Cross-sensor evaluation of hyperspectral and multispectral Ocean colour products in the northern Indian Ocean

Phytoplankton are the drivers of primary production in aquatic ecosystems, forming the foundation of the oceanic food web and playing a pivotal role in global carbon cycling. Satellite ocean colour is used to monitor the phytoplankton distribution and is critical for understanding ecosystem dynamics and climate interactions. The Moderate Resolution Imaging Spectroradiometer (MODIS) has provided invaluable multispectral ocean colour observations for over two decades. Recently, NASA launched the Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) mission introduces hyperspectral capabilities that greatly enhance spectral characterization and cloud–aerosol corrections. This study investigates cross-sensor consistency between MODIS-Aqua and PACE-OCI over the northern Indian Ocean, using a sequence alignment technique firstly applied to satellite imagery. Chlorophyll and remote sensing reflectance products from MODIS and PACE were intercompared for an 8-day period and validated using in-situ measurements from India’s Coastal Ocean Monitoring and Prediction System (COMAPS). The analysis integrates two approaches: (i) standard statistical metrics and clustering techniques, and (ii) a pixel-level comparison method using the Needleman–Wunsch algorithm (NWA), adapted from bioinformatics for spatially sensitive sequence alignment of satellite data. Results show a strong inter-sensor correspondence (R² > 0.9) in blue-green spectral bands (412–555 nm), with both sensors effectively capturing large-scale chlorophyll patterns and coastal–offshore gradients. Validation results indicate similar performance for both sensors, with PACE showing slightly better performance (R² = 0.88, MSE = 0.008). The NWA-derived similarity maps indicate spatial deviations mainly in nearshore zones, highlighting region-dependent sensor performance. The study on hyperspectral and multispectral sensor comparison reveals PACE’s potential to continue and enhance MODIS’s long-term ocean colour climate data record. The proposed sequence alignment approach offers a robust, directionally sensitive alternative to conventional statistical comparisons, enabling detailed cross-sensor validation for future ocean colour applications.