Effect of Correcting Radiometric Inconsistency between Input Images on Spatio-temporal Fusion of Multi-sensor High-resolution Satellite Images

In spatio-temporal fusion aiming at predicting images with both high spatial and temporal resolutions from multi-sensor images, the radiometric inconsistency between input multi-sensor images may affect prediction performance. This study investigates the effect of radiometric correction, which compensate different spectral responses of multi-sensor satellite images, on the spatio-temporal fusion results. The effect of relative radiometric correction of input images was quantitatively analyzed through the case studies using Sentinel-2, PlanetScope, and RapidEye images obtained from two croplands. Prediction performance was improved when radiometrically corrected multi-sensor images were used as input. In particular, the improvement in prediction performance was substantial when the correlation between input images was relatively low. Prediction performance could be improved by transforming multi-sensor images with different spectral responses into images with similar spectral responses and high correlation. These results indicate that radiometric correction is required to improve prediction performance in spatio-temporal fusion of multi-sensor satellite images with low correlation.