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001-es BibID:	BIBFORM105337
035-os BibID:	(WoS)000846416200001 (Scopus)85137317190 (cikkazonosító)1858
Első szerző:	Hateffard, Fatemeh (digital soil mapping)
Cím:	High-Resolution Mapping and Assessment of Salt-Affectedness on Arable Lands by the Combination of Ensemble Learning and Multivariate Geostatistics / Fatemeh Hateffard, Kitti Balog, Tibor Tóth, János Mészáros, Mátyás Árvai, Zsófia Adrienn Kovács, Nóra Szűcs-Vásárhelyi, Sándor Koós, Péter László, Tibor József Novák, László Pásztor, Gábor Szatmári
Dátum:	2022
ISSN:	2073-4395
Megjegyzések:	Soil salinization is one of the main threats to soils worldwide, which has serious impacts on soil functions. Our objective was to map and assess salt-affectedness on arable land (0.85 km2) in Hungary, with high spatial resolution, using a combination of ensemble machine learning and multivariate geostatistics on three salt-affected soil indicators (i.e., alkalinity, electrical conductivity, and sodium adsorption ratio (n = 85 soil samples)). Ensemble modelling with five base learners (i.e., random forest, extreme gradient boosting, support vector machine, neural network, and generalized linear model) was carried out and the results showed that ensemble modelling outperformed the base learners for alkalinity and sodium adsorption ratio with R2 values of 0.43 and 0.96, respectively, while only the random forest prediction was acceptable for electrical conductivity. Multivariate geostatistics was conducted on the stochastic residuals derived from machine learning modelling, as we could reasonably assume that there is spatial interdependence between the selected salt-affected soil indicators. We used 10-fold cross-validation to check the performance of the spatial predictions and uncertainty quantifications, which provided acceptable results for each selected salt-affected soil indicator (for pH value, electrical conductivity, and sodium adsorption ratio, the root mean square error values were 0.11, 0.86, and 0.22, respectively). Our results showed that the methodology applied in this study is efficient in mapping and assessing salt-affectedness on arable lands with high spatial resolution. A probability map for sodium adsorption ratio represents sodic soils exceeding a threshold value of 13, where they are more likely to have soil structure deterioration and water infiltration problems. This map can help the land user to select the appropriate agrotechnical operation for improving soil quality and yield.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk salt-affected soils digital soil mapping ensemble modelling geostatistics; uncertainty assessment satellite remote sensing unpiloted aerial vehicle
Megjelenés:	Agronomy-Basel. - 12 : 8 (2022), p. 1-19. -
További szerzők:	Balog Kitti Tóth Tibor Mészáros János Árvai Mátyás Kovács Zsófia Adrienn Szűcs-Vásárhelyi Nóra Koós Sándor (agrár) László Péter Novák Tibor (1973-) (geográfus) Pásztor László Szatmári Gábor (1988-) (geográfus)
Pályázati támogatás:	K-131820 and K-124290 OTKA
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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