Digital mapping of soil biological properties and wheat yield using remotely sensed, soil chemical data and machine learning approaches

Abstract

Mapping of soil properties by using novel machine learning (ML) algorithms and optimized environmental covariates is of great importance for agricultural management to enhance crop production. This research aimed at evaluating ML algorithms to predict spatial distribution of soil biological properties and wheat yield in the Southwest of Iran. Topsoil samples (0–30 cm) were collected from a total of 60 sampling locations and wheat grain yield (plot 1 × 1 m) was recorded at each location. Soil properties including urease (Ur), alkaline phosphatase (AP), basal respiration (BR), microbial biomass carbon (MBC), soil organic carbon (SOC), MBC:SOC ratio, and metabolic quotient (qCO2) were measured. At the first step, Random Forest (RF) model was employed to predict soil biological properties by using terrain attributes, remote sensing indices and soil properties as covariates. In this step, both Variance Inflation Factor (VIF) and Pearson regression were applied to select the most important covariates in predicting soil biological properties and to decrease the dimension of the input space with considering no reduction in prediction accuracy. Secondly, wheat grain yield was modeled using six ML algorithms; they were optimized and evaluated in Caret package with 10-fold cross validation. Results showed the highest prediction accuracy for qCO2 (R2adj = 0.80) and the lowest for BR (R2adj = 0.23). Compared to environmental predictors, soil covariates had a greater effect in modeling Ur, qCO2, MBC and MBC:SOC ratio, while, for AP and BR, bands 6 and Chanel Network Base Level were the most important factors, respectively. In prediction of wheat grain yield, both Stochastic Gradient Boosting (SGB) and RF models outperformed with R2adj of 0.89 and 0.88, respectively. Results indicated that the Ur and AP played the major roles in predicting wheat grain yield and explaining its spatial variability. Our modeling results suggested that soil biological properties and yield can be estimated easily with reasonable accuracy. Overall, their high resolution maps may be useful for decision makers, stakeholders and applicants in agricultural management practices towards precision agriculture.