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ssrn.4618992

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/saj2.20487openalex-extracted

Mapping phosphorus sorption and availability in California vineyard soils using an ensemble of machine learning models

inferredcites →https://doi.org/10.1007/978-3-319-44327-0openalex-extracted

Using R for Digital Soil Mapping

inferredcites →https://doi.org/10.1007/s10705-017-9870-xopenalex-extracted

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

inferredcites →https://doi.org/10.1007/s12665-018-7367-9openalex-extracted

Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model

inferredcites →https://doi.org/10.1016/j.catena.2019.104412openalex-extracted

Effects of long-term K fertilization on soil available potassium in East China

inferredcites →https://doi.org/10.1016/j.catena.2020.104771openalex-extracted

Temporal paradox in soil potassium estimations using spaceborne multispectral imagery

inferredcites →https://doi.org/10.1016/j.catena.2023.106932openalex-extracted

Mapping clay mineral types using easily accessible data and machine learning techniques in a scarce data region: A case study in a semi-arid area in Iran

inferredcites →https://doi.org/10.1016/j.geoderma.2007.06.011openalex-extracted

Silt and fine sand fractions dominate K fixation in soils derived from granitic alluvium of the San Joaquin Valley, California

inferredcites →https://doi.org/10.1016/j.geoderma.2008.05.008openalex-extracted

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.2009.11.005openalex-extracted

Soil type classification and estimation of soil properties using support vector machines

inferredcites →https://doi.org/10.1016/j.geoderma.2010.12.022openalex-extracted

A weathering intensity index for the Australian continent using airborne gamma-ray spectrometry and digital terrain analysis

inferredcites →https://doi.org/10.1016/j.geoderma.2012.09.009openalex-extracted

The utility of remotely-sensed vegetative and terrain covariates at different spatial resolutions in modelling soil and watertable depth (for digital soil mapping)

inferredcites →https://doi.org/10.1016/j.geoderma.2015.07.016openalex-extracted

Digital mapping of soil carbon in a viticultural region of Southern Brazil

inferredcites →https://doi.org/10.1016/j.geoderma.2019.01.018openalex-extracted

Land parcel-based digital soil mapping of soil nutrient properties in an alluvial-diluvia plain agricultural area in China

inferredcites →https://doi.org/10.1016/j.geoderma.2020.114890openalex-extracted

High resolution middle eastern soil attributes mapping via open data and cloud computing

inferredcites →https://doi.org/10.1016/j.geoderma.2021.115108openalex-extracted

Enhancing the accuracy of machine learning models using the super learner technique in digital soil mapping

inferredcites →https://doi.org/10.1016/j.geoderma.2021.115153openalex-extracted

Using bias correction and ensemble modelling for predictive mapping and related uncertainty: A case study in digital soil mapping

inferredcites →https://doi.org/10.1016/j.geodrs.2020.e00289openalex-extracted

Predicting spatial variability of selected soil properties using digital soil mapping in a rainfed vineyard of central Chile

inferredcites →https://doi.org/10.1016/j.geodrs.2020.e00295openalex-extracted

Could airborne gamma-spectrometric data replace lithological maps as co-variates for digital soil mapping of topsoil particle-size distribution? A case study in Western France

inferredcites →https://doi.org/10.1016/j.geodrs.2021.e00444openalex-extracted

Assessment of the soil fertility status in Benin (West Africa) – Digital soil mapping using machine learning

inferredcites →https://doi.org/10.1016/j.jenvman.2017.06.017openalex-extracted

Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings

inferredcites →https://doi.org/10.1016/j.still.2014.10.003openalex-extracted

Land use effects on organic carbon storage in soils of Bavaria: The importance of soil types

inferredcites →https://doi.org/10.1029/2011jf001977openalex-extracted

Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra

inferredcites →https://doi.org/10.1038/s41598-021-85639-yopenalex-extracted

African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning

inferredcites →https://doi.org/10.1080/09571269308717953openalex-extracted

Viticultural soils of California, with special reference to the Napa valley

inferredcites →https://doi.org/10.1081/css-120004288openalex-extracted

Estimating soil potassium availability for processing tomato production

inferredcites →https://doi.org/10.1111/j.1755-0238.2003.tb00265.xopenalex-extracted

A review of potassium nutrition in grapevines with special emphasis on berry accumulation

inferredcites →https://doi.org/10.1111/sum.12192openalex-extracted

Explaining and mapping total phosphorus content in French topsoils

inferredcites →https://doi.org/10.15666/aeer/1002_121140openalex-extracted

MODELLING THE IMPACT OF CLIMATE CHANGE ON THE HUNGARIAN WINE REGIONS USING RANDOM FOREST

inferredcites →https://doi.org/10.20870/oeno-one.2011.45.2.1488openalex-extracted

Temporal stability of within-field patterns of NDVI in non irrigated Mediterranean vineyards

inferredcites →https://doi.org/10.2136/sssaj2006.0060openalex-extracted

Potassium Fixation in San Joaquin Valley Soils Derived from Granitic and Nongranitic Alluvium

inferredcites →https://doi.org/10.2136/sssaj2012.0275openalex-extracted

High‐Resolution 3‐D Mapping of Soil Texture in Denmark

inferredcites →https://doi.org/10.2136/sssaj2014.05.0202openalex-extracted

Digital Mapping of Soil Particle‐Size Fractions for Nigeria

inferredcites →https://doi.org/10.2136/sssaj2017.04.0122openalex-extracted

Soil Property and Class Maps of the Conterminous United States at 100‐Meter Spatial Resolution

inferredcites →https://doi.org/10.32614/cran.package.rasteropenalex-extracted

raster: Geographic Data Analysis and Modeling

inferredcites →https://doi.org/10.3390/rs12071197openalex-extracted

Soil Color and Mineralogy Mapping Using Proximal and Remote Sensing in Midwest Brazil

inferredcites →https://doi.org/10.3733/ca.v062n04p195openalex-extracted

Soil-landscape model helps predict potassium supply in vineyards

inferredcites →https://doi.org/10.5281/zenodo.2567061openalex-extracted

inferredcites →https://doi.org/10.5344/ajev.1980.31.3.283openalex-extracted

A Hypothesis for the Presence, Activity, and Role of Potassium/Hydrogen, Adenosine Triphosphatases in Grapevines

inferredcites →https://doi.org/10.5344/ajev.1991.42.2.113openalex-extracted

Partitioning of Dry Weight, Nitrogen, and Potassium in Cabernet Sauvignon Grapevines From Anthesis Until Harvest

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.envc.2025.101160openalex-snapshot

Soil organic carbon (SOC) prediction using super learner algorithm based on the remote sensing variables

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