Hybrid Machine Learning for Integrating Pedological Knowledge into Digital Soil 1 Mapping to Advance Next-Generation Earth System Models

Abstract

[1]  S. Fatichi,et al.  On the Uncertainty Induced by Pedotransfer Functions in Terrestrial Biosphere Modeling , 2022, Water Resources Research.

[2]  G. Heuvelink,et al.  Global mapping of volumetric water retention at 100, 330 and 15 000 cm suction using the WoSIS database , 2022, International Soil and Water Conservation Research.

[3]  I. Prentice,et al.  Land-surface evapotranspiration derived from a first-principles primary production model , 2021, bioRxiv.

[4]  D. Or,et al.  Global Prediction of Soil Saturated Hydraulic Conductivity Using Random Forest in a Covariate‐Based GeoTransfer Function (CoGTF) Framework , 2021, Journal of Advances in Modeling Earth Systems.

[5]  H. Fathizad,et al.  High resolution middle eastern soil attributes mapping via open data and cloud computing , 2021 .

[6]  B. Magi,et al.  Warmer, Wetter Climates Accelerate Mechanical Weathering in Field Data, Independent of Stress‐Loading , 2020, Geophysical Research Letters.

[7]  C. Brungard,et al.  Prediction of soil water infiltration using multiple linear regression and random forest in a dry flood plain, eastern Iran , 2020 .

[8]  M. H. Salehi,et al.  Assessment of different digital soil mapping methods for prediction of soil classes in the Shahrekord plain, Central Iran , 2020 .

[9]  Pedro Walfir M. Souza Filho,et al.  Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine , 2020, Remote. Sens..

[10]  B. Minasny,et al.  Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm , 2020 .

[11]  M. Yoshikawa,et al.  Analyzing the Effects of "People also ask" on Search Behaviors and Beliefs , 2020, HT.

[12]  A. Hartemink,et al.  How deep is the soil studied – an analysis of four soil science journals , 2020, Plant and Soil.

[13]  B. Minasny,et al.  Machine learning for digital soil mapping: Applications, challenges and suggested solutions , 2020, Earth-Science Reviews.

[14]  Yongjiu Dai,et al.  A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling , 2019, Journal of Advances in Modeling Earth Systems.

[15]  M. Schaap,et al.  Estimation of saturated hydraulic conductivity with pedotransfer functions: A review , 2019, Journal of Hydrology.

[16]  Peter Finke,et al.  Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran , 2019, Geoderma.

[17]  J. Bundick.  National Aeronautics and Space Administration (NASA) , 2018, The Grants Register 2021.

[18]  M. R. P. Rad,et al.  Digital Soil Mapping , 2018, Handbook of Soil Sciences (Two Volume Set).

[19]  M. Seyfried,et al.  Predicting soil thickness on soil mantled hillslopes , 2018, Nature Communications.

[20]  T. Behrens,et al.  Spatial modelling with Euclidean distance fields and machine learning , 2018, European Journal of Soil Science.

[21]  Ying Zhao,et al.  Development and analysis of the Soil Water Infiltration Global database , 2018, Earth System Science Data.

[22]  M. Kearney,et al.  Can next-generation soil data products improve soil moisture modelling at the continental scale? An assessment using a new microclimate package for the R programming environment , 2018, Journal of Hydrology.

[23]  Thorsten Behrens,et al.  Updating a national soil classification with spectroscopic predictions and digital soil mapping , 2018 .

[24]  M. Schaap,et al.  Hydrophysical Database for Brazilian Soils (HYBRAS) and Pedotransfer Functions for Water Retention , 2018 .

[25]  T. O. Ferreira,et al.  Weathering and clay formation in semi-arid calcareous soils from Northeastern Brazil , 2018 .

[26]  A. Stein,et al.  Soil depth spatial prediction by fuzzy soil-landscape model , 2018, Journal of Soils and Sediments.

[27]  R. Poccard-Chapuis,et al.  Soil texture derived from topography in North-eastern Amazonia , 2017 .

[28]  T. Hengl,et al.  3D soil hydraulic database of Europe at 250 m resolution , 2017 .

[29]  Frédérique Seyler,et al.  Mapping soil organic carbon on a national scale: Towards an improved and updated map of Madagascar , 2017 .

[30]  Budiman Minasny,et al.  Chile and the Chilean soil grid: A contribution to GlobalSoilMap , 2017 .

[31]  H. Nõlvak,et al.  Elevated Air Humidity Changes Soil Bacterial Community Structure in the Silver Birch Stand , 2017, Front. Microbiol..

[32]  Marvin N. Wright,et al.  SoilGrids250m: Global gridded soil information based on machine learning , 2017, PloS one.

[33]  Laura Poggio,et al.  Assimilation of optical and radar remote sensing data in 3D mapping of soil properties over large areas. , 2017, The Science of the total environment.

[34]  S. Montenegro,et al.  Influence of oceanic-atmospheric interactions on extreme events of daily rainfall in the Sub-basin 39 located in Northeastern Brazil , 2016 .

[35]  A. Porporato,et al.  Vegetation response to rainfall seasonality and interannual variability in tropical dry forests , 2016 .

[36]  P. Vitousek,et al.  Climate‐driven thresholds for chemical weathering in postglacial soils of New Zealand , 2016 .

[37]  J. Fung,et al.  Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model , 2016 .

[38]  Alfred E. Hartemink,et al.  Total soil organic carbon and carbon sequestration potential in Nigeria , 2016 .

[39]  Peter A. Troch,et al.  Implementing and Evaluating Variable Soil Thickness in the Community Land Model, Version 4.5 (CLM4.5) , 2016 .

[40]  R. Kerry,et al.  Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran , 2016 .

[41]  Thomas F. A. Bishop,et al.  Digital mapping of pre-European soil carbon stocks and decline since clearing over New South Wales, Australia , 2016 .

[42]  Raghavan Srinivasan,et al.  Soil-Landscape Estimation and Evaluation Program (SLEEP) to predict spatial distribution of soil attributes for environmental modeling. , 2015 .

[43]  Alfred E. Hartemink,et al.  Digital Mapping of Soil Organic Carbon Contents and Stocks in Denmark , 2014, PloS one.

[44]  Martin Hitziger,et al.  Comparison of three supervised learning methods for digital soil mapping: Application to a complex terrain in the Ecuadorian Andes , 2014 .

[45]  Jin Li,et al.  Spatial interpolation methods applied in the environmental sciences: A review , 2014, Environ. Model. Softw..

[46]  Roland Hiederer,et al.  Global soil carbon: understanding and managing the largest terrestrial carbon pool , 2014 .

[47]  Dominique Arrouays,et al.  GlobalSoilMap : Basis of the global spatial soil information system , 2014 .

[48]  Alois Knoll,et al.  Gradient boosting machines, a tutorial , 2013, Front. Neurorobot..

[49]  J. Stape,et al.  Köppen's climate classification map for Brazil , 2013 .

[50]  Johan Bouma,et al.  Framing soils as an actor when dealing with wicked environmental problems , 2013 .

[51]  S. Beguería,et al.  Modeling the spatial distribution of soil properties by generalized least squares regression: Toward a general theory of spatial variates , 2013, Journal of Soil and Water Conservation.

[52]  Quirijn de Jong van Lier,et al.  Pedotransfer functions to estimate water retention parameters of soils in northeastern Brazil , 2013 .

[53]  Hossein Davarzani,et al.  Study of the effect of wind speed on evaporation from soil through integrated modeling of the atmospheric boundary layer and shallow subsurface , 2012, Water resources research.

[54]  Gerard B. M. Heuvelink,et al.  Efficiency comparison of conventional and digital soil mapping for updating soil maps , 2012 .

[55]  B. Lowery,et al.  Soil maps of Wisconsin , 2012 .

[56]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes , 2011 .

[57]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics , 2011 .

[58]  Alfred E. Hartemink,et al.  Predicting soil properties in the tropics , 2011 .

[59]  Ray Leuning,et al.  Diagnosing errors in a land surface model (CABLE) in the time and frequency domains , 2011 .

[60]  M. R. Coelho,et al.  Funções de pedotransferência para estimativa da densidade dos solos brasileiros. , 2010 .

[61]  D. W. Pribyl,et al.  A critical review of the conventional SOC to SOM conversion factor , 2010 .

[62]  J. M. Júnior,et al.  Hillslope curvature, clay mineralogy, and phosphorus adsorption in an Alfisol cultivated with sugarcane , 2009 .

[63]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[64]  G. Bonan.  Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests , 2008, Science.

[65]  R. G. Davies,et al.  Methods to account for spatial autocorrelation in the analysis of species distributional data : a review , 2007 .

[66]  E. Davidson,et al.  Modeling the effects of throughfall reduction on soil water content in a Brazilian Oxisol under a moist tropical forest , 2007 .

[67]  Elaine Cristina Cardoso Fidalgo,et al.  PEDOTRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY FROM EXISTING SOIL SURVEY REPORTS IN BRAZIL , 2007 .

[68]  Koen P. Overmars,et al.  Comparing Inductive and Deductive Modeling of Land Use Decisions: Principles, a Model and an Illustration from the Philippines , 2007 .

[69]  W. Rawls,et al.  Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions , 2006 .

[70]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[71]  M. Meirvenne,et al.  Predictive Quality of Pedotransfer Functions for Estimating Bulk Density of Forest Soils , 2005 .

[72]  Valentina Krysanova,et al.  Development of the ecohydrological model SWIM for regional impact studies and vulnerability assessment , 2005 .

[73]  Douglas M. Hawkins,et al.  The Problem of Overfitting , 2004, J. Chem. Inf. Model..

[74]  P. D’Odorico,et al.  On the effect of air humidity on soil susceptibility to wind erosion: The case of air‐dry soils , 2004 .

[75]  Peter Scull,et al.  Predictive soil mapping: a review , 2003 .

[76]  Christina Gloeckner,et al.  Modern Applied Statistics With S , 2003 .

[77]  P. T. K. Jacomine,et al.  Funções de pedotransferência para predição da umidade retida a potenciais específicos em solos do estado de Pernambuco , 2002 .

[78]  Javier Tomasella,et al.  Estimating soil water retention characteristics from limited data in Brazilian Amazonia , 1998 .

[79]  Paul E. Gessler,et al.  Soil-Landscape Modelling and Spatial Prediction of Soil Attributes , 1995, Int. J. Geogr. Inf. Sci..

[80]  Gary A. Peterson,et al.  Soil Attribute Prediction Using Terrain Analysis , 1993 .

[81]  C. Valentin,et al.  Morphology, genesis and classification of surface crusts in loamy and sandy soils , 1992 .

[82]  T. Zobeck,et al.  Management effects on wind-eroded sediment and plant nutrients , 1989 .

[83]  A. Newman.  The significance of clays in agriculture and soils , 1984, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[84]  Van Genuchten,et al.  A closed-form equation for predicting the hydraulic conductivity of unsaturated soils , 1980 .

[85]  A. Wayne Whitney,et al.  A Direct Method of Nonparametric Measurement Selection , 1971, IEEE Transactions on Computers.

[86]  M. Vastaranta,et al.  This is a non-peer reviewed preprint submitted to EarthArXiv , 2020 .

[87]  N. Rawlinson,et al.  THIS IS A NON-PEER REVIEWED PREPRINT SUBMITTED TO EarthArXiv The Future of Broadband Passive Seismic Acquisition , 2019 .

[88]  Felipe Ferreira Bocca,et al.  How accurate are pedotransfer functions for bulk density for Brazilian soils , 2018 .

[89]  EM Solos,et al.  Manual de métodos de análise de solo. , 2017 .

[90]  C. Ballabio,et al.  Mapping topsoil physical properties at European scale using the LUCAS database , 2016 .

[91]  Quirijn de Jong van Lier,et al.  Pedotransfer Functions for Brazilian Soils , 2014 .

[92]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[93]  M. Mendonça-Santos,et al.  Chapter 3 The State of the Art of Brazilian Soil Mapping and Prospects for Digital Soil Mapping , 2006 .

[94]  Philippe Lagacherie,et al.  Chapter 1 Spatial Soil Information Systems and Spatial Soil Inference Systems: Perspectives for Digital Soil Mapping , 2006 .

[95]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[96]  J. Tomasella,et al.  Pedotransfer functions for the estimation of soil water retention in Brazilian soils. , 2000 .

[97]  A. Barros,et al.  Levantamento de reconhecimento de baixa e média intensidade dos solos do Estado de Pernambuco. , 2000 .

[98]  David G. Tarboton,et al.  On the extraction of channel networks from digital elevation data , 1991 .

[99]  John R. Williams,et al.  EPIC-erosion/productivity impact calculator: 1. Model documentation. , 1990 .

[100]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[101]  C. Wardlaw,et al.  The Sugarcane , 1952, Nature.

[102]  J. Galloway.  A Review of the , 1901 .