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Potential of Sentinel Images to Evaluate Physicochemical Parameters Concentrations in Water Bodies—Application in a Wetlands System in Northern Colombia

César Padilla-Mendoza
Franklin Torres-Bejarano
Gabriel Campo-Daza
1 and
Luis Carlos González-Márquez
Environmental Engineering Department, Universidad de Córdoba, Montería 230002, Colombia
Engineering and Technology Department, Universidad Autónoma de Occidente, Unidad Regional Guasave, Guasave 81048, Sinaloa, Mexico
Author to whom correspondence should be addressed.
Water 2023, 15(4), 789;
Submission received: 20 October 2022 / Revised: 30 January 2023 / Accepted: 1 February 2023 / Published: 17 February 2023
(This article belongs to the Special Issue Ecohydrological Conditions and Modeling of Wetlands)


This research demonstrated the feasibility of applying Sentinel-2 images to generate empirical models and estimate physicochemical parameters concentration, particularly nutrients in the wetland system called Bajo Sinú wetlands complex, Colombia. Spearman correlations were determined between water quality parameters, which were monitored at 17 points in the wetland on 5 February 2021, with Sentinel-2 images reflectance values from the same monitoring date; the correlations allowed the identification of statistically significant bands in the multiple linear regression algorithm implementation to determine empirical water quality models. The results show significant correlations between the optically active parameters, TSS-Turbidity, which in turn correlated with the optically inactive parameters Turbidity-NO3 and TSS-DO, as well as non-optically active parameters among themselves, TDS-NO3 and TDS-TP; the empirical models presented higher than 74.5% fit (R2), particularly DO (R2 = 0.948), NO3 (R2 = 0.858) and TP (R2 = 0.779) were the models with the highest fits (R2). These models allowed us to properly estimate the spatial distribution of nutrient-forming compounds in the wetlands complex. The determinant role played by turbidity in this type of water body is highlighted; it acts as a connecting constituent that makes the estimation of water quality parameters without spectral response through remote sensing feasible. Sentinel-2 images and multiple linear regression algorithms have been shown to be effective in estimating the concentration of water quality parameters without spectral response, such as NO3 and TP in shallow tropical wetlands, due to the processes of transformation, interaction and dependence between the different environmental variables in aquatic ecosystems.

1. Introduction

Wetlands are land extensions with saturated or water-covered soils characterized by almost zero velocities. These systems are of great ecological importance because they host an important diversity of species and provide cultural, water-regulating, and agricultural and fisheries provisioning ecosystem services, which, if well managed, boost the nearby population’s economy [1,2]. They also play an important role in nutrient and sediment retention, due to the different biogeochemical processes involved and the retention periods that change depending on their hydrological variability [3,4,5,6].
For these reasons, nutrient analysis becomes more relevant in these ecosystems [7]. In recent decades, intense human activities resulting from the urban centers’ increase in number and size and agro-industrial development have caused many surface and groundwater systems to be polluted by wastewater (treated and untreated), agricultural runoff and the increased use of nutrient-rich fertilizers [8]. This has caused and accelerated water bodies’ eutrophication, becoming the most serious and widespread problem in aquatic ecosystems worldwide [6,9,10,11,12].
Other consequences of increased nutrients in wetlands include hindering the optimal growth of fish species [13] and fostering conditions for a rise in the population density of floating macrophytes [14], as observed in the Bajo Sinú Complex during monitoring.
For nutrients in tropical wetlands study, constant monitoring has been carried out to characterize the biochemical cycle of nitrogen and phosphorus in water due to the high complexity of interrelationships with the surrounding biomass [15]; some authors analyze the mechanisms of nitrogen and phosphorus retention and accumulation in wetlands [16,17], these studies with conventional nutrient monitoring have the limitations of small scales and high economic investment [18].
In situ water quality sampling is limited to a local scale, and conventional analytical methods used to analyze nitrogen and phosphorus compounds, although widely used, are expensive and require a lot of manpower [19]. In addition, due to the limited mobility in wetlands because of their shallow depth and presence of aquatic plants, accessing the entire water surface is difficult, hindering sampling and data collection and increasing the cost and time invested in water quality monitoring [20,21,22]. Therefore, remote sensing can increase the availability of data for proper water quality assessment by providing two-dimensional maps derived from geographic information systems that would otherwise require a large number of in situ water quality monitoring stations [8]. Likewise, different studies have shown the usefulness of remote sensing for estimating nutrient concentrations in water bodies [8,23,24].
For several decades, remote sensing has been applied to assess the state of water quality in inland and coastal surface ecosystems using different remote sensors, with Landsat being the most widely applied sensor, both for optically active (e.g., suspended solids, chlorophyll, turbidity) and inactive (e.g., nutrients, dissolved oxygen) parameters [24,25,26,27,28]. MODIS images have also been of great application for the study of different water quality parameters in coastal and inland areas [29,30]. Recently, the use of Sentinel 2 images has shown potential for water quality analysis. Several authors have used Sentinel 2 images to correlate Secchi disk depth and chlorophyll a [31,32], evaluate field parameters [33] describe coastal lagoon dynamics [34], and map coral reefs [35].
Although nutrients (nitrates and phosphates) have no spectral response they can be systematically linked in aquatic environments according to their main characteristics concerning other water quality parameters [8]. Their concentration can be correlated with optically active parameters such as chlorophyll [36,37], transparency [20,38,39], turbidity, and total suspended solids [40].
Water quality parameters analysis in wetlands using satellite images helps to understand their spatial and temporal variability, sometimes identifying nutrients’ behavior in water; from another perspective, it is an economical technique that allows constant and rapid monitoring to support decision-making [33,41]; furthermore, it serves as a complement to monitoring programs [34].
In Colombia, objectives for a good ecological state of rivers, lakes and coastal waters have not been proposed as in European countries [42]. However, the National Policy for Inland Wetlands of Colombia (PNHIC in Spanish) has as one of its goals the preservation of wetlands important for biodiversity conservation at the regional and national level; and eutrophication is one of the main disturbance factors to mitigate; therefore, there is a specific goal to carry out water quality monitoring that includes nutrients [43,44].
In light of this, in this research, the objective was to show the feasibility of applying Sentinel-2 images to generate empirical models and estimate nutrient concentration in northern Colombian wetlands. The models were generated from the relationship between nitrate (NO3), total phosphorous (TP), total Suspended Solids (TSS), turbidity, total dissolved solids (TDS), and dissolved oxygen (DO) concentrations measured in the wetland and the reflectance recorded in Sentinel-2A images obtained for the Bajo Sinú wetlands complex, Colombia.

2. Materials and Methods

2.1. Study Area

The Bajo Sinú Wetlands Complex is made up of a group of wetlands located in the north of the department of Córdoba, Colombia. This complex was declared a protected area in the category of Regional District of Integral Management of Natural Resources by the Regional Autonomous Corporation of the Sinú and San Jorge Valleys (CVS for its acronym in Spanish) through agreement No. 76 of 25 October 2007, due to its ecological and economic importance [45].
The research was carried out specifically in the Guartinaja (G), Sapal (S) and Momil (M) wetlands (Figure 1), which at the time of sampling (5 February 2021) had areas of 27.10 km2, 2.96 km2 and 6.63 km2 respectively. These water bodies are delimited by the municipalities of Lorica, with a population of 115,461, Purísima with 17,587 inhabitants, Momil with 20,117 inhabitants, and Chimá, with a population of 17,905, according to data from the National Administrative Department of Statistics (DANE for its acronym in Spanish) [46].
The wetlands complex receives inputs from the Bugre and Aguas Prietas streams, which are characterized by their low water velocities when approaching the floodplain, showing a quasi-lotic behavior in dry periods [47].
The Aguas Prietas stream has a bidirectional behavior in the section from the Guartinaja wetland to the Sinú River, which means that during periods of high water levels, it delivers water to the Sinú River with flows of up to 245 m3/s; and during the summer, it receives water from the river with flows of up to 80 m3/s [48]. In its upstream section, near Chimá municipality, the Aguas Prietas stream carries an average flow of approximately 10 m3/s. The Bugre stream flow rates are lower, transporting an average of approximately 50 m3/s [48].
The wetlands complex basin has minimum multiannual precipitation of 1206.44 mm, a maximum of 1579 mm, and an annual average of 1395.5 mm, with a wet period from early April to late October; the average temperature within the basin is 27.5 °C, the average annual evapotranspiration 1300 mm, and surface wind speed 2.5 m/s (, accessed on 19 September 2022).

2.2. Environmental Data Analysis

For water quality monitoring, 17 measurement and sampling stations were distributed in the wetlands: 9 in Guartinaja, 4 in Sapal, and 4 in Momil (Figure 1). The measurement and sampling campaign was conducted on 5 February 2021 to coincide with the moment the Sentinel-2 satellite would capture the image. In situ measurements of dissolved oxygen (DO), turbidity, and total dissolved solids (TDS) were taken at all stations using a calibrated Hanna model HI9829 multiparameter [49]. In the laboratory, total suspended solids (TSS) were analyzed by the SM 2540 D method, total phosphorus (TP) by SM 4500-P B.4 and SM 4500-P E methods, and nitrate (NO3) by the SM 4500-NO3-B method.
Turbidity and TDS were chosen because these parameters allow determining the amount of both dissolved and suspended solids in the water column, and, consequently, because they have active optical properties, they can be correlated with other optically non-active parameters, such as those described below. By measuring DO, the effective amount of gaseous oxygen in water bodies, which is vital for aquatic organisms, is known. With TP, the forms of reactive phosphorus, dissolved, and organic particle fractions are quantified. Moreover NO3 was chosen considering that around the studied wetland system, there are agricultural and livestock areas that generate direct, diffuse inputs of dissolved nitrate to the water body due to runoff.
For the water samples collection and transport, the guide for monitoring discharges, surface water, and groundwater weas used [50]. Samples were collected in 1 L glass bottles at approximately 0.3 m depth from the water surface, then stored and refrigerated at 4 °C in a portable cooler for transport to the laboratory.

2.3. Satellite Data Source

Satellite data from the Sentinel-2 mission multispectral instrument (MSI), consisting of the Visible and Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) bands, were used (Table 1). The Sentinel-2A image was downloaded from the Copernicus Open Access Hub platform with radiometric and atmospheric corrections [51]. All bands used in this study with a 20 m spatial resolution were standardized to 10 m.

2.4. Statistical Analysis

The Shapiro-Wilk test was performed, and its results showed that several parameters did not present normality; therefore, a Spearman’s bilateral correlation test (r) was performed, applicable when the data are not normal, where an (r) equal or close to (1) represents parameters with direct correlation. Alternatively, values equal or close to (−1) represent indirect relationships, being a null correlation that whose value of (r) is equal to zero. These analyses were performed with R software version 4.1.2.
Water quality parameter estimation models were generated and validated by implementing a multiple linear regression technique in Matlab R2021a software [52,53]. To construct the multiple regression models, bands and band ratios that indicated a good correlation with water quality parameters were selected.
The models were selected taking into account the best correlation values, the determination coefficient (R2), and the root mean square error (RMSE) between the observed and estimated values. These parameters indicate the model’s importance and the relationship nature between the variables [41]. In addition, regression graphs were created to verify the relationship between the data observed as independent variables and those estimated by the model as the dependent variable.

2.5. Water Quality Parameters Maps

Initially, wetlands contours were extracted from the Sentinel-2A image by applying the Normalized Difference Water Index (NDWI). Then, with the QGIS software version 3.16 raster calculator, the generated statistical models were applied to obtain the spatial distribution maps of the water quality parameters’ estimated concentration.

3. Results

3.1. Water Quality Parameters

Table 2 summarizes the main statistics values obtained for the different water quality parameters measured in the stations of Guartinaja (G), Sapal (S), and Momil (M) in the wetlands complex.
DO mean and standard deviation (mean ± SD) was 5.9 ± 0.8 mg/L with a minimum value of 5.1 mg/L (station G5) and a maximum of 7.9 mg/L (station G9). The measured turbidity values were low, in the 6.5 (in M5) and 33.8 NTU range (in S5), with a mean of 18.8 ± 10.3 NTU. These DO and turbidity concentrations are similar to those reported by [33] for the same wetlands complex. TSS mean concentration in this study was 18.1 ± 10.9 mg/L with minimum values of 4.5 mg/L (in G5) and maximum values of 34 mg/L (in S5); The TDS mean obtained in this study was 197.2 ± 19.2 mg/L with maximum and minimum values of 175.9 and 225.9 mg/L. The TP showed a mean of 0.12 ± 0.04 mg/L with concentrations from 0.05–0.17 mg/L. NO3 showed a concentration range between 0.86 (station S5) and 2.35 (at station G5) with a 1.62 ± 0.56 mg/L mean.

3.2. Correlation Analysis of Water Quality Parameters

Significant correlations were present between non-optically active parameters (Figure 2) TDS–NO3 (r = 0.63) and TP–TDS (r = −0.65); this correlation was also shown by [40]. Other correlations between optically active and non-optically active parameters were observed; Turbidity–NO3 (r = −0.71) showed a strong correlation, and similar results were obtained [54]. TSS–Turbidity showed a strong positive correlation (r = 0.71), also reported by [55]. Similar results were obtained by [56], who found that turbidity and TSS had strong positive correlations with each other. TSS–TP (r = −0.08) and TSS–NO3 (r = −0.64) showed no significant correlation, a condition associated with the lack of constant water inflow due to the dry period [57].

3.3. Regression Models

Table 3 shows two models for each parameter, and the first is the best fit. It is observed that the optically active water quality parameter, TSS, was related to the visible (VIS) and near-infrared (NIR) electromagnetic spectrum bands, while turbidity was only related to the NIR bands. Similarly, parameters that have no spectral response, such as DO, TP, and TDS, were related to the VIS and NIR spectrum bands; however, NO3 was only related to the NIR spectrum bands.
In addition, TSS, turbidity, and TDS had the NIR spectrum B8 band in common in their models similar to those results obtained by [58] and [33].
Considering the parameter pairs with the highest negative correlations, such as TDS-TP and Turbidity-NO3, in their individual statistical models, the B2 band is inversely related to TP and directly related to TDS; however, the most influential bands, such as B8 and B6, showed a direct relationship in both parameters for each empirical model. A similar case occurs with the Turbidity and NO3 parameters, which particularly, in their models, presented terms with the same bands (B8 and B6) and band ratio (B8/B6), but with different signs and coefficients; this evidences the relationship between optically active and non-active water quality parameters, considering the spectral bands found individually in each empirical model.
Overall, the obtained and selected multiple regression models showed an R2 > 0.7 between the reflectance of Sentinel-2A spectral bands and the observed water quality values (Figure 3). The statistical models with the best determination coefficients were DO, TDS, and NO3 with R2 > 0.78 (Figure 3a,c,f). Turbidity, TSS, and TP models (Figure 3a,c) showed R2 greater than 0.745.

3.4. Concentration Distribution Maps

By applying the empirical models to the Sentinel-2A image, DO, turbidity, TDS, TSS, TP, and NO3 concentration maps of the Bajo Sinú wetlands complex were obtained (Figure 4). They help visualize the spatial concentration variation in the entire water body and produce relevant information for water resource management and water quality assessments.
According to the DO concentration map (Figure 4a), it can be observed that the Guartinaja wetland has concentrations lower than 4 mg/L in the water body narrow zone. The Momil and Sapal wetlands have concentrations above 4.5 mg/L in a large part of their extension. However, in the Guartinaja wetland central area, between stations G7 and G8, estimated concentrations were below the quality criteria for flora and fauna preservation established in Decree 703 of 2018 of the Environment and Sustainable Development Sector, Colombia.
The study area in the measurement date was characterized by precipitation absence and wetlands’ low water levels (<1.3 m); these conditions can increase retention times and influence nutrient concentrations variation in water bodies [7,59,60]. In the Guartinaja wetland, an abundant presence of Najas arguta, a very common plant in this wetland complex, was observed [61]; characteristics that also contribute to turbidity concentration decrease [56].
The TSS concentration map shows that high values in the Guartinaja wetland are observed to the northwest (Figure 4d); this location is close to the mouth of the Aguas prietas stream to the Sinú River. This behavior occurred because, at the time of sampling, the river was generating water inputs through the Aguas Prietas stream to the wetland, which caused the TSS increase. In the Momil and Sapal wetlands, TSS values are more homogeneous and are above 15 mg/L. The high sediment concentrations’ most obvious effect on water quality is turbidity. TSS can increase during the rainy season and decrease in the absence of rain, becoming an important parameter to determine land use in a basin [55,62].
The Guartinaja wetland TDS concentrations (Figure 4c) show values ranging 195–210 mg/L in some areas and most of the water surface with values above 225 mg/L. This is explained by the shallow water depths and the wetland bottom nature [63]; on the contrary, the Momil and Sapal wetlands show concentrations ranging from 175 to 195 mg/L.
TP and NO3 (Figure 4e,f) in the central Guartinaja wetland areas show homogeneous distributions with high concentrations. The predominant concentrations for TP (Figure 4e) are between 0.01 and 0.08 mg/L in the Guartinaja wetland; the northwestern sector values were above 0.15 mg/L; in the Momil wetland, concentrations were above 0.15 mg/L with a homogeneous distribution. However, for the Sapal wetland, the values were between 0.10 mg/L and 0.15 mg/L. NO3 values on the Momil and Sapal wetlands shores are between 1.8 mg/L and 2.4 mg/L; however, in the central areas, they are between 0.8 mg/L and 1.4 mg/L (Figure 4f). In the Guartinaja wetland, NO3 values were high, ranging between 1.8 mg/L and 3 mg/L, which could induce eutrophication processes in the wetlands. These nutrient values, compared to other studies in tropical wetlands, range from 0.0005 to 6 mg/L [64,65,66,67,68,69,70].
Some authors state that tributaries that feed this type of wetlands carry a load of nutrients from agricultural, domestic, industrial, and livestock activities [71,72,73,74]; several of these activities take place in the Bajo Sinú wetland complex basin and in the main municipalities that comprise it.

4. Discussions

4.1. Statistical Correlations between Physicochemical Parameters and Sentinel 2 Bands

Bands B4 and B5 presented a good correlation with TSS, which through B4, is related to DO. B6 and B8 bands showed a strong correlation with NO3 and a moderate correlation with TDS (Figure 5). All the defined correlations presented a significance level of p ≤ 0.05.
TSS has little influence on nitrogen; however, at high concentrations, they can influence PO4 ion retention [75,76]. The relationships between TP with other parameters may vary according to the water body. Therefore, in this study, the linear correlation between TP and turbidity was not present; however, this does not mean that the correlation does not occur, as it may exist in a non-linear way, considering that the correlations are influenced by TP concentration, the depth of the water body, pH, and chlorophyll [77]. Turbidity can occur in both algal and inorganic forms, but in this wetland complex, turbidity is caused predominantly by inorganic substances and sediment inputs from the Sinú River through the Aguas Prietas stream [78]. Only Turbidity–NO3 showed a direct correlation between an optical and a non-optically active parameter (r = −0.71).
For the TDS-NO3 correlation, bands B6 and B8 were directly related to each parameter in the empirical models. Therefore, with the correlations and empirical models, it is inferred that TSS and Turbidity concentrations, as optically active parameters [79,80], present indirect relationships through the Sentinel 2 image bands, with parameters difficult to extract spectral characteristics such as DO, NO3, TDS, and TP [81,82].
For DO, a high relationship was obtained with the VIS and NIR spectrum B4 and B5 bands, with R2 = 0.948 and RMSE = 0.182 mg/L (Table 2). The authors of [83] show that there is a relationship between the NIR-VIS spectrum bands and the DO, implementing other robust algorithms in the absence of spectral response [84].
The model obtained for TP was related to B2, B4, B6, and B8 bands. The authors [85] relate it in their TP model to the B2 band, which showed a linear combination with this parameter, and argue that, although it is an optically inactive molecule, its solubility in water and interaction with dissolved particles in the medium allows it to be related to other parameters that are optically active. Meanwhile, [86] showed that the VIS and NIR spectrum wavelengths correlate with this parameter using the multiple linear regression method.
The NO3 model bands combination shows a strong relationship with B6 and B8 bands, i.e., with the NIR electromagnetic spectrum. Recently, [85] studied, through Landsat 8 images, the total nitrogen presence in water, obtaining a statistical model relating VIS and NIR wavelengths using deep learning techniques.
As for turbidity, the bands’ linear combination with the best correlation was those relating B6 and B8 bands. The electromagnetic spectrum considered in the turbidity model followed the study conducted by [27], with the difference that in this new model, only the NIR spectrum bands were included.
Finally, TDS were associated with B2, B6, and B8 bands; the interrelationship with these bands may be associated with algae presence, suspended particles, and shallow water. These characteristics were present on the wetland complex’s monitoring date, a condition that allows the water composition to be related to the VIS and NIR bands [87,88].

4.2. Water Quality Analysis

The values of DO mean that at all measurement points, the concentrations were above 2 mg/L, which is the minimum condition for sustaining aquatic life [89,90] and above hypoxia conditions [91,92]. Concerning turbidity (and TSS) increase significantly during and immediately after rainfall; algae increase results in higher water turbidity [53]. However, in the days before monitoring, there were no rainfall events, which explains the wetlands complex’s low concentrations and turbidity values. The low TSS concentrations are associated with the dry season and low velocities in the wetlands that favor the retention time increase, facilitating sedimentation and some pollutants removal, as well as a suspended solids decrease [3]. These TDS concentrations do not affect living organisms or water availability for human consumption [93,94,95]. Its measurement with other parameters allows fish yield estimation and biomass [96].
Variations in the NO3 concentration may be due to runoff from the basin and wastewater from nearby municipalities [97]. It can also be associated with wastewater discharge, agriculture [98], and organic soils because they are a source of nitrates [99]. These types of soils are characteristic of lacustrine floodplains but don’t cause eutrophication or human health risks [100]. An increase in NO3 values was observed in the Guartinaja wetland, which might be due to low algal turbidity promoting nitrogen consumption [101], as well as the DO concentrations that help the nitrification process [3], since they promote the conversion of NH4 to NO3 [102]. Of all forms of N that can be present in water, NO3 is the most available form of nitrogen for primary producers and is the best indicator when other forms of N are unavailable [6].
Phosphorus can accumulate in basins and be mobile even after restricting the sources that generate it [103]. Phosphorus inputs are also associated with aquifer geology since wetlands are fed directly or indirectly with groundwater that interacts with sulfur and iron biogeochemistry in the subsoil, causing phosphate eutrophication [104].

5. Conclusions

A multiple linear regression algorithm was implemented to generate empirical models considering water quality parameters and reflectance values from a Sentinel-2A satellite image. In this study, significant correlations were obtained between optically active parameters, TSS-Turbidity, which in turn correlated with optically inactive parameters, Turbidity-NO3 and TSS-DO, as well as non-optically active parameters such as TDS-NO3, TDS-TP.
Remote sensing combined with traditional water quality monitoring applied in the Guartinaja, Sapal and Momil wetlands proved efficient for estimating water quality parameters concentration without spectral response, such as NO3 and TP. This possibility is due to transformation, interaction, and dependence processes existing between the different environmental variables in aquatic ecosystems. The decisive role played by turbidity in this water body type is highlighted, which acts as a connecting constituent that makes the water quality parameters estimation without spectral response by remote sensing feasible.
The empirical models obtained from the multiple regression analysis presented good fits, making the water quality parameters estimation without spectral response by remote sensing optimal because they have some bands between parameters pairs in common that show relationships such as TSS-DO with the B4, as well as Turbidity-NO3 and TDS-NO3 with the B6 and B8 bands.
The linear regressions’ high fit values between measured and estimated data made it possible to obtain high spatial resolution concentration distribution maps to identify areas with high concentrations that could lead to intense alteration processes of the natural dynamics in the Bajo Sinú wetland complex.
Nitrogen and phosphorus (TP and NO3) monitoring in wetlands is significant because they help to describe the wetland environmental status so that estimating them by combining remote sensing techniques and statistical tools generates inputs that properly represent the water body’s real conditions; this reduces time and costs for monitoring, becoming an integral analysis alternative for the continuous supervision, preservation and possible adaptation forms of these aquatic ecosystems to climate change.

Author Contributions

C.P.-M. performed the literature review, processed the Sentinel-2 images to generate empirical models, analyzed results, and original draft preparation. F.T.-B. performed the experimental design and sampling campaigns, and directed the structure of the paper. G.C.-D. organized the methodology, analyzed the data statistically, and sampling campaigns. L.C.G.-M. revised the work critically for important intellectual content. All authors have read and agreed to the published version of the manuscript.


This research and The APC was funded by Research and Extension Department of University of Córdoba, Colombia. Grant number FI-01-19.

Data Availability Statement

The information and results of the project are not yet available. But, soon the project report will be available in the reposi-tory of the University of Cordoba.


The authors acknowledge and thanks the University of Córdoba, Colombia, for funding and supporting the research project FI-01-19 through its Research and Extension Department. This collaboration was invaluable for developing and obtaining these results.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Wetland’s complex location and sampling sites.
Figure 1. Wetland’s complex location and sampling sites.
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Figure 2. Spearman correlation correlogram; significance values at *** p < 0.001, ** p < 0.01, * p < 0.05.
Figure 2. Spearman correlation correlogram; significance values at *** p < 0.001, ** p < 0.01, * p < 0.05.
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Figure 3. Correlation of observed vs. estimated data with the models. (a) dissolved oxygen (DO); (b) turbidity; (c) total dissolved solids (TDS); (d) total suspended solids (TSS); (e) total Phosphorus (TP) and (f) nitrates (NO3).
Figure 3. Correlation of observed vs. estimated data with the models. (a) dissolved oxygen (DO); (b) turbidity; (c) total dissolved solids (TDS); (d) total suspended solids (TSS); (e) total Phosphorus (TP) and (f) nitrates (NO3).
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Figure 4. Water quality maps estimated from the models. (a) dissolved oxygen (DO); (b) turbidity; (c) total dissolved solids (TDS); (d) total suspended solids (TSS); (e) total Phosphorus (TP) and (f) nitrates (NO3).
Figure 4. Water quality maps estimated from the models. (a) dissolved oxygen (DO); (b) turbidity; (c) total dissolved solids (TDS); (d) total suspended solids (TSS); (e) total Phosphorus (TP) and (f) nitrates (NO3).
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Figure 5. Relationship between analyzed water quality parameters and Sentinel-2A image bands.
Figure 5. Relationship between analyzed water quality parameters and Sentinel-2A image bands.
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Table 1. Sentinel-2 bands description.
Table 1. Sentinel-2 bands description.
BandFunctionWavelength (nm)Bandwidth (nm)Resolution (m)
1Coastal aerosol4432060
5infrared- NIR 17051520
6NIR 27401520
7NIR 37832020
8NIR 484211510
8Ainfrared narrow (NIRn)8652020
9Water vapor9452060
11SWIR 116109020
12SWIR 2219018020
Table 2. Main statistics (mean, range, and standard deviation) for the following water quality parameters: nitrate (NO3), total phosphorus (TP), total suspended solids (TSS), turbidity, total dissolved solids (TDS), and dissolved oxygen (DO) measured during the field study.
Table 2. Main statistics (mean, range, and standard deviation) for the following water quality parameters: nitrate (NO3), total phosphorus (TP), total suspended solids (TSS), turbidity, total dissolved solids (TDS), and dissolved oxygen (DO) measured during the field study.
Main StatisticsTurbidityTDSDOTSSTPNO3
Table 3. Water quality models obtained from satellite image analysis.
Table 3. Water quality models obtained from satellite image analysis.
WQPModel Derived from Regression AnalysisR2RMSE
DO−39.2556 + 0.8061/B4 + 4288.3263 × (B4 × B5) + 19.4829 × (B4/B5)0.9480.182
−3.7611 + 57.4864 × B4 + 109.5176 × B8 + 4.0720 × (B4/B5)0.7540.394
Turbidity2787.4155 − 67370 × B6 + 76850 × B8 − 3160.9521 × (B8/B6)0.7694.693
191.1427 − 11.3280 × (1/B5) + 6150.3974 × (B4) − 96330 × (B4 × B5)0.6292.459
TDS191.4556 + 3475.4249 × B6 − 1449.3128 × B8 − 58.0480 × (B2/B8)0.7834.482
175.0792 + 2384.3749 × B6 − 50.7716 × (B2/B8)0.7738.66
TSS−52.5628 + 536.2086 × B5 + 30.2611 × (B4/B8)0.7455.218
30.2457 + 649.2762 × B4 − 80.0834 × (B8/B5) + 11.2173 × (B8/B4)0.7385.281
TP1.6130 − 0.030 × (1/B2) − 12.1973 × B6 − 0.2562 × (B4/B8)0.7790.018
1.4349 − 0.0276 × (1/B2) − 11.2153 × B8 − 0.2337 × (B4/B8)0.6880.021
NO3−88.3612 + 2119.4812 × B6 − 2338.2696 × B8 + 99.7217 × (B8/B6)0.8580.200
1.8516 + 50.4735 × B6 + 9.1044 × B8 − 1.8144 × (B2/B8)0.8330.217
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Padilla-Mendoza, C.; Torres-Bejarano, F.; Campo-Daza, G.; González-Márquez, L.C. Potential of Sentinel Images to Evaluate Physicochemical Parameters Concentrations in Water Bodies—Application in a Wetlands System in Northern Colombia. Water 2023, 15, 789.

AMA Style

Padilla-Mendoza C, Torres-Bejarano F, Campo-Daza G, González-Márquez LC. Potential of Sentinel Images to Evaluate Physicochemical Parameters Concentrations in Water Bodies—Application in a Wetlands System in Northern Colombia. Water. 2023; 15(4):789.

Chicago/Turabian Style

Padilla-Mendoza, César, Franklin Torres-Bejarano, Gabriel Campo-Daza, and Luis Carlos González-Márquez. 2023. "Potential of Sentinel Images to Evaluate Physicochemical Parameters Concentrations in Water Bodies—Application in a Wetlands System in Northern Colombia" Water 15, no. 4: 789.

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