# Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
## 5 Conclusions and Perspectives  
We have successfully implemented a method based on Bayes' theorem to estimate uncertainties associated with OC-SMART remote sensing reflectance (\(R_{\text{rs}}\)) retrievals. The OC-SMART platform uses a multi-layer neural network to solve the inverse problem, which saves significant calculation time when compared to typical Bayesian (optimal estimation) approaches. This methodology was applied to MODIS, OLCI, and VIIRS sensor data in various optically complex regions and \(R_{\text{rs}}\) retrievals with corresponding uncertainties were presented and discussed for these locations. The uncertainty estimates were dominated by the _a priori_ term, leading to a strong connection between large uncertainties and large \(R_{\text{rs}}\) and APE values.  
This framework for uncertainty estimation could be expanded to include other OC-SMART retrievals, such as aerosol optical depth, chlorophyll concentration, absorption coefficients, and backscattering coefficients. The inclusion of uncertainty estimates for aerosol optical depths, in particular, would involve only minor changes to the methods described in this paper, namely adjustments to the APE values in the _a priori_ term. For other retrieval parameters, such as chlorophyll concentrations and water IOPs new considerations will have to made for determining measurement errors and _a priori_, as well as calculations of the Jacobian. MODIS, OLCI, and VIIRS sensor data were used in this paper, but the calculations of uncertainties could also be performed for any sensor that OC-SMART is compatible with those mentioned in Section 2.  
Improvements could be made to our estimations by obtaining better information on the _a priori_. In our case, such improvement would imply a better match-up of MLNN retrievals to synthetic data which could be achieved by implementing additional IOP data to the training dataset of the neural network. We could also improve estimations if uncertainties associated with satellite radiance measurements were available. Such information would allow us to apply measurement error terms more accurately than assuming one percent Gaussian noise for all bands. Future work would include an expansion of our approach to encompass all applicable OC-SMART retrievals and to integrate uncertainty estimation into the currently available Python OC-SMART package and plug-ins.  
All authors contributed to conception and design of methodology presented in this paper. [PERSON], [PERSON] and [PERSON] formulated work involved with OC-SMART. [PERSON] and [PERSON] performed the mathematical analysis. [PERSON] wrote the first draft of the manuscript and produced all figures. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by NASA's OBB program through a grant to Stevens Institute of Technology. This study was also partially supported by NOAA grant NA19 NES4320002 (Cooperative Institute for Satellite Earth System Studies -CISESS) at the University of Maryland/ESSIC.  
Data Availability StatementData used in this paper is available upon request, please contact the corresponding author with any inquiries. The OC-SMART platform can be accessed from [[http://www.rtatmocn.com/oc-smart/](http://www.rtatmocn.com/oc-smart/)]([http://www.rtatmocn.com/oc-smart/](http://www.rtatmocn.com/oc-smart/)) (accessed on 23 May 2023).  
The Authors would like to acknowledge NASA's Distributed Active Archive Centers and ESA's Copernicus Open Access Hub for providing a platform for us to access data used in this paper.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.