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Two factors influence the diffuse transmittance (t) of water-leaving radiance (L(w)) to the top of the atmosphere: the angular distribution of upwelling radiance beneath the sea surface (L(u)) and the concentration and optical properties of aerosols in the atmosphere. We examine these factors and (1) show that the error in L(w) that is induced by assuming L(u) is uniform (i.e., in treating the subsurface reflectance by the water body as Lambertian) is significant in comparison with the other errors expected in L(w) only at low phytoplankton concentration and then only in the blue region of the spectrum; (2) show that when radiance ratios are used in biophysical algorithms the effect of the uniform- L (u) approximation is even smaller; and (3) provide an avenue for introducing accurate computation of the uniform L(u) diffuse transmittance into atmospheric correction algorithms. In an Appendix the reciprocity principle is derived for a medium in which the refractive index is a continuous function of position.

BackgroundKey opinion leaders (KOLs) are people who can influence public opinion on a certain subject matter. In the field of medical and health informatics, it is critical to identify KOLs on various disease conditions. However, there have been very few studies on this topic.ObjectiveWe aimed to develop a recommender system for identifying KOLs for any specific disease with health care data mining.MethodsWe exploited an unsupervised aggregation approach for integrating various ranking features to identify doctors who have the potential to be KOLs on a range of diseases. We introduce the design, implementation, and deployment details of the recommender system. This system collects the professional footprints of doctors, such as papers in scientific journals, presentation activities, patient advocacy, and media exposure, and uses them as ranking features to identify KOLs.ResultsWe collected the information of 2,381,750 doctors in China from 3,657,797 medical journal papers they published, together with their profiles, academic publications, and funding. The empirical results demonstrated that our system outperformed several benchmark systems by a significant margin. Moreover, we conducted a case study in a real-world system to verify the applicability of our proposed method.ConclusionsOur results show that doctors’ profiles and their academic publications are key data sources for identifying KOLs in the field of medical and health informatics. Moreover, we deployed the recommender system and applied the data service to a recommender system of the China-based Internet technology company NetEase. Patients can obtain authority ranking lists of doctors with this system on any given disease.

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