Deep-Learning-Based Retrieval of an Orange Band Sensitive to Cyanobacteria for Landsat-8/9 and Sentinel-2

Abstract

The lack of an orange band (∼620 nm) in the imagery captured by Landsat-8/9 and Sentinel-2 restricts the detection and quantification of harmful cyanobacterial blooms in inland waters. A recent study suggested the retrieval of orange remote sensing reflectance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_rs</i> (620), by assuming green, red, and panchromatic (Pan) bands of Landsat-8 as predictors through a linear model. However, this method is not applicable to Sentinel-2 imagery lacking a Pan band. Moreover, the Pan-based method does not account for the nonlinear relationships among the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_rs</i> data at different wavelengths. We propose a deep-learning model called DOABLE-Net (Deep OrAnge Band LEarning Network) that leverages a large training set of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_rs</i> data from radiative transfer simulations and in-situ measurements. The proposed DOABLE-Net is structured as five fully-connected layers and implemented either with or without the Pan band as an input feature, which only the latter applies to Sentinel-2. DOABLE-Net provided more accurate and robust retrievals than the Pan-based method on a wide range of independent validation datasets. The performance of DOABLE-Net on Landsat-8/9 data was minimally impacted by including the Pan band. The results from Sentinel-2 data analysis also confirmed that the DOABLE-Net provides promising results without using a Pan band.