Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Lipi Mukherjee,Meng Gao,Yongxiang Hu,Peng-Wang Zhai,Bryan A Franz,P Jeremy Werdell

doi:10.3390/rs12091421

Abstract

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

Highlights

The study of global ocean color measurements can help in understanding the ocean ecology and biogeochemistry which can subsequently help better understand the carbon cycle and its impact on climate change
We present an ocean reflectance model based on machine learning techniques, hereafter referred to as Neural Network Reflectance Prediction Model (NNRPM), which can be used to replace the radiative transfer simulation of ocean waters and expedite the retrieval algorithm significantly
Ocean polarized reflectances obtained from satellite sensors contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton types, and pollutants

Summary

Introduction

The study of global ocean color measurements can help in understanding the ocean ecology and biogeochemistry which can subsequently help better understand the carbon cycle and its impact on climate change. Once remote sensing reflectance is obtained, empirical and model based approaches [4,5,6,7,8,9,10,11] are developed to retrieve the inherent optical properties (IOPs). The algorithm quantifies water constituents as a function of blue and green light emanating from the ocean surface [4,12] Another empirical approach is based on a line-height color index that is the difference between Rrs in the green band and a reference formed linearly between remote sensing reflectance in the blue and red band [5]. The second approach performs better in areas with adjacency effects, such as sun glint or cloud edges Both empirical approaches are successful for open ocean where the phytoplankton and detrital are the main constituents of water, co-varying with each other [13]. They often perform less successfully for coastal ocean where the optical constituents like CDOM and suspended sediments do not co-vary with water primary productivity

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