remote sensing Article Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative Gábor Szatmári 1,2 , Zsófia Bakacsi 1, Annamária Laborczi 1,*, Ottó Petrik 3,Róbert Pataki 3, Tibor Tóth 1 and László Pásztor 1 1 Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, H-1022 Budapest, Hungary;
[email protected] (G.S.); zsofi@rissac.hu (Z.B.);
[email protected] (T.T.);
[email protected] (L.P.) 2 Department of Soil Science and Environmental Informatics, Georgikon Faculty, Szent István University, H-8360 Keszthely, Hungary 3 Lechner Knowledge Center, H-1149 Budapest, Hungary;
[email protected] (O.P.);
[email protected] (R.P.) * Correspondence:
[email protected] Received: 8 November 2020; Accepted: 10 December 2020; Published: 12 December 2020 Abstract: 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.