# Improving Colored Dissolved Organic Matter (CDOM) Retrievals by Sentinel2-MSI Data through a Total Suspended Matter (TSM)-Driven Classification: The Case of Pertusillo Lake (Southern Italy)

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## Abstract

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_{CDOM}(440), and reflectance band ratios using ground-based measurements. The Green-to-Red (B3/B4 and B3/B5) and Red-to-Blue (B4/B2 and B5/B2) band ratios showed good relationships (R

^{2}≥ 0.75), which were further improved according to sub-region division (R

^{2}up to 0.93). The best accuracy of B3/B4 in the match-ups between S2-MSI-derived and in situ band ratios proved the exportability on S2-MSI data of two B3/B4-based a

_{CDOM}(440) models, namely the fixed (for the whole PL) and the switching one (according to sub-region division). Although they both exhibited good agreements in a

_{CDOM}(440) retrievals (R

^{2}≥ 0.69), the switching model showed the highest accuracy (RMSE of 0.0155 m

^{−1}). Finally, the identification of areas exposed to different TSM patterns can assist with refining the calibration/validation procedures to achieve more accurate a

_{CDOM}(440) retrievals.

## 1. Introduction

_{bp}(λ)) or optimization approaches (i.e., radiative transfer models) that can increase CDOM retrieval uncertainties [8]. The empirical algorithms are based on a few basic relationships between apparent and inherent optical water properties and, thus are easier to be implemented compared with the semi-analytical ones. On the other hand, they are particularly sensitive to changes in boundary conditions [29]. For this reason, most of the regional studies for CDOM estimates in freshwaters used empirical methods (e.g., regression) based on remote sensing reflectance (Rrs) and CDOM absorption indices [30].

_{CDOM}) by S2-MSI data in several lakes of Estonia and Sweden, respectively. Xu et al. [8] and Shang et al. [30] have profitably adopted the B5 (665 nm central wavelength) to B2 (492 nm central wavelength) ratio to develop empirical CDOM models and characterize its spatial dynamics in Chinese water reservoirs. The above studies have corroborated the need to calibrate CDOM models at regional and locale scales by accounting for differences in water bio-chemical properties across geographical regions [30].

_{CDOM}(440) algorithms, namely an empirical [14] and a semi-analytical one [27] when ported on PL; (b) define a customized version of S2-MSI a

_{CDOM}(440) model by using in situ radiometric data and a

_{CDOM}(440) measurements; (c) maximize the accuracy of such a PL-tuned algorithm via a switchable scheme based on a pixel membership to the two sub-regions identified using the TSM-driven classification.

## 2. Materials and Methods

#### 2.1. Study Site

^{2}with approximately 80 m and 155 × 10

^{6}m

^{3}maximum depth and volume, respectively [40]. It serves the Apulian Aqueduct in providing drinking water to 3.5 million people as well as for irrigation and hydroelectric energy production [41].

#### PL Sub-Region Division: The ISODATA Classification

#### 2.2. In Situ Data Acquisition

_{CDOM}(440)) over the lake. Sub-surface water samples and radiometric parameters were acquired at planned stations (Figure 2) in the period from May 2017 to May 2018. Information regarding the in situ measurement campaigns (with distinction between the two different subset) is listed in Table 1.

#### 2.2.1. The a_{CDOM} (440) and TSM Measurements

_{CDOM}(λ), was obtained using Equation (1):

_{(λ)}the measured optical density and a

_{CDOM}(λ) represents the CDOM concentration at the computation wavelength λ. The CDOM spectral slope (S

_{CDOM}) was computed by using an exponential function as reported in Equation (2) [48]:

_{CDOM}is the fitted parameter for the exponential decay of a

_{CDOM}(λ) with increasing wavelength λ respect to λ

_{0}, that is the reference wavelength at 440 nm. In this study, we analyzed the spectral slope within the 350–500 nm wavelength range (i.e., S

_{CDOM}(350–500)) to ensure a sufficiently high signal-to-noise ratio [49].

_{CDOM}(440) and TSM derived from the six measurement campaigns and related to the whole area, the West and East subset, respectively.

_{CDOM}(440) values range from 0.1277 to 0.4145 m

^{−1}with higher mean values in the West subset than the East one while S

_{CDOM}(350–500 nm) records an averaged value of 0.0168 nm

^{−1}with negligible differences between sub-regions. The TSM estimations are within 0.6–7 g/m

^{3}and spatially differ with higher mean values in the West subset than the East one as for a

_{CDOM}(440).

#### 2.2.2. Radiometric Rrs(λ) Measurements

^{−1}), namely the ratio of water leaving radiance L

_{w}(θ,φ,λ) (Wm

^{−2}nm

^{−1}sr

^{−1}) to downwelling spectral irradiance Es(λ) (Wm

^{−2}nm

^{−1}) [51]:

#### 2.3. Satellite Data Acquisition and Processing

_{CDOM}(440) model. MSI is a multi-spectral instrument acquiring the emitted/reflected Earth radiance in 13 spectral bands with 10–60 m spatial resolution in the whole electromagnetic spectrum (i.e., 440–2202 nm) and 10–20 m within the visible domain of interest (i.e., 492–704 nm) for a

_{CDOM}(440) modeling (Table 3).

_{CDOM}(440) models.

#### 2.4. CDOM Estimation Algorithms

_{CDOM}(440) model, we first assessed the accuracy of two literature algorithms, namely an empirical style [14] and a semi-analytical one [27]. Among the empirical models viable in literature and tested by recent works [21,56], we selected the Ficek et al. [14] algorithm owing to its superior statistical indicators and error metrics within the satellite-in situ match-ups [56]. Such an algorithm relies on a band ratio of Green to Red, where a

_{CDOM}(440) can be defined as follows:

_{CDOM}(440) < 1 m

^{−1}) [27]. The QAA-CDOM scheme was developed from the original QAA [15,16], namely a sequential step-based scheme characterized by empirical (between Rrs and Inherent Optical Properites-IOPs) and analytical relationships.

_{CDOM}(440) can be estimated from the reported IOPs as follows:

_{p}, and a

_{w}are total, particulate, and pure water absorption, respectively, and b

_{bp}is the particulate backscattering. The above-mentioned a

_{CDOM}(440) algorithms were implemented on the in situ Rrs(λ) data considering the availability of a large enough dataset of measurements over the investigated area.

#### 2.5. Model Calibration and Validation

_{CDOM}(440) model on S2-MSI data relies on calibration/validation (cal/val) steps. Before calibration, a preliminary procedure was required to simulate the S2-MSI bands from the in-situ measured Rrs(λ) spectra. According to the spectral response functions of the S2-MSI bands [57], SRF(λ), we applied the following formula [58]:

_{e(i)}is the equivalent remote sensing reflectance for

_{i}-band of S2-MSI while Rrs(λ) is the in situ measured one; λ

_{min}and λ

_{max}are the lowest and highest wavelengths within the S2-MSI band range, respectively.

_{CDOM}(440) models. Finally, the performance of the S2-MSI-derived a

_{CDOM}(440) models was evaluated by validation match-ups with corresponding in situ a

_{CDOM}(440) measurements.

#### Performance Analysis of a_{CDOM} (440) Models

_{CDOM}(440) models firstly concerned the two literature-selected algorithms (Equations (4)–(6)). In this case, we did not apply any specific criteria for the match-up analysis because of the spatiotemporal concurrency of in situ Rrs—a

_{CDOM}(440) data used.

_{CDOM}(440) models, we considered the mean values within 3 × 3-pixel windows (hereinafter S2-MSI extracts) centered over the sampling locations. To ensure the best quality of data for validation, we retained only the S2-MSI extracts having a 50% minimum of valid pixels (i.e., at least 5 over the 9 pixels in the 3 × 3-pixel windows) [59]. Regarding the temporal criterion, we used a narrow time window (i.e., no more than ±3 h) for determining time proximity between in situ and S2-MSI data [59].

^{2}). They are defined as follows:

_{i}is the i

_{th}satellite/modeled value, y

_{i}is the i

_{th}in situ measurement, and N is the number of samples.

## 3. Results

#### 3.1. Assessment of a_{CDOM} (440) Algorithms

_{CDOM}(440) algorithms [14,27] revealed poor suitability in retrieving accurate a

_{CDOM}(440) estimations for the PL waters (a

_{CDOM}(440) < 1 m

^{−1}). The match-up analysis exhibited a clear overestimation for both the two models, as shown in Figure 3a,b.

_{CDOM}(440) models exhibited low performance, with R

^{2}between 0.23 and 0.26 and a strong overestimation and were proved by the r values ranging between 1.65 and 1.93 as well as the high values of APD (65.15–93.03%) and RMSE (0.14–0.22 m

^{−1}).

_{CDOM}(440) model that accounts for the PL optical properties. Considering the lack of measured in situ IOPs (e.g., phytoplankton absorption coefficient, a

_{ph}(λ) and/or particulate backscattering, and b

_{bp}(λ)), we aimed at developing a customized a

_{CDOM}(440) model based on an empirical-style algorithm by adopting the following cal/val procedures.

#### 3.2. Model Calibration with In Situ Rrs(λ) Data

_{e}(i.e., equivalent Rrs for S2-MSI bands) band ratios and measured a

_{CDOM}(440) values. Four literature band ratios (within the Blue–Red spectral range), namely B3/B4 [33], B3/B5 [36], B4/B2 [8], and B5/B2 [30], were retained for regression analyses. For each band ratio, the best-fit functions (i.e., linear, exponential, and power) recording the highest regression scores are reported in Table 5 with the distinction between the whole PL and the two subregions.

^{2}≥ 0.75) between in situ S2-MSI-simulated band ratios and a

_{CDOM}(440). However, when analysis was conducted according to the sub-region division (West and East subset), a further improvement in the correlation was achieved. Based on such a subset distinction, R

^{2}shifted from a maximum of 0.8 (for the whole PL) to a maximum of 0.93 and RMSE from a minimum of 0.016 to a minimum of 0.009 m

^{−1}recorded in the East subset. Focusing on the different band ratios, B3/B4 showed the best regression metrics in the PL and West subsets via the exponential and linear best-fit functions, respectively (Figure 4a,b). Otherwise, a linear relationship between B5/B2 and a

_{CDOM}(440) recorded the best performance in the East subset, as shown in Figure 4c.

#### 3.3. Model Validation with S2-MSI Data

^{2}≥ 0.74. Most of the scatterplots revealed a quite good correlation (with r values between 0.89 and 1.15), except for the B5/B2 ratio, which exhibits a clear overestimation (r = 1.41).

_{CDOM}(440) retrievals. Although the in situ B5/B2 revealed the best regression metrics of calibration for the East subset, we adopted, for validation, the B3/B4-based a

_{CDOM}(440) model (via exponential best fit), ensuring a comparable score in the regression rank (Table 5).

_{CDOM}(440) calibration models for the final validation phase. The three a

_{CDOM}(440) models refer to the whole PL, the West and East subset, respectively, and are defined as following:

#### Fixed vs. Switchable PL-Tuned Models

_{CDOM}(440) retrievals by S2-MSI data. The a

_{CDOM}(440) model defined by the Equation (12) represents a fixed scheme for all the pixels within the whole PL. The Equations (13) and (14) are used to define a “switchable” scheme of model on which the first algorithm (Equation (13)) is applied to the West subset pixels, while the second one (Equation (14)) to the East subset pixels. By exploiting an informative layer relied on a pixel membership to the two sub-regions (Figure 2), one of the two algorithms (i.e., Equation (13) or (14)) is alternatively implemented and automatically applied.

_{CDOM}(440) maps of 14 June 2017 and 12 October 2017 used for validation match-ups of the fixed (Equation (12) and switching (Equations (13) and (14)) a

_{CDOM}(440) models, respectively.

_{CDOM}(440) ≤ 0.25 m

^{−1}) with higher values in autumn than in summer as expected for reservoirs impacted by river flow fluctuations. Regardless of the model considered, a

_{CDOM}(440) shows a well-defined spatial variability with higher values in the PL’s Western side than the Eastern as already noted by the in situ a

_{CDOM}(440) measurements (Table 2).

_{CDOM}(440) values (depicted in yellow in Figure 6b) in the West sub-region. On 12 October 2017, the fixed model showed higher a

_{CDOM}(440) values (≈0.25 m

^{−1}) spatially distributed over the PL west-central zone. On both the validation days the East subset exhibited comparable a

_{CDOM}(440) spatial patterns with values not higher than 0.15–0.17 m

^{−1}.

_{CDOM}(440), even if the switching model performed better than the fixed one, exhibiting a higher determination coefficient R

^{2}(0.80 against 0.7), as shown in Figure 8a,b.

_{CDOM}(440) models are summarized in Table 7.

_{CDOM}(440) models exhibited APD values well below 10%, confirming a satisfactory accuracy in a

_{CDOM}(440) retrievals. Within an in-depth analysis, the implementation of a switching a

_{CDOM}(440) model (Equations (9) and (10)) allowed for a further improvement of the accuracy scores with APD decreasing from 8.75% to 6.79% and RMSE from 0.0194 m

^{−1}to 0.0155 m

^{−1}.

_{CDOM}(440) retrievals.

## 4. Discussion

#### 4.1. CDOM Modelling

_{CDOM}(440) model requires a preliminary performance assessment of the already published algorithms/models, considering there is no algorithm suitable for lakes at the global scale [56].

_{CDOM}(440) models with the best performance rank for the algorithm and water type according to the recently published European Space Agency (ESA) report [56]. However, both the algorithms showed large overestimations (APD higher than 65.15%) of a

_{CDOM}(440) for PL, confirming their sensitivity to changes in boundary conditions and suggesting the need to define a customized model. The lack of measured in situ IOPs (e.g., aph(λ) and/or bbp(λ)) did not allow for tuning a QAA-based model but the development of an empirical-style a

_{CDOM}(440) algorithm. The high uncertainties recorded by the Ficek et al. algorithm [14] prompted us to not work on the tuning of regression coefficients but to consider both other literature band ratios and suitable best-fit functions (i.e., linear, exponential, and power) within the calibration phase.

_{CDOM}(440), as all the best-fit functions generally showed a good determination coefficient (R

^{2}≥ 0.7) within regression analyses. The choice to separately consider the Western and Eastern sub-regions resulted profitable improvement in the performance of the a

_{CDOM}(440) models with noticeable differences in regression and error metrics (R

^{2}up to 0.93 and RMSE of approximately 0.009 m

^{−1}). The achieved results corroborated the need to identify sub-areas with similar bio-optical and topographical features to develop more customized and accurate a

_{CDOM}(440) models. However, only the match-up analysis between S2-MSI-derived and in situ band ratios allowed for a clear selection of the most suitable one for exportability on S2-MSI data. Such an analysis revealed the B3/B4 band ratio as the most suitable for estimating a

_{CDOM}(440) by S2-MSI data with good accuracy (APD of 9.86%). On the other hand, the Blue-based band ratios (i.e., B5/B2) showed minor capabilities for this purpose, probably due to the proven lower accuracy of the atmospheric correction at the blue wavelengths [12,40]. Furthermore, in this spectral region, the overlapping of phytoplankton (i.e., chlorophyll-a) and suspended matter (i.e., TSM) absorption spectra with that of CDOM could make difficult its retrievals in inland water bodies [68,69]. Selecting a relatively longer wavelength (>600 nm) as the denominator in the band ratio proved to significantly improve the accuracy of the empirical models in inland waters [29]. Since TSM usually presents high backscattering at these longer wavelengths, the exploitation of Red/Near Infrared bands allows for better accounting for particulate matter [70].

_{CDOM}(440) models, the switching scheme showed a better performance (with a minimum APD value of 6.79%) but required a further step in satellite data processing. Although the switching model needs a preliminary informative layer to work (i.e., map of the PL sub-regions), its dynamic and automated algorithm selection allows for exploiting the complementarity of two optimization algorithms by switching between them through the pixel-based membership to the two sub-regions. Despite its good accuracy, the customized switching model should be cautionary adopted especially close to the deltaic and shallower PL’s Western zone. In this sub-region, water level drawdown could determine areas of emergent bottom with frequent episodes of erosion and/or deposition [40]. Increases in reflectance contribution due to bottom and possible emergent vegetation could cause sources of noise in the Rrs(λ) spectra with potential implications on the derived switching a

_{CDOM}(440) model.

#### 4.2. Future Developments

_{CDOM}(440) retrievals by S2-MSI data. From a future perspective, the adoption of a B3/B4-based a

_{CDOM}(440) model could enable its easy exportability on Landsat 8/9 (L8/9)-Operational Land Imager (OLI). In particular, the spectral proximity of the S2-MSI Green (560 nm) and Red (665 nm) bands to the corresponding L8/9-OLI ones (at 561 nm and 655 nm, respectively) should facilitate the inter-calibration procedure to develop an MSI–OLI combined dataset of a

_{CDOM}(440) retrievals [40]. The joint exploitation of L8/9-OLI and S2-MSI data can contribute to minimizing any acquisition gap, thus ensuring an average revisit time of 2.9 days [71]. Finally, the potential exportability of such a switching a

_{CDOM}(440) model on Landsat8/9-OLI data can provide great advantages also for water quality monitoring [72] aimed at ensuring the good quality status of water bodies, as requested by the Water Framework Directive (WFD, 2000/60/EC and amendments).

_{CDOM}(440) data [40]) enabling the assessment of climate-related or human-induced PL water quality changes.

## 5. Conclusions

_{CDOM}(440) model by S2-MSI data adopting a previously achieved classification scheme on satellite TSM data [40].

^{2}≤ 0.26 and APD ≥ 65.15%) of two published a

_{CDOM}(440) algorithms (i.e., Ficek et al. [14] and QAA_CDOM [27]) suggested the development of a PL-customized version of S2-MSI a

_{CDOM}(440) model by using in situ radiometric data and a

_{CDOM}(440) measurements.

_{CDOM}(440) measurements allowed for defining the most suitable a

_{CDOM}(440) calibration models. The Green-to-Red (B3/B4 and B3/B5) and Red-to-Blue (B4/B2 and B5/B2) band ratios showed generally good performance (R

^{2}≥ 0.75), further improved when analysis was conducted according to the sub-region division (R

^{2}up to 0.93 and a minimum RMSE of approximately 0.009 m

^{−1}). Match-ups between S2-MSI-derived and in situ band ratios revealed the potential exportability of such calibration models on S2-MSI data. The unsatisfactory accuracy by B5/B2 and the good performance of B3/B4 resulted in the validation of two B3/B4-based a

_{CDOM}(440) models, namely the fixed and the switching one, which is based on a pixel membership to the two PL sub-regions identified by the TSM-driven classification. Both customized a

_{CDOM}(440) models exhibited satisfactory accuracy in a

_{CDOM}(440) retrievals, with APD values well below 10%. Within an in-depth analysis, the switching model recorded an improved accuracy with APD decreasing from 8.75% to 6.79% and RMSE from 0.0194 m

^{−1}to 0.0155 m

^{−1}as well.

_{CDOM}(440) retrievals.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A

_{rs}(λ) is the remote sensing reflectance just below water surface, a

_{w}is the pure water absorption [78,79], b

_{bw}is the pure water backscattering [80]. In this work, we adopted a

_{w}(560) = 0.062 m

^{−1}, b

_{bw}(560) = 0.000779 m

^{−1}.

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**Figure 1.**On the left is the PL location and on the right is the magnification of the study area (reported in the WGS 84 Coordinate Reference System (CRS)). The main rivers/tributaries (continuous black lines) and the bathymetry (in blue tones) are depicted. The villages (black triangles) and the onshore oil field (i.e., Centro Olio Val d’Agri) close to PL are also reported.

**Figure 2.**Map of the PL sub-regions derived from the ISODATA unsupervised classification (adapted from Ciancia et al. [40]). Dots represent the locations of the sampling stations designed for the in situ measurement campaigns. The yellow dots (from S1 to S6) are those falling into the PL western side while the red ones (from S7 to S10) are within the PL eastern side.

**Figure 4.**Most performing calibration models (in terms of R

^{2}and RMSE) for the whole PL (

**a**), the West (

**b**), and East (

**c**) subsets, respectively. The equations in the plots represent the mathematical “best-fitting” functions (dashed red line).

**Figure 5.**S2-MSI band ratios versus in situ ones for (

**a**) B3/B4, (

**b**) B3/B5, (

**c**) B4/B2, and (

**d**) B5/B2. Note that the analysis was performed without distinction between sub-regions (i.e., West and East). The regression and 1:1 lines are depicted by the continuous and dashed lines, respectively.

**Figure 6.**S2-MSI a

_{CDOM}(440) maps of 14 June 2017 for the CDOM fixed model (

**a**) and the switching one (

**b**), respectively. The black and red dots are the validation sampling stations falling into the West and East subsets, respectively.

**Figure 7.**S2-MSI a

_{CDOM}(440) maps of 12 October 2017 for the CDOM fixed model (

**a**) and the switching one (

**b**), respectively. The black and red dots are the validation sampling stations falling into the West and East subsets, respectively.

**Figure 8.**Validation match-ups of S2-MSI a

_{CDOM}(440) models based on a fixed PL-tuned algorithm (

**a**) and on a switching PL-tuned one (

**b**). The regression and 1:1 lines are depicted by the continuous and dashed lines, respectively.

**Table 1.**Sampling campaigns and data used. Note that Rrs(λ) data were not acquired in September 2017 (19th).

Measurement Campaigns | Number of Samples | In Situ Measurements | |
---|---|---|---|

West Subset | East Subset | ||

May 2017 (10th, 26th) | 12 | 8 | TSM, a_{CDOM} (440), Rrs(λ) |

June 2017 (14th, 15th) | 12 | 8 | TSM, a_{CDOM} (440), Rrs(λ) |

September 2017 (19th) | 5 | 4 | TSM, a_{CDOM} (440) |

October 2017 (12th) | 4 | 4 | TSM, a_{CDOM} (440), Rrs(λ) |

November 2017 (21st) | 4 | 4 | TSM, a_{CDOM} (440), Rrs(λ) |

May 2018 (17th) | 6 | 4 | TSM, a_{CDOM} (440), Rrs(λ) |

Parameter | Values | PL | West Subset | East Subset |
---|---|---|---|---|

a_{CDOM}(440) (m ^{−1}) | min | 0.1277 | 0.1414 | 0.1277 |

max | 0.4145 | 0.4145 | 0.2533 | |

mean | 0.2252 | 0.2450 | 0.1980 | |

stdv | 0.0678 | 0.0756 | 0.0396 | |

TSM (g/m ^{3}) | min | 0.6 | 1 | 0.6 |

max | 7 | 7 | 2.6 | |

mean | 2.0829 | 2.3679 | 1.7029 | |

stdv | 1.1270 | 1.3296 | 0.6252 |

S2-MSI Spectral Bands | Blue2 B2 | Green B3 | Red1 B4 | Red2 B5 | SWIR1 B11 |
---|---|---|---|---|---|

central wavelength (nm) | 492 | 560 | 665 | 704 | 1614 |

spatial resolution (m) | 10 | 10 | 10 | 20 | 20 |

**Table 5.**Calibration models for retrieving a

_{CDOM}(440) based on in situ S2-MSI-simulated band ratios. * Represents a statistically significant p-value < 0.001.

Dataset | Band Ratio | Function | Calibration Model | R^{2} | RMSE | n |
---|---|---|---|---|---|---|

PL | B3/B4 | exponential | y = 0.347 × exp(−0.16x) | 0.8 * | 0.016 | 28 |

B3/B5 | linear | y = −0.016x + 0.269 | 0.79 * | 0.0161 | 28 | |

B4/B2 | power | y = 0.291x^{0.537} | 0.79 * | 0.0162 | 28 | |

B5/B2 | power | y = 0.268x^{0.348} | 0.75 * | 0.0181 | 28 | |

West subset | B3/B4 | linear | y = −0.031x + 0.3 | 0.87 * | 0.012 | 15 |

B3/B5 | linear | y = −0.015x + 0.262 | 0.84 * | 0.0147 | 15 | |

B4/B2 | power | y = 0.275x^{0.505} | 0.79 * | 0.0149 | 15 | |

B5/B2 | power | y = 0.251x^{0.312} | 0.78 * | 0.0158 | 15 | |

East subset | B3/B4 | exponential | y = 0.424 × exp(−0.2x) | 0.88 * | 0.0121 | 13 |

B3/B5 | linear | y = −0.019x + 0.293 | 0.88 * | 0.0122 | 13 | |

B4/B2 | exponential | y = 0.091 × exp(1.68x) | 0.92 * | 0.009 | 13 | |

B5/B2 | linear | y = 0.302x + 0.089 | 0.93 * | 0.009 | 13 |

**Table 6.**Regression coefficients and error metrics for the four band ratios considered. * Represents a statistically significant p-value < 0.001.

Band Ratio | R^{2} | r | APD | %RMSE |
---|---|---|---|---|

B3/B4 | 0.77 * | 1.03 | 9.86 | 11.91 |

B3/B5 | 0.74 * | 0.89 | 16.90 | 23.88 |

B4/B2 | 0.94 * | 1.15 | 15.94 | 19.98 |

B5/B2 | 0.78 * | 1.41 | 41.62 | 48.25 |

**Table 7.**Regression indices and error metrics of the two PL-tuned a

_{CDOM}(440) models. * Represents a statistically significant p-value < 0.001.

Type | Dataset | CDOM Algorithm | R^{2} | r | RMSE | %RMSE | APD |
---|---|---|---|---|---|---|---|

fixed | PL | exponential | 0.7 * | 0.98 | 0.0194 | 10.52 | 8.75 |

switching | West | linear | 0.8 * | 0.99 | 0.0155 | 8.38 | 6.79 |

East | exponential |

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**MDPI and ACS Style**

Ciancia, E.; Campanelli, A.; Colonna, R.; Palombo, A.; Pascucci, S.; Pignatti, S.; Pergola, N.
Improving Colored Dissolved Organic Matter (CDOM) Retrievals by Sentinel2-MSI Data through a Total Suspended Matter (TSM)-Driven Classification: The Case of Pertusillo Lake (Southern Italy). *Remote Sens.* **2023**, *15*, 5718.
https://doi.org/10.3390/rs15245718

**AMA Style**

Ciancia E, Campanelli A, Colonna R, Palombo A, Pascucci S, Pignatti S, Pergola N.
Improving Colored Dissolved Organic Matter (CDOM) Retrievals by Sentinel2-MSI Data through a Total Suspended Matter (TSM)-Driven Classification: The Case of Pertusillo Lake (Southern Italy). *Remote Sensing*. 2023; 15(24):5718.
https://doi.org/10.3390/rs15245718

**Chicago/Turabian Style**

Ciancia, Emanuele, Alessandra Campanelli, Roberto Colonna, Angelo Palombo, Simone Pascucci, Stefano Pignatti, and Nicola Pergola.
2023. "Improving Colored Dissolved Organic Matter (CDOM) Retrievals by Sentinel2-MSI Data through a Total Suspended Matter (TSM)-Driven Classification: The Case of Pertusillo Lake (Southern Italy)" *Remote Sensing* 15, no. 24: 5718.
https://doi.org/10.3390/rs15245718