V.L. Mulder

Representing major soil variability at regional scale by constrained Latin Hypercube Sampling of remote sensing data

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.

Soil legacy data rescue via GlobalSoilMap and other international and national initiatives

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1km in 2014, followed by an update at a resolution of 250m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.