# The Effect of Mineral Sediments on Satellite Chlorophyll-a Retrievals from Line-Height Algorithms Using Red and Near-Infrared Bands

^{*}

## Abstract

**:**

^{3}and saturation of the index at Chl-a ~300 mg/m

^{3}; (3) suggested significant variability in the MCI:Chl-a relationship due to mineral scattering, resulting in an RMSE in predicted Chl-a of ~23 mg/m

^{3}; and (4) revealed elevated Chl a retrievals and potential false positive algal bloom reports for sediment concentrations > 20 g/m

^{3}. A novel approach combining both MCI and its baseline slope, MCIslope reduced the RMSE to ~5 mg/m

^{3}. A quality flag based on MCIslope was proposed to mask erroneously high Chl-a retrievals and reduce the risk of false positive bloom reports in highly turbid waters. Observations suggest the approach may be valuable for all line-height-based Chl-a algorithms.

## 1. Introduction

## 2. Methods

#### 2.1. Study Areas

#### 2.2. In Situ Biogeochemistry and Optical Measurements

_{rs}(λ, 0+) was calculated as Lu/Ed after extrapolation to the surface and correction for interactions at the air-water interface following Mueller et al. (2003) [34]. Profiles exhibiting ship shadow effects, low light levels, poor extrapolation, or otherwise erroneous profiles were quality flagged and removed from the analysis. The ECO-triplet sensors (Wetlabs, Philomath OR) were deployed for in situ spectral particulate backscatter measurements at six custom wavelengths (532, 630, 650, 676, 700 and 880 nm).

_{Total}, a*

_{Ph}, and a*

_{NAP}as the total, phytoplankton, and non-algal particulate (including both mineral and organic detrital material) absorption fractions, respectively. Here we assumed a*

_{NAP}was dominated by mineral absorption and, thus, denoted this as a*

_{MSPM}from this point on. Spectral absorption by CDOM was measured spectrophotometrically after filtration though 0.45 μm cellulose acetate filters, with distilled water at the same ambient temperature used as a reference. The ranges of measured a

_{CDOM(440)}(CDOM absorption at 440 nm) are shown in Table 1. The analysis of Chl-a was carried out by Environment and Climate Change Canada (ECCC)’s National Laboratory for Environmental Testing. Surface water was filtered through 47 mm GFC filters and frozen immediately. After extraction with 90% acetone, Chl-a concentrations were determined spectrophotometrically using the trichromatic equations in SCOR-UNESCO [37]. Suspended particulate matter (SPM) was measured gravimetrically after filtering surface water onto pre-rinsed and weighed 47 mm GFC filters. Filters were subsequently reweighed after drying overnight at 80 °C and again after ashing (3 h at 500 °C) to provide concentrations of total SPM and MSPM, respectively. Data from several inland water research and monitoring cruises between 2011 and 2015 on board the CCGS Limnos and MV Namao provided a wide range of biogeochemical and optical conditions on the two lakes (see Table 1). Table 1 presents the number of observations used in this study and the range of water constituent concentrations measured on both lakes, which were used both as input to Ecolight for model validation purposes and as a guide to determine the appropriate ranges over which reflectance was simulated.

#### 2.3. R_{rs} Modelling

_{rs}by defining the concentrations and absorption, scattering, and fluorescence properties for both Case 1 waters and for non-covarying components of Case 2 waters. This study used Ecolight, a faster version of Hydrolight which solves the azimuthally averaged radiative transfer equation to simulate nadir viewing R

_{rs}. A typical Case 2 model in Ecolight was employed, with four components: pure water (w), phytoplankton (Ph), CDOM, and MSPM. As input for the model, default water absorption and backscatter of pure water was chosen in Ecolight 5.2 [31,38]. User-defined absorption (a) and backscattering (b

_{b}) coefficients were used according to Equations (1)–(3).

_{b}* represent the concentration-specific inherent optical properties (SIOPs), [MSPM], [Chl-a] are the concentrations of MSPM, and chlorophyll-a respectively and [CDOM] is CDOM absorption at 440 nm. Considering the pigment package effect of chlorophyll-a [39,40], a chlorophyll-a dependent a*

_{Ph}(λ) was adopted as defined in Equation (3), following Bricaud et al. [41]. Concentration-specific absorption (a*) for CDOM and MSPM were determined by linear regression on their contributions to total absorption (a

_{CDOM}and a

_{MSPM}, respectively), while the A and B coefficients for a*

_{Ph}(Equation (3)) were fitted simultaneously using a least-squares optimization of the relationship between [Chl-a] and a*

_{Ph}(λ) calculated as a

_{Ph}(λ)/[Chl-a]. Multiple linear regression was used to obtain the average concentration-specific backscatter for phytoplankton (b

_{b}*

_{Ph}) and MSPM (b

_{b}*

_{MSPM}) for each of the spectral b

_{b}channels of the ECO-triplet sensors, and then linearly interpolated between those channels. With weak absorption of CDOM in the R-NIR, previous studies [24,42] have shown that a

_{CDOM(440)}< 1 m

^{−1}has limited effect on R

_{rs}simulation in this spectral range; therefore, a

_{CDOM(440)}was kept at a constant lake-average of 0.994 m

^{−1}for all simulations.

_{rs}simulation, including a constant solar zenith angle of 30°, average cloud cover of 39.5%, and an average wind speed of 4.85 m/s. The simulations consider inelastic scattering, including the chlorophyll-a fluorescence with a default fluorescence efficiency of 0.02. All simulations were set to be optically deep under the condition of negligible contribution to R

_{rs}from the substrate, a reasonable assumption for R-NIR wavelengths in turbid waters. Vertical profiles of biogeochemical constituents and optical properties were not available at all sites; therefore, constant vertical profiles equal to surface measures were used under the assumption of a vertically mixed water column. Such an assumption may not hold where calm conditions may have allowed algal biomass to accumulate at the surface under bloom conditions. The remaining parameters were set to default within Ecolight, including a RADTRAN-X sky model.

#### 2.4. MCI Calculation and Performance Assessment

_{2}, relative to a baseline extrapolated between bands either side of that spectral feature at λ

_{1}and λ

_{3}as shown in Equation (4) [14,17]:

_{1}, λ

_{2}, and λ

_{3}are centered at 681, 708 and 753 nm, respectively, for the MCI.

^{2}), root mean square error (RMSE), and mean absolute percentage error (MAPE), calculated according to Equations (5)–(7).

_{rs}, MCI, or [Chl-a] in different discussions in this study.

## 3. Results and Discussion

#### 3.1. Inherent Optical Properties

_{Ph}(Equation (3)) was defined by the A and B coefficients presented in Figure 1a. Derived a*

_{MSPM}and a*

_{CDOM}showed the typical exponential increase in absorption towards shorter wavelengths. Absorption spectra were consistent in shape and within the wide-ranging magnitude of previous inland water observations [35,40,43]. Particulate absorption measured in this study were corrected to zero NIR absorption, as has been assumed elsewhere [44,45]; however, it has been suggested that mineral sediments do exhibit significant absorption in the NIR ([46,47,48]). As such, it is possible that the a*

_{MSPM}adopted here may result in overestimations in our simulated reflectance, although the effect on simulated MCI is anticipated to be small (for example in turbid and productive water with [Chl-a] at 50 mg/m

^{3}and [MSPM] at 30 g/m

^{3}, the simulated MCI difference caused by an increase of a*

_{MSPM}at 0.005 m

^{−1}is 6.8%). Average b

_{b}*

_{MSPM}were comparable with observations of [49] and [50] but somewhat lower than reported in [43]. The magnitude and spectral shape of b

_{b}*

_{Ph}derived here fell within the range of observations from phytoplankton cultures measured by [51] and [52]. Spectral b

_{b}*

_{Ph}showed elevated backscatter at wavelengths greater than 700 nm, consistent with observations of gas vacuolate cyanobacteria [52,53,54], which are known to be prevalent in LW and LE. Small depressions in the b

_{b}*

_{MSPM}may be due to the anomalous dispersion effects due to the strong absorption features of chlorophyll-a [55] and are likely artifacts of the statistical partitioning of organic and inorganic backscatter.

#### 3.2. MCI Simulation and Validation

_{rs}between 600 nm and 885 nm at 5 nm increments and, subsequently, compute MCI for all stations. Ecolight-simulated MCI agreed well with MCI calculated on in situ measured R

_{rs}(R

^{2}= 0.885, RMSE = 9.5 × 10

^{−4}, at Y = X, Figure 2). The strong agreement between measured and modelled indices suggest here that we can have reasonable confidence in the model outcome. Nevertheless, care should be taken to consider various sources of uncertainty when interpreting the model results and in applying this approach to different systems. Figure 2 suggests some systematic underestimation of MCI especially with higher biomass. Model simulations were based on average SIOPs measured in two turbid eutrophic North American lakes and a default Chl-a fluorescence term. It is well known, however, that cyanobacteria have lower Chl-a fluorescence yield than eukaryotic phytoplankton [56], with most of the chlorophyll-a molecules belonging to the non-fluorescing photosystem I (PSI) [57]. In cyanobacteria, phycobiliproteins also contribute to fluorescence, which overlap with the spectrum of chlorophyll emission [58]. Using the default Ecolight fluorescence term may therefore overestimate R

_{rs}at 681 nm, leading to a reduction in simulated MCI as observed here. Enhancing the capabilities of Ecolight to enable multiple fluorescence terms (including phycocyanin) to be more representative of cyanobacteria dominated waters may allow further improvements in the model agreement.

_{rs}may be introduced by non-homogenous vertical distributions of cyanobacterial biomass [59].

#### 3.3. The Effect of MSPM on Simulated MCI

_{rs}were computed over the [Chl-a] and [MSPM] ranges of 0.1–300 mg/m

^{3}and 0.1–30 g/m

^{3}, respectively, consistent with the ranges of these two constituents observed in the field on LW and LE (Table 1). During the simulation, [Chl-a] had increments of 1 in the range 1–10 mg/m

^{3}, 2 from 10–30 mg/m

^{3}, 3 from 30–50 mg/m

^{3}, 5 from 50–100 mg/m

^{3}, and 10 from 100–300 mg/m

^{3}, while [MSPM] had increments of 1 from 1–30 g/m

^{3}. Figure 3a shows a non-linear relationship between simulated MCI and [Chl-a] which tends toward saturation at ~0.02 sr

^{−1}and [Chl-a] ~300 mg/m

^{3}. This is in good agreement with in situ observations of [24] from Lake of the Woods over the same [Chl-a] range. The saturation of line-height algorithms is due to the increasing contribution from absorption relative to backscatter with increasing algal/cyanobacterial biomass and the known migration of the fluorescence/backscatter peak toward longer wavelengths. Switching algorithms, such as the MPH, target the changing peak location and aim to extend the sensitivity of Chl-a detection across a broader [Chl-a] range. The series of curves in Figure 3a suggest significant sensitivity of the MCI–[Chl-a] relationship to [MSPM], leading to increases in MCI for any given [Chl-a] due to the effects of a*

_{MSPM}and b

_{b}*

_{MSPM}, which are not spectrally neutral across the MCI wavelength interval (see Figure 1). This variability in the relationship between MCI and [Chl-a] could lead to significant uncertainties in derived Chl-a under turbid water conditions.

^{3}when [MSPM] is greater than 20 g/m

^{3}, with the relative error decreasing substantially for [Chl-a] > 50 mg/m

^{3}.

#### 3.4. Global Performance Assessment of [Chl-a] Retrievals

_{rs}spectra were simulated with randomly varying concentrations of Chl-a and MSPM, approximately uniformly distributed over the ranges 0–300 mg/m

^{3}and 0–30 g/m

^{3}respectively. This dataset provides sufficient samples across the full concentration ranges observed in the field and is used to capture the joint effect of [Chl-a] and [MSPM] on MCI and assess the performance of MCI-retrieved Chl-a from a global fit to the dataset. The large population size makes it possible to test the fit of different types of regression models between MCI and Chl-a over a wide [Chl-a] range. Computed MCI were divided randomly into 10 groups for separate regressions, each group with 1000 data points, and then the coefficients and performance metrics were averaged to obtain a stable evaluation of each model fit. A number of model forms were tested to fit the MCI–[Chl-a] distribution (Figure 4a, Table 2); exponential, power, polynomial, and rational polynomial functions. All the functions fitted the MCI-[Chl-a] distribution similarly well with R

^{2}> 0.92, consistent RMSE of ~23 mg/m

^{3}and MAPE ranging from 25% for the exponential fit to 68% for the power fit. The rational polynomial regression was a variant of a typical first-order rational polynomial, keeping coefficients positive and including an intercept, consistent with the approximate theoretical solution to the MCI-[Chl-a] relationship. Because many published algorithms are based on limited in situ datasets, the relationship between MCI and [Chl-a] and indeed many of the line-height algorithms are often fitted with linear functions over a specific [Chl-a] range [1,4,22], thus this analysis provides evidence of the form of the relationship over a wide range of conditions. Figure 4b shows the resulting MAPE in [Chl-a] derived from the global exponential fit, demonstrating the underestimation of [Chl-a] at low [MSPM] and overestimation at higher [MSPM]. Consistent with Figure 3c, the greatest overestimation occurs at high [MSPM] and [Chl-a] < 50 mg/m

^{3}.

#### 3.5. A Novel Solution for Improved Chl-a Retrievals Using Both MCI and Its Slope

_{slope}is defined according to Equation (8).

^{3}, and the spectral backscatter properties of MSPM lead to an increasingly negative slope of the MCI baseline with increasing [MSPM]. Consequently, under mixed sediment-algae conditions (Figure 5c), increasing [MSPM] leads to the steepening negative slope of the MCI baseline while coincidentally increasing MCI.

_{slope}in the relationship between MCI and [Chl-a] over the range of [MSPM] shown in Figure 3a. As suggested by the individual spectra in Figure 5, MCI

_{slope}increases consistently with increasing [Chl-a], to a positive slope above [Chl-a] ~200 mg/m

^{3}. For a constant [Chl-a], MCI increases while MCI

_{slope}decreases with an increasing contribution from MSPM, with the greatest variability being for [Chl-a] < 50 mg/m

^{3}. At very low [Chl-a] (< ~2 mg/m

^{3}, Figure 6b), the relationship changes, where MCI decreases with increasing [MSPM] while the MCI

_{slope}continues to decrease. Figure 6c shows the distribution of the [Chl-a] residuals relative to modeled [Chl-a] at [MSPM] = 0, showing the large potential retrieval error when MCI

_{slope}was < ~−1.5 × 10

^{–4}nm

^{−1}. The low residual errors at MCI < 0 are again the product of the switch in the MCI:[Chl-a] relationship seen in Figure 6b. The MCI and MCI

_{slope}measured on in situ R

_{rs}show a distribution consistent with modeling results (Figure 6d).

_{slope}to reduce the uncertainties in derived [Chl-a] due to the spectral variations in R

_{rs}driven by MSPM. Figure 7a,b show the comparison of [Chl-a] derived from MCI using only an exponential model, and both MCI and MCI

_{slope}with a quadratic polynomial model. Model-simulated [Chl-a] (grey points in the figures) show a large variation around the 1:1 line in Figure 7a for the MCI only approach while the combined MCI and MCI

_{slope}approach substantially decreases the variation and forces the data closer to the 1:1 line. The combined MCI and MCI

_{slope}approach results in a significant reduction in the RMSE in derived [Chl-a], from 23.1 to 5.1 mg/m

^{3}for the simulated dataset and from 10.2 to 6.1 mg/m

^{3}for in situ derived MCI.

_{slope}approach, where the nature of the slope determination may be more sensitive to spectral atmospheric correction uncertainties than the line-height measure alone as well as errors propagated. This study has demonstrated the potential of using line-height measures and their baseline slopes to refine Chl-a retrieval algorithms using both in situ and modeled R

_{rs}. Where atmospheric correction over optically complex inland and coastal waters results in a reliable level-2 spectral R

_{rs}product, this approach may indeed provide enhanced Chl-a retrievals. However, due to the remaining challenges in atmospheric correction over highly turbid waters, as well as the successful application of line-height algorithms to TOA radiance or BRR products, we propose an alternate approach of using line-height slope parameters to define a quality flag. Slope thresholds would therefore mask potential areas of significant MSPM contamination and reduce the risk of erroneously high [Chl-a] and potential false positive algal bloom detection. A threshold MCI

_{slope}of <−1.5 × 10

^{–4}nm

^{−1}, as shown in Figure 6a,b, removes a large fraction of those Chl-a retrievals with high uncertainty due to MSPM contamination, and would limit false positive bloom detection errors while maintaining the advantages and simplicity of an MCI Chl-a retrieval. This threshold may apply to accurate atmospheric-corrected R

_{rs}products, but alternate thresholds are required for TOA radiance and/or BRR products. Furthermore, it is not yet known how a threshold might vary regionally depending on IOP variability; further work is required to confirm the stability and widespread applicability of a single threshold.

_{slope}for each day can be seen to decrease from the predominantly algal conditions of September 2017 to the sediment-laden image of May 2017. Applying a flag threshold to OLCI level-1 products of MCI > 0 mW m

^{–2}sr

^{−1}nm

^{−1}and MCI

_{slope}< −0.3 nm

^{−1}accurately masks those areas of potential sediment contamination and false positive blooms while preserving reliable Chl-a retrievals (Figure 8).

#### 3.6. Applicability to Other Line-Height Algorithms

_{rs}centered at 681 nm with bands at 665 nm and 709 nm forming the baseline [17]. The effect of MSPM on CI is consistent with that of the MCI (Figure 9), creating the potential for significant overestimates in CI-derived [Chl-a] in turbid waters. Like Figure 2, Figure 4, and Figure 7 for the MCI, Figure 9 provides the corresponding evaluation of the CI, which is very similar in many aspects to the MCI, including performing a satisfactory model simulation (Figure 9a), having a general variation in magnitude and shape of the CI:[Chl-a] relationship caused by [MSPM] (Figure 9b), and in the magnitude and distribution of the MAPE residual (Figure 9c). Consistent with the results from the MCI, a combined CI and CI

_{slope}approach results in a considerable improvement in Chl-a retrievals, with reductions in the RMSE in derived [Chl-a], from 22.2 to 2.2 mg/m

^{3}for the simulated dataset and from 9.3 to 6.0 mg/m

^{3}for in situ derived CI. Of note in Figure 9b and in agreement with observations for the MCI is the reversal of the CI:[Chl-a] relationship with MSPM at low [Chl-a], whereby MSPM at first causes a decrease in CI before increasing. This common feature suggests the unlikely occurrence of false positive bloom reports due to the low to moderate [MSPM] at low [Chl-a] using CI- or MCI-based algorithms. The OLCI images comparable to Figure 8 (not shown) suggest extreme sediment loads are also likely to cause erroneous CI-retrieved [Chl-a]. Like the MCI, variability in SIOPs used would result in some variability in the model outcomes, notably in the width of the CI range and the location of the inflection point, although the general trends would remain consistent.

## 4. Summary and Conclusions

_{rs}showed significant sensitivity to mineral sediments in the R-NIR range. At very low Chl-a (< ~2 mg/m

^{3}), model results and image observations suggest that false positive algal bloom reports are unlikely for MSPM < ~20g/m

^{3}(in fact, MCI and CI are inversely related to MSPM when Chl-a = 0 and imagery confirmed decreased MCI in moderate turbidity zones). For higher Chl-a, and/or heavily sediment laden waters with low Chl-a, the model results suggest MCI and CI are significantly enhanced by MSPM. Under conditions of mixed algal/mineral turbidity, as might be expected during wind-mixed conditions in shallow lakes (when cyanobacteria may be vertically uniform throughout the water column and sediment re-suspended from the bottom), Chl-a may be overestimated by these algorithm approaches. Wind events have been shown to lead to significant reductions in satellite-detected Chl-a due to the mixing of the surface algal biomass [61,62]. Results of the present study may further complicate the interpretation of satellite observations during wind events due to the potential for overestimated Chl-a caused by scattering-enhanced algal indices.

_{slope}(or CI and CI

_{slope}) resulted in considerable improvements to Chl-a retrieval accuracies compared with using the MCI (or CI) alone. Even if line-height slope parameters are not used to directly correct derived Chl-a, they can be used effectively as a method of flagging potentially erroneous Chl-a retrievals suspected of being contaminated by MSPM. Flag thresholds may be set on the basis of empirical evidence for both atmospherically and non-atmospherically corrected imagery. Quality flags based on a threshold MCI

_{slope}were able to accurately delineate those areas of OLCI imagery where MSPM led to erroneously high Chl-a retrievals to effectively limit false positive bloom reports.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**The average concentration-specific inherent optical properties (SIOPs) used in Ecolight for reflectance simulations: (

**a**) concentration-specific absorption coefficients, a*, and (

**b**) concentration-specific backscatter coefficients, b

_{b}*. A and B in (

**a**) are coefficients of a*

_{Ph}defined in Equation (3).

**Figure 2.**Comparison of in situ and Ecolight simulated MCI. N = 246, as described in Table 1.

**Figure 3.**The impact of MSPM on the MCI-[Chl-a] relationship. (

**a**) MCI-[Chl-a] relationship from simulated R

_{rs}; each isoline indicating a constant [MSPM] in the range of 0.1 to 30 g/m

^{3}according to the colored legend. (

**b**) [MSPM]-MCI relation with a [Chl-a] colored legend, and (

**c**) [Chl-a] residuals relative to the MCI:[Chl-a] relationship at [MSPM] = 0.

**Figure 4.**(

**a**) Ten thousand simulated MCI based on variable [Chl-a] and [MSPM] with global best-fit relationships, also reported in Table 2. (

**b**) Distribution of modelled [Chl-a] residual (MAPE) by applying the exponential fitting in (

**a**).

**Figure 5.**Modeled reflectance spectra and derived MCI and MCI

_{slope}for (

**a**) varying [Chl-a] in the absence of MSPM, (

**b**) varying [MSPM] in the absence of Chl-a, and (

**c**) [Chl-a] = 50 mg/m

^{3}and increasing [MSPM], showing the effect of MSPM on baseline slopes.

**Figure 6.**(

**a**) The variability of the MCI

_{slope}in the relationship between [Chl-a] and MCI and (

**b**) the same variability of the MCI

_{slope}in the relationship between [Chl-a] and MCI expanded to show in detail [Chl-a] < 20 mg/m

^{3}. (

**c**) The distribution of the [Chl-a] residual relative to derived [Chl-a] at [MSPM] = 0, in the [MCI, MCI

_{slope}] space, and (

**d**) in situ derived MCI and MCI

_{slope}superimposed over simulated values.

**Figure 7.**Comparison of global [Chl-a] fitting using (

**a**) only MCI and (

**b**) both MCI and its slope. The regression model used in (

**a**) is the best performance model (exponential) in Table 2. The regression model in (

**b**) is a quadratic polynomial regression from MCI and its slope to [Chl-a].

**Figure 8.**Three examples of the ocean and land colour instrument (OLCI) scenes showing an RGB true colour composite, MCIslope, L1 MCI-derived [Chl-a], MCIslope mask area overlaid on MCI, using MCI > 0 mW/(m

^{2}∙sr∙nm) and MCIslope < −0.15 nm

^{−1}.

**Figure 9.**(

**a**) Comparison of in situ and Ecolight simulated CI, (

**b**) modeled variability of the CI:[Chl-a] relationship in response to MSPM, (

**c**) the distribution of MAPE residual when applying an exponential CI:[Chl-a] best-fit model, (

**d**) modeled and in situ [Chl-a] from a global best-fit regression, and (

**e**) modeled [Chl-a] from combined CI and CI

_{slope}regression.

**Table 1.**A summary of in situ optical and water quality parameters from lakes Erie (LE) and Winnipeg (LW).

# Observations | Water Constituent | Concentration | |||||
---|---|---|---|---|---|---|---|

MEAN | MIN | MAX | SD | ||||

LE | Water | 372 | Chl-a (mg/m^{3}) | 13.1 | 0.5 | 161 | 20.4 |

IOPs | 292 | MSPM (g/m^{3}) | 4.3 | 0.01 | 24.7 | 5.1 | |

R_{rs} | 138 | a_{CDOM(440)} (m^{−1}) | 0.4 | 0.03 | 2.4 | 0.4 | |

LW | Water | 316 | Chl-a | 7.6 | 0.8 | 290 | 24.6 |

IOPs | 209 | MSPM | 7.0 | 0.01 | 31.6 | 6.6 | |

R_{rs} | 108 | a_{CDOM(440)} | 1.8 | 0.26 | 5.5 | 0.8 |

**Table 2.**Comparison of different theoretical models for the relationship between [Chl-a] and MCI. Note that MCI was multiplied by 10

^{3}for the coefficients below.

Model (Chl=) | a | b | c | R^{2} | RMSE (mg/m^{3}) | MAPE (%) |
---|---|---|---|---|---|---|

$a\xb7{e}^{b\xb7MCI}+c$ | 103 | 0.0685 | −96.8 | 0.928 | 23.1 | 25.5 |

$a\xb7MC{I}^{b}+c$ | 1.93 | 1.67 | 15.7 | 0.928 | 23.1 | 68.2 |

$a\xb7MC{I}^{2}+b\times MCI+c$ | 0.51 | 4.34 | 11 | 0.929 | 23 | 39.9 |

$a\xb7MCI/\left(b-MCI\right)+c$ | 332 | 41.8 | 3.09 | 0.928 | 23.2 | 32.1 |

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## Share and Cite

**MDPI and ACS Style**

Zeng, C.; Binding, C.
The Effect of Mineral Sediments on Satellite Chlorophyll-a Retrievals from Line-Height Algorithms Using Red and Near-Infrared Bands. *Remote Sens.* **2019**, *11*, 2306.
https://doi.org/10.3390/rs11192306

**AMA Style**

Zeng C, Binding C.
The Effect of Mineral Sediments on Satellite Chlorophyll-a Retrievals from Line-Height Algorithms Using Red and Near-Infrared Bands. *Remote Sensing*. 2019; 11(19):2306.
https://doi.org/10.3390/rs11192306

**Chicago/Turabian Style**

Zeng, Chuiqing, and Caren Binding.
2019. "The Effect of Mineral Sediments on Satellite Chlorophyll-a Retrievals from Line-Height Algorithms Using Red and Near-Infrared Bands" *Remote Sensing* 11, no. 19: 2306.
https://doi.org/10.3390/rs11192306