Digital Mapping of Soil Particle Size Distribution in

an Alluvial Plain Using the Random Forest Algorithm

Fuat KAYA*; Levent BAŞAYİĞİT

Isparta University of Applied Sciences, Faculty of Agriculture, Department of Soil

Science and Plant Nutrition, Çünür, Isparta 32260, Turkey fuatkaya@isparta.edu.tr,

leventbasayigit@isparta.edu.tr

Introduction

The spatial distribution of physical soil

properties is an important requirement

in practice as basic input data. Soil

texture is one of the most important

physical properties affecting water

holding capacity, nutrient availability,

and crop development. A spatial

distribution map of soil texture at a high

spatial resolution is essential data for

crop planning and management.

Traditionally, soil texture can be determined in the field by hand feel method,

however, it is confirmed by laboratory analysis of particle size fractions

(sand, silt, and clay) using the hydrometer method [1]. Conventional soil

texture analysis methods are expensive. Also, it requires a large number of

samples to obtain higher resolution spatial distribution of soil texture over

large areas.

Introduction

Digital soil maps can be used to show the soil’s ability to perform certain

functions. The digital soil mapping technique provides solutions for estimating

the soil properties and soil texture classes based on quantitative soil-landscape

models [2-3].

Introduction

Topography controls the

distribution and accumulation

of water and energy in the

pedosphere, so it has been

widely accepted in the

literature as the dominant

factor affecting soils and soil

properties [4-5].

Introduction

Method

Digital soil mapping is the method of spatial data generation with the

advantages of current technologies. It supplies fast, accurate, and

reproducible results.

In this study, a soil texture map with 30 m spatial resolution was produced

for an alluvial plain covering an area of approximately 10,000 ha. In the

study, 11 Topographic Environmental Variables obtained from NASA's

ASTER Global Digital Elevation model were used. Another input

parameters were clay, silt, and sand values determined for 91 soil samples

obtained through field studies.

Random Forest Algorithm offers interpretability for pedological information

extraction by determining the importance of environmental variables in

digital soil mapping.

Method

Environmental variable

extraction,

modeling,

and spatial mapping

R Core Environment (3.6.1) [7]

and related packages were used for

environmental variable extraction,

modeling, and spatial mapping.

Results - Soil properties descriptive statistics

n Mean Standart D Min. Maks. Skewness. Kurtosis Coefficient

Variation (%)

Clay(%) 91 36.91 13.68 13.28 73.76 0.82 -0.22 37.06

Silt (%) 91 25.81 7.16 12.47 51.94 1.13 1.64 27.72

Sand(%) 91 37.29 15.01 6.35 69.3 -0.34 -0.87 40.26

If it is desired to evaluate the degree of variability for soil properties by

considering the coefficient of variation, it is reported that there is a low

variation of 0-15%, medium variation of 16-35% and high variation coefficient

values (CV) of more than 36% [6]. Among the soil properties, clay and sand

have high variability in an alluvial plain.

Results -Model Accuracy

Results – Variable Importance

Results – Spatial Mapping

Conclusion

As a result of the study, digital soil maps obtained with the random forest

algorithm show compatibility with the study area.

The idea that the Random Forest Algorithm is compatible with the

compliance problem in small data sets is controversial.

Pixel-based soil properties maps obtained within the framework of soil

science can be controlled by creating soil texture class maps using

geographic information systems.

In this way, the evaluation of models produced in small data sets with

overfitting interpretation can be pedologically more effective.

References

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