Remote sensing data imputation using deep learning for multispectral imagery

Researchers compared deep learning models against linear interpolation for reconstructing missing spectral bands in PlanetScope SuperDove satellite imagery across four lakes with algal bloom histories. The study found that CNN-based architectures, particularly the standard CNN model, substantially outperformed traditional methods in imputing data gaps caused by cloud cover. This approach enables more reliable water monitoring by improving the completeness of optical satellite datasets critical for detecting algal blooms.

arXiv:2605.24003v1 Announce Type: new Abstract: Remote sensing techniques have been increasingly utilised in aquatic applications in recent years. A common challenge in using optical satellite data is the presence of missing observations due to cloud cover. These data gaps can lead to missed detection of critical events, such as algal blooms, in lakes of high interest to water authorities. As a result, enhancing the completeness of optical satellite datasets is crucial for improving the monitoring and prediction of algal blooms. In this study, we compared a traditional data imputation method i.e., linear interpolation with deep learning models for reconstructing missing spectral bands across four lakes with historical records of algal blooms. The deep learning models adopted include CNN-based architectures i.e., CNN, Inception Resnet, and Autoencoder and CNN-LSTM-based architectures i.e., CNN-LSTM, Resnet-LSTM, and Autoencoder-LSTM . Our results demonstrated that deep learning models substantially outperformed the baseline linear interpolation method in imputing spectral band values within artificially masked regions. Among these models, CNN delivered the best performance across most lakes. Furthermore, we evaluated the performance of algal bloom indices i.e., Green/Red and NDCI derived from the imputed imagery by comparing them with the observed data. Our results demonstrate that deep learning models are effective for imputing missing data in PlanetScope SuperDove imagery, enabling more reliable applications in water monitoring.