Inversion models applied for hyperspectral prediction of soil chromium include univariate regression, multiple linear regression, principal component regression, and partial least squares regression models. They are mostly based on the presumed homogeneous influence of heavy metal content on spectral reflectance at different locations. This presumption, however, ignores the spatial heterogeneity of correlation between soil chromium and spectral variables. In contrast, Geographically Weighted Regression(GWR) model effectively reveals the spatial heterogeneity among different variables, as is evidenced in many studies involving the spatial prediction of soil properties. In this study, we first analyzed the influence of different spectral resolutions and transformations on soil chromium-targeted hyperspectral prediction model. Thereafter, optimal spectral resolution and variables were selected to establish the GWR model for prediction of soil chromium content in Fuzhou City. In addition, the applicability and limitations of the model were assessed by comparing the predictions based on GWR and Ordinary Least Squares Regression(OLS)models separately. The conclusions finally drawn from the study are as follows:(1) At a resolution of 10 nm, with soil chromium content as a dependent variable and the second derivative of reflectance and reflectance reciprocal as independent variables, the GWR model displayed the best prediction performance. The values of R² and the adjusted R² were 0.821 and 0.716, respectively, which showed an increase of 0.529 and 0.450, respectively, above the corresponding values in the OLS model. The AIC was decreased by 22 units to 720.703, and the residual sum of squares was decreased by three quarters, an indication of significant improvement of the prediction performance. (2) The spectral resolution exerted obvious influence on the accuracy of chromium prediction models. The GWR model, with a spectral resolution of 10 nm, as against the OLS model, with a resolution of 3 nm, showed optimal prediction outcome and an evident increase in accuracy. The best resolution for the GWR model was 10 nm. (3) The spectral transformation of first-order differential effectively enhanced the spectral features of soil chromium. Whereas other spectral transformations failed to enhance the features, they significantly improved the prediction performance. (4) The optimal spectral resolution of 10 nm for GWR-based soil chromium prediction was up to the level of EO-1 Hyperion images. Moreover, the prediction performance of the GWR model showed a tendency to stabilize with the increase in the number of sample sites, which is suitable for soil chromium prediction in the regions featuring great spatial heterogeneity. Therefore, with hyper-spectral images, the application of GWR model can be extended from laboratory to the regional scale, making the spatial prediction of soil chromium on grid basis feasible.