Lotfollahi, Leila, Delavar, Mohammad Amir, Biswas, Asim, Jamshidi, Mohammad, Taghizadeh-Mehrjardi, Ruhollah, Fatehi, Shahrokh. (1402). Modeling the spatial distribution of sand, silt, and clay particles based on GlobalSoilMap and Limited Data. , 28(2), 243-262. doi: 10.22059/jdesert.2023.95533
Leila Lotfollahi; Mohammad Amir Delavar; Asim Biswas; Mohammad Jamshidi; Ruhollah Taghizadeh-Mehrjardi; Shahrokh Fatehi. "Modeling the spatial distribution of sand, silt, and clay particles based on GlobalSoilMap and Limited Data". , 28, 2, 1402, 243-262. doi: 10.22059/jdesert.2023.95533
Lotfollahi, Leila, Delavar, Mohammad Amir, Biswas, Asim, Jamshidi, Mohammad, Taghizadeh-Mehrjardi, Ruhollah, Fatehi, Shahrokh. (1402). 'Modeling the spatial distribution of sand, silt, and clay particles based on GlobalSoilMap and Limited Data', , 28(2), pp. 243-262. doi: 10.22059/jdesert.2023.95533
Lotfollahi, Leila, Delavar, Mohammad Amir, Biswas, Asim, Jamshidi, Mohammad, Taghizadeh-Mehrjardi, Ruhollah, Fatehi, Shahrokh. Modeling the spatial distribution of sand, silt, and clay particles based on GlobalSoilMap and Limited Data. , 1402; 28(2): 243-262. doi: 10.22059/jdesert.2023.95533

Modeling the spatial distribution of sand, silt, and clay particles based on GlobalSoilMap and Limited Data

Desert
مقاله 6، دوره 28، شماره 2، اسفند 2023، صفحه 243-262    اصل مقاله (1.45 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jdesert.2023.95533
نویسندگان
Leila Lotfollahi1؛ Mohammad Amir Delavar* 1؛ Asim Biswas2؛ Mohammad Jamshidi3؛ Ruhollah Taghizadeh-Mehrjardi4؛ Shahrokh Fatehi5
1Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran.
2School of Environmental Sciences, University of Guelph, Ontario N1G2W1, Canada
3Scientific Staff of Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
4Department of Geosciences, University of Tübingen, Rümelinstr. 19-23, Tübingen, Germany
5Soil and Water Research Department, Kermanshah Agricultural and Natural Resources Research and Education Center, AREEO, Kermanshah, Iran
چکیده
Many regions of Iran lack digital map of soil properties. The Chahardowli plain in western Iran is one of these areas. Due to the importance of sand, silt, and clay components, having quantitative and continuous data on abrupt changes in these two properties in this area is very critical. Therefore, to study sand, silt, and clay, samples were taken at depths of 0–5, 5–15, 15–30, 30–60, and 60–100 cm, according to GlobalSoilMap. Finally, 145 samples were collected from 30 soil profiles. The significant covariates were selected by Random Forest Recursive Feature Elimination (RF-RFE). Relationships between these characteristics and environmental predictors were modeled using random forest (RF), decision tree (DT), and multiple linear regression (MLR) models. The accuracy and precision of the models used for all three particles showed that the RF model had the most accurate prediction with R2 and RMSE of 0.82 and 2.34 for clay, 0.80 and 3.87 for sand, and 0.85 and 2.89 for silt, respectively. In this study, terrain-based variables had a greater impact on improving accuracy than remote-sensing variables. The current study showed that even with limited information, digital mapping of sand, silt, and clay particles under GlobalSoilMap and the use of environmental factors can provide acceptable results.
کلیدواژه‌ها
Arid and semiarid؛ Digital Soil Mapping؛ Random Forest؛ Decision Tree
مراجع
آمار
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