Environmental Science › Environmental Engineering

Soil Geostatistics and Mapping

Works Count: 40804

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Description

This cluster of papers represents advances in digital soil mapping techniques, including the use of geostatistics, remote sensing, spectroscopy, and machine learning for mapping and predicting soil properties. The focus is on spatial interpolation, global soil information, and the application of these techniques for soil security and terrain analysis.

Keywords

Digital Soil Mapping; Geostatistics; Remote Sensing; Soil Properties; Spectroscopy; Spatial Interpolation; Machine Learning; Soil Security; Global Soil Information; Terrain Analysis

The World Reference Base for Soil Resources

1998-01-01

Otto Spaargaren , Jozef Deckers

spatstat: AnRPackage for Analyzing Spatial Point Patterns

2005-01-01

Adrian Baddeley , Rolf Turner

spatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, model-fitting, and simulation. It is designed to handle realistic datasets, including inhomogeneous point patterns, … spatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, model-fitting, and simulation. It is designed to handle realistic datasets, including inhomogeneous point patterns, spatial sampling regions of arbitrary shape, extra covariate data, and

An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach

2011-08-04

Finn Lindgren , Håvard Rue , Johan Lindström

Summary Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical modelling and geostatistics. The specification through the covariance function gives an intuitive interpretation of the field … Summary Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical modelling and geostatistics. The specification through the covariance function gives an intuitive interpretation of the field properties. On the computational side, GFs are hampered with the big n problem, since the cost of factorizing dense matrices is cubic in the dimension. Although computational power today is at an all time high, this fact seems still to be a computational bottleneck in many applications. Along with GFs, there is the class of Gaussian Markov random fields (GMRFs) which are discretely indexed. The Markov property makes the precision matrix involved sparse, which enables the use of numerical algorithms for sparse matrices, that for fields in ℝ2 only use the square root of the time required by general algorithms. The specification of a GMRF is through its full conditional distributions but its marginal properties are not transparent in such a parameterization. We show that, using an approximate stochastic weak solution to (linear) stochastic partial differential equations, we can, for some GFs in the Matérn class, provide an explicit link, for any triangulation of ℝd, between GFs and GMRFs, formulated as a basis function representation. The consequence is that we can take the best from the two worlds and do the modelling by using GFs but do the computations by using GMRFs. Perhaps more importantly, our approach generalizes to other covariance functions generated by SPDEs, including oscillating and non-stationary GFs, as well as GFs on manifolds. We illustrate our approach by analysing global temperature data with a non-stationary model defined on a sphere.

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PRIMUS: a Windows PC-based system for small-angle scattering data analysis

2003-09-08

Petr V. Konarev , В. В. Волков , Anna Sokolova +2 more

A program suite for one-dimensional small-angle scattering data processing running on IBM-compatible PCs under Windows 9 x /NT/2000/XP is presented. The main program, PRIMUS , has a menu-driven graphical user … A program suite for one-dimensional small-angle scattering data processing running on IBM-compatible PCs under Windows 9 x /NT/2000/XP is presented. The main program, PRIMUS , has a menu-driven graphical user interface calling computational modules to perform data manipulation and analysis. Experimental data in binary OTOKO format can be reduced by calling the program SAPOKO , which includes statistical analysis of time frames, averaging and scaling. Tools to generate the angular axis and detector response files from diffraction patterns of calibration samples, as well as binary to ASCII transformation programs, are available. Several types of ASCII files can be directly imported into PRIMUS , in particular, sasCIF or ILL-type files are read without modification. PRIMUS provides basic data manipulation functions (averaging, background subtraction, merging of data measured in different angular ranges, extrapolation to zero sample concentration, etc. ) and computes invariants from Guinier and Porod plots. Several external modules coupled with PRIMUS via pop-up menus enable the user to evaluate the characteristic functions by indirect Fourier transformation, to perform peak analysis for partially ordered systems and to find shape approximations in terms of three-parametric geometrical bodies. For the analysis of mixtures, PRIMUS enables model-independent singular value decomposition or linear fitting if the scattering from the components is known. An interface is also provided to the general non-linear fitting program MIXTURE , which is designed for quantitative analysis of multicomponent systems represented by simple geometrical bodies, taking shape and size polydispersity as well as interparticle interference effects into account.

Kriging: a method of interpolation for geographical information systems

1990-07-01

Margaret A. Oliver , R. Webster

Geographical information systems could be improved by adding procedures for geostatistical spatial analysis to existing facilities. Most traditional methods of interpolation are based on mathematical as distinct from stochastic models … Geographical information systems could be improved by adding procedures for geostatistical spatial analysis to existing facilities. Most traditional methods of interpolation are based on mathematical as distinct from stochastic models of spatial variation. Spatially distributed data behave more like random variables, however, and regionalized variable theory provides a set of stochastic methods for analysing them. Kriging is the method of interpolation deriving from regionalized variable theory. It depends on expressing spatial variation of the property in terms of the variogram, and it minimizes the prediction errors which are themselves estimated. We describe the procedures and the way we link them using standard operating systems. We illustrate them using examples from case studies, one involving the mapping and control of soil salinity in the Jordan Valley of Israel, the other in semi-arid Botswana where the herbaceous cover was estimated and mapped from aerial photographic survey.

Near‐Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties

2001-03-01

Cheng-Wen Chang , David A. Laird , M. J. Mausbach +1 more

A fast and convenient soil analytical technique is needed for soil quality assessment and precision soil management. The main objective of this study was to evaluate the ability of near‐infrared … A fast and convenient soil analytical technique is needed for soil quality assessment and precision soil management. The main objective of this study was to evaluate the ability of near‐infrared reflectance spectroscopy (NIRS) to predict diverse soil properties. Near‐infrared reflectance spectra, obtained from a Perstrop NIR Systems 6500 scanning monochromator (Foss NIRSystems, Silver Spring, MD), and 33 chemical, physical, and biochemical properties were studied for 802 soil samples collected from four Major Land Resource Areas (MLRAs). Calibrations were based on principal component regression (PCR) using the first derivatives of optical density [log(1/ R )] for the 1300‐ to 2500‐nm spectral range. Total C, total N, moisture, cation‐exchange capacity (CEC), 1.5 MPa water, basal respiration rate, sand, silt, and Mehlich III extractable Ca were successfully predicted by NIRS ( r 2 > 0.80). Some Mehlich III extractable metals (Fe, K, Mg, Mn) and exchangeable cations (Ca, Mg, and K), sum of exchangeable bases, exchangeable acidity, clay, potentially mineralizable N, total respiration rate, biomass C, and pH were also estimated by NIRS but with less accuracy ( r 2 = 0.80∼0.50). The predicted results for aggregation (wt% > 2, 1, 0.5, 0.25 mm, and macroaggregation) were not reliable ( r 2 = 0.46∼0.60). Mehlich III extractable Cu, P, and Zn, and exchangeable Na could not be predicted using the NIRS–PCR technique ( r 2 < 0.50). The results indicate that NIRS can be used as a rapid analytical technique to simultaneously estimate several soil properties with acceptable accuracy in a very short time.

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Multivariable geostatistics in S: the gstat package

2004-05-31

Edzer Pebesma

Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting

1988-09-01

William S. Cleveland , Susan J. Devlin

Abstract Locally weighted regression, or loess, is a way of estimating a regression surface through a multivariate smoothing procedure, fitting a function of the independent variables locally and in a … Abstract Locally weighted regression, or loess, is a way of estimating a regression surface through a multivariate smoothing procedure, fitting a function of the independent variables locally and in a moving fashion analogous to how a moving average is computed for a time series. With local fitting we can estimate a much wider class of regression surfaces than with the usual classes of parametric functions, such as polynomials. The goal of this article is to show, through applications, how loess can be used for three purposes: data exploration, diagnostic checking of parametric models, and providing a nonparametric regression surface. Along the way, the following methodology is introduced: (a) a multivariate smoothing procedure that is an extension of univariate locally weighted regression; (b) statistical procedures that are analogous to those used in the least-squares fitting of parametric functions; (c) several graphical methods that are useful tools for understanding loess estimates and checking the assumptions on which the estimation procedure is based; and (d) the M plot, an adaptation of Mallows's Cp procedure, which provides a graphical portrayal of the trade-off between variance and bias, and which can be used to choose the amount of smoothing.

Statistica for Windows

1995-04-01

J. L. Francis

StatSoft™, 2325 E. 13th Street, Tulsa, OK 74104, USA. Tel: (918) 583–4149; Fax: (918) 583–4376. Price: $995 (a Macintosh version is available for $695). StatSoft™, 2325 E. 13th Street, Tulsa, OK 74104, USA. Tel: (918) 583–4149; Fax: (918) 583–4376. Price: $995 (a Macintosh version is available for $695).

Geostatistics: Modeling Spatial Uncertainty

2000-03-01

Michael L. Stein , Jean‐Paul Chilès , Pierre Delfiner

Preliminaries. Structural Analysis. Kriging. Intrinsic Model of Order k. Multivariate Methods. Nonlinear Methods. Conditional Simulations. Scale Effects and Inverse Problems. Appendix. References. Index. Preliminaries. Structural Analysis. Kriging. Intrinsic Model of Order k. Multivariate Methods. Nonlinear Methods. Conditional Simulations. Scale Effects and Inverse Problems. Appendix. References. Index.

Factors of Soil Formation: A System of Quantitative Pedology

1945-04-01

Rosemary L. Pendleton , Hans Jenny

Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties

2005-05-06

Raphael A. Viscarra Rossel , D.J.J. Walvoort , Alex B. McBratney +2 more

On digital soil mapping

2003-08-08

Alex B. McBratney , Maria de Lourdes Mendonça-Santos , Budiman Minasny

Dispersion on a Sphere

1953-05-07

Ronald Aylmer Fisher

Spatial pattern and ecological analysis

1989-06-01

Pierre Legendre , Marie Jos�e Fortin

Model-Based Geostatistics

1998-09-01

Peter J. Diggle , Jonathan A. Tawn , Rana Moyeed

SUMMARY Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S(x) based on observations Yi = S(xi) + … SUMMARY Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S(x) based on observations Yi = S(xi) + Zi at sampling locations xi, where the Zi are mutually independent, zero-mean Gaussian random variables. We describe two spatial applications for which Gaussian distributional assumptions are clearly inappropriate. The first concerns the assessment of residual contamination from nuclear weapons testing on a South Pacific island, in which the sampling method generates spatially indexed Poisson counts conditional on an unobserved spatially varying intensity of radioactivity; we conclude that a conventional geostatistical analysis oversmooths the data and underestimates the spatial extremes of the intensity. The second application provides a description of spatial variation in the risk of campylobacter infections relative to other enteric infections in part of north Lancashire and south Cumbria. For this application, we treat the data as binomial counts at unit postcode locations, conditionally on an unobserved relative risk surface which we estimate. The theoretical framework for our extension of geostatistical methods is that, conditionally on the unobserved process S(x), observations at sample locations xi form a generalized linear model with the corresponding values of S(xi) appearing as an offset term in the linear predictor. We use a Bayesian inferential framework, implemented via the Markov chain Monte Carlo method, to solve the prediction problem for non-linear functionals of S(x), making a proper allowance for the uncertainty in the estimation of any model parameters.

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World Reference Base for Soil Resources

2005-12-22

Paul Driessen , Freddy Nachtergaele , Otto Spaargaren +1 more

In 1998, the International Union of Sciences (IUSS) officially adopted the world reference base for soil resources (WRB) as the Union's system for soil correlation. The structure, concepts, and definitions … In 1998, the International Union of Sciences (IUSS) officially adopted the world reference base for soil resources (WRB) as the Union's system for soil correlation. The structure, concepts, and definitions of the WRB are strongly influenced by the FAO-UNESCO legend of the soil map of the world (1-2). At the time of itsinception, the WRB proposed 30 Soil Reference Groups accommodating more than 200 (second level) soil units. WRB (3-5) was endorsed by the IUSS in 1998 and provides an opportunity to create and refine a common and global language for soil classification. WRB aims to serve as a framework through which ongoing soil classification throughout the world can be harmonized. The ultimate objective is to reach international agreement on the major soil groups to be recognized at a global scale as well as on the criteria and methodology to be applied for defining and separating them. Such an agreement is needed to facilitate the exchange of information and experience, to provide a common scientific language, to strengthen the applications of soil science, and to enhance the communication with other disciplines and make the major soil names into household names

A review of assessing the accuracy of classifications of remotely sensed data

1991-07-01

Russell G. Congalton

STATISTICS FOR SPATIAL DATA

1992-09-01

Noel Cressie

Random forest classifier for remote sensing classification

2005-01-01

Mahesh Pal

Abstract Growing an ensemble of decision trees and allowing them to vote for the most popular class produced a significant increase in classification accuracy for land cover classification. The objective … Abstract Growing an ensemble of decision trees and allowing them to vote for the most popular class produced a significant increase in classification accuracy for land cover classification. The objective of this study is to present results obtained with the random forest classifier and to compare its performance with the support vector machines (SVMs) in terms of classification accuracy, training time and user defined parameters. Landsat Enhanced Thematic Mapper Plus (ETM+) data of an area in the UK with seven different land covers were used. Results from this study suggest that the random forest classifier performs equally well to SVMs in terms of classification accuracy and training time. This study also concludes that the number of user‐defined parameters required by random forest classifiers is less than the number required for SVMs and easier to define. Acknowledgment The author is grateful for the critical comments of two anonymous referees, whose advice has led to an improvement in the presentation of this paper.

A gentle introduction to quantile regression for ecologists

2003-10-01

Brian S. Cade , Barry R. Noon

Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between … Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.

Interpolation of Spatial Data: Some Theory for Kriging

2000-09-01

Stephen L. Rathbun , Michael L. Stein

1 Linear Prediction.- 1.1 Introduction.- 1.2 Best linear prediction.- Exercises.- 1.3 Hilbert spaces and prediction.- Exercises.- 1.4 An example of a poor BLP.- Exercises.- 1.5 Best linear unbiased prediction.- Exercises.- … 1 Linear Prediction.- 1.1 Introduction.- 1.2 Best linear prediction.- Exercises.- 1.3 Hilbert spaces and prediction.- Exercises.- 1.4 An example of a poor BLP.- Exercises.- 1.5 Best linear unbiased prediction.- Exercises.- 1.6 Some recurring themes.- The Matern model.- BLPs and BLUPs.- Inference for differentiable random fields.- Nested models are not tenable.- 1.7 Summary of practical suggestions.- 2 Properties of Random Fields.- 2.1 Preliminaries.- Stationarity.- Isotropy.- Exercise.- 2.2 The turning bands method.- Exercise.- 2.3 Elementary properties of autocovariance functions.- Exercise.- 2.4 Mean square continuity and differentiability.- Exercises.- 2.5 Spectral methods.- Spectral representation of a random field.- Bochner's Theorem.- Exercises.- 2.6 Two corresponding Hilbert spaces.- An application to mean square differentiability.- Exercises.- 2.7 Examples of spectral densities on 112.- Rational spectral densities.- Principal irregular term.- Gaussian model.- Triangular autocovariance functions.- Matern class.- Exercises.- 2.8 Abelian and Tauberian theorems.- Exercises.- 2.9 Random fields with nonintegrable spectral densities.- Intrinsic random functions.- Semivariograms.- Generalized random fields.- Exercises.- 2.10 Isotropic autocovariance functions.- Characterization.- Lower bound on isotropic autocorrelation functions.- Inversion formula.- Smoothness properties.- Matern class.- Spherical model.- Exercises.- 2.11 Tensor product autocovariances.- Exercises.- 3 Asymptotic Properties of Linear Predictors.- 3.1 Introduction.- 3.2 Finite sample results.- Exercise.- 3.3 The role of asymptotics.- 3.4 Behavior of prediction errors in the frequency domain.- Some examples.- Relationship to filtering theory.- Exercises.- 3.5 Prediction with the wrong spectral density.- Examples of interpolation.- An example with a triangular autocovariance function.- More criticism of Gaussian autocovariance functions.- Examples of extrapolation.- Pseudo-BLPs with spectral densities misspecified at high frequencies.- Exercises.- 3.6 Theoretical comparison of extrapolation and ointerpolation.- An interpolation problem.- An extrapolation problem.- Asymptotics for BLPs.- Inefficiency of pseudo-BLPs with misspecified high frequency behavior.- Presumed mses for pseudo-BLPs with misspecified high frequency behavior.- Pseudo-BLPs with correctly specified high frequency behavior.- Exercises.- 3.7 Measurement errors.- Some asymptotic theory.- Exercises.- 3.8 Observations on an infinite lattice.- Characterizing the BLP.- Bound on fraction of mse of BLP attributable to a set of frequencies.- Asymptotic optimality of pseudo-BLPs.- Rates of convergence to optimality.- Pseudo-BLPs with a misspecified mean function.- Exercises.- 4 Equivalence of Gaussian Measures and Prediction.- 4.1 Introduction.- 4.2 Equivalence and orthogonality of Gaussian measures.- Conditions for orthogonality.- Gaussian measures are equivalent or orthogonal.- Determining equivalence or orthogonality for periodic random fields.- Determining equivalence or orthogonality for nonperiodic random fields.- Measurement errors and equivalence and orthogonality.- Proof of Theorem 1.- Exercises.- 4.3 Applications of equivalence of Gaussian measures to linear prediction.- Asymptotically optimal pseudo-BLPs.- Observations not part of a sequence.- A theorem of Blackwell and Dubins.- Weaker conditions for asymptotic optimality of pseudo-BLPs.- Rates of convergence to asymptotic optimality.- Asymptotic optimality of BLUPs.- Exercises.- 4.4 Jeffreys's law.- A Bayesian version.- Exercises.- 5 Integration of Random Fields.- 5.1 Introduction.- 5.2 Asymptotic properties of simple average.- Results for sufficiently smooth random fields.- Results for sufficiently rough random fields.- Exercises.- 5.3 Observations on an infinite lattice.- Asymptotic mse of BLP.- Asymptotic optimality of simple average.- Exercises.- 5.4 Improving on the sample mean.- Approximating $$\int_0^1 {\exp } (ivt)dt$$.- Approximating $$\int_{{{[0,1]}^d}} {\exp (i{\omega ^T}x)} dx$$ in more than one dimension.- Asymptotic properties of modified predictors.- Are centered systematic samples good designs?.- Exercises.- 5.5 Numerical results.- Exercises.- 6 Predicting With Estimated Parameters.- 6.1 Introduction.- 6.2 Microergodicity and equivalence and orthogonality of Gaussian measures.- Observations with measurement error.- Exercises.- 6.3 Is statistical inference for differentiable processes possible?.- An example where it is possible.- Exercises.- 6.4 Likelihood Methods.- Restricted maximum likelihood estimation.- Gaussian assumption.- Computational issues.- Some asymptotic theory.- Exercises.- 6.5 Matern model.- Exercise.- 6.6 A numerical study of the Fisher information matrix under the Matern model.- No measurement error and?unknown.- No measurement error and?known.- Observations with measurement error.- Conclusions.- Exercises.- 6.7 Maximum likelihood estimation for a periodic version of the Matern model.- Discrete Fourier transforms.- Periodic case.- Asymptotic results.- Exercises.- 6.8 Predicting with estimated parameters.- Jeffreys's law revisited.- Numerical results.- Some issues regarding asymptotic optimality.- Exercises.- 6.9 An instructive example of plug-in prediction.- Behavior of plug-in predictions.- Cross-validation.- Application of Matern model.- Conclusions.- Exercises.- 6.10 Bayesian approach.- Application to simulated data.- Exercises.- A Multivariate Normal Distributions.- B Symbols.- References.

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An Introduction to Applied Geostatistics

1991-11-01

Richard A. Bilonick

(1991). An Introduction to Applied Geostatistics. Technometrics: Vol. 33, No. 4, pp. 483-485. (1991). An Introduction to Applied Geostatistics. Technometrics: Vol. 33, No. 4, pp. 483-485.

Sampling Errors in the Estimation of Empirical Orthogonal Functions

1982-07-01

Gerald R. North , Thomas L. Bell , Robert F. Cahalan +1 more

Empirical Orthogonal Functions (EOF's), eigenvectors of the spatial cross-covariance matrix of a meteorological field, are reviewed with special attention given to the necessary weighting factors for gridded data and the … Empirical Orthogonal Functions (EOF's), eigenvectors of the spatial cross-covariance matrix of a meteorological field, are reviewed with special attention given to the necessary weighting factors for gridded data and the sampling errors incurred when too small a sample is available. The geographical shape of an EOF shows large intersample variability when its associated eigenvalue is “close” to a neighboring one. A rule of thumb indicating when an EOF is likely to be subject to large sampling fluctuations is presented. An explicit example, based on the statistics of the 500 mb geopotential height field, displays large intersample variability in the EOF's for sample sizes of a few hundred independent realizations, a size seldom exceeded by meteorological data sets.

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The Combination of Estimates from Different Experiments

1954-03-01

William G. Cochran

When we are trying to make the best estimate of some quantity A that is available from the research conducted to date, the problem of combining results from different experiments … When we are trying to make the best estimate of some quantity A that is available from the research conducted to date, the problem of combining results from different experiments is encountered. The problem is often troublesome, particularly if the individual estimates were made by different workers using different procedures. This paper discusses one of the simpler aspects of the problem, in which there is sufficient uniformity of experimental methods so that the ith experiment provides an estimate xi of u, and an estimate si of the standard error of xi . The experiments may be, for example, determinations of a physical or astronomical constant by different scientists, or bioassays carried out in different laboratories, or agricultural field experiments laid out in different parts of a region. The quantity xi may be a simple mean of the observations, as in a physical determination, or the difference between the means of two treatments, as in a comparative experiment, or a median lethal dose, or a regression coefficient. The problem of making a combined estimate has been discussed previously by Cochran (1937) and Yates and Cochran (1938) for agricultural experiments, and by Bliss (1952) for bioassays in different laboratories. The last two papers give recommendations for the practical worker. My purposes in treating the subject again are to discuss it in more general terms, to take account of some recent theoretical research, and, I hope, to bring the practical recommendations to the attention of some biologists who are not acquainted with the previous papers. The basic issue with which this paper deals is as follows. The simplest method of combining estimates made in a number of different experiments is to take the arithmetic mean of the estimates. If, however, the experiments vary in size, or appear to be of different precision, the investigator may wonder whether some kind of weighted meani would be more precise. This paper gives recommendations about the kinds of weighted mean that are appropriate, the situations in which they

Statistics for Spatial Data, Revised Edition.

1994-03-01

Noel Cressie

Spatial Statistics

1981-04-22

B. D. Ripley

Multivariate Data Analysis

2016-01-01

Thomas Haslwanter

Statistics for Spatial Data

1993-09-10

Noel Cressie

Statistics for Spatial Data.

1993-01-01

Mike Rees , Noel Cressie

5. Statistics for Spatial Data. By N. Cressie. ISBN 0 471 84336 9. Wiley, Chichester, 1991. 900 pp. £71.00. 5. Statistics for Spatial Data. By N. Cressie. ISBN 0 471 84336 9. Wiley, Chichester, 1991. 900 pp. £71.00.

SoilGrids250m: Global gridded soil information based on machine learning

2017-02-16

Tomislav Hengl , Jorge Mendes de Jesus , G.B.M. Heuvelink +7 more

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions … This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression—as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10–fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

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Statistical Procedures for Agricultural Research.

1985-03-01

G. H. Freeman , K. A. Gomez , Á. Gómez

GSLIB: Geostatistical Software Library and User's Guide

1995-02-01

Eric R. Ziegel , Clayton V. Deutsch , André G. Journel

Geostatistics for Natural Resources Evaluation

2000-11-01

Timothy C. Coburn , Pierre Goovaerts

This book provides an advanced-level introduction to geostatistics and geostatistical methodology. The discussion includes tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and … This book provides an advanced-level introduction to geostatistics and geostatistical methodology. The discussion includes tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and stochastic simulation. It also details the theoretical background underlying most GSLIB programs.

Geostatistics for Environmental Scientists

2001-11-01

Preface 1 Introduction 2 Basic Statistics 3 Prediction and Interpolation 4 Characterizing Spatial Processes: The Covariance and Variogram 5 Modelling the Variogram 6 Reliability of the Experimental Variogram and Nested … Preface 1 Introduction 2 Basic Statistics 3 Prediction and Interpolation 4 Characterizing Spatial Processes: The Covariance and Variogram 5 Modelling the Variogram 6 Reliability of the Experimental Variogram and Nested Sampling 7 Spectral Analysis 8 Local Estimation or Prediction: Kriging 9 Kriging in the Presence of Trend and Factorial Kriging 10 Cross-Correlation, Coregionalization and Cokriging 11 Disjunctive Kriging 12 Stochastic Simulation (new file) Appendix A Appendix B References Index

Geostatistics

1999-03-24

Jean‐Paul Chilès , Pierre Delfiner

System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

2015-07-07

Olaf Conrad , Benjamin Bechtel , M. Bock +6 more

Abstract. The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, … Abstract. The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.

An Introduction to Applied Geostatistics.

1991-06-01

Carolyn M. Rutter , Edward H. Isaaks , R. Mohan Srivastava

Methods of Soil Analysis

1996-01-01

Methods of Soil Analysis

1982-02-01

Methods of Soil Analysis

1986-01-01

Methods of Soil Analysis

1965-01-01

Interpolation of Spatial Data

1999-01-01

Michael L. Stein

Spatial analysis and geostatistical characterization of nitrate pollution in Mahdia’s shallow aquifers

2025-06-25

Rania Soula , Ali Chebil , Mahmut Çetin +1 more | Environmental Science and Pollution Research

Transferring and Spiking of VNIR Models for Soil Property Predictions in Southern India

2025-06-24

Christopher M. Clingensmith , Sabine Grunwald | Communications in Soil Science and Plant Analysis

Modeling Soil pH at regional scale using environmental covariates and machine learning algorithm

2025-06-24

R. Vasundhara , S. Dharumarajan , Rajendra Hegde +7 more | Environmental Monitoring and Assessment

The use of Kappa statistics to assess long-term changes in the intrafield heterogeneity of the content of available forms of phosphorus and potassium

2025-06-24

Ilnas Sahabiev , K. G. Giniyatullin , Е. В. Смирнова | Agrarian science

Relevance . The introduction of robotic technology for differentiated application of fertilizers makes it necessary to study the intra-field heterogeneity of the availability of arable land with nutrients. It is … Relevance . The introduction of robotic technology for differentiated application of fertilizers makes it necessary to study the intra-field heterogeneity of the availability of arable land with nutrients. It is necessary to evaluate the structure of spatial heterogeneity and the direction of changes in the long term. The use of different methods for determining available forms of nutrients in different years makes it difficult to compare long-term data. Methods . Long-term (1965, 1987, 2021) data on the content of mobile phosphorus (P 2 O 5 ) and exchangeable potassium (K 2 O) in the soils of the crop testing site of the Republic of Tatarstan were used. The P 2 O 5 content was determined according to Truog (1965) and Kirsanov (1987, 2021), and the K 2 O content according to Maslova (1965, 1987) and Kirsanov (2021). Agreement in the assessment of availability was calculated using Cohen’s kappa coefficient. Results . The availability of P 2 O 5 by the Kappa coefficient shows a moderate coincidence between 1965 and 1987 (κ = 0.45), and a significant coincidence between 1987 and 2021 (κ = 0.62). The availability of K 2 O between 1965 and 1987 has a very weak coincidence (κ = 0.10), between 1987 and 2021 — a satisfactory coincidence (κ = 0.25). In contrast to P 2 O 5 , the structure of spatial heterogeneity of K 2 O availability remains weak during prolonged use. The availability of P 2 O 5 and K 2 O fields increased significantly from 1965 to 1987, and decreased from 1987 to 2021. The intra-field variability of the indicators increased from medium to high (to close to very high (P 2 O 5 ) from 1965 to 2021.

Developing Spatial Maps of Soil Fertility Status and Assessing Impact of Different Fertigation Strategies on Soil Fertility in Kerala, India

2025-06-23

M. Khamarunneesa , S. Sajeena , V. M. Abdul Hakkim | Journal of Agricultural Engineering (India)

Knowledge about soil nutrient status is vital in precision agriculture for site-specific nutrient management, which is the key to improve nutrient application efficiency and boost crop productivity. A study was … Knowledge about soil nutrient status is vital in precision agriculture for site-specific nutrient management, which is the key to improve nutrient application efficiency and boost crop productivity. A study was conducted in Malappuram district of Kerala, India where a total of 70 soil samples were collected at a grid interval of 50 m × 50 m to determine status of soil nutrients including pH, electrical conductivity (EC), organic carbon, available nitrogen, available phosphorus and available potassium. Then, spatial mapping of the soil parameters was accomplished by employing inverse distance weighting (IDW) method of spatial interpolation in geographic information system (GIS) environment. Furthermore, dynamics of variable soil nutrients due to fertigation in tomato crop was monitored at two test sites, one at high soil fertility zone and another at low fertility zone. The results indicated that soil pH in the study area ranged from strongly to slightly acidic with low EC values, which is considered safe for plant growth. The low EC value of soil is likely due to high rainfall and minimal salt accumulation in the soil. The results of nutrient analysis revealed that nitrogen and potassium levels were in the ‘low fertility’ class, while phosphorus was in ‘high fertility’ class. The nutrient dynamics study showed that concentrations of three primary nutrients were highest at the points close to the drippers, especially at 15-30 cm depth, which suggests efficient nutrient delivery through the watering system. Overall, nutrient levels declined with soil depth and distance from the dripper, which emphasized the need for precise fertilizer application. The contrasting fertility levels suggest that blanket fertilizer recommendations may be inefficient and potentially lead to nutrient imbalances or environmental stress. On the other hand, site-specific nutrient management strategies have the potential for promoting both economic savings and environmental stewardship.

Influence of Field Survey Principles on the Quality of Approximation of the Relationship between the Soil Organic Matter Content and the Multitemporal Spectral Characteristics

2025-06-23

Д. И. Рухович , Dmitry Borshchev , П. В. Королева +1 more | Eurasian Soil Science

A pragmatic workflow towards the generation of pXRF datasets for large-scale soil monitoring programs

2025-06-22

Xueyu Zhao , Uta Stockmann , Mark Farrell +1 more | Soil Research

Compared to conventional laboratory methods, portable X-ray fluorescence (pXRF) can rapidly estimate total elemental concentrations of soil samples. Developing an optimised and efficient data collection protocol is crucial for processing … Compared to conventional laboratory methods, portable X-ray fluorescence (pXRF) can rapidly estimate total elemental concentrations of soil samples. Developing an optimised and efficient data collection protocol is crucial for processing large sample numbers. Factors such as sample preparation methods, manufacturer calibration modes, and associated analysis times can influence pXRF performance. We evaluated the performance of different containers and detection modes (Soil vs Geochem) to determine the most time-efficient scanning protocol under standard instrument operation while maintaining acceptable accuracy for large-scale total elemental concentration analysis. Using 130 representative soil samples from the CSIRO National Soil Archive, results showed strong correlations for most elements (K, Ca, Ti, Fe, Cu, and Zr) across different containers. However, Mg, which has a low atomic number element, showed poor correlations (R2 = 0.05), likely due to the limits of detection (LOD) of the Geochem mode. Al and Si exhibited better R2 values but showed a low Lin’s Concordance Correlation Coefficient (LCCC) value of less than 0.1. Among different modes, elements including K, Ca, Ti, Fe, Cu and Zr maintained strong correlations (R2 > 0.65 and LCCC > 0.7). Scanning soil samples through plastic bags in Geochem mode is recommended due to its shorter measurement and sample preparation time and ability to detect lighter elements (Mg, Al, and Si). This optimised protocol will support national scale soil monitoring programs with large sample sizes (e.g. n > 3000 soil samples). For future work, elemental data acquired can support for example investigations of how soil mineralogy influences carbon storage capacity and provide insights to the biological-chemical stabilisation of soil organic carbon.

Integrated framework for assessment and spatial prediction of humus layer properties of forest soils

2025-06-21

Felix Thomas , Carina Becker , Rainer Petzold +3 more | Discover Soil.

Soil mosaic: A multi-modelling approach to understand the complex interactions that determine the spatial heterogeneity of soil health − An example from Serbia

2025-06-21

Velјko Perović , Vesna Mrvić , Dragan Čakmak +4 more | Computers and Electronics in Agriculture

Pathways to improving MIR prediction accuracy for soil carbon, nitrogen and pH using external libraries at national scale

2025-06-21

Felipe Bachion de Santana , Giulia Bondi , Karen Daly | Geoderma

How to solve small sample size problems in time-series soil organic carbon mapping: New insights from the Third Law of Geography

2025-06-21

Jingzhe Wang , Zipeng Zhang , Yankun Wang +4 more | Geoderma

Unlocking Soil Salinity Prediction With Remote Sensing Indices and Environmental Insights

2025-06-20

Tehseen Javed , Jinzhu Zhang , Li Wenhao +4 more | Land Degradation and Development

ABSTRACT Despite advances in salinity prediction, a knowledge gap exists in accurately integrating remote sensing indices and environmental factors for effective management strategies. Therefore, this study examines the relationship between … ABSTRACT Despite advances in salinity prediction, a knowledge gap exists in accurately integrating remote sensing indices and environmental factors for effective management strategies. Therefore, this study examines the relationship between soil salinity (EC e ) and remote sensing (RS) indices, soil texture properties, and ecological features. Several statistical techniques, such as Pearson correlation, Geographically Weighted Regression (GWR), Principal Component Analysis (PCA), and SHapley Additive exPlanations (SHAP), were used to investigate the capability of these indices and indicators for the prediction of soil salinity. The study revealed that the Decision Tree (DT) showed the highest accuracy for soil salinity prediction among the machine learning models, while XGBoost exhibited lower predictive performance. Evaluating the environmental indices with ECe, the Normalized Difference Salinity Index (NDSI) showed the highest positive correlation with ECe ( r = 0.88), reflecting its effectiveness in salinity prediction. Moderate positive correlations were observed with the Soil Salinity Index (SSI, r = 0.65), while the Bare Soil Index (BSI, r = −0.85) and Soil‐Adjusted Vegetation Index (SAVSI, r = −0.76) demonstrated strong negative correlations. Soil physicochemical properties, including clay, silt, sand, organic carbon, and bedrock, exhibited weak relationships with ECe, with R 2 values consistently below 0.04, indicating limited predictive power. PCA analysis revealed distinct contributions of RS indices to ECe variability, with NDSI and SSI positively influencing salinity variability, whereas SAVSI contributed inversely, aligning negatively along PC1. SHAP analysis further reinforced the predictive dominance of RS indices, assigning the highest importance value to NDSI (0.61), followed by BSI (0.28) and SAVSI (0.08). In contrast, soil texture properties and organic carbon exhibited minimal significance, with importance values under 0.02. NDSI was further tested across low‐ and high‐salinity farms, consistently outperforming other indices. These findings highlight its advantage in improving salinity mapping management strategies and advancing precision agriculture/environmental planning through modern analytical approaches.

Bridging the gap between laboratory VNIR-SWIR spectra and Landsat-8 bare soil composite image for soil organic carbon prediction

2025-06-19

Yongsheng Hong , Yiyun Chen , Songchao Chen +7 more | Remote Sensing of Environment

Spatial Analysis of Sample Data

2025-06-19

| Cambridge University Press eBooks

Spatial Analysis of Point and Quadrat Data

2025-06-19

| Cambridge University Press eBooks

AI-Based Soil Quality Monitoring and Improvement Techniques Predicting and Mitigating Soil Erosion and Degradation

2025-06-19

Ajay B. Gadicha , Vijay B. Gadicha , Mohammad Moin Maniyar | IGI Global eBooks

Soil health remains a cornerstone of sustainable agriculture and food production, yet it faces mounting challenges from nutrient depletion, erosion, and degradation, often driven by unsustainable farming practices, climate change, … Soil health remains a cornerstone of sustainable agriculture and food production, yet it faces mounting challenges from nutrient depletion, erosion, and degradation, often driven by unsustainable farming practices, climate change, and over-reliance on chemical inputs. The emergence of artificial intelligence (AI) in agriculture has introduced a transformative solution to these issues by enabling highly accurate soil quality assessments and predictive capabilities that aid in the conservation and improvement of soil health. This chapter explores the application of AI-based techniques in soil monitoring and improvement, focusing on how they predict, detect, and mitigate soil erosion and degradation to ensure long-term soil fertility and agricultural productivity.

Testing Elliptical Models in High Dimensions

2025-06-18

Siyao Wang , Miles E. Lopes | Journal of the American Statistical Association

Mapping basic properties of Danish sandy soils using on-the-go proximal sensors and terrain attributes

2025-06-18

Ameesh Khatkar , Amélie Beucher , Triven Koganti +2 more | Geoderma Regional

Comparison of multivariate machine learning models for major soil nutrients prediction using laboratory-based and airborne (AVIRIS-NG) visible near-infrared spectroscopy

2025-06-17

Bhabani Prasad Mondal , Rabi Narayan Sahoo , Bappa Das +5 more | European Journal of Agronomy

Development of Yield Modelling Techniques for Rabi Pulses using Geospatial Techniques

2025-06-17

Abhishek Danodia , T. Udaya Bhaskar Jayanth , N. R. Patel +1 more | Journal of the Indian Society of Remote Sensing

Remote sensing based on agricultural soil salinity assessment in Soc Trang province, Vietnam

2025-06-16

Thi Hong Diep Nguyen , Trọng Nguyễn Nguyễn , Tran Van Hung +1 more | Ecological Engineering & Environmental Technology

In Situ Quantification of Surface pH of Dry Soils and Minerals: An Innovative Approach Combining Indicators and Diffuse Reflectance Spectroscopy

2025-06-16

Pengfei Cheng , Kuan Cheng , Yang Yang +5 more | Langmuir

Most natural soils and minerals are typically dry or semidry, and their surface characteristics and interfacial interactions are distinct from those of slurry systems. The surface acidity of dry soils … Most natural soils and minerals are typically dry or semidry, and their surface characteristics and interfacial interactions are distinct from those of slurry systems. The surface acidity of dry soils and minerals cannot be measured by using traditional methods such as potentiometric and temperature-programmed desorption methods. In this article, we describe the development of an analytical method based on a combination of acid-base indicators and diffuse reflectance spectroscopy (DRS). The surface pH (pHsurf) values of dry clay minerals and metal oxides were obtained, which were 1.13-3.14 units lower than that of slurry systems. This result was confirmed by the pollutant degradation activity on the mineral surface with different water contents. The potentiometric titration and surface complexation models (SCM) were used to reveal the acidity characteristics of different minerals. The pHsurf values of soils from seven provinces in China were determined using three methods: (1) real soil samples with different colors were diluted in BaSO4, and pHsurf values were determined using DRS; (2) the pHsurf values of soil samples were also calculated using the pHsurf vs slurry pH curves, which were constructed using the pHsurf of six metal oxides and clay minerals obtained using DRS and their slurry pH values; (3) the pHsurf values of soil samples were determined using the pH values through extension from the slurry pH vs soil-water ratio plot. The pHsurf values calculated using the former two DRS methods were consistent and provided a better measure of the surface acidity of dry soil than slurry pH and its extension method. This review presents an in situ method for the quantitative surface-acidity measurement of dry soils and minerals and provides insights into acidity and interface research in other low-moisture systems.

Mapping Soil Organic Carbon in High‐standard Farmland Construction Areas Using Machine Learning Algorithms: Threshold and Interaction Effects of Environmental Variables

2025-06-16

Zening Wu , Bozhi Wang , Xiangyang Feng +4 more | Land Degradation and Development

ABSTRACT Accurate mapping soil organic carbon (SOC) in high‐standard farmland (HSF) construction areas is essential for optimizing agricultural management practices and achieving carbon sequestration. However, how to quantify the construction … ABSTRACT Accurate mapping soil organic carbon (SOC) in high‐standard farmland (HSF) construction areas is essential for optimizing agricultural management practices and achieving carbon sequestration. However, how to quantify the construction activities of HSF and predict SOC is still unclear. In this study, we proposed a framework to quantify the impacts of HSF construction activities on SOC from four perspectives: landscape pattern, agricultural infrastructure, soil property, and agricultural management. Using 298 high‐standard and conventional farmland samples collected in Peixian County, China, machine learning algorithms were employed to explore the threshold effect and interaction effect of HSF construction activities on SOC, and then map the SOC content. Results showed that SOC content in HSF was significantly higher than in conventional farmland ( p < 0.05 ). The gradient boosting decision tree model outperformed other models, with an R 2 of 0.49 on the test set. The SOC content was high in the eastern part and low in the western part of Peixian County, which was influenced by farmland landscape configuration and the degree of completeness of agricultural infrastructure. Non‐linear relationship analysis indicated that being close to water bodies and green land was beneficial to the accumulation of farmland SOC content, with average effective influence ranges of 448 m and 430 m, respectively. Friedman's H statistic reflected complex interaction mechanisms among environmental variables, with total phosphorus content demonstrating the highest interaction strength. These findings highlight that HSF construction increased SOC content. In particular, farmland with high connectivity, comprehensive irrigation and drainage measures, and proximity to water bodies and shelterbelts was conducive to carbon sequestration.

Pruned hierarchical Random Forest framework for digital soil mapping: Evaluation using NEON soil properties

2025-06-15

Chengcheng Xu , Jingyi Huang , Alfred E. Hartemink +1 more | Geoderma

Arazi toplulaştırma projelerinde kullanılan toprak indeks değerlerinin alansal dağılımının farklı enterpolasyon yöntemleri ile değerlendirilmesi

2025-06-15

Eyüp Uzunal , Hakan Arslan | Toprak Bilimi ve Bitki Besleme Dergisi

Toprak haritaları, sürdürülebilir tarım, arazi yönetimi ve arazi toplulaştırmalarının planlanmasında çok önemli yer tutmaktadır. Bu nedenle toprak özelliklerinin alansal dağılımlarının en az hata ile yapılması projenin başarısı için çok önemlidir. … Toprak haritaları, sürdürülebilir tarım, arazi yönetimi ve arazi toplulaştırmalarının planlanmasında çok önemli yer tutmaktadır. Bu nedenle toprak özelliklerinin alansal dağılımlarının en az hata ile yapılması projenin başarısı için çok önemlidir. Bu haritaların oluşturulmasında farklı enterpolasyon yöntemleri (IDW, Kriging, RBF vb.) kullanılmaktadır. Enterpolasyon yöntemleri, toplulaştırma sürecinde toprak sınıflarının belirlenmesi ve sınırların doğru çizilmesine olanak tanımaktadır. Bu çalışmada Devlet Su İşleri Genel Müdürlüğü tarafından Konya ili Altınekin ilçesi 1. Kısım Arazi Toplulaştırma ve TİGH Projesi kapsamında, 24.911 hektarlık alanda 1318 noktadan elde edilen toprak verileri kullanılmış ve bu verilerden arazi toplulaştırma da çok önemli bir aşama olan toprak indeks değerleri hesaplanmıştır. Çalışmada hesaplanmış olan toprak indeks değerlerinin alansal dağılım haritaları IDW, RBF ve Kriging enterpolasyon yöntemlerine ait toplam 12 farklı yöntem ile hazırlanmış ve en iyi enterpolasyon yöntemi belirlenmeye çalışılmıştır. Yöntemlerin karşılaştırılmasında RMSE değerleri kullanılmıştır. Yapılan analizlerde, IDW-3 yöntemi 12.399 ile en düşük RMSE değerine sahip olarak belirlenmiş ve çalışma için en uygun yöntem olarak öne çıkmıştır. Yöntemlere göre alansal dağılım arasında çok büyük farklılıklar olduğu belirlenmiştir. Bu durum alan bazlı çalışmalarda seçilen enterpolasyon yönteminin önemini ortaya koymuştur. Arazi toplulaştırma çalışmalarının toprak etüt ve haritalama aşamalarında klasik değerlendirme yerine enterpolasyon yöntemlerine göre yapılan haritaların kullanılması arazi toplulaştırma çalışmalarının başarısına önemli katkılar sağlayacağı düşünülmektedir.

Optimizing Greenhouse Gas Emissions Studies with Functional Data Analysis in Tropical Land Uses

2025-06-13

Mickaelle Maria de Almeida Pereira , Luiz Antônio Martinelli , Janaína Braga do Carmo +1 more | ACS Agricultural Science & Technology

Leveraging Remote Sensing and Geospatial Technologies in Soil and Water Resources Mapping and Characterization: A Review

2025-06-13

S. N. Ingle , Bhabani Prasad Mondal , Chirag Patel +1 more | Agricultural Reviews

Soil and groundwater, important natural resources, are essential for sustaining ecosystem health and maintaining agricultural production. However, both resources are under severe threat due to increasing demand for human consumption, … Soil and groundwater, important natural resources, are essential for sustaining ecosystem health and maintaining agricultural production. However, both resources are under severe threat due to increasing demand for human consumption, agricultural operations and industrial activities. This situation warrants accurate monitoring and mapping to ensure their sustainable use. Since conventional methods are expensive and cumbersome, there is a need for innovative alternative approaches. The integration of remote sensing, Geographical Information Systems (GIS) and advanced geospatial technologies offers effective solutions for mapping and monitoring soil and groundwater. These tools provide accurate, cost-effective methods for estimating soil properties, detecting land degradation processes such as soil salinization and identifying potential groundwater zones. High-resolution satellite imagery, combined with machine learning, enables digital soil mapping, improving assessments of soil variability and land suitability. The adoption of these advanced technologies has revolutionized soil and groundwater assessment. Digital soil mapping enhances understanding of land resources, while remote sensing and GIS facilitate environmental conservation and sustainable agriculture. This article systematically underscores the role of these technologies in soil and groundwater management, highlighting their importance in resource sustainability.

Soil penetration resistance prediction based on a comparative evaluation of individual and ensemble machine learning under varying tillage, fertilization and liming treatments

2025-06-13

Danijel Jug , Dorijan Radočaj , Irena Jug +6 more | Soil and Tillage Research

Assessing the Accuracy of Gaussian Transformations for Reproducing Statistical and Spatial Dependence Relationships in Multivariate Simulation

2025-06-12

Jordan Plaza-Carvajal , Mohammad Maleki , Farzaneh Khorram +1 more | Natural Resources Research

Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels

2025-06-10

Sébastien Coube-Sisqueille , Sudipto Banerjee , Benoît Liquet | Journal of Computational and Graphical Statistics

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