A distinctive spatial noise pattern in the form of parallel stripes exists commonly in the images that are acquired using pushbroom hyperspectral imaging systems. Passing through chemometric systems, it often resurfaces in resultant images of the spatial distributions of various chemical or quality indices, blocking or breaking the spatial details therein, and undermining consequent interpretation. In regard of this, an image de-striping calibration was investigated for its contribution to improving spatial chemometric predictions. The de-stripe calibration was first applied to the hyperspectral images of 155 ginkgo leaves before mapping the spatial distribution of water content (WC) using partial least squares regression-chemometric models. In comparison, the process was repeated twice, respectively, from either raw hyperspectral image without de-stripe calibration or those through a conventional image-enhancement of spatial smoothing-filtering. Results showed that neither the de-stripe calibration nor the conventional image enhancement would affect the accuracy of chemometric models, and that the coefficient of determination of prediction, or R2P, irrespective of different preprocessing in all three cases, were risen up with the increase of 〖JP2〗number of principal components (PCs), until peaking at the number of 10 PCs (R2P=0.93~0.94). However, difference emerged when applying chemometric models to the spectra at individual pixels to map the spatial distribution of WC over leaf-surface. As the number of PCs was increased from 6 to 10, so did the spectral gains of chemometric models causing strengthening stripy noise in the WC maps from either the un-treated or conventionally smooth-filtered images, with noise-stripes being the most prominent spatial feature at 8 PCs, and even deteriorating to the point, at 9 or 10 PCs, that any possible WC variation over a leaf would be totally blocked up. To the contrary, the de-stripe calibration successfully suppressed the distinctive noise patterns inherent from the pushbroom hyperspectral imaging system, so that no discernible stripes appeared in the WC maps from the destriped hyperspectral images and stunning spatial details were preserved in the maps derived from the relatively high accuracy chemometric model of 8 PCs (R2P=0.88). It may be safely concluded from this comparative study that de-stripe calibration of pushbroom hyperspectral images did contribute rich spatial details and high accuracy to spatial chemometric predictions through keeping spatial details intact while enabling the application of models with high spectral gains.