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001-es BibID:BIBFORM127416
Első szerző:Phinzi, Kwanele
Cím:Improving the spatial prediction of topsoil properties in a typical grazing area using multi-season PlanetScope spectral covariates and data mining techniques / Kwanele Phinzi, László Bertalan, Gashaw Gismu Chakilu, Szilárd Szabó
Dátum:2025
ISSN:1865-0473 1865-0481
Megjegyzések:Understanding the spatial distribution of topsoil properties in grassland ecosystems is essential for improving soil ecosystem services, quality, and erosion resilience. The availability of free, high-resolution satellite imagery and advanced data mining techniques offers new opportunities for efficient soil property assessment. This study aimed to evaluate the potential utility of multi-season PlanetScope imagery to predict soil organic carbon (SOC), pH, and calcium carbonate (CaCO3). Using random sampling, 121 topsoil samples (0-30 cm depth) were collected with an auger across grasslands, bare soil, and eroded areas within a typical grazing land use. Three data mining techniques: random forest (RF), extreme gradient boosting (XGB), and support vector machines (SVM), were applied and evaluated using a 10-fold cross-validation. The results indicated that multi-season spectral covariates considerably improved the accuracy of the target soil properties compared to single-season imagery. SVM was the most effective algorithm for predicting SOC, achieving a root mean square error (RMSE) of 0.52%, mean absolute error (MAE) of 0.24%, and R² of 0.92. RF was the best-performing algorithm for predicting soil pH (RMSE=0.22, MAE=0.17, and R² = 0.97) and CaCO3 (RMSE=0.55%, MAE=0.42%, and R² = 0.96). While XGB failed to capture the variability in soil pH, the other models generated interpretable maps that accurately represented the distribution of soil properties across different land cover categories. The green-red vegetation index (GRVI) was the most critical covariate for predicting SOC, while elevation and the topographic wetness index (TWI) were key predictors for soil pH and CaCO3, respectively. This study underscores the potential of multi-season PlanetScope imagery for accurately predicting soil properties and recommends conducting similar studies in diverse geographical settings to validate these findings and develop more generalizable models.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Digital soil mapping
Machine learning
Multispectral
Remote sensing
Soil property prediction
Megjelenés:Earth Science Informatics. - 18 : 2 (2025), p. 222. -
További szerzők:Bertalan László (1989-) (geográfus) Chakilu, Gashaw Gismu Szabó Szilárd (1974-) (geográfus)
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001-es BibID:BIBFORM084936
035-os BibID:(WoS)000539535700067 (Scopus)85083768890
Első szerző:Phinzi, Kwanele
Cím:Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach / Kwanele Phinzi, Abriha Dávid, Bertalan László, Holb Imre, Szabó Szilárd
Dátum:2020
ISSN:2220-9964
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
machine learning
gully erosion
pan sharpening
feature extraction
Megjelenés:ISPRS International Journal of Geo-Information. - 9 : 4 (2020), p. 1-20. -
További szerzők:Abriha Dávid (1995-) (geográfus) Bertalan László (1989-) (geográfus) Holb Imre (1973-) (agrármérnök) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:ED_18-1-2019-0028
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