HICO-Based NIR–Red Models for Estimating Chlorophyll-A Concentration in Productive Coastal Waters Wesley J
Total Page：16
File Type：pdf, Size：1020Kb
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 1 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, and Karen W. Patterson Abstract—We present here results that demonstrate the poten- in water is a key indicator of the biophysical status of a water tial of near-infrared (NIR)–red models to estimate chlorophyll-a body (e.g., [1]) and is one of the primary water quality parame- (chl-a) concentration in coastal waters using data from the space- ters. The optical complexity of turbid productive coastal waters borne Hyperspectral Imager for the Coastal Ocean (HICO). Since the recent demise of the MEdium Resolution Imaging Spectrome- renders conventional blue–green algorithms unreliable for es- ter (MERIS), the use of sensors such as HICO has become critical timating chl-a concentration (e.g., [2]). Numerous algorithms for coastal ocean color research. Algorithms based on two- and based on analytical and semi-analytical spectral inversion tech- three-band NIR–red models, which were previously used very niques and band combinations in the red and near-infrared successfully with MERIS data, were applied to HICO images. (NIR) regions of the spectrum have been recently developed The two- and three-band NIR–red algorithms yielded accurate estimates of chl-a concentration, with mean absolute errors that and successfully validated for estimating chl-a concentration in were only 10.92% and 9.58%, respectively, of the total range of inland and coastal waters. Matthews [3] and Odermatt et al. [4] chl-a concentrations measured over a period of several months have provided comprehensive lists of such algorithms and their in 2012 and 2013 on the Taganrog Bay in Russia. Given the associated accuracy values in estimating chl-a concentration. uncertainties in the radiometric calibration of HICO, the results Many of these algorithms were regionally tuned using data illustrate the robustness of the NIR–red algorithms and validate the radiometric, spectral, and atmospheric corrections applied to from specific water bodies and are limited in their bio-geo-optic HICO data as they relate to estimating chl-a concentration in scope of application, whereas some algorithms have demon- productive coastal waters. Inherent limitations due to the charac- strated a potential for quasi-universal application to turbid and teristics of the sensor and its orbit prohibit HICO from providing productive inland and coastal waters from various locations anywhere near the level of frequent global coverage as provided [5]–[10]. by standard multispectral ocean color sensors. Nevertheless, the results demonstrate the utility of HICO as a tool for determining The aforementioned reports [3], [4] contain several algo- water quality in select coastal areas and the cross-sensor appli- rithms that were developed based on data from the MEdium cability of NIR–red models and provide an indication of what Resolution Imaging Spectrometer (MERIS). MERIS has been could be achieved with future spaceborne hyperspectral sensors a reliable tool for monitoring water quality in coastal waters. in estimating coastal water quality. The availability of a spectral channel at 708 nm made MERIS Index Terms—Chlorophyll-a, International Space Station (ISS), preferable over the MODerate resolution Imaging Spectrora- near-infrared (NIR)–red algorithms, productive coastal waters, diometer (MODIS) for estimating chl-a concentration in turbid remote sensing. productive waters, particularly at low-to-moderate chl-a con- centrations, where the results from MODIS are unreliable (e.g., I. INTRODUCTION [5] and [6]). EMOTE sensing has become a very valuable and virtually The demise of MERIS in April 2012 has caused a gap in the R indispensable tool for determining water quality in inland availability of reliable ocean color data for coastal waters. This and coastal waters. The concentration of chlorophyll-a (chl-a) data gap is crucial because no hyperspectral or multispectral sensor with similar or better spectral, spatial, and temporal characteristics is scheduled to be launched in the immediate Manuscript received September 4, 2013; revised October 10, 2013; accepted future. The Ocean Land Colour Instrument, which is MERIS’ October 14, 2013. This work was supported in part by the Office of Naval Research, through the Karles Fellowship awarded by the U.S. Naval Research replacement and will have all the spectral channels of MERIS Laboratory to W. J. Moses, and by the NASA Land Cover Land Use Change in addition to a few extra channels, is scheduled to be launched Program extended to A. A. Gitelson. W. J. Moses, J. H. Bowles, E. J. Wagner, and K. W. Patterson are with the onboard the satellite Sentinel-3 in 2014. Several hyperspectral U.S. Naval Research Laboratory, Washington, DC 20375 USA. sensors, such as the Japanese mission Hyperspectral Imager A. A. Gitelson is with the Center for Advanced Land Management Infor- SUIte (HISUI), the Italian mission PRecursore IperSpettrale mation Technologies, School of Natural Resources, University of Nebraska- Lincoln, Lincoln, NE 68583 USA. della Missione Applicativa (PRISMA), and the German mis- S. Berdnikov, V. Povazhnyi, and V. Saprygin are with the Southern Scientific sion Environmental Mapping and Analysis Program (EnMAP), Center of the Russian Academy of Sciences, 344006 Rostov-on-Don, Russia. are either under design or development, with EnMAP being Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. the closest to a launch date, which is tentatively set around Digital Object Identifier 10.1109/LGRS.2013.2287458 2017–2018. 1545-598X © 2013 IEEE This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 2 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS TABLE I DATES OF IN SITU AND SATELLITE DATA COLLECTION and the spatiotemporal dynamics of these water bodies make Fig. 1. Map of the Taganrog Bay, with screenshots of HICO images taken from the sensor’s ascending and descending orbits overlaid. The yellow dots them uniquely suitable proving grounds for developing remote represent the stations where in situ measurements were taken. sensing techniques for turbid and productive coastal and inland waters. Hyperion, which was launched in 2000, has been used Algal blooms are common in the bay and occur throughout for coastal water studies. However, its signal-to-noise ratio is the year, particularly in summer. The Taganrog Bay receives a very low [11], and the sensor is unreliable for quantitatively significant amount of freshwater input through runoff from the estimating water quality parameters due to problems such as Don River, the Kalmius River, the Mius River, and the Yeya radiometric instability [12]. River. The rapid industrial development in the watershed area The Hyperspectral Imager for the Coastal Ocean (HICO), in recent years has resulted in increased influx of terrigenous built by the U.S. Naval Research Laboratory as a low-cost pro- nutrients into the bay [15], leading to enhanced eutrophication. totype spaceborne hyperspectral sensor specifically designed Variations in the freshwater input through precipitation and for coastal waters, has been operational since October 2009. fluvial runoff affect the spatiotemporal dynamics of the algal Spaceborne hyperspectral sensors offer unique advantages over population and the species diversity in the bay [16]. Changes multispectral and airborne hyperspectral sensors for detailed in the algal biomass and the species composition have conse- coastal water analysis that can be of much help to environmen- quent effects on the fish population. Intense eutrophication has tal decision makers and managers of coastal systems. caused drastically adverse effects on the fish population and, Bearing in mind that HICO is a demonstration mission and consequently, on the local economy in the region [17]. Dedi- was not designed as an alternative to sensors such as MERIS, cated efforts have been undertaken by the local authorities to we have used a multitemporal dataset to assess the potential implement integrated watershed management plans to monitor of HICO, in the absence of MERIS, as a tool for quantita- and regulate the water quality in the Taganrog Bay and the Sea tively determining water quality in coastal waters. Previous of Azov. studies involving large datasets spanning multiple years have Researchers at the Southern Scientific Center of the Russian demonstrated the consistently high accuracy and operational Academy of Sciences, in Rostov-on-Don, Russia, undertook potential of algorithms based on reflectances in the red and NIR five in situ data collection campaigns on the Taganrog Bay in spectral channels of MERIS to estimate chl-a concentration 2012 and 2013, resulting in data from 37 stations (see Table I). in the Taganrog Bay in Russia [7]–[9]. Using a single image Water samples were collected at each station and filtered and in situ data from only eight stations, Gitelson et al. [13] through Whatman GF/F glass filters. Chl-a was extracted from showed that HICO can be used to quantitatively detect chl-a the filters using hot ethanol, and its concentration was deter- and accessory pigments