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Assessments of specific inherent optical properties (SIOPs) and their variability in highly turbid and productive inland waters are essential for the accurate estimation of water quality. A new optical classification method including two classification criteria [i.e., normalized remote sensing reflectance slope (NS), and normalized remote sensing reflectance depth (ND)] was developed to divide remote sensing reflectance into four classes, i.e., class 1 (NS < −0.0017 and ND < 0.21) is low turbid and productive water; class 2 (NS < −0.0017 and ND > 0.21) is low turbid and high productive water; class 3 (NS > −0.0017 and ND < 0.09) is high turbid and low productive water; and class 4 (NS > −0.0017 and ND > 0.009) is high turbid and high productive water. The relationships between phytoplankton absorption at 440 nm [ a(440)] and chlorophyll- a concentration [ C] as well as between particle backscattering coefficient at 440 nm [ b(440)] and total suspended matter concentration ( C) after classification were obtained from a large number of in situ data in Lake Taihu. The measured specific phytoplankton absorption $$[ {a_{\text{ph}}^{*} \left( \lambda \right)} ]$$ and particle backscattering coefficient $$[ {b_{\text{bp}}^{*} \left( \lambda \right)} ]$$ show significant variations even within the same class. The mean values of $$a_{\text{ph}}^{*} \left( \lambda \right)$$ at 440 nm $$[ {a_{\text{ph}}^{*} \left( {440} \right)} ]$$ for each class are 0.048 ± 0.013, 0.060 ± 0.012, 0.083 ± 0.021, and 0.056 ± 0.017 m/mg, respectively. The mean values of $$b_{\text{bp}}^{*} \left( \lambda \right)$$ at 440 nm $$[ {b_{\text{bp}}^{*} \left( {440} \right)} ]$$ for each class are 0.035 ± 0.01, 0.024 ± 0.004, 0.041 ± 0.009, and 0.038 ± 0.009 m/g, respectively. The power functions of SIOPs and water constituents' concentration indicate that $$a_{\text{ph}}^{*} \left( {440} \right)$$ and $$b_{\text{bp}}^{*} \left( {440} \right)$$ vary with C and C. The validation results show that our proposed values for $$a_{\text{ph}}^{*} \left( {440} \right)$$ and $$b_{\text{bp}}^{*} \left( {440} \right)$$ cover a very wide range of water optical properties, which are characterized from clear water to highly turbid productive water. The validation results also suggest that the retrieval accuracy of C and C bio-optical model was improved after classification. The root mean square error (RMSE) of C was improved from 14.18 to 7.43 μg/L (mean value of all classes) and RMSE of C was improved from 32.98 to 26.10 mg/L (mean value of all classes). Thus, the temporal and spatial variation of $$a_{\text{ph}}^{*} \left( {440} \right)$$ and $$b_{\text{bp}}^{*} \left( {440} \right)$$ should be considered in the bio-optical retrieval model of water quality. Graphical Abstract: In complex optical properties of inland water, retrieving the water constituents with high accuracy needs to classify the water optical properties from the remote sensing spectrum by optical classification method. The figure shows the water color examples of each class[Figure not available: see fulltext.]. [ABSTRACT FROM AUTHOR]

Abstract: Significant advances have been made in ocean color remote sensing of turbidity and water clarity for estuarine waters, yet accurate estimations of chlorophyll-a concentrations (Chla in mgm−3) has been problematic, posing a challenge to the research community and an obstacle to managers for long-term water quality assessment. Here, a novel empirical Chla algorithm based on a Red-Green-Chorophyll-Index (RGCI) was developed and validated for MODIS and SeaWiFS observations between 1998 and 2011. The algorithm showed robust performance with two independent datasets, with relative mean uncertainties of ~30% and ~50% and RMS uncertainties of ~40% and ~65%, respectively, for Chla ranging between 1.0 and >30.0mgm−3. These uncertainties are comparable or even lower than those reported for the global open oceans when traditional blue-green band ratio algorithms are used. A long-term Chla time series generated from SeaWiFS and MODIS observations showed excellent agreement between sensors and with in situ measurements. Substantial variability in both space and time was observed in the four bay segments, with higher Chla in the upper bay segments and lower Chla in the lower bay segments, and higher Chla in the wet season and lower Chla in the dry season. On average, river discharge could explain ~60% of the seasonal changes and ~90% of the inter-annual changes, with the latter mainly driven by climate variability (e.g. El Niño and La Niña years) and anomaly events (e.g. tropical cyclones). Significant positive correlation was found between monthly mean Chla anomalies and monthly Multivariate ENSO Index (MEI) (Pearson correlation coefficient=0.43, p<0.01, N=147), with high Chla associated with El Niño and lower Chla associated with La Niña. Further, a Water Quality Decision Matrix (WQDM) was established from satellite observations, providing complementary and more reliable information to the existing WQDM based on less synoptic and less frequent field measurements. The satellite-derived WQDM and long-term time-series data support the decision making efforts of the management agencies that regulate nutrient discharge to the bay. Similar approaches may be established for other estuaries where field data are much more limited than for Tampa Bay. [Copyright &y& Elsevier]

Abstract: Despite recent advances in using satellite data for continuous monitoring of estuarine water quality parameters such as turbidity and water clarity, estimating chlorophyll-a concentrations (Chla) has remained problematic due to the optical complexity of estuarine waters and imperfect atmospheric correction. This poses a significant challenge to the community as synoptic and frequent Chla “measurements” from satellites are in high demand by various government agencies and environmental groups to help make management decisions. Here, using 10years of in situ and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements from a moderately sized, turbid estuary, Tampa Bay (Florida, USA), we developed and validated a new algorithm specifically designed for retrieving Chla from MODIS data. The algorithm takes the red-to-green remote-sensing reflectance (Rrs(λ)) band ratio of [Rrs(667)+ Rrs(678)]/[Rrs(531)+ Rrs(547)] as the independent variable, and estimates Chla through the non-linear regression function: Ln(Chla)=1.91Ln(x)+3.40 (R 2 =0.87, N =97, p <0.01, 1.5

Abstract: Accurate assessment of phytoplankton chlorophyll a (Chla) concentration in turbid waters by means of remote sensing is challenging due to optically complexity and significant variability of case 2 waters, especially in inland waters with multiple optical types. In this study, a water optical classification algorithm is developed, and two semi-analytical algorithms (three- and four-band algorithm) for estimating Chla are calibrated and validated using four independent datasets collected from Taihu Lake, Chaohu Lake, and Three Gorges Reservoir. The optical classification algorithm is developed using the dataset collected in Taihu Lake from 2006 to 2009. This dataset is also used to calibrate the three- and four-band Chla estimation algorithms. The optical classification technique uses remote sensing reflectance at three bands: Rrs(G), Rrs(650), and Rrs(NIR), where G indicates the location of reflectance peak in the green region (around 560nm), and NIR is the location of reflectance peak in the near-infrared region (around 700nm). Optimal reference wavelengths of the three- and four-band algorithm are located through model tuning and accuracy optimization. The three- and four-band algorithm accuracy is further evaluated using other three independent datasets. The improvement of optical classification in Chla estimation is revealed by comparing the performance of the two algorithms for non-classified and classified waters. Using the slopes of the three reflectance bands, the 138 reflectance spectra samples in the calibration dataset are classified into three classes, each with a specific spectral shape character. The three- and four-band algorithm performs well for both non-classified and classified waters in estimating Chla. For non-classified waters, strong relationships are yielded between measured and predicted Chla, but the performance of the two algorithms is not satisfactory in low Chla conditions, especially for samples with Chla below 30mg m−3. For classified waters, the class-specific algorithms perform better than for non-classified waters. Class-specific algorithms reduce considerable mean relative error from algorithms for non-classified waters in Chla predicting. Optical classification makes that there is no need to adjust the optimal position to estimate Chla for other waters using the class-specific algorithms. The findings in this study demonstrate that optical classification can greatly improve the accuracy of Chla estimation in optically complex waters. [Copyright &y& Elsevier]

Accurate estimation of phytoplankton chlorophyll a (Chla) concentration from remotely sensed data is particularly challenging in turbid, productive waters. The objectives of this study are to validate the applicability of a semi-analytical three-band algorithm in estimating Chla concentration in the highly turbid, widely variable waters of Taihu Lake, China, and to improve the algorithm using a proposed four-band algorithm. The improved algorithm is expressed as [Rrs(λ 1)−1 −Rrs(λ 2)−1][Rrs(λ 4)−1 −Rrs(λ 3)−1]−1. The two semi-analytical algorithms are calibrated and evaluated against two independent datasets collected from 2007 and 2005 in Taihu Lake. Strong linear relationships were established between measured Chla concentration and that derived from the three-band algorithm of [Rrs−1(660)−Rrs−1(692)]Rrs(740) and the four-band algorithm of [Rrs−1(662)−Rrs−1(693)][Rrs−1(740)−Rrs−1(705)]−1. The first algorithm accounts for 87% and 80% variation in Chla concentration in the 2007 and 2005 datasets, respectively. The second algorithm accounts for 97% of variability in Chla concentration for the 2007 dataset and 87% of variation in the 2005 dataset. The three-band algorithm has a mean relative error (MRE) of 43.9% and 34.7% for the 2007 and 2005 datasets. The corresponding figures for the four-band algorithm are 26.7% and 28.4%. This study demonstrates the potential of the four-band model in estimating Chla even in highly turbid case 2 waters. [Copyright &y& Elsevier]

To overcome the retrieval problems in complex water, dual working wavelengths are required instead of a single wavelength in oceanic lidar. The wavelength optimization method of detecting chlorophyll a and Colored Dissolved Organic Matter (CDOM) absorption coefficient with a dual-wavelength lidar is studied in this paper. The inversion methods of chlorophyll a and CDOM absorption are developed based on the water absorption characteristics, which then lead to the inversion error equations. The effects of the wavelength on the inversion errors are studied. For the case in which λ1 and λ2 are both random, the errors are relatively small when λ1 is chosen between 420 and 560 nm and λ2 is selected under 420 nm. For the case in which λ1 is fixed at 532 nm, the errors generally decrease with decreasing λ2, with minimums around 300 and 356–360 nm under different water conditions. The wavelength optimization method discussed in this paper and the penetration depth criterion will be beneficial to the design of the dual-wavelength lidar. [ABSTRACT FROM AUTHOR]

Summary Based on the bio-optical properties of highly turbid productive inland waterbody, Taihu Lake, a novel bio-optical optimization algorithm was developed to estimate chlorophyll- a concentration ( C chla ) and total suspended matter concentration ( C TSM ) after optical classification. Between 2006 and 2013, 1080 in situ samples collected from four inland lakes in China were utilized to test this optimization algorithm. All data were classified into four classes based on a new bio-optical classification method. The retrieval results of C TSM and C chla exhibit a good consistency with in situ measured C TSM and C chla . C TSM retrieval accuracies (evaluated by the root mean square of percentage errors: RMSPs) of classes 1, 2, 3, and 4 were 35.77%, 16.09%, 28.42%, and 26.86% for data1 and were 32.15%, 33.14%, 47.71%, and 34.89% for data3, respectively. C chla retrieval accuracies (RMSPs) of classes 1, 2, 3, and 4 were 32.49%, 20.05%, 42.01%, and 34.85% for data1, were 44.71%, 32.59%, 47.92%, and 38.11% for data2, and were 33.12%, 25.65%, 70.88%, and 23.57% for data3, respectively. The optimization algorithm was also tested by the simulated data of Medium Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer, and Sea-viewing Wide Field-of-view Sensor satellite sensors’ center wavelengths. The validation shows a good correlation with the measured C TSM and C chla . All these examinations demonstrate that the bio-optical optimization algorithm and classification are valid and robust for both the in situ data and the simulated satellite data. The optical relationships of a ph (4 4 0) to C chla and b bp (4 4 0) to C TSM are reasonable and effective. In summary, the results present that the bio-optical optimization algorithm proposed in this study shows high potential application to various water types and satellite sensors. The retrieval accuracy of C TSM and C chla derived by bio-optical optimization algorithm was significantly improved after classification. [ABSTRACT FROM AUTHOR]