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001-es BibID:BIBFORM112775
035-os BibID:(cikkazonosító)165879
Első szerző:Hateffard, Fatemeh (digital soil mapping)
Cím:Applicability of machine learning models for predicting soil organic carbon content and bulk density under different soil conditions / Fatemeh Hateffard, Gábor Szatmári, Tibor József Novák
Dátum:2023
ISSN:2300-4975
Megjegyzések:A reliable overview of the spatial distribution of soil properties is a straightforward approach in soil policies and decision-making. Soil organic carbon (SOC) content, SOC stock and bulk density (BD) directly affect soil quality and fertility. Therefore, an accurate assessment of these crucial soil parameters is required. To do this, we used machine learning algorithms (MLAs) including, multiple linear regression (MLR), random forest (RF), artifi cial neural network (ANN), and support vector machine (SVM) with the help of environmental covariates to predict SOC content, BD, and SOC stock. The study was conducted in two different areas, Látókép and Westsik (East Hungary), both experimental research fi elds but different from physio geographic points of view. Thirty topsoils (0?10 cm) samples were collected for each study area using conditioned Latin Hypercube Sampling strategy. Environmental covariates were extracted from a digital elevation model (DEM) and satellite images based on the representation of soil forming factors. We validated the results by randomly splitting the dataset into a train (two-third) and test (one-third) and calculated the root mean square error and R2. Our results showed that RF provided the most accurate spatial prediction with R2 of about 80% for each soil property in both study areas. This study highlighted the importance of terrain attributes (including plan and profi le curvature, elevation and valley depth) and NDVI derived from satellite images in presenting a spatial distribution of selected soil properties in two different areas. We conclude that comparing these methods can help to determine the most accurate maps under diverse geographical conditions and heterogeneities at different scales, which can be used in precision soil quality management.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Digital soil mapping
Soil variations
Machine learning
Soil properties
Random forest
Megjelenés:Soil Science Annual. - 74 : 1 (2023), p. 1-11. -
További szerzők:Szatmári Gábor Novák Tibor (1973-) (geográfus)
Pályázati támogatás:TKP2021-NKTA-32
FIKP
Internet cím:Szerző által megadott URL
DOI
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2.

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
Pályázati támogatás:K-131820 and K-124290
OTKA
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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