A Preliminary Study of Global Sea Surface Chlorophyll Concentration Estimation from GNSS-R Data

Chlorophyll concentration serves as a key indicator of marine primary productivity, and accurate measurement of chlorophyll is essential for monitoring ocean ecosystems and understanding the impact of climate change on oceans. While optical sensors, such as Moderate Resolution Imaging Spectro-radiometer (MODIS), provide valuable chlorophyll concentration information by calculating reflectance values at different bands, they are limited by cloud cover and other environmental factors. In contrast, Global Navigation Satellite System Reflectometry (GNSS-R) is not affected by cloud cover but the use of GNSS-R data for global sea surface chlorophyll concentration retrieval remains unexplored. This study addresses this gap by analyzing the feasibility of using GNSS-R data and conducting experiments with CYGNSS data. The results demonstrate the potential of the random forest model in estimating chlorophyll concentrations from GNSS-R data, achieving a root mean square difference (RMSD) of 0.17 and a correlation coefficient (CC) of 0.89.