Moderate Imaging Spectroradiometer Aqua Research Articles

An optical remote sensing approach for ecological monitoring of red and green Noctiluca scintillans.

An ecosystem disruptive bloom of red Noctiluca scintillans (hereafter Noctiluca) was observed in coastal waters of the north-western Bay of Bengal during April 2014. Based on the principle of phytoplankton group/species specific remote sensing reflectance (Rrs), a technique of detecting green Noctiluca and diatom was developed earlier using Rrs at 443, 488, and 531nm of Moderate Imaging Spectroradiometer-Aqua (MODIS). This was appropriately modified to detect bloom of red Noctiluca in coastal waters of the Bay of Bengal. Additional Rrs data at longer wavelengths viz. 667 and 678nm were included in the existing algorithm, and the spectral shapes were accounted to detect the bloom of red Noctiluca. The classification scheme discriminates red Noctiluca from the green form of the same species and diatom. Phytoplankton group/species products were generated using the modified approach and validated with the reported events of red and green Noctiluca blooms in the Indian coastal waters. The present study also highlights two specific results based on MODIS retrieved time-series phytoplankton group/species image analysis: first, the observation of coexistence of diatom, red, and green Noctiluca in coastal waters of the north-western Bay of Bengal, and the second, phytoplankton community shift resulting in red/green Noctiluca proliferation following diatom.

Performance of Semianalytical Algorithm and Associated Inherent Optical Properties in Coastal Waters of North Western Bay of Bengal

The present study was carried out with an aim to assess two semianalytical algorithms, Garver–Siegel–Maritorena (GSM) and Generalized IOP (GIOP) and their association with Inherent Optical Properties (IOP) such as phytoplankton absorption coefficient (aph), detritus absorption (adg) and particulate backscattering (bbp). Ten years of Moderate Imaging Spectroradiometer-Aqua (MODISA) data was processed to retrieve Chlorophyll_a (Chl_a) from GSM and GIOP. Subsequently the Rrs from MODISA was inverted to compute aph, adg and bbp. Both the algorithms showed similar trend at an annual scale. However in magnitude, Chl_a from GSM was three times higher than that from GIOP. Further the Chl_a retrieved from GSM was closer to in situ values. The variability of aph and adg was similar at long-term time scale. The peak observed in variability of aph and adg during pre-monsoon was due to the occurrence of bloom whereas the peak during monsoon was due to nutrient discharge from river. The observed peak in bbp during monsoon can be also attributed to the increasing concentration of total particulate matter from river as well as from bottom re-suspension. The poor association of IOP with Chl_a explains the limited accuracy of satellite retrieved Chl_a from these semianalytical algorithms. Hence it is require generating IOP at the regional scale and tuning the semianalytical model for better accuracy.