Enhancing Soil Texture and Bulk Density Mapping Using Soil Grids and Machine Learning: A Comparative Analysis with Observed Data

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Digital soil mapping plays a crucial role in understanding soil variability and informing sustainable land management practices. This study focuses on the Kurdistan Region of Iraq (KRI), evaluating the accuracy of SoilGrids, a global-scale soil mapping initiative, and exploring the efficacy of machine learning algorithms in refining soil properties estimations. The aim of this research was to assess and represent the physical parameters of soils effectively by comparing ground truth soil sampling data with data obtained from SoilGrids regarding clay, silt, and sand fractions and bulk density. Comparative analyses were conducted between ground truth soil sampling data and SoilGrids predictions, revealing significant differences across soil mineral fractions including clay, silt, sand fractions, and bulk density. The results showed that the mean clay fraction in the ground truth dataset differed notably from SoilGrids estimation, with a Mean Absolute Deviation (MAD) of 124.0 g kg-1 and Root Mean Square Error (RMSE) of 152.5. However, the integration of machine learning algorithms, particularly the Extreme Gradient Boosting (XG Boost) algorithm, showed promising results in improving accuracy. The XG Boost algorithm exhibited a relatively low MAD of 97.9 g kg-1 for clay fractions, indicating a better approximation of observed values compared to SoilGrids. Significant percent improvements in RMSE and Mean Absolute Percentage Error (MAPE) values were observed across soil fractions and bulk density measurements, ranging from approximately 15% for clay to 35% for sand fractions and 20% for bulk density. These findings highlight the importance of integrating advanced mapping techniques and machine learning algorithms to enhance soil mapping methodologies. Moving forward, efforts to expand ground truth datasets through targeted soil sampling campaigns and develop international collaboration initiatives will be crucial for improving the accuracy and reliability of soil mapping products in the KRI. By incorporating advanced mapping approaches, we can better support sustainable land management practices and environmental conservation efforts in the region.