Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 433627
Title Representing major soil variability at regional scale by constrained Latin Hypercube Sampling of remote sensing data
Author(s) Mulder, V.L.; Bruin, S. de; Schaepman, M.E.
Source International Journal of applied Earth Observation and Geoinformation 21 (2013). - ISSN 0303-2434 - p. 301 - 310.
DOI http://dx.doi.org/10.1016/j.jag.2012.07.004
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2013
Keyword(s) design-based estimation - spatial prediction - classification tree - optimization - landscape - attributes - strategies - variables - desert - model
Abstract This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin Hypercube Sampling (cLHS) to assess variability in soil properties at regional scale. The method optimizes the sampling scheme for a defined spatial population based on selected covariates, which are assumed to represent the variability of the target variables. The optimization also accounts for specific constraints and costs expressing the field sampling effort. The approach is demonstrated using a case study in Morocco, where a small but representative sample record had to be collected over a 15,000 km2 area within 2 weeks. The covariate space of the Latin Hypercube consisted of the first three principal components of ASTER imagery as well as elevation. Comparison of soil properties taken from the topsoil with the existing soil map, a geological map and lithological data showed that the sampling approach was successful in representing major soil variability. The cLHS sample failed to express spatial correlation; constraining the LHS by a distance criterion favoured large spatial variability within a short distances resulting in an overestimation of the variograms nugget and short distance variability. However, the exhaustive covariate data appeared to be spatially correlated which supports our premise that once the relation between spatially explicit remote sensing data and soil properties has been modelled, the latter can be spatially predicted based on the densely sampled remotely sensed data. Therefore, the LHS approach is considered as time and cost efficient for regional scale surveys that rely on remote sensing-based prediction of soil properties.
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