Machine Learning Model for Revealing the Characteristics of Soil Nutrient and Aboveground Biomass of Northeast China.

The purpose of this study was to determine the soil physical and chemical properties of mature broad-leaved forest in the cold temperate zone of Mt. Changbai, Jilin Province, by measuring pH, NH4 +, organic matter (%), C/N, available phosphorus, alkali-hydrolysable N, rapidly available K, and Cr etc., analysing species diversity characteristics, and estimating aboveground biomass (AGB) of tree species with machine learning models.

本研究旨在测定吉林省长白山寒温带成熟阔叶林的土壤理化性质，通过测定pH值、铵态氮（NH4+）、有机质（%）、碳氮比（C/N）、有效磷、碱解氮、速效钾及铬（Cr）等指标，分析物种多样性特征，并利用机器学习模型估算乔木树种的地上生物量（Aboveground Biomass, AGB）。