A Multidisciplinary Remote Sensing Ocean Color Sensor: Analysis of User Needs and Recommendations for Future Developments

Advancing remote sensing into the future to serve the expanding needs of the scientific community requires enhancing current capabilities of space-borne sensors and sustaining ocean color observations for diverse applications. In this study, an extensive set of experimental and space-borne measurements is analyzed for describing the operating characteristics of a global multidisciplinary ocean color sensor. Essential requirements for a successful mission include sensor characterization, high signal-to-noise ratio (SNR) and dynamic range, sensor stability, minimal polarization sensitivity, on-orbit calibration, vicarious calibration, atmospheric and in-water algorithms, product validation, and widely distributed regional and global products. To achieve high-quality products, an in-depth analysis of some key design aspects is provided which include spectral band sets, radiance levels, SNR, and dynamic range. These are essential characteristics of satellite sensors for measuring atmospheric signal in both ocean color and aerosol bands without saturation yet allowing high sensitivity measurements of water-leaving radiances and enabling ocean color measurements in aerosol and sun-glint contaminated regions of the ocean. Provisions to meet other requirements for improving retrievals of chlorophyll and phycocyanin fluorescence, partitioning algal and nonalgal color signals, and for implementing new approaches in atmospheric correction are further discussed. With these new capabilities and key design features, it will become feasible to resolve ocean color signals under most environmental circumstances and answer science questions related to changing conditions in the coastal and marine ecosystems and biogeochemical cycles due to climate change.

[1]  Quinten Vanhellemont,et al.  Variability of suspended particulate matter in the Bohai Sea from the geostationary Ocean Color Imager (GOCI) , 2012, Ocean Science Journal.

[2]  Robert A. Barnes,et al.  SeaWiFS prelaunch radiometric calibration and spectral characterization , 1995 .

[3]  Palanisamy Shanmugam,et al.  A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters , 2014 .

[4]  Lin Li,et al.  Remote quantification of phycocyanin in potable water sources through an adaptive model , 2014 .

[5]  Bo-Cai Gao,et al.  Vicarious calibrations of HICO data acquired from the International Space Station. , 2012, Applied optics.

[6]  K. Ruddick,et al.  Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8 , 2015 .

[7]  Sachidananda Mishra,et al.  A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms , 2014 .

[8]  Zhongfeng Qiu,et al.  A simple optical model to estimate suspended particulate matter in Yellow River Estuary. , 2013, Optics express.

[9]  M. Kahru,et al.  Ocean Color Chlorophyll Algorithms for SEAWIFS , 1998 .

[10]  P Jeremy Werdell,et al.  Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing. , 2010, Optics express.

[11]  Peter V. Ridd,et al.  A simple, binary classification algorithm for the detection of Trichodesmium spp. within the Great Barrier Reef using MODIS imagery , 2011 .

[12]  Chuanmin Hu,et al.  Spectral and spatial requirements of remote measurements of pelagic Sargassum macroalgae , 2015 .

[13]  Jun Zhao,et al.  Characterization of harmful algal blooms (HABs) in the Arabian Gulf and the Sea of Oman using MERIS fluorescence data , 2015 .

[14]  Y. Ahn,et al.  SeaWiFS sensing of hazardous algal blooms and their underlying mechanisms in shelf-slope waters of the Northwest Pacific during summer , 2008 .

[15]  Marvin E. Bauer,et al.  Influence of Chlorophyll and Colored Dissolved Organic Matter (CDOM) on Lake Reflectance Spectra: Implications for Measuring Lake Properties by Remote Sensing , 2006 .

[16]  P. Shanmugam,et al.  A model for estimating size-fractioned phytoplankton absorption coefficients in coastal and oceanic waters from satellite data , 2015 .

[17]  C. McClain A decade of satellite ocean color observations. , 2009, Annual review of marine science.

[18]  Zhongping Lee,et al.  Effect of spectral band numbers on the retrieval of water column and bottom properties from ocean color data. , 2002, Applied optics.

[19]  B. Nechad,et al.  CALIBRATION AND VALIDATION OF AN ALGORITHM FOR REMOTE SENSING OF TURBIDITY OVER LA PLATA RIVER ESTUARY, ARGENTINA , 2011 .

[20]  Y. Ahn,et al.  Derivation and analysis of the fluorescence algorithms to estimate phytoplankton pigment concentrations in optically complex coastal waters , 2007 .

[21]  J. Cihlar,et al.  Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors , 2002 .

[22]  D. Tanré,et al.  Remote Sensing of Tropospheric Aerosols from Space: Past, Present, and Future. , 1999 .

[23]  S. Maritorena,et al.  Consistent merging of satellite ocean color data sets using a bio-optical model , 2005 .

[24]  S. Phinn,et al.  A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans , 2014 .

[25]  Howard R. Gordon,et al.  In-Orbit Calibration Strategy for Ocean Color Sensors , 1998 .

[26]  Y. Ahn,et al.  Detecting the red tide algal blooms from satellite ocean color observations in optically complex Northeast-Asia Coastal waters , 2006 .

[27]  S. Maritorena,et al.  Atmospheric correction of satellite ocean color imagery: the black pixel assumption. , 2000, Applied optics.

[28]  B. Franz,et al.  Sensor-independent approach to the vicarious calibration of satellite ocean color radiometry. , 2007, Applied optics.

[29]  Frederick S. Patt,et al.  Assessment of tilt capability for spaceborne global ocean color sensors , 1994, IEEE Trans. Geosci. Remote. Sens..

[30]  P. Shanmugam New models for retrieving and partitioning the colored dissolved organic matter in the global ocean: Implications for remote sensing , 2011 .

[31]  S. Hooker An overview of SeaWiFS and ocean color , 1992 .

[32]  Palanisamy Shanmugam,et al.  A new theory and its application to remove the effect of surface-reflected light in above-surface radiance data from clear and turbid waters , 2014 .

[33]  Xiaoxiong Xiong,et al.  An overview of MODIS radiometric calibration and characterization , 2006 .

[34]  B. Franz,et al.  Oceanography: Century of phytoplankton change , 2010, Nature.

[35]  Y. Kaufman,et al.  Passive remote sensing of tropospheric aerosol and atmospheric , 1997 .

[36]  TANGJunwu,et al.  The statistic inversion algorithms of water constituents for the Huanghai Sea and the East China Sea , 2004 .

[37]  Y. Ahn,et al.  Reference solar irradiance spectra and consequences of their disparities in remote sensing of the ocean colour , 2007 .

[38]  David A. Siegel,et al.  Assessing requirements for sustained ocean color research and operations , 2011 .

[39]  E. Fry,et al.  Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements. , 1997, Applied optics.

[40]  R. Evans,et al.  Coastal zone color scanner “system calibration”: A retrospective examination , 1994 .

[41]  Xiaoxiong Xiong,et al.  On-Orbit Calibration and Performance of Aqua MODIS Reflective Solar Bands , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Menghua Wang Remote sensing of the ocean contributions from ultraviolet to near-infrared using the shortwave infrared bands: simulations. , 2007, Applied optics.

[43]  R. W. Austin,et al.  Nimbus-7 Coastal Zone Color Scanner: System Description and Initial Imagery , 1980, Science.

[44]  Menghua Wang,et al.  Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. , 1994, Applied optics.

[45]  J. Gower,et al.  Distribution of floating Sargassum in the Gulf of Mexico and the Atlantic Ocean mapped using MERIS , 2011 .

[46]  Sheng Ma,et al.  Merging Satellite Ocean Color Data With Bayesian Maximum Entropy Method , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[47]  Bryan A. Franz,et al.  Moderate Resolution Imaging Spectroradiometer on Terra: limitations for ocean color applications , 2008 .

[48]  H. Gordon,et al.  Atmospheric Correction of Ocean Color Imagery: Test of the Spectral Optimization Algorithm with the Sea-Viewing Wide Field-of-View Sensor. , 2001, Applied optics.

[49]  E. Boss,et al.  Bio-Optical sensors on Argo Floats. Reports of the international ocean-colour coordinating group , 2011 .

[50]  Zhongfeng Qiu,et al.  Estimating phycocyanin pigment concentration in productive inland waters using Landsat measurements: a case study in Lake Dianchi. , 2015, Optics express.

[51]  S. Maritorena,et al.  Merged satellite ocean color data products using a bio-optical model: Characteristics, benefits and issues , 2010 .

[52]  Jay Gao,et al.  Hyperspectral Remote Sensing of the Pigment C-Phycocyanin in Turbid Inland Waters, Based on Optical Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[53]  P. Shanmugam,et al.  A robust method for removal of glint effects from satellite ocean colour imagery , 2014 .

[54]  B. Franz,et al.  Evaluation of shortwave infrared atmospheric correction for ocean color remote sensing of Chesapeake Bay , 2010 .

[55]  J. Gower,et al.  Global monitoring of plankton blooms using MERIS MCI , 2008 .

[56]  Palanisamy Shanmugam,et al.  OSABT: An Innovative Algorithm to Detect and Characterize Ocean Surface Algal Blooms , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[57]  H. Gordon Remote sensing of ocean color: a methodology for dealing with broad spectral bands and significant out-of-band response. , 1995, Applied optics.

[58]  F. D’Ortenzio,et al.  Assessment of uncertainty in the ocean reflectance determined by three satellite ocean color sensors (MERIS, SeaWiFS and MODIS-A) at an offshore site in the Mediterranean Sea (BOUSSOLE project) , 2008 .

[59]  M. He,et al.  Determination of Primary Spectral Bands for Remote Sensing of Aquatic Environments , 2007, Sensors.

[60]  B. Franz,et al.  Effects of Spectral Bandpass on SeaWiFS-Retrieved Near-Surface Optical Properties of the Ocean. , 2001, Applied optics.

[61]  F. Muller‐Karger,et al.  Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters: A Practical Method , 2000 .

[62]  Menghua Wang,et al.  Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data , 2009 .

[63]  Gerhard Meister,et al.  Comparison of SeaWiFS measurements of the Moon with the U.S. Geological Survey lunar model. , 2004, Applied optics.

[64]  Ricardo M Letelier,et al.  An analysis of chlorophyll fluorescence algorithms for the moderate resolution imaging spectrometer (MODIS) , 1996 .

[65]  H. Gordon Atmospheric correction of ocean color imagery in the Earth Observing System era , 1997 .

[66]  Craig S. Tucker,et al.  Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: A quasi-analytical approach , 2013 .

[67]  T. Platt,et al.  Detection of phytoplankton pigments from ocean color: improved algorithms. , 1994, Applied optics.

[68]  P. J. Werdell,et al.  A multi-sensor approach for the on-orbit validation of ocean color satellite data products , 2006 .

[69]  P. Shanmugam A new bio-optical algorithm for the remote sensing of algal blooms in complex ocean waters , 2011 .

[70]  Christian D. Kummerow,et al.  The Remote Sensing of Clouds and Precipitation from Space: A Review , 2007 .

[71]  A. Morel,et al.  Surface pigments, algal biomass profiles, and potential production of the euphotic layer: Relationships reinvestigated in view of remote‐sensing applications , 1989 .

[72]  Zheng Qu,et al.  HATCH: results from simulated radiances, AVIRIS and Hyperion , 2003, IEEE Trans. Geosci. Remote. Sens..

[73]  T. Westberry,et al.  An algorithm for detecting Trichodesmium surface blooms in the South Western Tropical Pacific , 2011 .

[74]  Parry Moon,et al.  Proposed standard solar-radiation curves for engineering use , 1940 .

[75]  S. Andréfouët,et al.  Optical Algorithms at Satellite Wavelengths for Total Suspended Matter in Tropical Coastal Waters , 2008, Sensors.

[76]  B. Nechad,et al.  Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters , 2010 .