Advancing climate change Research: Robust methodology for precise mapping of sea level rise using satellite-derived bathymetry and the google Earth Engine API

Abstract

Sea level rise (SLR), linked to climate change, poses risks to coastal areas and requires urgent action. Traditional methods to measure SLR, such as tide gauges, satellite altimetry, and GNSS-based techniques, have limitations in coverage, accuracy, and data continuity. This study applies Random Forest regression in Google Earth Engine (GEE) to automate satellite-derived bathymetry (SDB) prediction for accurate SLR mapping and time-series analysis. The SDB has been predicted using Landsat series satellite data and derived products, including Chlorophyll, Total Suspended Material, and Turbidity, for the years 1993, 2003, 2013, and 2023. The results demonstrated high accuracy, strong correlation coefficients between in-situ bathymetry and SDB, and low error measures. The correlation coefficients with in-situ bathymetry were 0.8924 in 1993, 0.9386 in 2003, 0.9638 in 2013, and 0.9444 in 2023. Tidal correction was applied to the SDB maps to calculate SLR changes between 1993 and 2023. The analysis delineated a consistent rise in mean SDB values, suggesting a potential increase in sea level over the past four decades. A robust methodology for SLR time-series analysis has been proposed, with all codes accessible for deployment through Landsat collections and temporal parameters.