Calibration and Validation from Ground to Airborne and Satellite Level: Joint Application of Time-Synchronous Field Spectroscopy, Drone, Aircraft and Sentinel-2 Imaging

Non-invasive investigation of surfaces from drones and manned aircrafts used as camera platforms is a well-established remote-sensing practice. However, cross-comparison of multispectral reflectance from different camera systems across different platforms, locations, and times can be challenging. We investigate reflectance retrieved from Sentinel-2 and two airborne camera systems with respect to the mobile, radiometrically calibrated, two-channel hemispherical-conical field-spectrometer system RoX. This spectrometer system serves in combination with a nine-panel grey scale as ground reference and transfer instrument. In the first step, the ground reference was validated against Sentinel-2 reflectance including atmospheric compensation. Our results suggest significant differences in the uncorrected reflectance from the two airborne sensors with respect to instantaneous calibration across 22 mixed targets. In the second step, those differences were reduced to a median discrepancy below 10% using the proposed in-field empirical line correction method (ELC). Continuous irradiance correction further improved the agreement across the validation targets and achieved a coherent reflectance dataset from all four different sensor systems, from the satellite level to the ground and airborne level, considering the limitations of instrument and in-field handling. NDVI maps created from drone and manned aircraft achieved an agreement around 89% and 95% compared to the satellite after calibration and correction. We consider in-field calibration with additional, continuous down-welling radiance correction of reflectance promising to support fusion of information across four sensors and platforms. Thus, field-spectrometer systems serve as transfer instruments and bridge the gap of information from the satellite down to the ground and airborne scale in future airborne mapping and classification efforts.