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

The lack of an orange band (similar to 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, R-rs (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 R-rs 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 R-rs 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.