# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
## 4 Results and Discussions
### LULC and Assembled Bare Soil and Cropland Maps  
Figure 3a shows the LULC classification map for the Batifa region. Figure 3b displays a map of the bare soil and cropland types derived from the LULC map. The availability of such a detailed map is essential for efficient land management in the region. Table 3 provides the image classification accuracy metrics and the confusion matrix. The rows show the real LULC patterns, whereas the columns represent the LULC patterns used to classify the images. The resulting user accuracy was significant for all classes, indicating a high likelihood that a pixel identified as a specific class on the map belongs to that class. Similarly, the resultant producer accuracy was impressive, indicating the likelihood of a particular feature on the ground being correctly classified. The overall accuracy of the classification was 93%, and the Kappa coefficient was 0.91, which was considered successful when compared to previous research. According to previous studies [32; 45], for LULC classification, it is typically deemed acceptable to have an overall accuracy rate of more than 85 percent and a Kappa coefficient greater than 0.80.  
Sparse forests cover the largest area (approximately 239.42 km\({}^{2}\)), followed by bare soil (202.4 km\({}^{2}\)). The south has more soil, whereas the north has sparser forest. Dense forest covers 37.64 km\({}^{2}\), mainly in the north, and rock covers 17.21 km\({}^{2}\), mostly in the central and southern regions. Cropland covers 14.29 km\({}^{2}\) and is dominant in the south. The water and built-up areas had the smallest areas of 6.91 km\({}^{2}\) and 10.08 km\({}^{2}\), respectively (Figure 4). These results can be deemed satisfactory and align with the findings of [PERSON] and [PERSON] [22].  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Variables** & **Min** & **Max** & **Median** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **CV\%** \\ \hline
**B1** & 0.05 & 0.12 & 0.09 & 0.08 & 0.02 & 19.55 \\
**B2** & 0.05 & 0.14 & 0.09 & 0.09 & 0.02 & 23.71 \\
**B3** & 0.08 & 0.22 & 0.14 & 0.14 & 0.03 & 25.17 \\
**B4** & 0.10 & 0.31 & 0.20 & 0.20 & 0.05 & 25.12 \\
**B5** & 0.18 & 0.41 & 0.29 & 0.30 & 0.05 & 19.60 \\
**B6** & 0.19 & 0.41 & 0.34 & 0.31 & 0.05 & 17.87 \\
**B7** & 0.13 & 0.33 & 0.24 & 0.24 & 0.04 & 18.44 \\
**B10** & 34.21 & 43.01 & 39.64 & 39.52 & 1.99 & 5.04 \\
**B11** & 33.19 & 41.84 & 38.71 & 38.63 & 1.91 & 4.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**LULC** & & & & & **Classified** & & & \\ \cline{2-7}  & **Dense** & **Sparse** & **Rock** & **Soil** & **Water** & **Cropland** & **Built-Up** &
\begin{tabular}{c} **User’s** \\ **Accuracy** \\ \end{tabular} \\ \hline
**Dense Forest** & 65 & 1 & 0 & 0 & 0 & 0 & 0 & 98.48 \\
**Sparse Forest** & 1 & 31 & 0 & 4 & 0 & 0 & 0 & 86.11 \\
**Rock** & 0 & 0 & 32 & 1 & 0 & 0 & 0 & 96.96 \\
**Soil** & 0 & 1 & 4 & 60 & 0 & 2 & 0 & 89.55 \\
**Water** & 1 & 0 & 0 & 0 & 14 & 0 & 0 & 93.33 \\
**Cropland** & 0 & 0 & 0 & 1 & 0 & 19 & 0 & 95 \\
**Built-up** & 0 & 0 & 1 & 0 & 1 & 0 & 18 & 90 \\
**Producer’s Accuracy** & 97.01 & 93.93 & 86.48 & 90.90 & 93.33 & 95 & 100 \\
**Overall Accuracy** & & & & & & & **93\%** \\
**Kappa Coefficient** & & & & & & **0.91** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix (accuracies) of the classified LULC of the image.  
Figure 4: Area of Batifa land types.  
Figure 3: (**a**) Map of LULC and (**b**) map of bare soil and cropland.