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An explainable machine learning framework to analyze the influence of environmental drivers on vegetation condition: Applications in agricultural drought assessment | ||
| Iran Agricultural Research | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 27 تیر 1405 اصل مقاله (1.05 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22099/iar.2026.54487.1718 | ||
| نویسندگان | ||
| Amirhossein Haddadi* ؛ Behanm Rahimi | ||
| Department of Civil Engineering, Sharif University of Technology, Tehran, I. R. Iran | ||
| چکیده | ||
| Effective crop management depends on a clear understanding of the interactions among environmental variables and their combined effects on plant health. This study aims to evaluate the influence of environmental drivers—such as precipitation, air temperature, solar radiation, and aerosol optical depth (AOD)—on vegetation condition by integrating analysis of variance (ANOVA) within a multiple linear regression (MLR) framework and machine learning approach, SHAP (Shapley Additive exPlanations) on XGBoost model, using satellite and reanalysis data. Monthly mean values of multi-source remote sensing data were analyzed for the Shushtar sub-basin from 2001 to 2024, including ERA5-Land meteorological variables, AOD from Terra and Aqua satellites, and Landsat-derived NDVI used to compute the Vegetation Condition Index (VCI). The final results show that XGBoost outperformed MLR, achieving a higher accuracy (R² = 0.86, RMSE = 9.32) compared to MLR (R² = 0.29, RMSE = 19.06). The results of the MLR based on fitted coefficients indicate that volumetric soil water and precipitation exert the strongest linear influence on VCI, while wind speed shows non-significant effect. In addition, the ANOVA results identify AOD (F = 20.11, P < 0.01) and surface pressure (F = 18.19, P < 0.01) as the most statistically significant environmental drivers of VCI. Furthermore, SHAP-based result reveals that solar radiation (mean |SHAP| ≈ 3.0) and air temperature (mean |SHAP| ≈ 1.6) contribute most significantly to the XGBoost model’s predictions. Overall, this framework enables interpretable quantification of complex and nonlinear relationships on vegetation condition, supporting data-driven agricultural monitoring and management. | ||
| کلیدواژهها | ||
| ANOVA؛ Remote sensing؛ SHAP analysis؛ Vegetation condition؛ XGBoost | ||
| مراجع | ||
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