**3.1 Algorithm for quantitative pedology (AQP)**

The AQP package allows to gather a set of functions to work and to analyze large soil profile collections. Depth functions of soil key properties used to distinguish the profiles are presented in **Figure 2** and **Figure 3**. The percentage values plotted along the profile shows the relative quantity of profiles used in the soil-depth function to calculate the statistics (median and quartiles) at each depth.

The study area presents shallow soils, sometimes with high base saturation; but the main limitation for agricultural use is the soil texture and high clay activity, which will influence the soil moisture to adequately manage the soils due to their high plasticity.

The pH and the calcium (Ca) content presented a linear trend of median values along depth (**Figure 2**), even though a smaller number of soil profiles (less than 25%) were used in the estimative for deeper than 150 cm depth. However, different patterns were shown for potassium (K), sodium (Na), electrical conductivity (EC), and cation exchange capacity (CEC). The soil key properties Ca, K, and CEC showed more variability around the median value, thus presenting a better predictive potential to distinguishing soils with different parent materials and high clay activity, respectively.

It is pertinent to select which soil key properties could indicate differences in soil behavior along depth and are relevant for land management. From the analyses of **Figure 3**, it is possible to conclude that clay and sand contents have an opposite and large variability among the profiles of the collection; thus, they have great potential to aid in the definition of soil management zones. Since both properties are

**Figure 2.** *Depth functions for soil key chemical properties. EC, electrical conductivity; CEC, cation exchange capacity.*

NDVI, b3/b7 ratio, and GSI (**Table 2**). These results do not agree with [44], who found no correlation between sand content and the Landsat 5 TM image data. Only the covariate b3/b7 was strongly correlated (r = 0.58 and 0.49, topsoil and subsoil, respectively) with the sand content [54], whereas the NDVI was moderately correlated (r = 0.39 and 0.36, topsoil and subsoil, respectively) and the other covariates weakly correlated, with r values below 0.27 (positive or negative)

*Pedometric Tools Applied to Zoning Management of Areas in Brazilian Semiarid Region*

*DOI: http://dx.doi.org/10.5772/intechopen.88526*

Inverse relationships were observed between clay content and the environmental covariates, but magnitudes were the same as those observed for sand (**Table 2**), except for the covariate b4 (topsoil) which had no correlation with sand; similar results were reported by [42, 55]. The most relevant covariates were b3/b7

(r = 0.56 and 0.51, topsoil and subsoil, respectively) and NDVI (r = 0.36 and 0.37, topsoil and subsoil, respectively). These results vary among authors in the literature. Significant correlations were obtained between clay, the NDVI index, and b3/b2 and b5/b7 band ratios by [44], while there was no correlation between clay and the b3/b7 ratio band. On the other hand, Ahmed and Iqbal [56] found significant correlations between clay and bands 4 and 6 using Landsat 5 TM.

The CEC was significantly correlated with the covariates b4, b5, b7, NDVI, and b3/b7 and b5/b7 ratios (**Table 2**). Only the covariate b3/b7 was strongly correlated (r = 0.45 and 0.44, topsoil and subsoil, respectively) with the CEC [54], whereas the other covariates were weakly correlated, with r values below 0.27 (positive or negative) (**Table 3**). A strong correlation was observed by [55] between the CEC and covariates measured by ASTER spectral bands (1–8). The Na content was significantly correlated with most of the covariates, except with b7 (topsoil) and b3/ b2 and b5/b7 ratios (**Table 2**). However, none of the covariates had a strong or moderate correlation with Na content (**Table 3**), which may explain the very low performance of the random forest model in the prediction of this soil property.

The study performed by Demattê et al. [57] highlighted that the correlation with

**Sand Clay CEC Na**

a particular spectral band is directly related with soil characteristics in specific

Depth (cm) 0–20 20–60 0–20 20–60 0–20 20–60 0–20 20–60 b1 0.06 0.07 0.07 0.03 0.06 0.03 0.19 0.32 b2 0.08 0.06 0.05 0.08 0.01 0.02 0.20 0.34 b3 0.06 0.05 0.03 0.06 0.05 0.02 0.19 0.32 b4 0.12 0.12 0.15 0.11 0.18 0.14 0.14 0.27 b5 0.23 0.20 0.25 0.19 0.24 0.20 0.10 0.20 b7 0.21 0.18 0.24 0.18 0.27 0.23 0.09 0.19 NDVI 0.39 0.36 0.36 0.37 0.22 0.23 0.21 0.27 B3/b2 0.07 0.07 0.02 0.06 0.13 0.11 0.10 0.12 b3/b7 0.58 0.49 0.56 0.51 0.45 0.44 0.24 0.29 b5/b7 0.02 0.00 0.06 0.02 0.21 0.20 0.03 0.10 GSI 0.27 0.26 0.21 0.24 0.03 0.04 0.15 0.23

*Pearson's correlation coefficient (r) between soil properties (sand, clay, CEC, and Na) and environmental*

regions, explaining the differences between study cases.

**Variables Soil properties**

*covariates (b1, b2, b3, b4, b5, b7, NDVI, b3/2, b3/b7, b5/b7, and GSI).*

(**Table 3**).

**Table 3.**

**47**

#### **Figure 3.** *Depth functions for soil key physical properties. PWP, permanent wilting point.*

important to irrigation and mechanization practices, they were selected as criteria for agricultural zoning.
