Evaluation of mathematical models for predicting particle size distribution using digital soil mapping in semiarid agricultural lands. Issue 26 (13th December 2022)

• Record Type: Journal Article
• Authors: Cheshmberah, Fatemeh; Zolfaghari, Ali A.; Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas
• Is Part Of: Geocarto international. Volume 37:Issue 26(2023)
• Page Start: 13016
• Page End: 13038
• Publication Date: 2022-12-13
• Subjects: Digital soil mapping -- models parameters -- probability distribution -- Random Forest; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2022.2076911
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Soil Particle Size Distribution (PSD) is a fundamental physical property that can affect soil hydraulic properties, soil structure characterization, and available water. Many models have been applied to define the PSD curve, but predicting the spatial distribution information of PSD has been rarely investigated. Therefore, the main objective of the current study was to predict soil texture fractions using the most accurate PSD models. First, the performance of 16 mathematical PSD models was evaluated. Then, Random Forest (RF) was used to determine the relationship between covariates (i.e., remote sensing and the digital elevation model) and georeferenced measurements of the best PSD models' parameters. Results indicated that a PSD model may be acceptable for some particle diameters or even whole particles, but not necessarily be suitable for other particles. For example, in the estimation of sand content, the best model was Simple Lognormal, while the Fred-4p was the best model in the estimation of the clay fraction. Importantly, the Jaky model with only one parameter of P did a great job in predicting soil particle fractions. Further, the spatial distribution of clay, silt, and sand contents was accurately derived from the predicted map of P (R 2 for Sand = 0.86). Consequently, the current research indicated that the combination of PSD models and digital soil mapping techniques can be used to quantify the spatial distribution of the PSD curve in other similar agroclimatological regions.