Estimation of chlorophyll-a concentrations in diverse water bodies using ratio-based NIR/Red indices

Yang, Zhiming; Reiter, Michael A.; Munyei, Nicolas

doi:10.1016/j.rsase.2017.04.004

Cited by 14 publications

(17 citation statements)

References 47 publications

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“…Findings from numerous published studies have indicated that biological and chemical water quality parameters such as chlorophyll-a have distinctive spectral characteristics and can be measured using spectral indices. But these indices appear to be less reliable in diverse water bodies including lakes, ponds, rivers and streams in coastal regions [28]. A variety of spectral indices derived from remote sensing data based on empirical or semi-empirical relationships have been developed for transforming spectral data into water quality parameters.…”

Section: Introductionmentioning

confidence: 99%

“…However, the majority of spectral indices are based on the reflectance ratios of two spectral bands (near infrared and red) for operational purposes. A band ratio between the near infrared (NIR,~0.7 µm) and red (~0.6 µm) has frequently been used to estimate chlorophyll-a in waters due to a positive reflectivity of chlorophyll-a in the NIR and an inverse behavior in the red [35,36] while near infrared (NIR) and red bands are involved in most indices [28,37] . Monitoring of water quality components in coastal and inland waters (case-2 waters) is a complicated and challenging task since inflows from streams introduce different organic/inorganic sediments, which modify the physical and biological processes in coastal waters and lakes [38].…”

Section: Introductionmentioning

confidence: 99%

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An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

et al. 2018

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In-situ monitoring of lake water quality in synergy with satellite remote sensing represents the latest scientific trend in many water quality monitoring programs worldwide. This study investigated the suitability of the Operational Land Imager (OLI) instrument onboard the Landsat 8 satellite platform in accurately estimating key water quality parameters such as chlorophyll-a and nutrient concentrations. As a case study the largest freshwater body of Greece (Trichonis Lake) was used. Two Landsat 8 images covering the study site were acquired on 30 October 2013 and 30 August 2014 respectively. Near concurrent in-situ observations from two water sampling campaigns were also acquired from 22 stations across the lake under study. In-situ measurements (nutrients and chlorophyll-a concentrations) were statistically correlated with various spectral band combinations derived from the Landsat imagery of year 2014. Subsequently, the most statistically promising predictive models were applied to the satellite image of 2013 and validation was conducted using in-situ data of 2013 as reference. Results showed a relatively variable statistical relationship between the in-situ and reflectances (R logchl-a: 0.58, R NH 4 + : 0.26, R chl-a: 0.44). Correlation coefficient (R) values reported of up to 0.7 for ammonium concentrations and also up to 0.5 and up to 0.4 for chl-a concentration and chl-a concentrations respectively. These results represent a higher accuracy of Landsat 8 in comparison to its predecessors in the Landsat satellites series, as evidenced in the literature. Our findings suggest that Landsat 8 has a promising capability in estimating water quality components in an oligotrophic freshwater body characterized by a complete absence of any quantitative, temporal and spatial variance, as is the case of Trichonis lake. Yet, even with the presence of a lot of ground information as was the case in our study, a quantitatively accurate estimation of water quality constituents in coastal/inland waters remains a great challenge. The launch of sophisticated spaceborne sensing systems, such as that of Landsat 8, can assist in improving our ability to estimate freshwater lake properties from space.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

et al. 2018

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“…BRI, based on NIR/Red, has frequently been used in estimating chl-a concentration in coastal waters because there is a reflection peak in the NIR and an absorption behavior in the red of chl-a; the expression is shown in Equation 1, such as NIR (716 nm)/Red (667 nm) [10] and NIR (708 nm)/Red (665 nm) [11]. In this study, B8 and B9 are denoted by λ1, and B11 is denoted by λ2.…”

Section: Input Variablesmentioning

confidence: 99%

“…However, for case II waters, besides phytoplankton, total suspended solids (TSSs) and colored dissolved organic matter (CDOM) also affect the spectrum signal of water [9]. Thus, the ratio-based near infrared (NIR)-Red [10][11][12] and normalized difference chlorophyll index (NDCI) [12,13] were successively proposed and had proven highly reliable for the estimation of chl-a concentration for turbid productive waters. In addition, fluorescence remote sensing algorithms have been frequently used to retrieve chl-a concentration since the launch of third-generation watercolor satellites (MODIS, MERIS, etc.…”

Section: Introductionmentioning

confidence: 99%

Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Chen

et al. 2021

Remote Sensing

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Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new machine learning model, Light Gradient Boosting Machine (LightGBM), for estimating time-series chl-a concentration in Fujian’s coastal waters using multitemporal OLCI data and in situ data. We applied the Case 2 Regional CoastColour (C2RCC) processor to obtain OLCI band reflectance and constructed four spectral indices based on OLCI feature bands as supplementary input features. We also used root-mean-square error (RMSE), mean absolute error (MAE), median absolute percentage error (MAPE), and R2 as performance indicators. The results indicate that the addition of spectral indices can easily improve the prediction accuracy of the model, and normalized fluorescence height index (NFHI) has the best performance, with an RMSE of 0.38 µg/L, MAE of 0.22 µg/L, MAPE of 28.33%, and R2 of 0.785. Moreover, we used the well-known band ratio and three-band methods for chl-a estimation validation, and another two OLCI chl-a products were adopted for comparison (OC4Me chl-a and Inverse Modelling Technique (IMT) Neural Net chl-a). The results confirmed that the LightGBM model outperforms the traditional methods and OLCI chl-a products. This study provides an effective remote sensing technique for coastal chl-a concentration estimation and promotes the advantage of OLCI data in ocean color remote sensing.

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“…Other metrics used for quantifying blooms are species-specific (morphological identification), toxic metrics, and remote sense-based metrics which can estimate chlorophyll-concentration data in areas where are out of reach (Ho and Michalak, 2015). Regarding remote sensing, several studies have developed different algorithms for Chlorophyll-a estimation for use as an indicator for HABs (Bohn et al., 2018; Chen et al., 2017; Ogashawara et al., 2017; Palmer et al., 2015; Shen et al., 2012; Wang et al., 2016; Yang and Anderson, 2016; Yang et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Retrospective analysis of Chlorophyll-a and its correlation with climate and hydrological variations in Mindu Dam, Morogoro, Tanzania

Kimambo

Chikoore

Gumbo

et al. 2019

Heliyon

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The measurement of Chlorophyll-a in aquatic systems has usually correlated to harmful algae in water bodies. Harmful algal blooms (HABs) are as a result of massive proliferation of blue-green algae (Cyanobacteria). Harmful algal blooms (HABs) pose threats to both the environment as well as human health, and despite this well-known fact, their monitoring and management are still challenging. Climate change, extreme weather events, and hydrological changes are the main drivers and predicted to benefits HABs dynamics in most parts of the world. In Tanzania, studies of HABs proliferation and their possible correlation with variability in climate and hydrology still lag behind despite high demand for developing predicting tools and prevention of HABs proliferation. The present study reports on the retrospective analysis of HABs variation in Mindu Dam located in Morogoro, Tanzania using remote sensing techniques. In the present study comparison between in situ measurement and ocean color (OC2) Chlorophyll-a with the surface reflectance's (band and band combinations) of Landsat 7 and Landsat 8 Operational Land Imager (OLI), was performed. Another approach involved searching for patterns and trends, and teleconnection between Chlorophyll-a index (best band ration) and the climate and hydrological variations in the catchment. The findings demonstrated that minimum and maximum temperatures, solar radiation, Chlorophyll-a concentration registered significant increasing trends. Wind speed and directions, water levels for Mindu Dam showed a significant decreasing trend. On the other hand, rainfall showed no trend. The patterns suggest that there are link and causality between the HABs variations and meteorological parameters such as temperatures, solar radiations, and water levels. The study, therefore, contributes to the application of recent advances in remote sensing and retrospectively analysis of bloom dynamics and search for their link with climate and hydrological changes.

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Estimation of chlorophyll-a concentrations in diverse water bodies using ratio-based NIR/Red indices

Cited by 14 publications

References 47 publications

An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Retrospective analysis of Chlorophyll-a and its correlation with climate and hydrological variations in Mindu Dam, Morogoro, Tanzania

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