Operational NIR-red algorithms for estimating chlorophyll-a concentration from satellite data in inland and coastal waters

We present here results that strongly support the use of NIR-red algorithms, developed based on the spectral channels of MERIS, as standard tools for estimating chlorophyll-a (chl-a) concentration in turbid productive waters. We used an extensive set of MERIS imagery and in situ data collected between 2008 and 2010 in the Azov Sea and the Taganrog Bay, Russia. The overall estimation errors were only 5.5% of the total range of chl-a concentrations measured, illustrating the high accuracy of the MERIS-based NIR-red algorithms without the need for case-specific re-parameterization. The NIR-red algorithms were also applied to a series of images acquired by HICO over the same region in 2012–13, after the demise of MERIS. The results demonstrated the strong potential of HICO as a reliable tool for determining coastal water quality.

[1]  Anatoly A. Gitelson,et al.  Satellite Estimation of Chlorophyll-$a$ Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study , 2009, IEEE Geoscience and Remote Sensing Letters.

[2]  W. Moses.  SATELLITE-BASED ESTIMATION OF CHLOROPHYLL-a CONCENTRATION IN TURBID PRODUCTIVE WATERS , 2009 .

[3]  Anatoly A. Gitelson,et al.  HICO-Based NIR–Red Models for Estimating Chlorophyll- $a$ Concentration in Productive Coastal Waters , 2014, IEEE Geoscience and Remote Sensing Letters.

[4]  Jim Aiken,et al.  The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: Application to MERIS , 1999 .

[5]  Anatoly A. Gitelson,et al.  Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters — The Azov Sea case study , 2012 .

[6]  M. Schaepman,et al.  Review of constituent retrieval in optically deep and complex waters from satellite imagery , 2012 .

[7]  Marcos J. Montes,et al.  New algorithm for atmospheric correction of hyperspectral remote sensing data , 2001, SPIE Defense + Commercial Sensing.

[8]  Curtis D. Mobley,et al.  A numerical model for the computation of radiance distributions in natural waters with wind‐roughened surfaces , 1988 .

[9]  Anatoly A. Gitelson,et al.  Remote estimation of chl-a concentration in turbid productive waters — Return to a simple two-band NIR-red model? , 2011 .

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

[11]  M. Matthews.  A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters , 2011 .

[12]  Wesley J Moses,et al.  NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study. , 2011, Water research.

[13]  C. Yentsch,et al.  The Estimation of Phytoplankton Production in the Ocean from Chlorophyll and Light Data1 , 1957 .

[14]  Alexander A Gilerson,et al.  Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. , 2010, Optics express.

[15]  A. Gitelson,et al.  Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands , 2005 .

[16]  E. Leppäkoski,et al.  Chapter 30 European Enclosed and Semi-enclosed Seas , 2009 .

[17]  G. F. Humphrey,et al.  New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton , 1975 .