable selection]]> 2026-01-28T00:00:00Z Original Research Reda MokereMohamed GhassanIssam Barra https://www.frontiersin.org/articles/10.3389/fsoil.2025.1653400 https://www.frontiersin.org/articles/10.3389/fsoil.2025.1653400 <![CDATA[Mapping soil salinity using machine learning and remote sensing data in semi-arid croplands]]> 2025-11-26T00:00:00Z Original Research Abdelwahed ChaaouHamza Ait-IchouSaid El HachemyMohamed ChikhaouiMustapha NaimiMohammed HssaisouneMohammed El HafyaniYassine Ait BrahimLhoussaine Bouchaou https://www.frontiersin.org/articles/10.3389/fsoil.2025.1673628 https://www.frontiersin.org/articles/10.3389/fsoil.2025.1673628 <![CDATA[Ensemble machine learning for digital mapping of soil pH and electrical conductivity in the Andean agroecosystem of Peru]]> 2025-11-06T00:00:00Z Original Research Carlos Carbajal-LlosaAntony BarjaSamuel Pizarro 0.93 and reducing RMSE by approximately 72%. Among standalone models, RF and XGBoost performed best for pH, while SVM performed the best for EC. ANN models were the least effective. Uncertainty analysis indicated high confidence in pH predictions but moderate to high uncertainty in EC predictions, suggesting that EC is more challenging to predict. Ensemble models with optimized weighting provide robust and accurate mapping of spatially autocorrelated soil properties. The high-confidence pH maps are reliable for soil management decisions, while EC predictions, though more uncertain, effectively identify priority areas for future sampling and investigation.]]> https://www.frontiersin.org/articles/10.3389/fsoil.2025.1634647 https://www.frontiersin.org/articles/10.3389/fsoil.2025.1634647 <![CDATA[Accumulation of potentially toxic elements in vegetable-cultivated soils from an agri-intensive region of southwest Punjab, India: contamination status and the effect of crop rotation]]> 2025-10-01T00:00:00Z Original Research Prafulla Kumar SahooHarish Chandra BarmanHemant KumarLamnganbi NgangomUmakant Chaudhari https://www.frontiersin.org/articles/10.3389/fsoil.2025.1612908 https://www.frontiersin.org/articles/10.3389/fsoil.2025.1612908 <![CDATA[Spatiotemporal prediction of soil moisture content at various depths in three soil types using machine learning algorithms]]> 2025-09-30T00:00:00Z Original Research Tarek AlahmadMiklós NeményiAdrienn SzélesNour AliOmar HijaziAnikó Nyéki https://www.frontiersin.org/articles/10.3389/fsoil.2025.1668732 https://www.frontiersin.org/articles/10.3389/fsoil.2025.1668732 <![CDATA[Rapid assessment of soil traits in hyperarid areas via XRF and locally weighted PLSR]]> 2025-09-17T00:00:00Z Original Research Baptiste KerfridenStéphane BoivinOscar MalouYassine FendaneHassan BoukcimSami D. AlmalkiShauna K. ReesBenjamin P. Y.-H. LeeAhmed MohamedAbdalsamad Aldabaa https://www.frontiersin.org/articles/10.3389/fsoil.2025.1629686 https://www.frontiersin.org/articles/10.3389/fsoil.2025.1629686 <![CDATA[Improving plant-available water estimation using model averaging of national soil water models]]> 2025-08-26T00:00:00Z Original Research Brendan P. MaloneRoss D. SearleSiyuan TianThomas F. BishopYi Yu https://www.frontiersin.org/articles/10.3389/fsoil.2025.1642004 https://www.frontiersin.org/articles/10.3389/fsoil.2025.1642004 <![CDATA[Variations in maximum freezing depth in Northeast China from 1975 to 2024 using a machine learning model]]> 2025-08-12T00:00:00Z Original Research Shuo WangAihemaitijiang TuerhongNueraili MaimaitituersunZuo-Jun Ning 160 cm displayed a decreasing trend. The results not only reveal the impact of climate change on freezing depths, but also provide a scientific basis for environmental management and ecological protection in frozen ground areas. Changes in freezing depth directly affect many sectors such as agriculture, construction, and transportation, making accurate prediction essential for developing climate adaptation strategies. Considering the lack of data regarding the MFD of SFG in Northeast China for the past 50 years, the MLM provided an effective method for predicting changes in MFD using meteorological data and remote sensing reanalysis data.]]> https://www.frontiersin.org/articles/10.3389/fsoil.2025.1617526 https://www.frontiersin.org/articles/10.3389/fsoil.2025.1617526 <![CDATA[Rapid and low-cost geochemical indices for tracing iron mining tailings within fluvial sediments: a case study from the Paraopeba River after the B1 Dam burst in Brumadinho, Minas Gerais, Brazil]]> 2025-08-08T00:00:00Z Original Research Fernando Luís PantuzzoFernando Verassani LaureanoCaíque Lima CabralGabriel Negreiros SalomãoRoberto Dall’AgnolVitor Brognaro PimentaLucas Pereira Leão https://www.frontiersin.org/articles/10.3389/fsoil.2025.1557566 https://www.frontiersin.org/articles/10.3389/fsoil.2025.1557566 <![CDATA[AutoRA: an innovative algorithm for automatic delineation of reference areas in support of smart soil sampling and digital soil twins]]> 2025-04-03T00:00:00Z Original Research Hugo RodriguesMarcos Bacis CeddiaGustavo Mattos VasquesSabine GrunwaldEbrahim Babaeian https://www.frontiersin.org/articles/10.3389/fsoil.2025.1540941 https://www.frontiersin.org/articles/10.3389/fsoil.2025.1540941 <![CDATA[Comprehensive analysis of sediment grain features and their engineering implications in the Yangtze River source area]]> 2025-02-25T00:00:00Z Original Research Zhijing LiXiaoxue WangYujiao LiuWenqi LiXian ZhouChaonan LvGuiqiao WangJunxiao Ma https://www.frontiersin.org/articles/10.3389/fsoil.2025.1539477 https://www.frontiersin.org/articles/10.3389/fsoil.2025.1539477 <![CDATA[Geogenic perspectives on potassium dynamics and plant uptake: insights from natural and submerged conditions across different soil types with machine learning predictions]]> 2025-01-24T00:00:00Z Original Research Saibal GhoshGourav MondalShreya ChakrabortySonali BanerjeeSumit KumarRiddhi BasuPradip Bhattacharyya https://www.frontiersin.org/articles/10.3389/fsoil.2024.1536797 https://www.frontiersin.org/articles/10.3389/fsoil.2024.1536797 <![CDATA[Editorial: Digital soil mapping using electromagnetic sensors]]> 2024-12-17T00:00:00Z Editorial Triven KogantiPhilippe De SmedtMohammad FarzamianMaria KnadelJohn TriantafilisAnders Vest ChristiansenMogens H. Greve https://www.frontiersin.org/articles/10.3389/fsoil.2024.1421661 https://www.frontiersin.org/articles/10.3389/fsoil.2024.1421661 <![CDATA[Modeling the electrical conductivity relationship between saturated paste extract and 1:2.5 dilution in different soil textural classes]]> 2024-12-04T00:00:00Z Original Research Moh’d M. OmarMawazo J. ShitindiBoniface H. J. MassaweOle PedersenJoel L. MeliyoKadeghe G. Fue 0.95). The Root Mean Square Error values were low (1.4 < RMSE), and the Mean Absolute Error values were similarly low (0.85 < MAE). Therefore, the regression models developed provide a practical means of estimating ECe for various soil textural classes, thereby enhancing soil salinity assessment and management strategies.]]> https://www.frontiersin.org/articles/10.3389/fsoil.2024.1407502 https://www.frontiersin.org/articles/10.3389/fsoil.2024.1407502 <![CDATA[Groundwater fluoride prediction modeling using physicochemical parameters in Punjab, India: a machine-learning approach]]> 2024-07-18T00:00:00Z Original Research Anjali KerkettaHarmanpreet Singh KapoorPrafulla Kumar Sahoo https://www.frontiersin.org/articles/10.3389/fsoil.2024.1239497 https://www.frontiersin.org/articles/10.3389/fsoil.2024.1239497 <![CDATA[Comparison of multi-coil and multi-frequency frequency domain electromagnetic induction instruments]]> 2024-03-15T00:00:00Z Original Research Guillaume BlanchyPaul McLachlanBenjamin MaryMatteo CensiniJacopo BoagaGiorgio Cassiani