HICO-based NIR-red models for estimating chlorophyll-a concentration in productive coastal waters

Wesley J. Moses, Anatoly A Gitelson, Sergey Berdnikov, Jeffrey H. Bowles, Vasiliy Povazhnyi, Vladislav Saprygin, Ellen J. Wagner, Karen W. Patterson

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

We present here results that demonstrate the potential of near-infrared (NIR)-red models to estimate chlorophyll-a (chl-a) concentration in coastal waters using data from the spaceborne Hyperspectral Imager for the Coastal Ocean (HICO). Since the recent demise of the MEdium Resolution Imaging Spectrometer (MERIS), the use of sensors such as HICO has become critical for coastal ocean color research. Algorithms based on two- and three-band NIR-red models, which were previously used very successfully with MERIS data, were applied to HICO images. The two- and three-band NIR-red algorithms yielded accurate estimates of chl-a concentration, with mean absolute errors that were only 10.92% and 9.58%, respectively, of the total range of chl-a concentrations measured over a period of several months in 2012 and 2013 on the Taganrog Bay in Russia. Given the uncertainties in the radiometric calibration of HICO, the results illustrate the robustness of the NIR-red algorithms and validate the radiometric, spectral, and atmospheric corrections applied to HICO data as they relate to estimating chl-a concentration in productive coastal waters. Inherent limitations due to the characteristics of the sensor and its orbit prohibit HICO from providing anywhere near the level of frequent global coverage as provided by standard multispectral ocean color sensors. Nevertheless, the results demonstrate the utility of HICO as a tool for determining water quality in select coastal areas and the cross-sensor applicability of NIR-red models and provide an indication of what could be achieved with future spaceborne hyperspectral sensors in estimating coastal water quality.

Original languageEnglish (US)
Article number6670037
Pages (from-to)1111-1115
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume11
Issue number6
DOIs
StatePublished - Jun 1 2014

Fingerprint

Chlorophyll
Image sensors
coastal water
near infrared
chlorophyll a
Infrared radiation
ocean
sensor
Water
Sensors
MERIS
ocean color
Water quality
Spectrometers
Color
water quality
Imaging techniques
atmospheric correction
Orbits
Calibration

Keywords

  • Chlorophyll-a
  • International Space Station (ISS)
  • Near-infrared (NIR)-red algorithms
  • Productive coastal waters
  • Remote sensing

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

Cite this

Moses, W. J., Gitelson, A. A., Berdnikov, S., Bowles, J. H., Povazhnyi, V., Saprygin, V., ... Patterson, K. W. (2014). HICO-based NIR-red models for estimating chlorophyll-a concentration in productive coastal waters. IEEE Geoscience and Remote Sensing Letters, 11(6), 1111-1115. [6670037]. https://doi.org/10.1109/LGRS.2013.2287458

HICO-based NIR-red models for estimating chlorophyll-a concentration in productive coastal waters. / Moses, Wesley J.; Gitelson, Anatoly A; Berdnikov, Sergey; Bowles, Jeffrey H.; Povazhnyi, Vasiliy; Saprygin, Vladislav; Wagner, Ellen J.; Patterson, Karen W.

In: IEEE Geoscience and Remote Sensing Letters, Vol. 11, No. 6, 6670037, 01.06.2014, p. 1111-1115.

Research output: Contribution to journalArticle

Moses, WJ, Gitelson, AA, Berdnikov, S, Bowles, JH, Povazhnyi, V, Saprygin, V, Wagner, EJ & Patterson, KW 2014, 'HICO-based NIR-red models for estimating chlorophyll-a concentration in productive coastal waters', IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 6, 6670037, pp. 1111-1115. https://doi.org/10.1109/LGRS.2013.2287458
Moses, Wesley J. ; Gitelson, Anatoly A ; Berdnikov, Sergey ; Bowles, Jeffrey H. ; Povazhnyi, Vasiliy ; Saprygin, Vladislav ; Wagner, Ellen J. ; Patterson, Karen W. / HICO-based NIR-red models for estimating chlorophyll-a concentration in productive coastal waters. In: IEEE Geoscience and Remote Sensing Letters. 2014 ; Vol. 11, No. 6. pp. 1111-1115.
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