# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
## 1 Introduction  
The presence of soil organic matter (SOM) has a considerable impact on various aspects of soil management, including soil mapping, interpretation of soil features, and the application of fertilizers and agricultural chemicals. While soil carbon evaluation, simulations, and modelling of the landscape [1, 2] directly measure or estimate SOM content, other techniques like precision farming leverage these estimates for optimized soil management. Nevertheless, many of these approaches may not be feasible for large-scale applications due to factors like cost. As a solution, remote sensing data emerge as a promising alternative for assessing SOM levels.  
Remote sensing has been an essential data source for mapping soil and research for several decades [3]. [PERSON] et al. [4] demonstrated that a linear association can be created between data obtained from remote sensing and soil organic carbon content. [PERSON] et al. [5] also applied remote sensing to evaluate the relationship between organic matter and reflection spectra. According to their results, the ratio of organic matter content to image sensitivity showed a high level of correlation (R\({}^{2}\) = 0.98) with the assessed data. Numerous studies have explored techniques to determine SOM. For instance, [PERSON] and [PERSON] [6]analyzed the red and near-infrared (NIR) bands' reflectance and intensity to gauge soil depth, formulating an equation to link pixel distances on soil surfaces with SOM. [PERSON] et al. [7] predicted topsoil organic matter content using boosted regression tree models on Landsat Thematic Mapper images. Elevation, influencing vegetation types, indirectly affects the soil's organic matter absorption [8]. To refine SOM estimates, research has integrated multispectral remote sensing data with other datasets, including topography and climate [9; 10].  
Traditional methods, like laboratory examination, aid digital soil mapping (DSM), but integration with spectral data can enhance soil mapping precision [11]. However, characterizing vast areas with spectral data remains a challenge due to the complexities of natural soil surfaces [8].  
There is a growing emphasis on machine learning (ML) algorithms, like support vector machines and random forest (RF), to analyze spectral data for SOM prediction [12; 13; 14]. [PERSON] et al. [13] highlighted ML's capability to handle intricate data relationships, delivering enhanced accuracy over linear models. These algorithms, combined with other factors such as terrain, offer improved SOM predictions. Recent advancements in ML present opportunities to establish non-linear relationships between soil parameters and remote sensing data [15]. Tree-based models, especially RF and extreme gradient-boosted decision trees (XGBoost), stand out for their predictive accuracy [15]. While RF reduces overfitting risks, XGBoost offers efficient solutions to regression challenges [16; 17].  
In the Iraqi Kurdistan region, despite extensive SOM research, there is a noticeable absence of detailed digital SOM maps. While [PERSON] and [PERSON] [18] employed the ASD Field Spec 3 spectroradiometer for SOM levels in Duhok province, others still rely on time-tested laboratory methods [19; 20].  
The purpose of this study was to use advanced ML algorithms to predict SOM content in the Batifa region. To accomplish this, the study uses innovative techniques, such as RF and XGBoost models, in combination with remotely sensed data, to create a SOM model. Furthermore, this study aimed to produce a DSM of SOM for the Batifa region in the Iraqi Kurdistan region.