Machine learning and genetic algorithm for mapping soil available phosphorus in coastal provinces in Southeast China

Abstract

Soil available phosphorus (SAP) is a crucial nutrient for sustaining plant growth, and accurate prediction of its spatial distribution is vital for agricultural production and environmental management. Here, we comprehensively integrate multiple environmental variables impacting the phosphorus cycle, including climate, remote sensing imagery, vegetation indices, topography, and anthropogenic factors. Employing two geostatistical methods, three machine learning algorithms, and incorporating genetic algorithm (GA), the research aims to precisely forecast SAP content and generate spatial distribution maps. The GA successfully identifies the optimal indices of environmental variables, comprising Band3, Brightness Index, land use type, distance to water, and mean annual precipitation. In performance evaluation, the combination of GA with Gradient Boosting Decision Tree exhibits the best results (R2 = 0.60, MAE = 2.79, RMSE = 3.64). High-value SAP areas are predominantly located in the western region, demonstrating a correlation with land use patterns. Validating the model’s applicability in coastal provinces in Southeast China, covering Fujian, Zhejiang, and Guangdong, underscores the profound insights of this study into the dynamics and geographical correlations of SAP. Our findings demonstrate that GA exhibit outstanding stability and performance in mapping SAP, contributing to a better understanding of SAP dynamics, supporting improved soil management practices and sustainable agriculture.

Keywords

• Genetic algorithm
• Spatial distribution
• Machine learning
• Environment variables
• Soil available phosphorus