Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative
Autor: | Ottó Petrik, Róbert Pataki, Annamária Laborczi, Gábor Szatmári, Tibor Tóth, Zsófia Bakacsi, László Pásztor |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
salt-affected soils
Multivariate statistics multivariate geostatistics 010504 meteorology & atmospheric sciences Science uncertainty assessment 01 natural sciences Cross-validation Subsoil 0105 earth and related environmental sciences Hungary Topsoil Global Map 04 agricultural and veterinary sciences Random forest machine learning digital soil mapping Digital soil mapping Soil water 040103 agronomy & agriculture 0401 agriculture forestry and fisheries General Earth and Planetary Sciences Environmental science Physical geography |
Zdroj: | Remote Sensing; Volume 12; Issue 24; Pages: 4073 Remote Sensing, Vol 12, Iss 4073, p 4073 (2020) |
ISSN: | 2072-4292 |
DOI: | 10.3390/rs12244073 |
Popis: | 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. |
Databáze: | OpenAIRE |
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