Primary productivity plays a pivotal role in the global carbon cycle and the marine food chain. Chlorophyll-a concentration (chl-a) is considered as a proxy for the biomass, which requires reliable estimation. Remote sensing of ocean colour provides a valuable source of chl-a over the global oceans. However, the ocean colour measurements are often hindered by the presence of clouds, thereby denying information on various spatial scales. Data Interpolating Empirical Orthogonal Function (DINEOF) is a robust technique to reconstruct the data in gaps. The present study demonstrates the application of DINEOF-based reconstruction of the chl-a concentration data derived from the Ocean Colour Monitor-2 (OCM-2) onboard Oceansat-2 satellite for the period 2016–2019 over the northern Indian Ocean. The reconstructed and raw chl-a (Level-2) from OCM-2 are compared and found to be comparing well with a correlation of 0.93 and 0.9 in the Arabian Sea and Bay of Bengal, respectively. The seasonal and inter-annual variability of chl-a over the study region is examined to showcase the application of reconstructed data for spatio-temporal analysis on different scales.
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Alvera-Azcárate, A., Barth, A., Rixen, M., & Beckers, J. M. (2005). Reconstruction of incomplete oceanographic data sets using Empirical Orthogonal Functions. Application to the Adriatic Sea. Ocean Modelling, 9, 325–346. https://doi.org/10.1016/j.ocemod.2004.08.001.
Alvera-Azcárate, A., Barth, A., Sirjacobs, D., Lenartz, F., & Beckers, J. M. (2011). Data Interpolating Empirical Orthogonal Functions (DINEOF): A tool for geophysical data analyses. Mediterranean Marine Science, 12, 5–11. https://doi.org/10.12681/mms.64.
Beckers, J. M., & Rixen, M. (2003). EOF calculations and data filling from incomplete oceanographic datasets. Journal of Atmospheric and Oceanic Technology, 20, 1839–1856. https://doi.org/10.1175/1520-0426(2003)020%3c1839:ECADFF%3e2.0.CO2.
Chauhan, P., Mohan, M., Nayak, S. R., & Navalgund, R. R. (2002). Comparison of ocean colour chlorophyll algorithms for IRS-P4 OCM sensor using in-situ data. Journal of Indian Society of Remote Sensing, 30, 81–94. https://doi.org/10.1007/BF02989980.
Dutkiewicz, S., Hickman, A. E., Jahn, O., Henson, S., Beaulieu, C., & Monier, E. (2019). Ocean colour signature of climate change. Nature Communications, 10, 578. https://doi.org/10.1038/s41467-019-08457-x.
Ganzedo, U., Alvera-Azcárate, A., Esnaola, G., Ezcurra, A., & Sáenz, J. (2011). Reconstruction of Sea surface temperature by means of DINEOF. A case study during the fishing season in the Bay of Biscay. International Journal of Remote Sensing, 32, 933–950. https://doi.org/10.1080/01431160903491420.
Goes, J. I., Thoppil, G. P., Gomes, H. R., & Fasullo, J. T. (2005). Warming of the Eurasian landmass is making the Arabian Sea more productive. Science, 308, 545–547.
Gregg, W. W., & Rousseaux, C. C. (2014). Decadal trends in global pelagic ocean chlorophyll: A new assessment integrating multiple satellites, in situ data, and models. Journal of Geophysical Research (Oceans), 119, 5921–5933. https://doi.org/10.1002/2014JC010158.
Henn, B., Raleigh, M. S., Fisher, A., & Lundquist, J. D. (2013). A comparison of methods for filling gaps in hourly near-surface air temperature data. Journal of Hydrometeorology, 14, 929–945. https://doi.org/10.1175/JHM-D-12-027.1.
Hilborn, A., & Costa, M. (2018). Applications of DINEOF to satellite-derived chlorophyll-a from a productive coastal region. Remote Sensing, 10, 1449. https://doi.org/10.3390/rs/10091449.
Jayaram, C., Priyadarshi, N., Pavan Kumar, J., Udaya Bhaskar, T. V. S., Raju, D., & Joseph, K. A. (2018). Analysis of gap-free chlorophyll-a data from MODIS in Arabian Sea, reconstructed using DINEOF. International Journal of Remote Sensing, 39, 7506–7522. https://doi.org/10.1080/01431161.2018.1471540.
Jayaram, C., & Dinesh Kumar, P. K. (2018). Spatio-temporal variability of upwelling along the southwest coast of India based on satellite observations. Continental shelf research, 156, 33–42. https://doi.org/10.1016/j.csr.2018.02.003.
Li, Y., & He, R. (2014). Spatial and temporal variability of SST and Ocean color in the Gulf of Maine based on cloud-free SST and chlorophyll reconstructions in 2003–2012. Remote Sensing of Environment, 144, 98–108. https://doi.org/10.1016/j.rse.2014.01.019.
Liu, X., & Wang, M. (2018). Gap filling of missing data for VIIRS global ocean color products using the DINEOF method. IEEE Transactions on Geoscience and Remote Sensing, 56, 4464–4476. https://doi.org/10.1109/TGRS.2018.2820423.
McClain, C. R., Feldman, G. C., & Hooker, S. B. (2004). An overview of the SeaWiFS project and strategies for producing a climate research quality global oceanbio-optical time series. Deep Sea Research Part II, 51, 5–42. https://doi.org/10.1016/j.dsr2.2003.11.001.
Nagamani, P. V., Hussain, M. I., Choudhury, S. B., Panda, C. R., Sanghamitra, P., Kar, R. N., et al. (2013). Validation of chlorophyll-a algorithms in the coastal waters of Bay of Bengal initial validation results from OCM-2. Journal of Indian Society of Remote Sensing, 41, 117–125. https://doi.org/10.1007/s12524-012-0203-x.
Prakash, P., Prakash, S., Rahaman, H., Ravichandran, M., & Nayak, S. (2012). Is the trend in chlorophyll-a in the Arabian Sea decreasing? Geophysical Research Letters, 39, L23605.
Prasad, T. D. V., Latha, T. P., Rao, K. H., Choudhury, S. B., & Nagamani, P. V. (2012). Processing of Oceansat-2 Ocean colour monitor data using SeaDAS. National Remote Sensing Centre Technical Report No: NRSC/ECSA/AOSG/OSD/December-2012/TR-488.
Roxy, M. K., Modi, A., Murthugudde, R., Valsala, V., Panickal, S., Kumar, S. P., et al. (2016). A reduction in marine primary productivity driven by rapid warming over the tropical Indian Ocean. Geophysical Research Letters, 43, 826–833.
Viswanath, S. K., Tripathi, N. K., & Salin, K. R. (2018). Mapping of marine chl-a and suspended solid concentration using OCM-2 sensor. Journal of Indian Society of Remote Sensing, 46, 675–685. https://doi.org/10.1007/s12524-017-0742-2.
Zhao, Y., & He, R. (2012). Cloud-free sea surface temperature and colour reconstruction for the Gulf of Mexico: 2003–2009. Remote Sensing Letters, 3, 697–706. https://doi.org/10.1080/01431161.2012.666638.
The authors thank NICES programme of National Remote Sensing Centre, ISRO, for making the OCM-2 chlorophyll-a concentration data available. We gratefully acknowledge the support and encouragement provided by the General Manager of RRSC-East, Chief General Manager (RCs) & Deputy Director, ECSA, the Directors of NRSC and INCOIS. The authors are thankful to the anonymous reviewers and the editor for their critical comments and suggestions that has helped in improving the manuscript.
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Jayaram, C., Pavan Kumar, J., Udaya Bhaskar, T.V.S. et al. Reconstruction of Gap-Free OCM-2 Chlorophyll-a Concentration Using DINEOF. J Indian Soc Remote Sens (2021). https://doi.org/10.1007/s12524-021-01317-6
- Chlorophyll-a concentration
- Gap filling
- North Indian Ocean