The relevant range of scales for multi-scale contextual spatial modelling.

Clicks: 324
ID: 80875
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #374 of 1,628 articles by views in Scientific reports

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,628 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Spatial autocorrelation in the residuals of spatial environmental models can be due to missing covariate information. In many cases, this spatial autocorrelation can be accounted for by using covariates from multiple scales. Here, we propose a data-driven, objective and systematic method for deriving the relevant range of scales, with distinct upper and lower scale limits, for spatial modelling with machine learning and evaluated its effect on modelling accuracy. We also tested an approach that uses the variogram to see whether such an effective scale space can be approximated a priori and at smaller computational cost. Results showed that modelling with an effective scale space can improve spatial modelling with machine learning and that there is a strong correlation between properties of the variogram and the relevant range of scales. Hence, the variogram of a soil property can be used for a priori approximations of the effective scale space for contextual spatial modelling and is therefore an important analytical tool not only in geostatistics, but also for analyzing structural dependencies in contextual spatial modelling.
Reference Key
behrens2019thescientific Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Behrens, Thorsten;Viscarra Rossel, Raphael A;Kerry, Ruth;MacMillan, Robert;Schmidt, Karsten;Lee, Juhwan;Scholten, Thomas;Zhu, A-Xing;
Journal Scientific reports
Year 2019
DOI
10.1038/s41598-019-51395-3
URL
Keywords
uav ph quantile regression apparent electrical conductivity (eca) boundary-line law of minimum

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.