Results 221 to 230 of about 32,345 (261)

Can regression determination, nugget-to-sill ratio and sampling spacing determine relative performance of regression kriging over ordinary kriging?

CATENA, 2019
Abstract Regression kriging (RK), a popular digital soil mapping method which combines regression and ordinary kriging (OK) to predict, did not always perform better than OK purely. A lot of studies tried to establish approaches for determining relative improvement (RI) of RK over OK in terms of two of three factors, i.e., regression determination ...
Xiao-Lin Sun, Qian Yang, Hui-Li Wang, Yun-Jin Wu   +3 more
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Regression-kriging for characterizing soils with remotesensing data

Frontiers of Earth Science, 2011
In precision agriculture regression has been used widely to quantify the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually violates a basic assumption of regression: sample independence.
Yufeng Ge, J. Alex Thomasson, Ruixiu Sui, James Wooten   +3 more
openaire   +1 more source

High-resolution satellite image fusion using regression kriging

International Journal of Remote Sensing, 2010
Image fusion is an important component of digital image processing and quantitative image analysis. Image fusion is the technique of integrating and merging information from different remote sensors to achieve refined or improved data. A number of fusion algorithms have been developed in the past two decades, and most of these methods are efficient for
Qingmin Meng, Bruce Borders, Marguerite Madden   +2 more
openaire   +1 more source

Nonparametric Regression, Kriging and Process Optimization

1995
Thin plate splines and kriging models are proposed as methods for approximating unknown response functions in the context of process optimization. Connections between the methods are discussed and implementation of the models using S-PLUS is described.
M. O’Connell, P. Haaland, S. Hardy, D. Nychka   +3 more
openaire   +1 more source

Bayesian model averaging for Kriging regression structure selection

Probabilistic Engineering Mechanics, 2019
Abstract Kriging metamodels are widely used to approximate the response of computationally-intensive engineering models for a variety of applications, ranging from system design to uncertainty quantification. Their predictions are formulated by combining a regression that captures global trends of the true model with a Gaussian process approximation ...
Zhang, Jize, Taflanidis, Alexandros A.
openaire   +2 more sources

[Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging].

Ying yong sheng tai xue bao = The journal of applied ecology, 2016
Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control.
Shun-hua, Yang, Hai-tao, Zhang, Long, Guo, Yan, Ren   +3 more
openaire   +1 more source

Estimating Soil Organic Matter Content by Regression Kriging

2010
In Mediterranean countries soil organic matter (SOM) depletion is a key factor in land degradation. Here, climate (temperate winter/dry summer) and water scarcity give rise to faster mineralization rates and lower accumulation intensities, particularly in association with intensive and non-conservative agronomic practices.
A. Marchetti, C. Piccini, R. Francaviglia, S. Santucci, I. Chiuchiarelli   +4 more
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Linear Regression and Simple Kriging

1998
Linear regression is presented in the case of two variables and then extended to the multivariate case. Simple kriging is a transposition of multiple regression in a spatial context. The algebraic problems generated by missing values in the multivariate case serve as a motivation for introducing a covariance function in the spatial case.
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Estimating Spatial Precipitation Using Regression Kriging and Artificial Neural Network Residual Kriging (RKNNRK) Hybrid Approach

Water Resources Management, 2015
A hybrid model, combining regression kriging and neural network residual kriging (RKNNRK), is developed for determining spatial precipitation distribution. The RKNNRK model is compared with current spatial interpolation models, including simple kriging (SK), ordinary kriging (OK), universal kriging (UK), regression kriging (RK) and neural network ...
Youngmin Seo, Sungwon Kim, Vijay P. Singh   +2 more
openaire   +1 more source

A comparison of kriging, co-kriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations

Geoderma, 1995
Abstract We compared the performances of kriging and two methods of interpolation which allow to account for an auxiliary variable: co-kriging, and kriging combined with regression. These two methods were applied to improve the interpolation of the soft layers depth (Dsl) measured by augering, using the bulk soil electrical conductivity (ECa ...
Knotters, M., Brus, D.J., Oude Voshaar, J.H.   +2 more
openaire   +2 more sources