Assessment and Prediction of Soil Fertility in Urban Areas of the Loess Plateau Based on Machine Learning Methods

ABSTRACT The Loess Plateau, a vital ecological region in China, suffers from severe soil pollution and erosion. The soil fertility index (SFI) is a key indicator for assessing soil conditions, and understanding its spatial distribution and influencing factors is crucial for effective soil management. Machine learning methods, capable of analyzing complex and high‐dimensional data, offer potential for large‐scale SFI prediction. This study focuses on Lanzhou, a representative city on the Loess Plateau, using soil samples and the data of five key factors screened from environmental big data to train three machine learning models (random forest [RF], LightGBM, and XGBoost) for SFI prediction. The results show that all models effectively matched reference data trend, with XGBoost achieving the highest performance ( R 2 > 0.81). Notably, normalized difference vegetation index (NDVI) and soil organic carbon density (SOCD) emerged as the dominant predictors, collectively contributing over 80% to SFI prediction accuracy. Predicted SFI values in Lanzhou ranged from 0.09 to 0.91, with medium and lower quality soils predominantly located in central and north‐central regions, highlighting the need for soil quality improvement. This study provides a theoretical basis and scientific support for large‐scale SFI prediction.