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Efficient prediction and precise depiction of heavy metal concentrations in urban soil are essential for mitigating non-point source pollution and safeguarding public health. Therefore, this research investigated the estimation of soil heavy metal concentrations derived from Gaofen-5 (GF-5) hyperspectral images calibrated by the direct standardization (DS) algorithm. The inversion strategy for soil heavy metal concentrations in response to the two-dimensional soil spectral index (2D-SSI) was proposed by coupling Pearson correlation coefficient (r) and competitive adaptive reweighting algorithm (CARS) for feature selection. The results indicated that the optimal models based on 2D-SSI outperform the models based on calibrated, filtered original spectral bands. For Pb, Cu, Cd, and Hg, the optimal model determination coefficients for the validation data set (RV2) were 0.871 (SVM), 0.883 (BPNN), 0.834 (PLSR), and 0.907 (PLSR), respectively. The spectral features were highlighted in the two-dimensional feature space, and the predicted distribution of heavy metal concentrations was aligned with the observed ground measurements. This study revealed that the prediction strategy based on DS-corrected GF-5 AHSI images with constructed 2D-SSI features can serve as a reliable technical approach for soil heavy metal prediction and pollution prevention.