Remote Sensing Observations of Ocean Surface Features and Productivity in the Southeast Arabian Sea Around Lakshadweep

Southeastern Arabian Sea (SEAS) shows the variation of chlorophyll concentration (CC) associated with Sea Surface Temperature (SST) due to the seasonal effects of wind and current patterns. The present study carried out using MODIS-Aqua datasets (2017–2019) and discussed the seasonal variation of CC and SST in SEAS. Seasonal trend of CC at SEAS shows higher concentration during southwest monsoon months (July-September, 5.0 mgm− 3), which is supported by cold pool of SST (gradient of 1°C) and starts dissipating during northeast monsoon (November-January). The causative factor for the cold pool of SST is due to the uplift of bottom cold waters, strong alongshore wind stress and WICC (West Indian Coastal Currents). Satellite based observation of Sea Surface Height anomaly (SSHa) showed cold core circulation with negative anomalies (< -5 cm) in regions of cold pool of SST at the SEAS (July-August). This leads to the formation of Lakshadweep Low (LL). During August 2018, the extent of low SST (27°C) and negative SSHa (-10.5 cm) extended up to 15°N, whereas it confined up to 10°N during 2017 (-9 cm) and negligible during 2019 (+ 0.7 cm) due to strong positive Indian Ocean Dipole (IOD). The reversal of above phenomenon occurred during northeast monsoon (November-January) and inter-monsoon months (February-May), leads to the formation of Lakshadweep High (LH). The present study observed low CC ( 0.50 mg m− 3) around LH with positive SSHa and higher SST. The above mechanism is evident in SSHa images showed positive SSHa ( > + 16 cm) at the southern tip during November and propagates northwestward with positive anomaly. A warm pool observed with very high SST (30–31°C) and it spreads to offshore regions associated with NMC (northeast monsoon currents). Multiple regression analysis accomplished between CC, SST and SSHa over coastal and offshore sub-regions and entire study region. Gaussian fit resulted that SST and SSHa correlated better (R2 = 0.83 and SEE = 20.09