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

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, Rrs (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 Rrs data at different wavelengths. We propose a deep-learning model called Deep OrAnge Band LEarning Network (DOABLE-Net) that leverages a large training set of Rrs data from radiative transfer simulations and in situ measurements. The proposed DOABLENet 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.