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<p>The present study focuses on retrieval of sea surface nitrate (SSN) in the northern Indian Ocean (NIO) using satellite-based chlorophyll-<i>a</i> (Chl-<i>a</i>), sea surface temperature (SST) and reanalysed mixed layer depth (MLD). The derived SSN is validated with <i>in-situ</i> nitrate observations and showed good agreement within the range of 1 to 5 µ mol l<sup>−1</sup> with root mean square error (RMSE) of 0.4 µ mol l<sup>−1</sup>. The SSN maps strongly reflect the known seasonal variability in the NIO region, particularly mimicking upwelling-induced higher nitrate levels in the western Arabian Sea (AS) and the terrestrial runoff-dominated Bay of Bengal (BoB). In addition, the study employs SSN data to estimate potential ‘<i>new production</i>’ (NP) in both the AS and the BoB, showcasing the capability of SSN data to align with the measured NP rates and effectively capture both seasonal and episodic phenomena. This study underscores the utility of remote sensing data in comprehensively understanding oceanic biogeochemical properties and their implications on oceanic carbon budget and trophic dynamics on a broad scale, which remain poorly constrained due to the paucity of data.</p>

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Estimation of Sea Surface Nitrate and Derived Potential New Production in the Northern Indian Ocean Using Remote Sensing

  • 期刊论文

  • 发表日期：

    2025年5月27日

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  • Chiranjivi Jayaram,
  • Rajdeep Roy

摘要

The present study focuses on retrieval of sea surface nitrate (SSN) in the northern Indian Ocean (NIO) using satellite-based chlorophyll-a (Chl-a), sea surface temperature (SST) and reanalysed mixed layer depth (MLD). The derived SSN is validated with in-situ nitrate observations and showed good agreement within the range of 1 to 5 µ mol l−1 with root mean square error (RMSE) of 0.4 µ mol l−1. The SSN maps strongly reflect the known seasonal variability in the NIO region, particularly mimicking upwelling-induced higher nitrate levels in the western Arabian Sea (AS) and the terrestrial runoff-dominated Bay of Bengal (BoB). In addition, the study employs SSN data to estimate potential ‘new production’ (NP) in both the AS and the BoB, showcasing the capability of SSN data to align with the measured NP rates and effectively capture both seasonal and episodic phenomena. This study underscores the utility of remote sensing data in comprehensively understanding oceanic biogeochemical properties and their implications on oceanic carbon budget and trophic dynamics on a broad scale, which remain poorly constrained due to the paucity of data.