Marine ecological monitoring is essential for understanding and managing ocean ecosystems. The advent of ocean colour satellites has made remote sensing a powerful tool for assessing marine ecosystems on a large spatio-temporal scale. This chapter comprehensively reviews ocean colour satellite-retrieved chlorophyll-a (chl-a) and related bio-optical algorithms in Indian coastal waters. It covers the use of satellite-retrieved chl-a for research, retrieval algorithms, performance assessment, and applications, addressing their challenges and limitations. The review highlights the major use of satellite-retrieved chl-a in monitoring marine ecosystem health, tracking algal blooms, and understanding biogeochemical processes of Indian coastal waters, with significant operational applications in identifying potential fishing zones. A scientometric analysis (1990–2024) of satellite-retrieved chl-a in Indian coastal waters shows the growing interest and importance of using satellite data. Among the 154 publications screened, approximately 65% focused on the western coast of India and 35% on the eastern coast. Satellite-retrieved chl-a analysis has successfully tracked long-term phytoplankton trends, the evolution, decline of blooms, the spatial spread of algal blooms, impacts of extreme events on algal productivity, and algal size distribution. Regarding sensor and algorithm performance, studies indicate that MODIS-Aqua and its sensor-default algorithm OC3M perform well across the entire Indian coastal waters. The recent OLCI ocean colour sensor onboard Sentinel-3A, using a neural network-based algorithm, has shown promising results for chl-a retrieval, as evidenced by research in the eastern Arabian Sea’s coastal waters. Evaluations of satellite and biophysical model-retrieved chl-a data demonstrate strong correlations with field measurements, emphasizing the effectiveness of combining satellite and model data for understanding biogeochemical variability and primary productivity. This review emphasizes the need for regional bio-optical algorithms validated with regional calibration and validation datasets, considering seasonal and regional variations and their integration with existing monitoring programmes.