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Open AccessArticle

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

1
Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, H-1022 Budapest, Hungary
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
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(24), 4073; https://doi.org/10.3390/rs12244073
Received: 8 November 2020 / Revised: 7 December 2020 / Accepted: 10 December 2020 / Published: 12 December 2020
(This article belongs to the Special Issue Global Gridded Soil Information Based on Machine Learning)
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. View Full-Text
Keywords: salt-affected soils; digital soil mapping; uncertainty assessment; machine learning; multivariate geostatistics; Hungary salt-affected soils; digital soil mapping; uncertainty assessment; machine learning; multivariate geostatistics; Hungary
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MDPI and ACS Style

Szatmári, G.; Bakacsi, Z.; Laborczi, A.; Petrik, O.; Pataki, R.; Tóth, T.; Pásztor, L. Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative. Remote Sens. 2020, 12, 4073. https://doi.org/10.3390/rs12244073

AMA Style

Szatmári G, Bakacsi Z, Laborczi A, Petrik O, Pataki R, Tóth T, Pásztor L. Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative. Remote Sensing. 2020; 12(24):4073. https://doi.org/10.3390/rs12244073

Chicago/Turabian Style

Szatmári, Gábor; Bakacsi, Zsófia; Laborczi, Annamária; Petrik, Ottó; Pataki, Róbert; Tóth, Tibor; Pásztor, László. 2020. "Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative" Remote Sens. 12, no. 24: 4073. https://doi.org/10.3390/rs12244073

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