Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative

Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS ma...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-12, Vol.12 (24), p.4073
Main Authors: Szatmári, Gábor, Bakacsi, Zsófia, Laborczi, Annamária, Petrik, Ottó, Pataki, Róbert, Tóth, Tibor, Pásztor, László
Format: Article
Language:English
Subjects:
Algorithms
Digital mapping
digital soil mapping
Electrical conductivity
Electrical resistivity
Geomorphology
Hungary
Initiatives
Laboratories
Machine learning
Mapping
Modelling
Monitoring systems
multivariate geostatistics
Remote sensing
Salinity
Salt
salt-affected soils
Salts
Satellite imagery
Soil mapping
Soil sciences
Soils
Spatial distribution
Subsoils
Topsoil
uncertainty assessment
Vegetation
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Summary:Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12244073