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Determination of Chlorophyll Content and Trophic State of Lakes Using Field Spectrometer and IRS-1C Satellite Data in the Mecklenburg Lake District, Germany
Abstract
Multitemporal data sets of the LISS-III sensor mounted on the Indian Remote Sensing Satellite (IRS-1C) and field reflectance spectra have been evaluated for estimating chlorophyll-a content in lakes. The results were compared to laboratory analyses of in situ water samples. Quantification from field reflectance spectra was carried out using the 678 nm absorption maximum and the 705 nm reflectance peak. For the evaluation of the LISS-III satellite data three approaches were compared: spectral height of the green peak, supervised maximum likelihood classification, and linear spectral unmixing. The latter gave the best results with the highest certainty measure of R2=0.85 and was applied to all five LISS-III data sets. The results are maps of the chlorophyll-a content in 10 μg/l classes for each of the five dates. For comparison of the accuracy of the different methods for water quality analysis, the trophic state index was calculated based on chlorophyll-a determination from laboratory, field spectra, and satellite data. Regarding the five lakes for which all the data were available, each method shows similar results for the estimation of trophic state
... Over the past four decades, remote sensing technology combined with various advanced computer algorithms and multi-source satellite images has made an unprecedented rapid development, and become a valuable technique for TSI monitoring from time and space [8,[10][11][12][13]. However, because of the satellite platform, sensor performance, atmospheric conditions, and many other irresistible factors, satellite remote sensing approaches have their own limitations. ...
... Chlorophyll a presents unique spectral characteristic with noticeable peaks in the blue (nearly at 440 nm) and red wavelengths (at nearly 675 nm), representing the physical basis for Chla estimation from blue-to-green ratios or red-to-near infrared (NIR) ratios of remote sensing reflectance of inland waters. Moreover, three-band, four-band, quasi-analytical algorithm (QAA), and machine learning algorithms have been developed to derive Chla [11,13,15,20]. More studies chose to use Chla to indicate the trophic level of water rather than SDD or TP [2,13,21]. ...
... MRE = 42.43%, RMSE = 1.53 µg L −1 ), consistent with previously reported band ratios [11,19], indicating the good performance of the band ratio model. The relationship between the estimated and measured TSI for each parameter is shown in Fig. 6(a), (b), (c). ...
The Trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Estimating TSI by remote sensing is still a challenge due to the multivariate complexity of the eutrophication process. A comprehensive in situ spectral-biogeochemical dataset for 7 lakes in Northeast China was collected in October 2020. The dataset covers trophic states from oligotrophic to eutrophic, with a wide range of total phosphorus (TP, 0.07–0.2 mg L⁻¹), Secchi disk depth (SDD, 0.1–0.78 m), and chlorophyll a (Chla, 0.11–20.41 μg L⁻¹). Here, we propose an empirical method to estimate TSI from remote sensing data. First, TP, SDD, and Chla were estimated by band ratio/band combination models. Then TSI was estimated using the Carlson model with a high R² (0.88), a low RMSE (3.87), and a low MRE (6.83%). Synergistic effects between TP, SDD, and Chla dominated the trophic state, changed the distribution of light in the water column, affected the spectral characteristics. Furthermore, the contribution of each parameter for eutrophication were different among the studied lakes from ternary plot. High Chla concentration was the main reason for eutrophication in HMT Lake with 45.4% of contribution more than the other two parameters, However, in XXK Lake, high TP concentrations were the main reason for eutrophication with 66.8% of contribution rather than Chla and SDD. Overall, the trophic state was dominated by TP, and SDD accounted for 85.6% of contribution in all sampled lakes. Additionally, we found using one-parameter index to evaluate the lake trophic state will lead to a great deviation, even with two levels of difference. Therefore, multi-parameter TSI is strongly recommended for the lake trophic state assessment. Summarily, our findings provide a theoretical and methodological basis for future large-scale estimations of lake TSI using satellite image data, help with water quality monitoring and management.
... The remotely sensed signal used in empirical modeling may be obtained from a portable spectrometer or a satellite image. Portable spectrometers may be handheld or attached to a vessel and are deployed to carry out reflectance measurements above the water surface (Jiao et al., 2006;Thiemann and Kaufmann, 2000;Rundquist et al., 1996). Portable spectrometers are hyperspectral (i.e. are able to measure reflectances across several bands of spectra) with the advantage of being able to record extremely narrow wavebands allowing for detection of very small variations in received energy (Dahanayaka et al., 2014). ...
... The protocol used was that at each station the spectrometer was pointed vertically up to the source of light (sun), then a dark measurement was taken to carry out internal calibration then the spectrometer was pointed perpendicular to the water surface to take the sample measurement. Caution was taken to capture spectra away from sun glint and boat shadow (Thiemann and Kaufmann, 2000). The reflected radiation field over the collected samples in the different stations was assumed to be Lambertian (Lacava et al.,2018). ...
... The results are also similar to Rundquist et al. (1996) who identified the best wavelengths for modeling Chl a to be between 530 nm and 600 nm. Similar studies have identified respective positive and negative correlations as follows: 667 nm and 719 nm (Jiao et al., 2006), 680 nm and 708 nm (Thiemann and Kaufmann, 2000), 679 nm and 706 nm (Li et al., 2002). The difference between our results and these studies could be attributed to the fact that their studies considered highly eutrophic water bodies with significantly higher concentrations of Chl a than Lake Victoria. ...
We detail our attempts at empirical modeling of MODIS derived Chlorophyll a (Chl a) distribution on Lake Victoria in East Africa and consequently its trophic status. This was motivated by the need for Lake Victoria specific algorithms, as the current satellite based standard algorithms overestimate derived Chl a. In situ Chl a data was hence collected in three field campaigns in November 2014, March 2015 and July 2015. In situ reflectances were collected during the July campaign only. We first developed models from in situ reflectances and in situ Chl a, which when applied to MODIS bands performed dismally (R² = 0.03). We then proceeded to derive empirical models by directly comparing MODIS bands with in situ Chl a based on data collected in November 2014 and July 2015. The March 2015 dataset couldn’t be used due to cloud cover hence no matchups could be obtained. The best model derived (R² = 0.88) was based on the ratio 488 nm/645 nm, and was then used to determine the trophic status of Lake Victoria using Carlson’s Chl a Trophic State Index (TSI). The results show that large areas of the lake are mesotrophic with eutrophic displays closer to the shores. The modeled TSI was then validated against in situ TSI derived from the March dataset and posted an 80% matchup. One of the main challenges, however is the prevalence of cloud cover, which hinders synoptic mapping of the lake. That notwithstanding, the study demonstrates the potential of earth observation in providing accurate TSI information for improved management of Lake Victoria.
... As algal blooms are potentially a source for misclassification, we also analyze the lake spectra with high chl-a concentration ≥20 µg/L. The occurrence of considerable algal blooms are related to an chl-a concentration of at least 20 µg/L [56]. ...
... Whereas lakes with calcite precipitation are mostly turquoise, on some dates, the lake color appears more greenish than turquoise or is even bright green color (e.g., FH on 1 June 2008). Those green colors can be explained by (a mixture of calcite precipitation and) high chl-a concentrations: Phytoplankton scatters diffusely within the algal biomass (additive effect to the spectra), but also absorbs in blue and red [10,56,57]. Lake spectra with high chl-a are characterized by an peak around 700 nm (red edge) [56]. ...
... Those green colors can be explained by (a mixture of calcite precipitation and) high chl-a concentrations: Phytoplankton scatters diffusely within the algal biomass (additive effect to the spectra), but also absorbs in blue and red [10,56,57]. Lake spectra with high chl-a are characterized by an peak around 700 nm (red edge) [56]. This peak cannot be detected using Landsat imagery because of the missing red-edge band of the Landsat sensors [42,43]. ...
Calcite precipitation is a common phenomenon in calcium-rich hardwater lakes during spring and summer, but the number and spatial distribution of lakes with calcite precipitation is unknown. This paper presents a remote sensing based method to observe calcite precipitation over large areas, which are an important prerequisite for a systematic monitoring and evaluation of restoration measurements. We use globally archived satellite remote sensing data for a retrospective systematic assessment of past multi-temporal calcite precipitation events. The database of this study consists of 205 data sets that comprise freely available Landsat and Sentinel 2 data acquired between 1998 and 2015 covering the Northeast German Plain. Calcite precipitation is automatically identified using the green spectra and the metric BGR area, the triangular area between the blue, green and red reflectance value. The validation is based on field measurements of CaCO3 concentrations at three selected lakes, Feldberger Haussee, Breiter Luzin and Schmaler Luzin. The classification accuracy (0.88) is highest for calcite concentrations ≥0.7 mg/L. False negative results are caused by the choice of a conservative classification threshold. False positive results can be explained by already increased calcite concentrations. We successfully transferred the developed method to 21 other hardwater lakes in Northeast Germany. The average duration of lakes with regular calcite precipitation is 37 days. The frequency of calcite precipitation reaches from single time detections up to detections nearly every year. False negative classification results and gaps in Landsat time series reduce the accuracy of frequency and duration monitoring, but in future the image density will increase by acquisitions of Sentinel-2a (and 2b). Our study tested successfully the transfer of the classification approach to Sentinel-2 images. Our study shows that 15 of the 24 lakes have at least one phase of calcite precipitation and all events occur between May and September. At the lakes Schmaler Luzin and Feldberger Haussee, we illustrated the influence of ecological restoration measures aiming at nutrient reduction in the lake water on calcite precipitation. Our study emphasizes the high variance of calcite precipitation in hardwater lakes: each lake has to be monitored individually, which is feasible using Landsat and Sentinel-2 time series.
... Carlson [13] developed a continuous scale 0-100 to express the trophic state of the lake based on either Secchi disk transparency, Chl-a concentration or total phosphorus content. A TSI ranged from 40 to 50 can be assigned to the mesotrophic state, whereas values of more than 70 termed as hypereutrophic conditions [14,15,48]. The following equation calculated the TSI based on Chl-a concentration that are given below [13] TSI CHL ¼ 10 6 À 2:04 À 0:68ln ChlÀa ð Þ ln 2 ! ...
... Two methods give results of TSI for the consecutive three months at the close level and between 65 and 75. The comparison of these results (Fig. 6) showed that the accuracy is satisfactory in this study which is very similar to the findings of other studies [14,48]. In consistent with the present study, the TSI of the Lakes Bramin, Kagar and Schwarz from Germany was ranged from 50 to 70 [48]. ...
... The comparison of these results (Fig. 6) showed that the accuracy is satisfactory in this study which is very similar to the findings of other studies [14,48]. In consistent with the present study, the TSI of the Lakes Bramin, Kagar and Schwarz from Germany was ranged from 50 to 70 [48]. Figure 7 gave a high correlation (R = 0.88; P \ 0.0001) of TSI determined from satellitepredicted data with TSI from laboratory reference data of the validation points with R 2 of 0.76 and RMSE of 1.11 mg/L in Nalban Lake. ...
Landsat operational land imager (OLI) data and consequent laboratory measurements were used to predict chlorophyll-a (Chl-a) concentration and the trophic states for an inland lake within the East Kolkata Wetland, India (a Ramsar site). The most suitable band ratio was identified by performing Pearson correlation analysis between Chl-a concentrations and possible OLI band and band ratios from the study points. The results showed highest correlation coefficient from the band ratio OLI5/OLI4 with an R value of 0.85. The prediction model was then developed by applying regression analysis between the band ratio OLI5/OLI4 and Chl-a concentration of the study points. The reflectance ratios of the validation points were given as input on the prediction model and the model output was considered as predicted Chl-a values of the validation points to check the efficiency of the prediction model. The regression model between laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points revealed a high correlation with an R² value of 0.78. Trophic State Index (TSI) of the lake was also calculated from laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points. The study presented a high correlation of TSI determined from predicted data with TSI from laboratory reference data (R = 0.88). The TSI values of the lake ranged from 65 to 75 which indicate that the lake is appeared to be eutrophic to hypereutrophic conditions. This empirical study showed that Landsat 8 OLI imagery can be effectively applied to estimate Chl-a levels and trophic states for inland lakes.
... In the assessment of water quality of any aquatic system, a number of parameters are considered important. Some of these parameters include Chlorophyll_a (Chl_a) that exists in all algae groups and is also an indicator of bio production of inland water bodies (Thiemann and Kaufmann, 2000;Odermatt et al, 2010); turbidity which is caused by soil erosion and leads to a concentration of suspended particulate material (SPIM) and Dissolved Organic Matter (DOM) in freshwater that absorbs light and affects water transparency. Lake Surface Temperature (LST) is important because it gives an indication of a lake's biological and chemical activity (MacCallum and Merchant, 2012;Stefouli and Charou, 2012). ...
... As already mentioned, Chl_a is contained in all species of phytoplankton and can be regarded as the total amount of phytoplankton biomass (Thiemann and Kaufmann, 2000). Chl_a enables the monitoring of the mass generation of phytoplankton and is used as an indicator of eutrophication (Koponen et al., 2001). ...
... Carlson's index is one of the common indices used to categorize trophic levels (Trophic State Index) in fresh waters. The Carlson's index for lakes (Carlson, 1977) yields continuous values scaled between 0 and 100, based either on secchi disk transparency, Chl_a concentration or total phosphorus content (Thiemann and Kaufmann, 2000). The index enables the comparison of the trophic state of lakes where only one parameter is measured and is a good measure for the nutrient supply and change detection in eutrophic waters (Thiemann and Kaufmann, 2000).The Carlson index for Chl_a uses the algal biomass as an objective classifier of a lake's trophic status (Carlson, 1977). ...
Lake Victoria is one of the key ecosystems in East Africa. With a size of 68,800 km2, it is the largest lake in Africa. It supports the livelihoods of more than 20 million people directly and indirectly as a source of portable water and fish, for recreation, industrial use etc. This renders the monitoring of its water quality of paramount interest. Traditionally water quality testing is carried out by in-situ measurements or taking of water samples for further testing in the laboratory. This approach has been seen to be costly, cumbersome, it is irregularly carried out and does not give a synoptic perspective of the water quality variation on Lake Victoria, especially given its size. This has motivated the need to explore the use of MODIS satellite imagery in monitoring water quality on the lake. This paper explores the use of archived MODIS satellite imagery to study Lake Surface Temperature (LST) and Chlorophyl_a (Chl_a) variation from 2003 – 2010. The results show that from the time series dataset, in general the northern region of the lake exhibits annual seasonal LST variation which can be characterized as bimodal. These seasonal peaks coincide with the occurrence of the region’s rain season, which information could potentially be useful in modeling experiments. The Ocean Color (OC v5) algorithm was used to extract Chl_a from the dataset. The daily Chl_a extracts were averaged over a year and mapped. These annual images were then reclassified according to Carlton’s Index for Chl_a. The results show that on average, closer to the shores the lake is largely hypertrophic whereas the lake is largely eutrophic. The lake also exhibited traces of Mesotrophic behaviour in some of the years. This has potential implications about the identification of breeding/fishing zones. These results show that the use of satellite imagery in monitoring water quality, its challenges notwithstanding, can be operationalized for the effective management of Lake Victoria.
... Instead of individual parameters, several studies (e.g., Morel and Prieur, 1977;Gurlin et al., 2011;Huang et al., 2014;Sass et al., 2007;Thiemann and Kaufmann, 2000;Yin et al., 2018) have also provided empirical relationships expressed as band combinations or baseline methods to acquire Chl a, transparency or nutrients related to potential TSI calculations in regional lakes. However, the accuracy of these empirical relationships for transferring knowledge from some representative lakes to large-scale lake groups is limited by large uncertainties (i.e., in areas with different water quality concentrations and atmospheric component influences, fewer lakes can be used with more heterogeneous influences and uniform algorithms) (Oliver et al., 2017). ...
... It is difficult and costly to make field measurements in lakes in remote locations. The TSI calculation does not need all of these trophic parameters, but just one, e.g., Chl a (Thiemann and Kaufmann, 2000), SDD (Olmanson et al., 2008;Song et al., 2020), TP (Kutser et al., 1995) and total absorption coefficients (Lee et al., 1999;Shi et al., 2019). There have been many lake studies (Chl a and SDD, Sheela et al., 2011;Chl a, SDD and TP, Song et al., 2012) where two or three water quality parameters were mapped, which would allow us to subsequently gather them to calculate a comprehensive TSI. ...
Assessing eutrophication in lakes is of key importance, as this parameter constitutes a major aquatic ecosystem integrity indicator. The trophic state index (TSI), which is widely used to quantify eutrophication, is a universal paradigm in the scientific literature. In this study, a methodological framework is proposed for quantifying and mapping TSI using the Sentinel Multispectral Imager sensor and fieldwork samples. The first step of the methodology involves the implementation of stepwise multiple regression analysis of the available TSI dataset to find some band ratios, such as blue/red, green/red and red/red, which are sensitive to lake TSI. Trained with in situ measured TSI and match-up Sentinel images, we established the XGBoost of machine learning approaches to estimate TSI, with good agreement (R2= 0.87, slope = 0.85) and fewer errors (MAE = 3.15 and RMSE = 4.11). Additionally, we discussed the transferability and applications of XGBoost in three lake classifications: water quality, absorption contribution and reflectance spectra types. We selected XGBoost to map TSI in 2019–2020 with good-quality Sentinel-2 Level-1C images embedded in the ESA to examine the spatiotemporal variations of the lake trophic state. In a large-scale observation, 10 m TSI products from 555 lakes in China facing eutrophication and unbalanced spatial patterns associated with lake basin characteristics, climate and anthropogenic activities were investigated. The methodological framework proposed herein could serve as a useful resource for continuous, long-term and large-scale monitoring of lake aquatic ecosystems, supporting sustainable water resource management.
... The studied lakes are located about 100 km north of Berlin ( Figure 1). The lakes within this area cover various sizes, shapes, depths and a wide range in trophic state and biogeochemical characteristics ( Table 1) [30]. Several of these lakes have been previously measured by the European Multi Lake Survey (EMLS) [31], which confirms the diversity in the biogeochemical characterization observed in the early 2000s. ...
... The studied lakes are located about 100 km north of Berlin ( Figure 1). The lakes within this area cover various sizes, shapes, depths and a wide range in trophic state and biogeochemical characteristics (Table 1) [30]. Several of these lakes have been previously measured by the European Multi Lake Survey (EMLS) [31], which confirms the diversity in the biogeochemical characterization observed in the early 2000s. ...
Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can be used to improve monitoring, especially after the launch of the MultiSpectral Instrument (MSI) on Sentinel-2. In this study, we compared the estimation of chlorophyll-a (chl-a) from different bio-optical algorithms using hyperspectral proximal remote sensing measurements, from simulated MSI responses and from an MSI image. For the satellite image, we also compare different atmospheric corrections routines before the comparison of different bio-optical algorithms. We used in situ data collected in 2019 from 97 sampling points across 19 different lakes. The atmospheric correction assessment showed that the performances of the routines varied for each spectral band. Therefore, we selected C2X, which performed best for bands 4 (root mean square error—RMSE = 0.003), 5 (RMSE = 0.004) and 6 (RMSE = 0.002), which are usually used for the estimation of chl-a. Considering all samples from the 19 lakes, the best performing chl-a algorithm and calibration achieved a RMSE of 16.97 mg/m3. When we consider only one lake chain composed of meso-to-eutrophic lakes, the performance improved (RMSE: 10.97 mg/m3). This shows that for the studied meso-to-eutrophic waters, we can reliably estimate chl-a concentration, whereas for oligotrophic waters, further research is needed. The assessment of chl-a from space allows us to assess spatial dynamics of the environment, which can be important for the management of water resources. However, to have an accurate product, similar optical water types are important for the overall performance of the bio-optical algorithm.
... Several studies have attempted to employ remote sensing technology to assess lake TSI, which greatly limits traditional approaches (Matthews et al., 2012;Sass et al., 2007;Sheela et al., 2011;Song et al., 2012;Thiemann and Kaufmann, 2000;Watanabe et al., 2015;Wezernak et al., 1976). Wezernak et al. (1976) demonstrated that remote sensing technology has great potential for assessing the water TSI of inland waters and discussed the concept, framework and structure of TSI remote sensing for the first time. ...
... Olmanson et al. (2008) built a 20year SDD database of Minnesota's 10,000 lakes using Landsat imagery and then assessed the variations in TSI based on the remotely estimated SDD from 1985 to 2005 for these lakes. Thiemann and Kaufmann (2000) assessed the TSI of five lakes in the Mecklenburg Lake District (Germany) using Chla values determined from Indian Remote Sensing (IRS) LISS III data. Similarly, Watanabe et al. (2015) evaluated the TSI of the Barra Bonita Hydroelectric reservoir by means of Chla derived from Landsat 8 OLI imagery. ...
The trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Thus, information on the spatial and temporal distribution of TSI is critical for supporting scientifically sound water resource management decisions. We proposed a semi-analytical approach to remotely estimate TSI based on Landsat 8 OLI data for inland waters. The approach has two major steps: deriving the total absorption coefficient of optically active constituents (OACs) and building the relationship between the total absorption coefficient and TSI. First, version 6.0 of the Quasi-Analytical Algorithm (QAA_V6, developed by Zhongping Lee) was implemented with Landsat 8 OLI data to derive the total absorption coefficients of the OACs. Second, we modeled TSI using the total absorption coefficients of OACs at 440 nm based on a large in situ measurement dataset. The total absorption coefficient of OACs at 440 nm gave satisfactory validation results for modeling TSI with a mean absolute percent error of 6% and a root-mean-square error of 5.77. Then, we performed this approach in three inland waters with various eutrophic statuses to validate its results, and the approach demonstrated a robust and satisfactory performance. Finally, an application of the approach was demonstrated in Lake Qiandaohu. Our semi-analytical approach has a sound optical mechanism and extensive application for different trophic inland waters.
... For instance, the well-recognised ambit values ranged from 0 to 1 NTU, 1 to 5 NTU, 5 to 10 NTU and 10 to 1000 NTU corresponding to the low, average, high and extremely high turbidity levels (Table 2) (Chapman, 1996;Burlingame et al., 1998). The turbidity index is calculated similar to relationship of TSI (Shapiro and Maloney, 1979;Thiemann and Kaufmann, 2000) and takes the form: ...
... For deriving the equations of TSI, TI and HFI, the minimum and maximum values of respective input parameters (namely, chlorophyll concentration, turbidity and a CDOM (443) were used to fix the lower and upper limits such that each indices reflect a standardized common scale ranging from 0 to 100 (as shown in Tables 1 and 2). It must be noted that standardized equations were developed based on logarithmic relationship to achieve higher sensitivity at lower values of input parameters which yields higher accuracy and more reliable evaluation of WQI by facilitating systematic classification of the indices (Carlson, 1977;Shapiro and Maloney, 1979;Thiemann and Kaufmann, 2000). Fig. 6 shows a typical output space for WQI with fuzzified sets of triangular MFs representing the UD (along x-axis) and the degree of membership (along y-axis) of each element in the respective MFs. ...
Accurate assessment and monitoring of coastal and inland water quality by satellite optical remote sensing is challenging due to improper atmospheric correction algorithm, inaccurate quantification of in-water constituents’ concentration and a lack of efficient models to predict the water quality status. The present study aims to address the latter two parts in conjugation with an appropriate atmospheric correction algorithm to assess trophic status and water quality conditions of two coastal lagoons using Landsat-8 OLI data. Three vital underwater light attenuating factors, directly related to water quality, are considered namely, turbidity, chlorophyll and colored dissolved organic matter (aCDOM). These water quality parameters are quantified based on certain sensitive normalised water-leaving radiance band ratios and threshold values. To assess the accuracy of the derived products, these algorithms were applied to independent in-situ data and statistical evaluation of the results showed good agreement between the estimated and measured values with the errors within desirable limits. Being a primary nutrient indicator, the chlorophyll concentration was used to evaluate Trophic State Index. The Water Quality Index was derived from three parameters namely, chlorophyll concentration, turbidity and aCDOM(443) which were expressed in terms of Trophic State Index, Turbidity Index and Humic-Fulvic Index, respectively. The Water Quality Index maps, derived using a Fuzzy Inference System based on the Centre of Gravity method, provided insights into spatial structures and temporal variability of water quality conditions of the coastal lagoons which are influenced by anthropogenic factors, hydrographic changes and land-ocean-atmospheric interaction processes.
... Consequently, TSI has been applied extensively and demonstrated its superiority to assess the trophic state of urban lakes (Brown and Simpson 2001;Chen, Huang, and Tang 2020;Zhu and Mao 2021) Remote sensing has emerged as an advanced approach for assessing the trophic state of urban lakes over time and space because it provides a synoptic view of data with consistent temporal coverage (Bhagowati and Uddin Ahamad 2019). Many studies have employed remote sensing data to estimate water trophic indicators like C Chla and Z SD , and subsequently to diagnose the trophic state of lakes based on these estimations (Cheng and Lei 2001;Duan et al. 2007Duan et al. , 2008Hadjimitsis, Clayton, andToulios 2010, Ren et al. 2018;Patra et al. 2017;Thiemann and Kaufmann 2000;Watanabe et al. 2015). Intermediate indicators, such as the absorption of dissolved organic matter (a CDOM ) or the Forel-Ule Index (FUI), which exhibit a robust correlation with TSI, have also been extracted from remote sensing data to monitor lake eutrophication Zhang et al. 2018;Zhou et al. 2021). ...
... Since the introduction of remote sensing technology, remote sensing has been employed as a supplement to conventional approaches due to its convenient acquisition, long-term dynamic monitoring, and affordable characteristic. Thiemann used satellite spectral data to invert the chlorophyll-a concentration of lakes in Mecklenburg, Germany, and combined it with the Carlson model to determine the degree of eutrophication in the area [2]. Chlorophyll-a was the water parameter that researchers most frequently studied since its optically active characteristic eases the use of satellite data for monitoring, and it has a direct relationship with the occurrence of algae bloom [3][4][5][6][7][8][9][10][11][12]. ...
Water quality monitoring of medium-sized inland water is important for water environment protection given the large number of small-to-medium size water bodies in China. A case study was conducted on Yuandang Lake in the Yangtze Delta region, with a surface area of 13 km2. This study proposed utilising a multispectral uncrewed aerial vehicle (UAV) to collect large-scale data and retrieve multiple water quality parameters using machine learning algorithms. An alternate processing method is proposed to process large and repetitive lake surface images for mapping the water quality data to the image. Machine learning regression methods (Random Forest, Gradient Boosting, Backpropagation Neural Network, and Convolutional Neural Network) were used to construct separate water quality inversion models for ten water parameters. The results showed that several water quality parameters (CODMn, temperature, pH, DO, and NC) can be retrieved with reasonable accuracy (R2 = 0.77, 0.75, 0.73, 0.67, and 0.64, respectively), although others (NH3-N, BGA, TP, Turbidity, and Chl-a) have a determination coefficient (R2) less than 0.6. This work demonstrated the tremendous potential of employing multispectral data in conjunction with machine learning algorithms to retrieve multiple water quality parameters for monitoring medium-sized bodies of water.
... Up to now, several studies have attempted to use a variety of sensor data (e.g., IRS-1c, SPOT, TM, ETM, and EO-1) for water quality monitoring [19,[23][24][25][26][27]. These studies have shown that turbidity, Chl-a, and SPM could be used to indicate the deterioration of water quality caused by eutrophication [9,20,28], and water quality parameters are highly correlated with turbidity, Chl-a, and SPM [29]. ...
To estimate the key water quality parameters on a large scale, based on Pearson’s correlation analysis and band ratio, this study first obtains multiple sensitive band combinations (R ≥ 0.30, p < 0.01) for three key water quality parameters: dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Then, principal component analysis is used to reduce the dimensions and analyze multiple optimal combinations, and the first three principal components (PCs) of the optimal combinations are selected to analyze the water quality parameters. Finally, the water quality parameter models of DO, TN, and TP are proposed and compared based on spectral analysis and field measured water quality data respectively using Gaussian process regression and PCs for each parameter. Through model verification and by comparing the performance of the three models, it is found that the TP model performed well (R = 0.9824, p < 0.01), and TP grade accuracy rate is up to 94.97%. Through the error analysis of TN and DO, it is found that 93.0% of error samples occurs when TP < 0.1 mg/L in the water quality. These results would provide a scientific basis for water quality monitoring and water environment management in the study area and could also be used as a reference for water quality monitoring in other basins.
... Hence, water with different kinds and levels of pollutants differs from spectral information and the pollution level can be characterized by water quality parameters, which can be applied to retrieve parameters indicative of water quality. Using satellite-based technology to measure water parameters has been conducted for many decades [14][15][16][17][18]. However, there are two main limitations of the application of satellite imagery. ...
During accelerated urbanization, the lack of attention to environmental protection and governance led to the formation of black-odor water. The existence of urban black-odor water not only affects the cityscape, but also threatens human health and damages urban ecosystems. The black-odor water bodies are small and hidden, so they require large-scale and high-resolution monitoring which offers a temporal and spatial variation of water quality frequently, and the unmanned aerial vehicle (UAV) with a multispectral instrument is up to the monitoring task. In this paper, the Nemerow comprehensive pollution index (NCPI) was introduced to assess the pollution degree of black-odor water in order to avoid inaccurate identification based on a single water parameter. Based on the UAV-borne multispectral data and NCPI of sampling points, regression models for inverting the parameter indicative of water quality were established using three artificial intelligence algorithms, namely extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR). The result shows that NCPI is qualified to evaluate the pollution level of black-odor water. The XGBoost regression (XGBR) model has the highest fitting accuracy on the training dataset (R2 = 0.99) and test dataset (R2 = 0.94), and it achieved the best retrieval effect on image inversion in the shortest time, which made it the best-fit model compared with the RF regression (RFR) model and the SVR model. According to inversion results based on the XGBR model, there was only a small size of mild black-odor water in the study area, which showed the achievement of water pollution treatment in Guangzhou. The research provides a theoretical framework and technical feasibility for the application of the combination of algorithms and UAV-borne multispectral images in the field of water quality inversion.
... Table 7 presented several algorithms that were developed to estimate turbidity. 645 [193] 705 [201] 709 Two-band ratio ∝ ( 2 )/ ( 1 ) 565 660 [194] 678 705 [202] 565 825 [203] 545 840 [204] 550 850 [205] 645 555 [206] ∝ ( ( 1 ) + ( Chapter Three : Methodology ...
In Jordan, a country facing severe challenges in securing reliable water sources for its dramatically growing population, water quality monitoring and management becomes a fundamental obligation that the kingdom must meet effectively. Remote sensing technology is considered a useful tool for a more efficient assessment of water quality in aquatic ecosystems. Its techniques need less time, effort, and cost than traditional water quality assessment methods.
This thesis evaluates the potential of two types of remote sensing data (hyper-spectral and multispectral) to estimate Chlorophyll a, Colored dissolved organic matter (CDOM) and turbidity, taking King Talal Dam as a case study. Multispectral remote sensing data were obtained from Sentinel-2 and Landsat-8 satellites while the hyper-spectral data were measured from each water sample location using the ASD FieldSpec Hand Held-2.
While assessing water bodies using remote sensing, the atmospheric correction is recognised as a critical process because of its significant effect on the radiance received by the sensor. Therefore, one of the aims of this research is to evaluate the output of several atmospheric correction methods; Dark Spectrum Fitting (DSF), Dark Object Subtraction(DOS), Atmospheric and Topographic Correction (ATCOR), and Exponential Extrapolation(EXP) to find the satisfactory remote sensing reflectance for the estimation of Chl-a, aCDOM and Turbidity. The output results demonstrate that DOS and DSF showed the best performance for Sentinel-2 and Landsat-8, respectively.
Data from 52 surveyed sampling sites throughout the lake in (October 2018, January and July 2019) demonstrated the appropriateness of Sentinel-2 red and NIR reciprocal band product (1/B4*B6) for estimating Chl-a. This was shown through a strong correlation of corresponded field measured reflectance combination with Chl-a by a power function (R²=0.819 and RMSE=12.9 mg/m3).
For Landsat-8, it has been shown that the red-to NIR ratio (B4/B5) is the best performing model for estimating Chl-a using a power function with R²=0.7 and RMSE =39.880 mg/m3. On the other hand, the hyper-spectral data provided more accurate algorithms mainly the three-band ratio model (1/R670-1/R710)*R740) with R²=0.94, RMSE=6.57 mg/m3.
For the estimation of turbidity, it has been shown that the single red band algorithm (B4) of Sentinel-2 is appropriate for fitting the best tradeline with R²=0.812 and RMSE=1.268 NTU. However, the Blue-to-green ratio model (B1/B3) using Landsat-8 demonstrates a good efficiency to estimate turbidity with R²=0.644, RMSE=0.902 NTU. Finally, for hyper-spectral data, the two-band algorithm (R705/R678) has been identified as the best algorithm for estimating turbidity with the R²=0.803, and RMSE=1.286 NTU.
The results of aCDOM estimation have shown the weak potential of Sentinel-2, Landsat-8 and Hyperspectral data to be used to monitor our selected water quality parameters in our case of study. This may be explained by the high concentration of Chl-a in the lake; several reports establish the link between high Chl-a concentrations and low correlation with CDOM.
Mapping water quality parameters using the performance algorithms of Sentinel 2 illustrated the high effect of the adjacent land on the calculated water parameters relative to the much deeper water near the dam.
... Recently some researcher has used different satellite sensor like SPOT (Système Pour observation de la Terre)-High resolution vertical (Chacon- Torres et al. 1992;Dekker et al. 2002), IRS-1C LISS III (Linear Image Self Scanner) (Thiemann 2000), MODIS Aqua (Dorji et al. 2016); NOAA AVHRR (Advanced Very High-Resolution Radiometer) (Bolgrien 1995) to estimate the water quality, water temperature and also monitor the spatial distribution of suspended matters in dam and freshwaters. Many studies have been conducted on the Landsat series of satellite images from Landsat 4, Landsat 5 TM (Thematic Mapper), and Landsat 7 ETM + (Enhanced Thematic Mapper) to assess and evaluate the water quality parameters in surface water bodies using remote sensing techniques (Gitelson et al. 1993;Bustamante et al. 2009;Nas et al. 2010). ...
Inland lake of Vembanad has benefited from continuous monitoring to evaluate water quality which has declined due to increased anthropogenic activities and climate change. Remote sensing techniques can be used to estimate and monitor inland water quality both spatially and temporally. An empirical model is presented in Vemaband lake that retrieves the specific water quality parameters through correlations between various spectral wavelengths of Sentinel-2MSI (S2MSI) with field-measured water quality parameters. This approach includes the combinations of various bands, band ratios, and band arithmetic computation of satellite sensors of spectral datasets. The specific inland water quality parameters such as chlorophyll-a (chl-a), total suspended solids (TSS), turbidity, and secchi disc depth (SDD) were retrieved from the developed water quality model through Sentinel-2A remote sensing reflectance. The result illustrates that Specific Inland Water Quality Parameters (SIWQP) strongly correlated with S2MSI reflection spectral wavelengths. The SIWQP models are constructed for TSS (R² = 0.8008), Chl-a (R² = 0.8055), Turbidity (R² = 0.6329) and SDD (R² = 0.7174).The spatial distribution of SIWQPs in Vembanad lake for March 2018 is mapped and shows the lake's water quality distribution. The research from Sentinel-2, MSI has potential and is appropriate in high spectral and spatial characteristics for retrieving and continuous monitoring of water quality parameters in the regional scale of inland water bodies.
... Numerous studies have tried to use remote sensing imagery to retrieve trophic-state-related factors for calculating TSI. The widely used water quality parameters retrieved from remote sensing images include secchi depth (Olmanson et al., 2008;Papoutsa et al., 2014), Chl-a (Duan and Zhang, 2008;Guan et al., 2020;Thiemann and Kaufmann, 2000;Wang et al., 2008), and TP (Du et al., 2018;Song et al., 2012). In order to avoid the large bias that might be contributed from a single index, two or more indices have been adopted for trophic state evaluation by some studies (Cheng and Lei, 2001;Lillesand et al., 1983;Sheela et al., 2011). ...
Eutrophication is a severe environmental pollution problem for inland waters and poses significant threats to the water safety. Monitoring trophic state of inland waters using optical remote sensing generally requires the inversion of water quality parameters, such as chlorophyll-a, secchi depth, etc. However, the accurate inversion of these individual indicators remains challenging, while the associated retrieval errors can propagate and degrade the evaluation of trophic state. Hence, we proposed a novel monitoring method by developing a Trophic State Index (TSI) based on optical remote-sensing parameters, i.e., Forel-Ule index (FUI) and non-water absorption coefficient at 674 nm (referred to as at−w(674)) retrieved from Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery. The estimated TSI showed favorable correspondence with observed water quality data, including coefficient of determination (r² = 0.91), root mean squared error (RMSE = 5.54), and mean absolute percentage error (MAPE = 10.69%). Using the Sentinel-3 OLCI data, the proposed method also had very good performance in the field spectrum (MAPE = 5.25 % , RMSE = 3.36). The monthly trophic state evaluation also showed congruence (MAPE = 12.51 % , RMSE = 6.41) with surface water quality monthly report (SWQMR) from the Ministry of Environment and Ecology of the People's Republic of China. The monthly TSI showed favorable agreement for 23 ungauged lakes (RMSE = 7.26, MAPE = 12.78%), indicating potential utility for regional lake water quality monitoring. The proposed method was then applied to 47 other large (>50 km²) water bodies in the Middle-and-Lower watershed of Yangtze River and the Huaihe watershed to evaluate the spatial and temporal variation of trophic state from 2016 to 2020. The TSI results revealed several lakes, such as Lake Honghu and Lake Luoma, with rapidly deteriorating water quality during the study period, while other lakes show relative improvement (e.g., Xiashan Reservoir), indicating unbalanced environmental pressure over the region. Overall, this study showed promising performance and potential for satellite-based monitoring of regional aquatic environments.
... Several RS-based studies have assessed water trophic state assessments by biooptical modelling (Feng et al., 2005;Shi et al., 2019). Many researchers monitored water trophic state by using a water quality parameter (WQP) retrieved from satellite data, i.e., through satellite-retrieved chlorophyll-a (Chl-a) (Duan et al., 2007;Matthews & Odermatt, 2015;Thiemann & Kaufmann, 2000), or Secchi disk depth (SDD) (Christiana et al., 2014). ...
With the rapid development of economy, many lakes in Wuhan have been polluted to different degrees and suffer from eutrophication. The main objective of this study was to conduct the monthly trophic state assessments of waters in Wuhan from March 2019 to June 2020 using 111 Sentinel-2 images. The Forel-Ule index (FUI) and empirical Gaussian process regression (GPR) were, respectively, applied to obtain monthly area percentage (AP) of waters with each trophic state. Both FUI-derived and empirical GPR-retrieved results showed that majority of water bodies (>90%) in Wuhan were in mesotrophic and eutrophic states. The GPR-retrieved results based on FUI-derived water types were more reliable than the retrieval without classification, which reduced RMSE of trophic-level index (TLI) from 9.2 to 5.8, and MAPE from 14% to 9% (N = 213). Severe eutrophication occurred in the summer and early autumn (June–October). Stepwise multiple linear regression analysis indicated that temperature and wind speed were the two most important meteorological factors influencing eutrophication variability: the temperature accounted for 63% and 55% dynamic eutrophication from FUI-derived and GPR-retrieved results, respectively; the wind speed explained 44% and 52% variability of FUI-derived and GPR-retrieved results.
... The increase in algal biomass causes changes in the optical characteristics of a water body that can be detected using remote sensing (RS) spectral reflectance, which is the basis for RS eutrophication monitoring in lakes [18]. Different methods have been applied to evaluate lake eutrophication and calculate TSI: (1) the estimation of Chla concentration [19][20][21], transparency or SDD [22,23], suspended particulate matters (SPM) [24], and other water quality parameters using remote sensors; (2) direct establishment of a single band or multiband-derived TSI [25]; (3) use of the Forel-Ule index [26,27], absorption of optical active components (aOACs) [11,28,29] or machine learning [30] to estimate TSI. Due to the complex optical characteristics of inland waters, there are still some limitations in monitoring lake eutrophication based on RS: (1) the precision of estimates can be limited, and the indirect method may produce larger uncertainties than direct inversion [31][32][33][34][35]; (2) the lack of high-performance algorithms with great capability and transmissibility to simulate water quality parameters or TSI for a broader area [31,36]. ...
Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland waters. First, the turbid water index was applied for the determination of optical water types on each pixel, and water bodies are divided into two categories: algae-dominated water (Type I) and turbid water (Type II). The algal biomass index (ABI) was then established based on water classification to derive the trophic state index (TSI) proposed by Carlson (1977). The results showed a considerable precision in Type I water (R2 = 0.62, N = 282) and Type II water (R2 = 0.57, N = 132). The ABI-derived TSI outperformed several band-ratio algorithms and a machine learning method (RMSE = 4.08, MRE = 5.46%, MAE = 3.14, NSE = 0.64). Such a model was employed to generate the trophic state index of 146 lakes (> 10 km2) in eastern China from 2013 to 2020 using Landsat-8 surface reflectance data. The number of hypertrophic and oligotrophic lakes decreased from 45.89% to 21.92% and 4.11% to 1.37%, respectively, while the number of mesotrophic and eutrophic lakes increased from 12.33% to 23.97% and 37.67% to 52.74%. The annual mean TSI for the lakes in the lower reaches of the Yangtze River basin was higher than that in the middle reaches of the Yangtze River and Huai River basin. The retrieval algorithm illustrated the applicability to other sensors with an overall accuracy of 83.27% for moderate-resolution imaging spectroradiometer (MODIS) and 82.92% for Sentinel-3 OLCI sensor, demonstrating the potential for high-frequency observation and large-scale simulation capability. Our study can provide an effective trophic state assessment and support inland water management.
... Water-leaving reflectance was calculated for each sampling site based on the paired digital numbers recorded by ASD for the reference white board and water surface, respectively. The equation for the calculation is [47]: ...
Turbidity is an important indicator of riverine conditions, especially in a fragile environment such as the Tibetan Plateau. Remote sensing, with the advantages of large-scale observations, has been widely applied to monitor turbidity change in lakes and rivers; however, few studies have focused on turbidity change of rivers on the Tibetan Plateau. We investigated the pattern of turbidity change in the middle reaches of the Yarlung Zangbo River, southern Tibetan Plateau, based on multispectral satellite imagery and in situ measurements. We developed empirical models from in situ measured water leaving reflectance and turbidity, and applied the best performed s-curve models on satellite imagery from Sentinel-2, Landsat 8, and Landsat 5 to derive turbidity change in 2007–2017. Our results revealed an overall decreasing spatial trend from the upper to lower streams. Seasonal variations were observed with high turbidity from July to September and low turbidity from October to May. Annual turbidity showed a temporally slightly declining trend from 2007 to 2017. The pattern of turbidity change is affected by the confluence of tributaries and the changes in precipitation and vegetation along the river. These findings provide important insights into the responses of riverine turbidity to climate and environmental changes on the Tibetan Plateau.
... The accuracy of the inversion model using a single band as the independent variable is not as good as that using the band combination as the independent variable (Sun et al., 2009). The empirical model method is the most widely used remote sensing modeling method for water quality parameters (Dekker and Peters, 1993;Thiemann and Kaufmann, 2000). The multivariate linear model enables the band combination used in the empirical model method to establish a better relationship with the Chl-a concentration, and the fitting precision meets the needs of remote monitoring (Brezonik et al., 2005). ...
Satellite remote sensing technology presents advantages of macroscopicity, timeliness and cost effectiveness and has been increasingly used in lake water quality monitoring. In this paper, an empirical model for the remote sensing inversion of the chlorophyll a (Chl-a) concentration in Erhai Lake was established using ground monitoring Chl-a concentration data and multispectral remote sensing data from environmental satellites from 2010 to 2017. The average absolute error and relative error were 1.92 mg/m³ and 22%, respectively. A 10-year remote sensing inversion of Erhai blooms identified the temporal and spatial distribution characteristics of blooms and showed that the occurrence frequency of blooms was 37%, and they were mainly in the form of light algal blooms at a local scale. Moderate and severe blooms occurred at a frequency of 42%, mainly from Oct. to Jan. Moderate algal blooms were distributed along the southern and northern coasts and in coastal areas, while severe algal blooms were distributed in the northern section and across the entire lake. During the bloom period, the growth rate of the bloom area reached 102 km²/d, which was faster than the reduction rate (90 km²/d). Bloom events generally lasted for 6–37 days. The inflow of pollution sources led to a higher frequency of blooms in the coastal zone than in the lake center, and the frequency in the northern section was nearly twice as high as that in the southern section. Most blooms in Erhai Lake occurred from late summer to winter (i.e., Jul. to Jan. of the following year) because of the higher average air temperature (AT) and lower wind speed (WS) in winter and spring and the amount of precipitation in summer and autumn. The remote sensing method captured the high-risk areas and the spatial-temporal evolution trend of algal blooms, and the model provided support for the prevention and control of lake algal blooms; however, this work should be complemented by ground monitoring data for cloudy days.
... A trophic state classification can be applied using the estimated Chl-a. [39] evaluated the potential of a field spectrometer and IRS-1C satellite image to monitor the Chl-a content and trophic state of a lake in Germany. This lake belonged to a complex aquatic system and presented oligotrophic characteristics, with algal blooms during specific periods of the year. ...
... Other parameters such as chlorophyll-a (chl-a) and Colored Dissolved Organic Matter (CDOM)) have also been covered in various studies (i.e. Brezonik et al., 2005;Thiemann and Kaufmann, 2000;Li et al., 2002;Dona et al., 2014). ...
Progressive anthropogenic intrusion and increasing water demand necessitate frequent water quality monitoring for sustainability management. Unlike laborious, time consuming field-based measurements, remote sensing-based water quality retrieval proved promising to overcome difficulties with temporal and spatial coverage. However, remotely estimated water quality parameters are mostly related to visibility characteristic and optically active property of water. This study presents results of an investigated approach to derive oxygen –related water quality parameter, namely Dissolved Oxygen (DO), in a shallow inland water body from satellite imagery. The approach deduces DO levels based on interrelated optical properties that dictate oxygen consumption and release in waters. Comparative analysis of multiple regression algorithms was carried out, using various combinations of parameters; namely, Turbidity, Total Suspended Solids (TSS), Chlorophyll-a, and Temperature. To cover the wide range of conditions that is experienced by Edku coastal lake, ground truth measurements covering the four seasons were used with corresponding satellite imageries. While results show successful statistically significant correlation in certain combinations considered, yet optimal results were concluded with Turbidity and natural logarithm of temperature. The algorithm model was developed with summer and fall data (R2 0.79), then validated with winter and spring data (R2 0.67). Retrieved DO concentrations highlighted the variability in pollution degree and zonation nature within that coastal lake, as related to boundary interactions and irregularity in flow dynamics within. The approach presented in this study encourages expanded applications with space-based earth observation products for exploring non-detectable water quality parameters that are interlinked with optically active properties in water.
... Several studies monitoring aquatic systems through RS focus on estimating chlorophyll-a concentration (chl-a; a photosynthetic pigment present in all phytoplankton species [13]) [14][15][16]. Chl-a is the most commonly derived parameter in RS water quality mainly because it is the most comprehensive index of aquatic system trophic status [17][18][19]. Moreover, chl-a concentration can be used as a proxy for phytoplankton biomass [20,21]. ...
Using remote sensing for monitoring trophic states of inland waters relies on the calibration of chlorophyll-a (chl-a) bio-optical algorithms. One of the main limiting factors of calibrating those algorithms is that they cannot accurately cope with the wide chl-a concentration ranges in optically complex waters subject to different trophic states. Thus, this study proposes an optical hybrid chl-a algorithm (OHA), which is a combined framework of algorithms for specific chl-a concentration ranges. The study area is Ibitinga Reservoir characterized by high spatiotemporal variability of chl-a concentrations (3–1000 mg/m3). We took the following steps to address this issue: 1) we defined optical classes of specific chl-a concentration ranges using Spectral Angle Mapper (SAM); 2) we calibrated/validated chl-a bio-optical algorithms for each trophic class using simulated Sentinel-2 MSI (Multispectral Instrument) bands; 3) and we applied a decision tree classifier in MSI/Sentinel-2 image to detect the optical classes and to switch to the suitable algorithm for the given class. The results showed that three optical classes represent different ranges of chl-a concentration: class 1 varies 2.89–22.83 mg/m3, class 2 varies 19.51–87.63 mg/m3, and class 3 varies 75.89–938.97 mg/m3. The best algorithms for trophic classes 1, 2, and 3 are the 3-band (R² = 0.78; MAPE - Mean Absolute Percentage Error = 34.36%), slope (R² = 0.93; MAPE = 23.35%), and 2-band (R² = 0.98; MAPE = 20.12%), respectively. The decision tree classifier showed an accuracy of 95% for detecting SAM’s optical trophic classes. The overall performance of OHA was satisfactory (R2 = 0.98; MAPE = 26.33%) using in situ data but reduced in the Sentinel-2 image (R2 = 0.42; MAPE = 28.32%) due to the temporal gap between matchups and the variability in reservoir hydrodynamics. In summary, OHA proved to be a viable method for estimating chl-a concentration in Ibitinga Reservoir and the extension of this framework allowed a more precise chl-a estimate in eutrophic inland waters.
... Previous studies on using remote sensing for monitoring water bodies include NOAA/AVHRR (Yan and Jing, 2000), Sea WiFS (Vos et al., 2003), Landsat MSS/TM (Carpenter and Carpenter, 1983;Lindell et al., 1985;Doerffer et al., 1989;Lathrop et al., 1991;Keener and Yan, 1998;Lathrop, 1992;Dekker and Peters, 1993;Baban, 1993;Gitelson et al., 1993;Lavery et al., 1993;Keiner and Yan, 1998;Dekker et al., 2001), IRS-1C (Thiemann and Kaufmann, 2000), ERS-1 SAR (Nilsson and Tildesley, 1995), ERS-2 SAR (Zhang et al., 2002), CASI (Flink et al., 2001). Dekker and Peters, 1993 conducted that the downward and upwelling irradiance at TM bands 1,2,3 wavelengths are absorbed and reflected according to the optical characteristic of chlorophyll-a, accessory pigments and aquatic humus substances in the water itself. ...
Remote sensing technology offers advantages over conventional methods in monitoring different environmental changes in being relatively cheaper, could reach areas not easily accessible and have the capability of repetitive coverage of the same area which permits depicting short and long term changes. Landsat-7 ETM+ satellites image data were compared with in situ measurements and laboratory analysis of some water samples. The water quality parameters of interest included, Total Phosphorous (TP), Ammonia (NH 3), Nitrite (NO 2-), Nitrate (NO 3-), Total Suspended Solids (TSS), Chlorophyll-a, Chlorophyll-b, Dissolved Oxygen (DO), pH, Total Dissolved Solids (TDS), Temperature, Turbidity and water depth. Correlation matrices, and stepwise multiple regression were used to explore the relationship between the water quality parameters and Landsat-7 ETM+ reflectance data (independent variables). These relationships were extended to the entire study area to producing a series of class maps which were grouped and color-coded to represent the spatial distribution of water quality parameters. Some water quality parameters were significantly correlated with Lansat-7 ETM+ reflectance data. Subsequently, multiple linear regression models were used to generate the spatial distribution of Bathymetry (water depth), Chlorophyll-a, Total Phosphorous (TP) and Turbidity for Manzala lagoon.
... Therefore, Chl-a estimation using empirical blue-green band-ratio algorithms from satellite measurements are subject to large uncertainties (Magnuson et al., 2004;Werdell et al., 2009;Le et al., 2013a). Some other algorithms have been developed to avoid this issue using the red and near-infrared wavelengths (Thiemann and Kaufmann, 2000;Dall'Olmo et al., 2005;Le et al., 2009). Most of the ocean color satellites sensors do not have the required spectral bands (e.g. ...
Chabahar Bay is a strategic and productive estuary in the south-east of Iran (north of Oman Sea). It is an optically complex bay, and its Chl-a content variations never studied. In this study, Red-Green Chlorophyll Index (RGCI) algorithm for estimating accurate Chl-a content was tested, validated and applied to Moderate Resolution Imaging Spectrometer (MODIS) data, using in situ bio-optical data collected seasonally from 2007 to 2015 in 35 stations. Chl-a concentrations varied from 7.7 to 31.3 mg m⁻³ with a mean value of 16.4 ± 5.9 mg m⁻³. The mean absorption at 443 nm and 555 nm data showed that the detrital particles and CDOM contents is relatively low and the mean absorption is dominated by phytoplankton and non-living particles. The RGCI algorithm was tuned for green band center position of 555 nm, and showed improvement over the traditional blue-green band ratio algorithms (e.g. OC3) with mean relative error of 37.4% and RMSE of 73.2% for Chl-a ranging between 2 and 80 mg m⁻³. The Wavelet Transform (WT) techniques were utilized to analyze the spatio-temporal stability and abnormality of MODIS Chl-a extracted using the tuned RGCI algorithm. Significant variability in time and space was observed, with higher Chl-a in the eastern segment and lower Chl-a in the middle of bay. The highest and lowest Chl-a concentrations were observed in summer and winter, respectively. WT components analysis and anomaly detection revealed the strong correlation of Chl-a concentrations and patterns with turbidity contents and adjacent river discharge. This study showed that the accuracy of RGCI algorithm depends on water body constituents and optimized green waveband position, and therefore the algorithm must be tuned regionally with in situ Chl-a data.
... Other parameters such as chlorophyll-a (chl-a) and Colored Dissolved Organic Matter (CDOM)) have also been covered in various studies (e.g. [8] [11] [12] and [13]). ...
... Chlorophyll-a concentration analysis with empirical band ratio algorithms furnish important sign of water ecosystems condition. Many scholars have quantified chlorophyll-a concentration using various sensors, such as IRS-1C [15], NOAA/AVHRR [16], CASI [17][18][19], SeaWiFS [20], MODIS [21], IRS-P4 OCM [22],OCM-2 [23], AISA [24], MODIS, [21], RADARSAT-2, Envisat, TerraSAR-X, ERS-1/2, and RADARSAT-1, [25][26][27], SPOT [28] and MERIS [29]. It has been found that the ratios of R rs (443)/R rs (555) and R rs (490)/R rs (555) are frequently being used [30]. ...
Chlorophyll-a concentration is a significant conditioning factor for analysing variation of water quality. It is also an important indicator for examining phytoplankton and biomass both in inland and oceanic waters. The study aims at developing an approach to quantify chlorophyll-a concentration using Landsat-8 Optical land imager sensor data in Densu River, West Africa. Twelve water samples across Densu River were collected to measure chlorophyll-a concentration. Satellite data base chlorophyll-a concentration was determined using NIR-red algorithm. The chlorophyll-a concentration obtained through this algorithm was validated with laboratory-measured chlorophyll-a concentration. Regression analysis between laboratory-measured and modelled chlorophyll-a concentration revealed strong relationship. Thus, NIR-red algorithm has proved an effective tool in measuring and mapping chlorophyll-a concentration. The algorithm can also be utilised for assessing quality of different water bodies at spatial scales.
... Shuttle Radar Topographic Mission (SRTM), for example, has created an unparalleled data set of global elevations that are freely available for modeling and environmental applications [9]. Also, a growing number of researchers [11][12][13][14] have used different types of spectrometers, combined with varying methods of measurement to obtain the spectral data of a given water body [15]. The physics and chemical characteristics of water can be determined from spectral signatures [16]. ...
Abstract This study implements remote sensing (RS) and geographic information system techniques in deriving physical and spectral characteristics of a catchment to aid in water quality monitoring. This approach is conducted by utilizing RS datasets like digital elevation model (DEM), satellite images, and on-site spectral measurements. A Shuttle Radar Topography Mission DEM was used for extracting physical profiles while Landsat Operational Land Imager was utilized to extract land cover information. This method was tested in a 22,000-ha catchment with dominant agricultural lands where large-scale mining companies are also operating actively. The land cover classification has an overall accuracy of 97.66%. Forest (50%) and cropland (32%) are the most dominant land cover within the catchment. The spectral signature of waters at designated sampling points was measured to evaluate its correlation to water quality data like pH and dissolved oxygen (DO). The correlation between the level of pH and reflectance implies a positive relationship (R2 of 0.548) while that of DO and reflectance gives a negative correlation (R2 of 0.634). Results of this study demonstrate the practical advantage of exploiting remotely-sensed data in profiling and characterizing a catchment as it provides valuable information in understanding and mitigating contamination in an area. Through these RS-derived catchment profiles, insights on the contaminant’s concentration and possible sources can be identified. The graphical and statistical analysis of the spectral data prove its potential in developing water quality models and maps.
... Hulsbeek varies from 3.81 mg.m -3 to 23.7 mg.m -3 . The trophic state of each of the lakes were determined based on the Chl-a measurements (results) from the laboratory (Thiemann & Kaufmann, 2000). The ...
The use of satellite remote sensing in monitoring chlorophyll-a pigments in all marine and fresh open inland water has been a significant issue in the past decades for preserving and monitoring ecological issues related to aquatic systems.
Three small lakes in the Netherlands were used to study Chlorophyll-a (absorption coefficient of pigments/phytoplankton) estimation based on band ratio algorithms using concurrent in-situ on one hand, and Landsat-8 and SPOT6 data. This study identified, adapted and tested the performance of four (4) empirical models based on optimal bands using both satellite sensor data and field radiometric data for proper assessment of Chlorophyll-a. Each of the algorithms specifically requires an optimal band(s) and these bands differ among the algorithms. These algorithms includes Maximum Chlorophyll-a Index (MCI), the Three Band Model (TBS/3B), Normalized Differential Index Model (NDCI), and the Four Band Model (FBS). Ground sourced data including spectral reflectance data and absorption coefficient of chlorophyll-a pigments/phytoplankton were used for further analysis. The models were tuned and validated using the Geo-Cal/Val method.
In-situ variations of absorption coefficient of chlorophyll-a were compared with modelled/estimated absorption coefficient of chlorophyll-a pigments/phytoplankton from satellite data using four different band ratio algorithms. Good correlation was found when models were applied on in-situ data than concurrent Landsat-8 and SPOT6 data. The FBS/4B algorithm performed well with in-situ data, with an R2, of 0.83 respectively and a minimal percentage error of (rMAE) 12.35%. However, the MCI model performed better than the 4B as well as the 3B and NDCI models when applied to both Landsat-8 and SPOT6 data. Only few images processed through FLAASH and was used to depict the distribution and variation of Chl-a concentration (a_phy) over Lake Binnenschelde, Markiezaatsmeer and Hulsbeek retrieved via the application of the MCI band ratio-ing. It is also found that FLAASH is not suitable for atmospheric correction of SPOT6 images intended for this study.
Comparison of sensors showed that Landsat-8 performed better that SPOT6 in the derivation of absorption coefficient of chlorophyll-a pigments/phytoplankton. The MCI model maintained its consistency in working well with all the various data set and was found better than all other models, hence it was chosen as the best model. It had an R2 of 0.69 when applied on in-situ data, and R2 of 0.75 for Landsat-8 data as well 0.58 for SPOT6 data. The MCI percentage error was comparatively low for Landsat-8 (21.29%) and in-situ data (18.34%) showing only 3% increase in error but error doubled when MCI was applied on SPOT6 data. In all, most of the algorithms used in this study were sensitive to estimating absorption coefficients of Chl-a from both in-situ and Landsat-8 data.
Keywords: Chlorophyll-a, absorption of chlorophyll-a, Landsat-8, SPOT6, remote sensing, FLAASH, algorithms, models, lakes
... Eutrophication and increased productivity typically result in changes in the optical properties of water; therefore, RS approaches have been employed for water trophic state assessment (Baban, 1996;Papoutsa et al., 2014), in particular through the retrieval of Chl-a concentrations (Chen, 2003;Duan et al., 2007;Matthews and Odermatt, 2015;Pulliainen et al., 2001;Thiemann and Kaufmann, 2000;Wang et al., 2008). In addition, SD, which is one of the most commonly measured trophic state indicators, has also been used to assess water trophic states (Binding et al., 2015;Knight and Voth, 2012;Lillesand et al., 1983;Olmanson et al., 2008;Papoutsa et al., 2014;Sheela et al., 2011b). ...
Eutrophication of inland waters is considered a serious global environmental problem. Satellite remote sensing (RS) has been established as an important source of information to determine the trophic state of inland waters through the retrieval of optically active water quality parameters such as chlorophyll-a (Chl-a). However, the use of RS techniques for assessment of the trophic state of inland waters on a global scale is hindered by the performance of retrieval algorithms over highly dynamic and complex optical properties that characterize many of these systems. In this study, we developed a new RS approach to assess the trophic state of global inland water bodies based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and the Forel-Ule index (FUI). First, the FUI was calculated from MODIS data by dividing natural water colour into 21 indices from dark blue to yellowish-brown. Then the relationship between FUI and the trophic state index (TSI) was established based on in-situ measurements and MODIS products. The water-leaving reflectance at 645 nm band was employed to distinguish coloured dissolved organic matter (CDOM)-dominated systems in the FUI-based trophic state assessment. Based on the analysis, the FUI-based trophic state assessment method was developed and applied to assess the trophic states of 2058 large inland water bodies (surface area >25 km2) distributed around the world using MODIS data from the austral and boreal summers of 2012. Our results showed that FUI can be retrieved from MODIS with a considerable accuracy (92.5%, R2 = 0.92) by comparing with concurrent in situ measurements over a wide range of lakes, and the overall accuracy of the FUI-based trophic state assessment method is 80.0% (R2 = 0.75) validated by an independent dataset. Of the global large water bodies considered, oligotrophic large lakes were found to be concentrated in plateau regions in central Asia and southern South America, while eutrophic large lakes were concentrated in central Africa, eastern Asia, and mid-northern and southeast North America.
... According to [7], several types of satellite-borne sensors have been used in the last three decades for monitoring aquatic environments. However most of these studies are based on assessing the quality of marine and coastal waters [5,6,8,9]. ...
... According to [7], several types of satellite-borne sensors have been used in the last three decades for monitoring aquatic environments. However most of these studies are based on assessing the quality of marine and coastal waters [5,6,8,9]. ...
Water quality monitoring through remote sensing with UAVs is best conducted using multispectral sensors; however, these sensors are expensive. We aimed to predict multispectral bands from a low-cost sensor (R, G, B bands) using artificial neural networks (ANN). We studied a lake located on the campus of Unisinos University, Brazil, using a low-cost sensor mounted on a UAV. Simultaneously, we collected water samples during the UAV flight to determine total suspended solids (TSS) and dissolved organic matter (DOM). We correlated the three bands predicted with TSS and DOM. The results show that the ANN validation process predicted the three bands of the multispectral sensor using the three bands of the low-cost sensor with a low average error of 19%. The correlations with TSS and DOM resulted in R 2 values of greater than 0.60, consistent with literature values.
... Jest to oczywiste w świetle literatury przedmiotu dotyczącej polskich pojezierzy (WILGAT i in., 1992;CHOIŃSKI, 1995;JAŚKOWSKI, SOŁTYSIK, 2003;KOLADA i in., 2005): Mazurskiego, Pomorskiego, Wielkopolskiego, Łęczyńsko-Włodawskiego, Świętokrzyskiego, ale także w porównaniu z innymi pojezierzami strefy klimatu umiarkowa-nego (GONSIORCZYK i in., 1998;TIPPING i in., 1998;TIPPING i in., 2002;MORENO, 2000;THIEMANN, KAUF-MANN, 2000;SCHNAIBERG i in., 2002;PIENIMÄKI, LEP-PÄKOSKI, 2004), a tym bardziej krainami jezior występującymi poza zasięgiem tej strefy klimatycznej. Antropogeneza obiektów limnicznych Górnośląskiego Pojezierza Antropogenicznego kontrastuje z genezą naturalnych klasycznych pojezierzy w Skandynawii (PIENIMÄKI, LEPPÄKOSKI, 2004), Anglii (TIPPING i in., 1998, Niemczech (GONSIOR-CZYK i in., 1998;KAPFER, 1998;BOEHRER i in., 2000;THIEMANN, KAUFMANN, 2000;DUIS, OBEREMM, 2001), Ameryce (SCHNAIBERG i in., 2002) lub skupisk jezior niespełniających kryteriów genetycznych pojezierzy (CURTIS i in., 2005;HANASAKI i in., 2006). ...
... Dekker and Peters 1993; Han et al. 1994;Kallio et al. 2003;Thiemann & Kaufmann 2000) or the ratio 498 of blue and green wavebands (e.g. Hedger et al. 1996;George 1997b). ...
Phosphorus and nitrogen are key nutrients that affect abundance and growth of aquatic primary producers but cannot be directly remotely sensed as their dissolved or organic forms do not interact with the remote sensing signal. In addition, other lake water quality variables such as chlorophyll a and Secchi disk depth, have been previously successfully estimated with remote sensing, but the retrieval algorithms are site-, season-, and/or scene-specific. Such algorithms do not take into account lake typological features, which can affect the sensitivity of lake to change, or catchment characteristics, for example, land cover that is a major driver of lake water quality change. Here we propose a novel approach that utilises remotely sensed land cover information in the catchment to estimate phosphorus, nitrogen and chlorophyll a concentrations in lake waters. We use land cover type-specific nutrient export coefficients and the NASA MODIS (Moderate Resolution Imaging Spectroradiometer) Land Cover Type product showing that nutrient loading based on remote sensing can explain up to 75% of variability in lake nutrient concentrations and 58% of variability in lake chlorophyll a concentrations. In addition, we show that land cover information, supplemented by satellite measurements and lake morphometry data are good predictors of chlorophyll a (R² = 0.77) and Secchi disk depth (R² = 0.87) in lakes with different trophic statuses and in different months and years.
... The following are the essential differences between Class 1 and Class 3: Class 3 is dominated by nonliving particulate matter with no obvious pigment absorption characteristics, while Class 1 is also significantly affected by phytoplankton, with obvious spectral characteristics at around 630 nm and 676 nm. Thus, Rrs (695)/Rrs (676), which is positively correlated with chl-a [64][65][66], and Rrs (630), which is related to phycocyanin [45,67], are selected as indices. Also note that the Rrs (λ) spectra in Class 3 are typically flat between 560 and 695 nm. ...
... The index number can be calculated from any of several parameters including Secchi depth (SD) transparency, chlorophyll (Chl-a), and total phosphorus (TP). The Carlson TSI has been widely used to evaluate many lakes and reservoirs [60][61][62][63][64][65][66][67]. ...
Lake Manzala, the greatest Egyptian coastal lakes, is considered as one of the most valuable fish sources in Egypt. Recently, the water quality status of the lake has been sharply deteriorated due to the excessive discharge of industrial, agricultural, and municipal wastewater. Moreover, the lake is considered vulnerable to the impacts of future climatic changes, which will affect its hydrodynamic and water quality characteristics. This study has two main objectives: assessing the lake water quality status and quantifying the future climatic change impacts on the hydrodynamic and water quality characteristics of the lake. A comprehensive water quality assessment of the lake, based on water quality index (WQI) and trophic status index (TSI) approaches, has been presented to spatially assign the lake water quality conditions. A calibrated hydrodynamic water quality model (MIKE21 modeling system) and future projected estimates of the climatic changes have been used to investigate the impacts of climate change on the lake characteristics. The results revealed the critical and very bad water quality status and the high and very high trophic conditions, particularly in the southern and eastern zones due to the drainage of the polluted drains. The developed model results closely mimic the measured profiles of the simulated parameters. Severe spatial changes of the lake water temperature, water depth, and salinity due to future climatic changes are noticed. Based on the study results, an urgent water quality management strategy should be implemented for the lake, and an adaptation plan for the Egyptian coastal lakes should be investigated.
... Maximum Likelihood classifier was found to produce the best accuracy in this study. Many researchers choose the Maximum Likelihood method in their studies [Saura and Miguel-Ayanz, (2002), Pal and Mohanty, (2002), Donoghue and Mironnet, (2002), Thiemann and Kaufmann, (2000) and Guerschman, et al., (2003)]. The monitoring task can be accomplished by supervised classification techniques, which have been proven to be effective categorization tools (Bruzzone, et al., 2002). ...
... The reflectance spectrum peak near 700 nm had a strong correlation with Chl-a concentration [42,50,51]. Several previous studies of inland water quality also proved these wavelengths have the potential to predict Chl-a and TSS concentrations [52][53][54]. This study brings obvious evidence that the ISE-PLS model may be considered as a unified approach for remote quantification of constituent concentrations in water quality assessment. ...
Concentrations of chlorophyll-a (Chl-a) and total suspended solids (TSS) are significant parameters used to assess water quality. The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds in Higashihiroshima, Japan, using field hyperspectral measurements and statistical analysis. Field experiments were conducted in six ponds and spectral readings for Chl-a and TSS were obtained from six field observations in 2014. For statistical approaches, we used two spectral indices, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI), and a partial least squares (PLS) regression. The predictive abilities were compared using the coefficient of determination (R 2), the root mean squared error of cross validation (RMSECV) and the residual predictive deviation (RPD). Overall, iterative stepwise elimination based on PLS (ISE–PLS), using the first derivative reflectance (FDR), showed the best predictive accuracy, for both Chl-a (R 2 = 0.98, RMSECV = 6.15, RPD = 7.44) and TSS (R 2 = 0.97, RMSECV = 1.91, RPD = 6.64). The important wavebands for estimating Chl-a (16.97% of all wavebands) and TSS (8.38% of all wavebands) were selected by ISE–PLS from all 501 wavebands over the 400–900 nm range. These findings suggest that ISE–PLS based on field hyperspectral measurements can be used to estimate water Chl-a and TSS concentrations in irrigation ponds.
... Algorithms based on red and NIR reflectances measured using field spectrometers have been shown to yield accurate estimates of Chl-a concentration in turbid productive waters, irrespective of variations in the bio-optical characteristics of water. Thiemann and Kaufmann (2000) compared field reflectance spectra to linear imaging self-scanning sensor (LISS-III) satellite data for estimating Chl-a contents in lakes. Quantification from field reflectance spectra was carried out using the 678-nm absorption maximum and the 705-nm reflectance peak. ...
Inland waters represent complex and highly variable ecosystems, which are also of immense recreational and economic values to humans. The maintenance of high quality of inland water status necessitates development of means for rapid quality monitoring. Imaging spectrometry techniques are proven technology that can provide useful information for the estimation of inland water quality attributes due to fast speed, noninvasiveness, ease of use, and in situ operation. Although there have been many studies conducted on the use of imaging spectrometry for marine water quality monitoring and assessment, relatively few studies have considered inland water bodies. The aim of this review is to present an overview of imaging spectrometry technologies for the monitoring of inland waters including spaceborne and airborne and field or ground-based hyperspectral systems. Some viewpoints on the current situation and suggestions for future research directions are also proposed.
... The index number can be calculated from any of several parameters including Secchi depth transparency (SD), chlorophyll (Chl-a), and total phosphorus (TP). The Carlson TSI has been widely used to evaluate many lakes and reservoirs [107][108][109][110][111][112][113][114]. Galloway and Green [115] used TSI (TP) and TSI (CHL) to assess the trophic status of lakes Maumelle and Winona, Arkansas, USA. ...
Egypt is highly dependent on the River Nile as the main source of freshwater. The Aswan High Dam (AHD) was constructed to control the River Nile. AHD reservoir was formed due to the construction of the dam; it is considered as one of the largest man-made lakes in the world. There is currently rising awareness regarding the water quality status of River Nile and in particular the AHD reservoir, the sole reservoir in Egypt. In this work, a comparative study to assess the water quality and trophic state of the southern part of AHD reservoir, Lake Nubia, has been done during low flood periods of 3 successive years (2006–2008). Two water quality indices (NSF WQI and CCME WQI) and two trophic status indices (Carlson TSI and LAWA TI) were used. The results show that the water quality status of Lake Nubia ranges from excellent (according to the Egyptian water quality standards for surface fresh waterways) to good, while the trophic status of the reservoir is eutrophic. A spatial change in results can be noticed due to the morphological and hydrological characteristics of the reservoir. It is recommended that the reservoirs’ different zones should be assigned to different water uses based on comprehensive water quality studies.
Quality water plays a huge role in human life. Chlorophyll-a (Chl-a) in water bodies is a direct reflection of the population size of the primary productivity of various phytoplankton species in the water body and can provide critical information on the health of water ecosystems and the pollution status of water quality. Case 2 Regional CoastColour (C2RCC) is a networked atmospheric correction processor introduced by the Sentinel Application Platform for various remote sensing products. Among them, the Extreme Case-2 Waters (C2X) process has demonstrated advantages in inland complex waters, enabling the generation of band data, conc_chl product for Chl-a, and kd_z90max product for Secchi Depth (SD). Accurate in situ data are essential for the development of reliable Chl-a models, while in situ data measurement is limited by many factors. To explore and improve the uncertainties involved, we combined the C2X method with Sentinel-2 imagery and water quality data, taking lakes in Wuhan from 2018 to 2021 as a case. A Chl-a model was developed and validated using an empirical SD model and a neural network incorporating Trophic Level Index (TLI) to derive the predicted correction result, Chl-a_t. The results indicated that (1) the conc_chl product measured by C2X and in situ Chl-a exhibited consistent overall trends, with the highest correlation observed in the range of 2–10 μg/L. (2) The corrected Chl-a_t using the conc_chl product had a mean absolute error of approximately 10–15 μg/L and a root-mean-square error of approximately 8–10 μg/L, while using in situ Chl-a had a root-mean-square error (RMSE) of approximately 15 μg/L and a mean absolute error (MAE) of approximately 20 μg/L; both errors decreased by double after correction. (3) The correlation coefficient (R) between Chl-a_t and each data point in the Chl-a model results was lower than that of SD-a_t with each data point in the SD model results. Additionally, the difference in R-value between Chl-a_t and each data point (0.45–0.60) was larger than that of SD-a_t with each data point (0.35–0.5). (4) When using corrected Chl-a_t data to calculate the TLI estimation model, both RMSE and MAE decreased, which were 1μg/L lower than those derived from uncorrected data, while R increased, indicating an improvement in accuracy and reliability. These findings demonstrated the presence of in situ errors in Chl-a measurements, which must be acknowledged during research. This study holds practical significance as some of these errors can be effectively corrected through the use of C2X atmospheric correction on spectral bands.
A qualidade da água de um manancial influencia todo o ecossistema aquático, promovendo interferência nos sistemas de macrodrenagem das cidades, abastecimento e conservação da área; consequentemente acarretando impactos para os usuários. Rios e riachos no Semiárido brasileiro são, em sua maioria, irregulares, cujo fluxo de água superficial desaparece durante o período de estiagem e, em áreas urbanas integram a rede de drenagem local, além de receberem lançamentos de efluentes tratados ou não, portanto sujeitos a alterações na qualidade de suas águas. Este trabalho objetivou determinar o Índice de Estado Trófico (IET CL) das águas do Riacho das Porteiras, Petrolina, PE a partir das concentrações de clorofila-a em cinco pontos de amostragem, buscando determinar o nível de trofia dos pontos de coletas e realizar o comparativo entre os teores desse pigmento em cada um destes pontos. A partir da metodologia estabelecida por Yunes e Araújo, a qual utiliza de medições do espectrofotômetro, volume de pigmentos e volume de filtragem determinou-se a concentração de clorofila-a presente na água, diagnosticando o seu nível de eutrofização. Evidenciou-se grande aporte de nutrientes presentes em cada um dos cinco pontos citados, comprovando níveis de trofia hipereutróficos, com valores de clorofila-a em dissonância com a Resolução 357/2005 do CONAMA, corroborando elevadas concentrações de matéria orgânica e nutrientes decorrentes das causas e efeitos do uso e ocupação do solo. Palavras-chave: Eutrofização. Ecossistemas aquáticos. Hipereutrófico.
It is done a analysis of the current state of the subject related to the study of the space-time variation of ecosystems related to water is carried out. This activity is part of a sectoral project executed by the Department of Geosciences of the Technological University of Havana "José Antonio Echeverría" in collaboration with the National Institute of Hydraulic Resources.
To improve the accuracy of remotely sensed estimates of the trophic state index (TSI) of inland urban water bodies, key environmental factors (water temperature and wind field) were considered during the modelling process. Such environmental factors can be easily measured and display a strong correlation with TSI. Then, a backpropagation neural network (BP-NN) was applied to develop the TSI estimation model using remote sensing and environmental factors. The model was trained and validated using the TSI quantified by five water trophic indicators obtained for the period between 2018 and 2019, and then we selected the most appropriate combination of input variables according to the performance of the BP-NN. Our results demonstrate that the optimal performance can be obtained by combining the water temperature and single-band reflection values of Sentinel-2 satellite imagery as input variables (R2 = 0.922, RMSE = 3.256, MAPE = 2.494%, and classification accuracy rate = 86.364%). Finally, the spatial and temporal distribution of the aquatic trophic state over four months with different trophic levels was mapped in Gongqingcheng City using the TSI estimation model. In general, the predictive maps based on our proposed model show significant seasonal changes and spatial characteristics in the water trophic state, indicating the possibility of performing cost-effective, RS-based TSI estimation studies on complex urban water bodies elsewhere.
O trabalho teve como objetivo avaliar o uso potencial de imagens LISS-III/Resourcesat-1 na estimativa de constituintes opticamente ativos da água do reservatório Passo Real. Com as imagens de satélite utilizadas para a análise da refletância da água foi possível a extração de informações em pixels com resolução espacial de 23,5 metros. A seleção de imagens foi feita a partir da disponibilidade de cenas sem presença de nuvens em datas mais próximas à realização dos trabalhos de campo. Os processamentos das imagens para correção dos efeitos atmosféricos e transformação dos números digitais em valores de reflectância foram realizados no software ENVI 5.0 As espacializações dos dados limnológicos obtidos in situ foram realizadas por interpolação no software Spring 4.3.3 e as espacializações dos dados estimados por satélite foram gerados por fatiamento da imagem gerada pela inserção das equações obtidas nos modelos. A partir do conjunto de dados disponível foi possível produzir modelos capazes de estimar o total de sólidos em suspensão, a turbidez e a transparência da água com apenas uma única banda espectral, a banda 3 (620 a 680 nm) do sensor LISS-III/Resourcesat-1. Com a aplicação da técnica de razão de bandas espectrais foi possível gerar um modelo para estimativa da concentração de clorofila-a.
In agriculture, remote sensing data using earth observation satellites have many advantages over other methods in terms of time, space, and efficiency. This study analyzed the changes of reflectance and vegetation index according to atmospheric correction of images before using satellite images in agriculture. Top OF Atmosphere (TOA) reflectance and surface reflectance through atmospheric correction were calculated to compare the reflectance of each band and Normalized Vegetation difference Index (NDVI). As a result, the NDVI observed from field measurement sensors and satellites showed a higher agreement and correlation than the TOA reflectance calculated from surface reflectance using atmospheric correction. Comparing NDVI before and after atmospheric correction for multi-temporal images, NDVI increased after atmospheric corrected in all images. garlic and onion cultivation area and forest where the vegetation health was high area NDVI increased more 0.1. Because the NIR images are included in the water vapor band, atmospheric correction is greatly affected. Therefore, atmospheric correction is a very important process for NDVI time-series analysis in applying image to agricultural field.
The seasonal variation characteristics of eutrophication based on HJ-1 data and synchronous observation data on the ground were studied to provide a theoretical basis for the restoration and management of eutrophication in Dongting Lake. The remote sensing model of chlorophyll-a concentration inversion was established through the comparative analysis of the linear model, exponential model, logarithmic model and quadratic polynomial model by 38 kinds of band combinations. The average relative error was 22.81%. The RMSE of 13.71 mg/m ³ was far lower than the average concentration of 34.16 mg/m ³ . The results showed the following. (i) From the perspective of time distribution, the eutrophication degree decreased successively in summer, autumn, spring, and winter. Dongting Lake is mainly in the state of moderate nutrition, which may be attributed to its unique hydrological conditions (short water cycle and fast flow rate). (ii) Eutrophicated water bodies are mainly distributed in the East Dongting Lake and inner lake where the water flow is relatively slow. The water bodies with pool nutrient status are mainly distributed in the outer lake areas where the water flows faster, such as West Dongting Lake.
The spectral characteristics of agriculture water can reflect the water quality indirectly. How to observe the spectrum quickly and accurately is basis of evaluating aquaculture water quality by remote sensing technology. Many investigations indicate that the spectrum of several specific bands can reflect some water quality conditions. In this article, a real-time and automatic spectrum observation system is built based on high-precision optical sensor, flash storage technology, GPRS and RS485 wireless data transmission technology to observe the spectrum of specific bands. Through continuous observation of 5 ponds, the data is compared with data measured by ASD spectrometer in bands 680 nm, 700 nm and 769 nm. The data measured by ASD spectrometer is set as the standard value. The accuracy of the data measured by this system is above 98%. The result shows that, this system could take the place of spectrometer to measure the spectrum of specific bands. It can realize remote, real-time data observation.
A numerical trophic state index for lakes has been developed that incorporates most lakes in a scale of 0 to 100. Each major division ( 10, 20, 30, etc. ) represents a doubling in algal biomass. The index number can bc calculated from any of several parameters, including Secchi disk transparency, chlorophyll, and total phosphorus. My purpose here is to present a new ap- proach to the trophic classification of lakes. This new approach was developed because of frustration in communicating to the pub- lic both the current nature or status of lakes and their future condition after restoration when the traditional trophic classification system is used. The system presented hcrc, termed a trophic state index (TSI), in- volves new methods both of defining trophic status and of determining that status in lakes. All trophic classification is based on the division of the trophic continuum, howcvcr this is defined, into a series of classes termed trophic states. Traditional systems divide the continuum into three classes: oligotrophic, mesotrophic, and cutrophic. There is often no clear delineation of these divisions. Determinations of trophic state are made from examination of several di- verse criteria, such as shape of the oxygen curve, species composition of the bottom fauna or of the phytoplankton, conccntra- tions of nutrients, and various measures of biomass or production. Although each changes from oligotrophy to eutrophy, the changes do not occur at sharply defined places, nor do they all occur at the same place or at the same rate. Some lakes may be considered oligotrophic by one criterion and eutrophic by another; this problem is
Landsat TM images were analysed to assess the scope and limitations of their use for inland water quality detection. The spectral information in the six visible to middle infrared bands is discussed. The relatively wide spectral bands, 1 to 4, of the TM, in the optical wavelength domain contain a mixture of spectrally opposing features of absorption and scatteringQualitative and quantitative analysis is carried out. The limiting factors in the quantitative determination of water quality parameters are in first instance the radiometric resolution and in second instance the spectral resolution. Combinations of spectral bands of the TM are discouraged for analysis purposes as long as a physical explanation of the result is lacking. Under ideal circumstances the TM can be used to assess seston dry weight, sum of chlorophyll a and phaeopigments, Secchi depth transparency with limited accuracy.
The water quality parameters chlorophyll-a, total phosphorus, Secchi disk depth, suspended solids, salinity and temperature in the Norfolk Broads have been studied using Landsat TM data. An empirical approach of relating TM data with ground referenced data for these parameters through regression analysis was employed. Significant relationships were established between them. These models were used to predict and map these parameters in 27 or the Norfolk Broads. All the predicted values are consistent with the available general knowledge about these Broads
Interpretation techniques have been developed to process the spectra of upwelling radiation from water in the visible. Spectral indices to retrieve the concentration of optically active components have been suggested. They make it possible to retrieve the chlorophyll-a concentration with a standard error of less than 2.6mgm, mineral suspended matter concentration less than 4mgl, and dissolved organic matter concentration less than O.5mgCl. The instrumentation is described for measurments of the upwelling radiation as well as for operational determination of the concentrations of phytoplankton chlorophyll-a, mineral suspended matter and dissolved organic matter.
High-resolution reflectance spectra in the range of 400-850 nm were obtained from Lake Kinneret during a period when dense populations of the dinoflagellate Peridinium gatunense dominated the phytoplankton. Chlorophyll (Chl) concentrations ranged from 5.1 to 185 mg m-3 and from 2.4 to 187.5 mg m-3 in the samples of two independent experiments. The most prominent features of the reflectance spectra were: (i) a wide minimum from 400 to 500 nm; (ii) a maximum at 550-570 nm, which did not surpass 3% in samples with high Chl concentration (>20mg m-3), indicating a strong absorption by pigments in the green range of the spectrum; (iii) a minimum at 676 nm; this was ̃1% and was almost insensitive to variation in Chl concentration >10 mg m-3; (iv) a maximum reflectance showed near 700 nm; its magnitude and position were highly dependent on chlorophyll concentration. High-spectral-resolution data were used as a guideline for selection of the most suitable spectral bands for chlorophyll remote sensing. Models were devised, based on the calculation of the integrated area above the baseline from 670 to 850 nm and the reflectance maximal height within this range. Some algorithms already used m previous studies were tested and showed a plausible degree of accuracy when applied to the current data base. However, novel models devised in this study improved substantially the accuracy of Chl estimation by remotely sensed data, by reducing the estimation error from >11 to 6.5 mg m-3 Those models were validated by an independent data set where Chl concentration ranged over two orders of magnitude. The use of three relatively narrow spectral bands was sufficient for Chl mapping in Lake Kinneret. Therefore, a relatively simple sensor, measuring only a few bands will be employed in future applications for Chl monitoring in inland waters. Radiometric data were also used to simulate radiances in the channels of TM Landsat and to find the algorithm for Chl assessment. The ratio of channel 4 to channel 3 was used and enabled Chl estimation with an error of <15mg m-3 This algorithm was employed to map Chl in the entire area of Lake Kinneret with 10 gradations.
This study presents an experiment to determine the major origin of
the reflectance peak near 700 nm that can be observed in all
chlorophyll-a laden waters. For simulation of chlorophyll-a within an
increasing water mass, mountain maple leaves have been fixed on a plate,
gradually lowered into the water, and spectrally measured at different
water depths. The more water influences the spectra the more the red
edge of vegetation is being reduced to the reflectance peak as known
from algae laden waters. To make the crosscheck, also a white panel is
being lowered and spectrally measured in the same steps. The white panel
spectra have been normalised to retrieve pure water reflectance with
increasing absorption along with depth. Again normalising the spectra of
leaves covered with water by the increasing water absorption, common
vegetation spectra are being provided. Therefore the reflectance maximum
near 700 nm is mainly due to increasing water absorption that reduces
the red edge of plants
The Mecklenburg Lake District in Germany was used to verify MIDORI
AVNIR's potential for water quality assessment. By means of remote
sensing, water quality can be assessed by estimating chlorophyll
content. Chlorophyll shows spectral characteristics with relatively high
reflectance in the green and near-infrared spectral region. Chlorophyll
concentration is one parameter to estimate the trophic state of a lake
and it can be related to the amount of algae in the water. Complementary
data sets (CASI, AVNIR, IRS-1C PAN and LISS) and a spectral database
acquired by a field spectrometer are available for the test site. In
this study, AVNIR data are being evaluated regarding water quality.
Relative chlorophyll concentration of the lakes is differentiated by
techniques of linear spectral unmixing on atmospherically corrected
data. Results are in accordance to field measurements. Even though it is
not feasible to estimate absolute chlorophyll concentration using only 4
wavebands, groups of lakes with different states of bioproduction can be
differentiated
REFERENCES Chl-a using satellite remote sensing seems to be an ap-propriate method for the determination of trophic state
- Irs-1c / Liss-Iii Unmixing
Determination of Chl-a content from linear spectral unmixing of IRS-1C/LISS-III data. (a) Chl-a on 18 August 1996; (b) Chl-a on 4 May 1997; (c) Chl-a on 2 June 1997; (d) Chl-a on 1 September 1997; (e) Chl-a on 25 September 1997. REFERENCES Chl-a using satellite remote sensing seems to be an ap-propriate method for the determination of trophic state. Baban, S. M. J. (1993), Detecting water quality parameters in the Norfolk Broads, U.K., using Landsat imagery. Int. J. Re-Chlorophyll Content of Lakes in Mecklenburg, Germany 235
A fast atmospheric correction
- Seattle
- Wa
- R Richter
AVNIR data. Proceedings of the IGARSS'98, Seattle, WA, Richter, R. (1990), A fast atmospheric correction applied to USA, published on CD-ROM.
A fast atmospheric correction applied to Landsat TM images
- Richter