Over the past two decades, robotic technology such as Argo floats have revolutionized operational autonomous measurement of the oceans. Recently, Biogeochemical Argo floats (BGC-Argo floats) have measured optical and biogeochemical quantities down to a depth of 2,000 m. Among these parameters, are measurements of the underwater light field from which apparent optical properties (AOPs), such as the diffuse attenuation coefficient for downwelling irradiance Kd(λ), can be derived. Presently, multispectral observations are available on this platform at three wavelengths (with 10–20 nm bandwidths) in the ultraviolet and visible part of the spectrum plus the Photosynthetically Available Radiation (PAR; integrated radiation between 400 and 700 nm). This article reviews studies dealing with these radiometric observations and presents the current state-of-the-art in Argo radiometry. It focus on the successful portability of radiometers onboard Argo float platforms and covers applications of the obtained data for bio-optical modeling and ocean color remote sensing. Generating already high-quality datasets in the existing configuration, the BGC-Argo program must now investigate the potential to incorporate hyperspectral technology. The possibility to acquire hyperspectral information and the subsequent development of new algorithms that exploit these data will open new opportunities for bio-optical long-term studies of global ocean processes, but also present new challenges to handle and process increased amounts of data.

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Modern oceanography uses, amongst other platforms, automated diving devices, which are drifting with the ocean current whilst continuously collecting vertical profiles of environmental parameters. One of the important parameters is photosynthetically available radiation (PAR). It was studied in this work whether the PAR values can be reconstructed by combinations of measurements from the remaining onboard sensors with specific wavelength. If a reconstruction of PAR is possible, this would allow allocating the sensor with a further specific wavelength instead of PAR. Having available more spectral information could for example enable natural science researchers to better distinguish phytoplankton or UV radiation. Therefore, data from three different expeditions from different regions of the world were used to model PAR using multiple linear regression and regression trees (RT). multiple linear regression achieved an R2 value of 0.970 for the combined dataset and RT achieved an R2 value of 0.960. Hence, the models are accurate enough to predict the PAR parameter without the need for a dedicated PAR sensor. Thus the PAR sensor could be allocated instead with a further specific wavelength.

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