Improved estimation of irrigated field soil water (SWC) and salt content (SSC) from Sentinel-2 imagery by combining multi-dimensional spectra decomposition with ensemble learning

Abstract

Multispectral satellite imagery is an indispensable tool for understanding irrigated soil water (SWC) and salt content (SSC) processes in agricultural areas. However, the water-salt interaction effect on crop growth obscures the original mapping relationship between spectra and water-salt, introducing uncertainty into diagnosis results. To address this issue, a hybrid model of interaction effect decomposition and ensemble learning was proposed for evaluating the irrigated field SWC and SSC dynamic assessment. Firstly, the water-salt interaction effect on spectra index was quantified by nonlinear regression equations. Then, SWC and SSC was derived from the linear decomposition of two/three-dimensional spectra index combination. Finally, the derived results from chosen combination were used to predict SWC and SSC dynamics using ensemble learning. The result shows that: (1) The water-salt interaction effect on spectra index was significant (p < 0.05); (2) By the linear decomposition, two/three-dimensional spectra index combinations perform close relationship with SWC and SSC (SWC:R² = 0.28–0.62;SSC:R² = 0.31–0.61). ENDVI-GRVI-CIG and SI5-SI10-NDVI_gb were the optimal combinations for SWC and SSC, respectively; (3) Compared to decomposition result from single multiple-dimensional spectra index combination, the stacking ensemble learning enables reliable SWC and SSC estimation (SWC:R² = 0.76, RMSE = 1.08 %, MAE = 8 %; SSC:R² = 0.71,RMSE = 0.07 %,MAE = 14 %). In conclusion, this study demonstrated the potential of proposed method on SWC and SSC estimation in saline-affected irrigation areas, providing a new insight for precision agriculture management.