1. Introduction

#### 1.1. Soil properties

Difference of soils in physical and chemical determined what type of plants grows in a soil or what particular crops grow in a region. Jack pine (Pinus banksiana), for example, occurs on coarse sands, poor drainage, and shallow soils, and sugar maple (Acer saccharum) grows best on deep, fertile, moist, well-drained soils in Ref. [1]. The most important soil properties included soil texture, soil drainage, and soil organic carbon (SOC).

Soil texture is defined as relative proportions (percentages) of clay, sand, and silt contents. These percentages are used to confirm soil textural classes in a soil texture triangle (Figure 1).

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Figure 1. Canadian soil texture triangle in Ref. [2].

Soil texture not only directly affects the porosity of soil, but also determines water-holding and nutrient-holding capacity, flow characteristics, and long-term soil nutrient regime. For example, soils with heavy clay in general have higher percentage of smaller pores, higher water-holding capacity at lower water potentials and are often associated with poorly drained conditions with limited aeration for plant growth. As a contrast, soils with heavy sand normally have relatively higher percentage of larger pores with lower water-holding capacity under relatively dry conditions. Soil texture also affects the risk of soil erosion and soil erodibility.

Soil drainage was defined as the frequency and duration of periods of water saturation or partial saturation, and soil drainage classes reflect average soil moisture conditions in Ref. [3]. Soil drainage is associated with water-holding and nutrient-holding capacities, flow characteristics, and solute transport. Soil drainage is also directly related to plant growth. For example, plants grown on soil with poor drainage often suffer from reduced growth, leaf dieback as a result of root suffocation, and root disease in Ref. [4]. Plants experiencing root decline from excess water are also more susceptible to attack by secondary diseases and insects in Ref. [5]. Under natural conditions, soil drainage characteristic is one important factor that determines which types of plants grow on a particular landscape site. For precision forestry and precision agriculture, high-resolution soil maps are especially important in Ref. [6]. Soil drainage classes are closely related soil texture and slope position (Figure 2).

Soil organic carbon refers to the carbon (C) occurring in soil organic matter of the soil. SOC can help to improve soil physical properties by increasing water-holding capacity, stabilizing soil structure in Ref. [8], soil chemical properties, and nutrients holding capacity in Ref. [9]. From a land management perspective, SOC plays important roles in reducing soil erosion and improving crop productivity. For this reason, SOC content has been used as a required input variable

Figure 2. Generalized soil drainage patterns and drainage classes for soils with coarse texture soil (A) and fine texture soil (B) as influenced by slope position in Ref. [7].

for a number of hydrological simulation models in Ref. [10] and many landscape level models for estimating soil water retention, cation exchange capacity, and soil bulk density in Ref. [11].

### 1.2. Mapping soil properties

Soil texture not only directly affects the porosity of soil, but also determines water-holding and nutrient-holding capacity, flow characteristics, and long-term soil nutrient regime. For example, soils with heavy clay in general have higher percentage of smaller pores, higher water-holding capacity at lower water potentials and are often associated with poorly drained conditions with limited aeration for plant growth. As a contrast, soils with heavy sand normally have relatively higher percentage of larger pores with lower water-holding capacity under relatively dry

Soil drainage was defined as the frequency and duration of periods of water saturation or partial saturation, and soil drainage classes reflect average soil moisture conditions in Ref. [3]. Soil drainage is associated with water-holding and nutrient-holding capacities, flow characteristics, and solute transport. Soil drainage is also directly related to plant growth. For example, plants grown on soil with poor drainage often suffer from reduced growth, leaf dieback as a result of root suffocation, and root disease in Ref. [4]. Plants experiencing root decline from excess water are also more susceptible to attack by secondary diseases and insects in Ref. [5]. Under natural conditions, soil drainage characteristic is one important factor that determines which types of plants grow on a particular landscape site. For precision forestry and precision agriculture, high-resolution soil maps are especially important in Ref. [6]. Soil drainage classes

Soil organic carbon refers to the carbon (C) occurring in soil organic matter of the soil. SOC can help to improve soil physical properties by increasing water-holding capacity, stabilizing soil structure in Ref. [8], soil chemical properties, and nutrients holding capacity in Ref. [9]. From a land management perspective, SOC plays important roles in reducing soil erosion and improving crop productivity. For this reason, SOC content has been used as a required input variable

conditions. Soil texture also affects the risk of soil erosion and soil erodibility.

are closely related soil texture and slope position (Figure 2).

Figure 1. Canadian soil texture triangle in Ref. [2].

52 Advanced Applications for Artificial Neural Networks

Field soil surveys have been the primary method for determination of soil properties, including soil texture, soil drainage, and SOC. For mapping purposes, soil surveys are normally conducted with point samples, either systematically or randomly over a given area, and then the point data are usually interpolated to produce soil maps. Various interpolation methods have been used to produce soil maps, especially the kriging method in Ref. [12]. There is a major limitation about interpolation method, i.e., the assumption that the spatial distributions and changes of the interpolated properties are continuous. Therefore, large amount of data are often required to produce accurate high-resolution soil maps. With the purpose of improving the interpolation accuracy with sparsely distributed sample points, various improved kriging methods have been developed in Ref. [13]. However, the methods still require substantial amounts of field samples to define the spatial autocorrelation and the precision of the resultant maps will still depend upon the density and distribution of original data points in Ref. [14]. Due to high spatial variability of soil characteristics, large numbers of sampling points are required to generate an accurate high-resolution soil map. Although the accuracy of a soil map may be increased with increasing data points, intensive field surveys are expensive and timeconsuming. Furthermore, the accuracy is affected by the quality of the data, which, to a great extent, depends on the field experience of the soil surveyors in Ref. [15]. As an alternative, various models have been developed to produce soil property maps.

Statistical models with predictive powers could potentially overcome the problem of interpolation methods in Ref. [16]. Bell et al., for example, related soil drainage class to parent material, terrain, and surface drainage with the help of discriminant function analysis in Pennsylvania, USA in Ref. [17]. According to this method, soil drainage probability maps were predicted well when compared with published soil drainage maps. Campling et al. applied a logistic model to successfully predict the probability of drainage classes in a tropical area using terrain properties (elevation, slope, distance-to-the-river channel) and vegetation indices from a Landsat TM image in Ref. [18]. By applying discriminant function analysis and a co-kriging method, Kravchenko et al. created soil drainage maps using topographical data, i.e., slope, curvature, and flow accumulation, and soil electrical conductivity data in central Illinois, USA in Ref. [19]. But empirical models derived with traditional statistical methods may hinder the real relationships between soil properties and independent data because the relationships are rarely linear in nature.

#### 1.3. Artificial neural networks

In recent years, artificial neural network (ANNs) have been increasingly used to overcome non-linear problems. The ANN is a form of artificial intelligence that was inspired by the studies of the human neuron and has been used to analyze biophysical data in Ref. [20]. ANNs have the ability to auto-analyze the relationships between multi-source inputs (including combinations of qualitative and quantitative data) by self-learning, and produce results without hypothesis. Some ANNs have been successfully used to map soil properties in Ref. [21]. For example, in Licznar and Nearing's study, soil loss was predicted quantitatively from natural runoff plots with the ANN method in Ref. [22]. The results showed that correlation coefficients (predicted soil loss versus measured values) were in the range of 0.7–0.9. Ramadan et al. applied two different multivariate calibration methods (PCA and back-propagation ANN) to predict soil properties (sand, silt, clay, etc.) with the help of DNA data from microbial community in Ref. [23].

#### 1.4. Objectives

In the chapter, we focused on describing a general approach for using ANNs to produce highresolution soil properties, from preparing data, building ANN structure, training ANNs, optimizing networks, to simulating ANNs.
