Generating hyperspectral reference measurements for surface reflectance from the LANDHYPERNET and WATERHYPERNET networks

Abstract

The LANDHYPERNET and WATERHYPERNET networks (which together make up the HYPERNETS network) consist of a set of autonomous hyperspectral spectroradiometers (HYPSTAR®) acquiring fiducial reference measurements of surface reflectance at various sites covering a wide range of surface types (both land and water) for use in satellite Earth observation validation and remote sensing applications. This paper describes the processing algorithm for the HYPSTAR® data products. The hypernets_processor is a Python software package to process the LANDHYPERNET and WATERHYPERNET in-situ hyperspectral raw data, collected from the measurement network under the standard measurement protocols, to the designated products, through data transmission and conversion, application of calibration, evaluation of reflectance and other variables, and, archiving for distribution to users. In order to achieve fiducial reference measurement quality, uncertainties are propagated through each step of the processing chain, taking into account temporal and spectral error-covariance. Such detailed uncertainty information is unique for any satellite validation network. We also describe the HYPSTAR® products acquired until 2023–04–31, consisting of 12,190 LANDHYPERNET sequences and 55,514 WATERHYPERNET sequences (of which respectively 11,802 and 44,412 were successfully processed to surface reflectance).