Python algorithms and dataset of empirical line method applied to inland water hyperspectral images combining reference targets and in situ water measurements

Empirical line methods are frequently used to correct images from aerial remote sensing. Remote sensing of aquatic environments captures only a small amount of energy because the water absorbs much of it. The small signal response of the water is proportionally smaller when compared to the other land surface targets. This dataset presents some resources of a new approach to calibrate empirical lines combining reference calibration panels with a water point. We optimize the method using python algorithms until reaches the best result. We evaluated several combinations of targets using both linear and exponential fit. All results are included: python scripts to calibrate and validate the ELM and also the entire dataset of 26 water espectrum.