Seasonal nitrate algorithms for nitrate retrieval using OCEANSAT-2 and MODIS-AQUA satellite data

Abstract

In situ datasets of nitrate, sea surface temperature (SST), and chlorophyll a (chl a) collected during the monthly coastal samplings and organized cruises along the Tamilnadu and Andhra Pradesh coast between 2009 and 2013 were used to develop seasonal nitrate algorithms. The nitrate algorithms have been built up based on the three-dimensional regressions between SST, chl a, and nitrate in situ data using linear, Gaussian, Lorentzian, and paraboloid function fittings. Among these four functions, paraboloid was found to be better with the highest co-efficient of determination (postmonsoon: R2=0.711, n=357; summer: R2=0.635, n=302; premonsoon: R2=0.829, n=249; and monsoon: R2=0.692, n=272) for all seasons. Based on these fittings, seasonal nitrate images were generated using the concurrent satellite data of SST from Moderate Resolution Imaging Spectroradiometer (MODIS) and chlorophyll (chl) from Ocean Color Monitor (OCM-2) and MODIS. The best retrieval of modeled nitrate (R2=0.527, root mean square error (RMSE)=3.72, and mean normalized bias (MNB)=0.821) was observed for the postmonsoon season due to the better retrieval of both SST MODIS (28 February 2012, R2=0.651, RMSE=2.037, and MNB=0.068) and chl OCM-2 (R2=0.534, RMSE=0.317, and MNB=0.27). Present results confirm that the chl OCM-2 and SST MODIS retrieve nitrate well than the MODIS-derived chl and SST largely due to the better retrieval of chl by OCM-2 than MODIS.

Overview: Recent advances in the understanding of the northern Eurasian environments and of the urban air quality in China – a Pan-Eurasian Experiment (PEEX) programme perspective

“…Although these assessments are done without explicitly considered nutrient concentrations (NCs), implicitly NCs were among the FFs. Indeed, arguably, variations in SWT, SSS, CHL, MLD, and surface current speed/advection (tested as FFs) indirectly account for the variations in NCs as well in such CCS parameters as alkalinity and basicity (Durairaj et al, 2015;Pozdnyakov et al, 2019, and references therein).…”

The Phenomenon Of Emiliania Huxleyi In Aspects Of Global Climate And The Ecology Of The World Ocean

“…sea surface temperature and salinity (SST & Sal), PAR, water surface geostrophic current speed, mixed layer depth, and concentration of phytoplankton chlorophyll. Although the tested set of FFs did not explicitly include nutrient concentrations (NCs), the authors assumed that variations in the above variables indirectly account for the variations in NCs as well via such carbonate chemistry system parameters as alkalinity and basicity (Durairaj et al 2015;Pozdnyakov et al 2019). The representativeness of the employed FFs is supported by the fact that over the twenty years of observations the selected FFs have not failed to explain the patterns of either the areal extent or PIC content in E. huxleyi blooms.…”

Overview: Recent advances on the understanding of the Northern Eurasian environments and of the urban air quality in China - Pan Eurasian Experiment (PEEX) program perspective

“…Although these assessments are done without explicitly considered nutrients concentrations (NCs), implicitly NCs were among the FFs. Indeed, arguably, variations in SWT, SSS, CHL, MLD, and surface current speed/advection (tested as FFs) indirectly account for the variations in NCs as well in such CCSs parameters as alkalinity and basicity (Durairaj et al, 2015;Pozdnyakov et al, 2019, and references therein).…”