# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
## 4 Results and Discussions
### Variable Importance Analysis  
The relative importance of auxiliary data variables in predicting SOM with the XGBoost and RF models is presented in Figure 7. Notably, the order of significance differed between XGBoost and RF, depending on their predicted SOM values. In our study, the prominence of the B10 band from Landsat 8, which represents the thermal infrared-1 spectrum, was particularly evident when using the XGBoost algorithm. [PERSON] et al. (2018) underscored this phenomenon by identifying the thermal band's substantial correlation with SOM, making it a pivotal variable for accurate predictions. The thermal infrared band can capture variations in surface temperature, which in turn can be influenced by soil moisture and organic content, thus providing valuable information about SOM levels. Additionally, [PERSON] et al. (2018) noted that Landsat bands can capture spatial variations in vegetation. Based on the characteristics of each variable and their significance in the mode, the order of relative relevance of the variables for XGBoost was B10 > SAVI > B1 > DEM > NDVI > B5. Our analysis determined that the most important factor for the RF model was DEM, followed by SAVI, B11I, NDVI, B10, and B6. According to previous research ([PERSON] et al., 2018; [PERSON] et al., 2018), the topographical features represented by the DEM play a significant role in influencing the spatial distribution of SOM. The variations in elevation and slope can impact factors such as soil moisture, temperature, and erosion rates, all of which can in turn influence SOM distribution. For instance, lower elevations or depressions might accumulate more organic material due to water runoff and reduced erosion. Conversely, higher elevations or slopes might have reduced SOM due to increased erosion and lesser water retention. Such topographical influences have been well-documented in the soil science literature. Our findings that emphasize DEM's role align with previous research conducted by [PERSON] et al. (2018), who also identified DEM as a pivotal predictor for SOM modelling. This study determined that SAVI, DEM, B10, and NDVI were the most prominent predictors of SOM content in both the RF and XGBoost models. Among those four important variables, DEM may not capture soil-specific properties resulting from soil management practices. However, DEM included in this study as one of the predictors was based on previous studies [7,56] that have found elevation data to sometimes be indirectly associated with SOM due to drainage, moisture retention, and other terrain-driven factors. These variables exhibit heightened susceptibility to diverse soil characteristics, rendering them pivotal predictors for accurate SOM estimation. The efficacy of both SAVI and NDVI as tools for the accurate prediction of SOM levels within various contexts is well-established. Several studies, including that conducted by [PERSON] et al. [57], have demonstrated the utility of SAVI in this regard. Concurrently, the importance of NDVI, a measure of vegetation vigor, is emphasized by findings such as those presented by [PERSON] et al. [58]. NDVI often correlates positively with SOM, as healthier vegetation generally grows in soils with a higher organic content. In contrast, SAVI, which accounts for the soil's red reflectance particularly in low vegetation areas, might have an inverse relationship with SOM. [PERSON] et al. [15] further illustrated that SAVI can refine predictions of agricultural soil organic carbon content. In the context of our study area, it is essential to understand the relationship between these indices. The interconnectedness of these indices with biotic factors, such as vegetation type and agroecosystems, and abiotic factors, such as climate and relief, makes them significant in SOM prediction. However, it is noteworthy that the variables B2, B3, B4, and BI, derived from satellite images captured during specific periods in the Batifa region, displayed a minimal impact on SOM levels in both models.