Towards a global machine learning model to fill gaps in flood frequency: Random Forest to estimate MNDWI in Bangladesh

An understanding of historical trends of flooding is essential to capture current risk as more people are exposed to flood hazards than ever before. Flood modeling relies on accurate and frequent remote sensing inputs to produce reliable flood risk results. However, remote sensing datasets are limited by temporal resolution which, in the case of Landsat, leaves 87% of days without observation. This study aims to address these gaps in observation using a Random Forest model to predict the Modified Normalized Difference Water Index (MNDWI) in Landsat images by fusing gridded precipitation and elevation data. The trained model is then used to estimate MNDWI for when there are no Landsat observations. The model was evaluated on a 50 by 50 pixel test patch achieving a residual squared error between the true average MNDWI and the estimated average MNDWI of 0.48.