RESEARCH ARTICLE Accuracy Assessment of Bio-optical Models to Retrieve Backscattering Coefficients in Case 1 Waters of the Bay of Bengal Nikhil Kumar Baranval 1 • P. V. Nagamani 1 • P. Rama Rao 2 • S. B. Choudhury 1 Received: 19 July 2018 / Accepted: 2 January 2019 Ó Indian Society of Remote Sensing 2019 Abstract The backscattering coefficient (b b ) has been obtained either in situ observations/measurements or semi-analytical/analytical and empirical algorithms that depend on the relationship between ‘b b ’ and the remote sensing reflectance (R rs ). Several models were developed to estimate backscattering coefficient from the satellite imagery. The present study aims to assess the accuracy of bio-optical models and infer the best suitable model for Ocean Colour Monitoring (OCM-2) and for the upcoming OCM-3 sensors of Indian Space Research Organization. For this purpose, the bio-optical algorithms/models such as quasi-analytical algorithm (QAA), QAA version 5, Generalized Inherent Optical Properties (GIOP) and optimization of semi-analytical models in case 1 waters of the Bay of Bengal (BoB) were considered. For this analysis, three satellite sensors Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua, Visible Infrared Imaging Radiometer Suite (VIIRS), and OCM-2 datasets has been used to assess the model’s accuracy. The results showed that the optimization technique performs better (bias = 0.002, 0.179 and root-mean-square error (RMSE) = 8%, 20%) for MODIS-A and OCM- 2, respectively, whereas GIOP performs better (bias = 0.08, RMSE = 14%) for VIIRS in case 1 waters of the BoB. From the statistical analysis of each model for all stations, it is recommended to use the optimization technique rather than GIOP technique for estimation of satellite based ‘b b ’ in Indian waters. Keywords Backscattering coefficient  QAA  QAA_v5  GIOP  Optimization  Case 1 waters Introduction Inherent optical properties (IOPs) are one of the most important parameters for linking remote sensing reflec- tance (R rs ) with concentration of water constituents, e.g. chlorophyll-a and suspended sediment concentration. The IOPs mainly, absorption ‘a’ and backscattering coefficient ‘b b ’ are analytically related with R rs (Mobley 1994; Bricaud et al. 1995; Gordon et al. 1988; Lee et al. 2002). The IOPs, which are independent of the light field distri- bution, depend on composition of water constituents and their physicochemical properties. Over the past few dec- ades, numerous algorithms have been developed for the retrieval of IOPs from R rs but not yet achieved best one. For accurate estimation of IOPs, it is required better understanding of bio-optical properties (Doxaran et al. 2006). Inversion approaches to retrieve IOPs from remotely sensed data are divided into two categories: empirical and model based. Empirical methods derive a relationship between optical measurements and constituent’s concen- tration based on observations from experimental datasets. Model-based techniques exploit bio-optical models that capture the connection between water constituents and spectra of water-leaving radiance/reflectance and a radia- tive transfer model that describes light propagation through the water and the atmosphere. & Nikhil Kumar Baranval baranvalnikhil@gmail.com P. V. Nagamani pvnagamani@gmail.com P. Rama Rao raorpaluri@gmail.com S. B. Choudhury sarojnrsa@gmail.com 1 National Remote Sensing Centre, Hyderabad 500037, India 2 Department of Geophysics, Andhra University, Vishakhapattanam 530003, India 123 Journal of the Indian Society of Remote Sensing https://doi.org/10.1007/s12524-019-00937-3