Satellite-derived ocean color data for monitoring pCO2 dynamics in the North Indian Ocean

Abstract

The partial pressure of carbon dioxide (pCO₂) in the North Indian Ocean (NIO) undergoes significant variations due to factors such as biological activity, ocean circulation patterns, and atmospheric influences. Understanding these variations is crucial for assessing the ocean role in the global carbon cycle and their impact on climate change. Estimating pCO₂ through in-situ platforms is challenging due to the time-consuming, expensive, and complex nature of water sample collection, particularly under rough oceanic conditions. Conversely, remote sensing technology offers high spatiotemporal resolution data over extensive synoptic scales, making it a valuable tool for pCO₂ estimation. Current models for estimating pCO₂ in the NIO region are limited due to the improper selection of model parameters and the scarcity of in-situ measurements, highlighting the need for a more accurate approach. This study develops a Multiparametric Linear Regression (MLR) method, integrating satellite and in-situ observations of sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chla) concentration. To develop and validate this model, in-situ data were sourced from the Global Ocean Data Analysis Project (GLODAP). Validation results showed that the proposed MLR approach outperformed existing global models, achieving low mean relative error (MRE = 0.08), mean normalized bias (MNB = 0.013), and root mean square error (RMSE = 7.26 μatm), with a high correlation coefficient (R² = 0.96). This study has the potential to improve understanding of carbon dynamics in the NIO region and its contribution to the global carbon cycle. The pCO₂ maps generated in this study improve climate modeling and monitoring, supporting predictions and mitigation efforts. This accurate model also aids policy-making, environmental management, and ecological assessments.