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Remote sensing reflectance ( Rrs ) and inherent optical properties (IOPs) conversions are fundamental in accurate satellite measurements to guarantee the semi-analytical retrieval quality of IOPs and the biogeochemical products. Traditionally, the  Rrs -IOPs conversions are determined by a quadratic polynomial function with two model parameters ( Gx  = 0,1 ). However,  Gxvalues vary in time and location, which are attributed to the spatial and temporal variability inherent to illumination conditions, sea surface properties, and meteorological states. To improve the performance of three classical existing models used for  Rrs -IOPs conversions, we designed two novel neural network models (NNGx  = 1,2 ) to quantitatively calculate  Gxfrom the  Rrs  spectrum pixel by pixel without requirement of any auxiliary illumination and meteorological data, and then proposed for  Rrs -IOPs conversions. We evaluated these approaches with numerical simulations and field measurements, and the results show that the NNGxmodels are more effective in semi-analytically converting  Rrs  into IOPs than the three existing models. Furthermore, we applied the NNGxmodels to satellite images to understand the downstream influence of the  Gxvalues on IOPs estimates for the global oceans. We further confirm that the  Gxvalues dramatically change for the global ocean, which is especially true for very oligotrophic gyres, coastal waters, and high latitude oceans. When we use a constant  Gxfor the  Rrs -IOPs conversions, it leads to substantial uncertainty of up to 30% in the IOPs retrievals for China’s coastal regions. Our results suggest that it is possible to improve the data quality of IOPs for the global oceans by providing accurate pixel-level  Gxvalues using NNGxmodels.

Estimating the model parameters for remote sensing reflectance pixel by pixel: a neural network approach for optically deep waters