Eutrophication is the leading cause of freshwater impairment. Of China’s major cities, Wuhan has the largest coverage of the lake area, while lake eutrophication is increasing. Thus, the objective of this study was to monitor and assess lake eutrophication in Wuhan. For this purpose, the lake eutrophication was quantified using the Trophic Level Index (TLI). The TLI was modeled by building a radial basis function neural network (RBFNN) model with a set of indices derived from Sentinel-2 Multispectral Instrument (MSI) imagery and a parameter of lake environmental characteristics. Applied MSI indices are the Maximum Chlorophyll Index (MCI) and the spectral band ratio of b5/b4 and b3/b4. In addition, the applied lake environmental parameter is the chlorophyll-a dependent month. Based on 54 MSI images from April 2018 to March 2020, the lake eutrophication was successfully determined. The main findings are as follows: 1) The higher percentage of eutrophic area was registered in the autumn (from September to November) and the highest was registered on September 12^th, 2019; 2) Compared to the same period in 2018, the eutrophic area in the autumn of 2019 was significantly higher. In the autumn of 2018, from 74.94% to 86.38% of the lakes were eutrophic, while from 83.54% to 93.24% of the lakes were eutrophic in the autumn of 2019; 3) The TLI values were between 30 and 70 for the majority of lakes in Wuhan, suggesting that most lakes were mesotrophic, light eutrophic, or medium eutrophic, with few oligotrophic and heavy eutrophic lakes. Most oligotrophic and mesotrophic lakes were located in northern Wuhan, while eutrophic lakes were located in central and southern Wuhan. Our result indicates that the developed algorithm from this study could be applied for rapid and effective monitoring of eutrophication in lakes.

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 importance of atmospheric correction is pronounced for retrieving physical parameters in aquatic systems. To improve the retrieval accuracy of trophic level index (TLI), we built eight models with 43 samples in Wuhan and proposed an improved method by taking atmospheric water vapor (AWV) information and Landsat-8 (L8) remote sensing image into the input layer of radical basis function (RBF) neural network. All image information taken in RBF have been radiometrically calibrated. Except model(a), image data used in the other seven models were not atmospherically corrected. The eight models have different inputs and the same output (TLI). The models are as follows: (1) model(a), the inputs are seven single bands; (2) model(c), besides seven single bands (b1, b2, b3, b4, b5, b6, b7), we added the AWV parameter k1 to the inputs; (3) model(c1), the inputs are AWV difference coefficient k2 and the seven bands; (4) model(c2), the input layers include seven single bands, k1 and k2; (5) model(b), seven band ratios (b3/b5, b1/b2, b3/b7, b2/b5, b2/b7, b3/b6, and b3/b4) were used as input parameters; (6) model(b1), the inputs are k1 and seven band ratios; (7) model(b2), the inputs are k2 and seven band ratios; (8) model(b3), the inputs are k1, k2, and seven band ratios. We estimated models with root mean squared error (RMSE), model(a) > model(b3) > model(b1) > model(c2) > model(c) > model(b) > model(c1) > model(b2). RMSE of the eight models are 12.762, 11.274, 10.577, 8.904, 8.361, 6.396, 5.389, and 5.104, respectively. Model b2 and c1 are two best models in these experiments, which confirms both the seven single bands and band ratios with k2 are superior to other models. Results also corroborate that most lakes in Wuhan urban area are in mesotrophic and light eutrophic states.

The Forel–Ule index (FUI) is one of the indicators used to assess the trophic state of inland waters, which can be objectively determined based on satellite-retrieved corrected hue angles (α). However, the functions for α correction have been proposed based on a dataset from Dutch and the North Sea, which might be inadequate for the precise FUI-derived trophic state. This study proposed a method for FUI retrieval through improved α correction. First, the daily surface spectral reflectance product (MOD09GA) was corrected through simple band subtraction. The results showed that the FUI retrieved from the corrected MOD09GA was more reliable than those without corrections. Second, we obtained new offset functions through the fit between the sensor (α) and the error with α derived from the corrected MOD09GA. The results showed that: (1) for three atmospheric correction methods, the C2RCC (case 2 regional coast color) processor and the iCOR (image correction for atmospheric effects) performed better than 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) in FUI calculations; and (2) with Pearson’s r square (r²) of 0.72, and percentage error (PE) values of 5.80% for FUI derived from Landsat 8, r² of 0.52 and PE of 7.81% for results derived from Sentinel-2, r² of 0.60 and PE of 19.40% for results retrieved from Sentinel-3, the FUI determined by the new offset-corrected greatly improved the assessment of trophic states for five waterbodies in and around Wuhan, China. Hue angles retrieved by the corrected-MOD09GA are available as references and the results also imply that hue angle correction the functions can be achieved in any place, even if without an in-situ hyperspectral dataset. The waters of the Xiajiasi Reservoir and the Daoguanhe Reservoir were mostly in mesotrophic status; while East Lake, Tangxun Lake and Liangzi Lake were eutrophic.

Classification-based methods for estimating water quality parameter (WQP) using remote sensing have shown great application potential in inland waters. Water classification algorithms have seen progress in water remote sensing. In this paper, we conducted the Secchi Depth value (Z_SD) estimation based on a global water typology for the Wuhan area. Firstly, we classified the water into seven types using the Sentinel-2 data. The procedure was based on spectral angle mapping (SAM) of the 13 inland water optical types (IWOTs). Afterwards, seven IWOTs were summarized into four categories for Wuhan area. We then developed empirical models for each water category by stepwise multiple linear regression, generalized regression neural network (GRNN), and sparse spectrum Gaussian process regression (SSGPR), and applied the better approaches (GRNN and SSGPR) to three full satellite images (27 October 2018, 10 May 2019, and 29 July 2019). Finally, the retrieved results were validated using in situ-satellite match-ups and compared with the results based on unclassified imagery. With root-mean-square error (RMSE) of three satellite-derived results reduced from 0.32 m (without classification) to 0.16 m (with classification), and mean absolute percentage error (MAPE) reduced from 52% to 18%, from 0.51 m (MAPE = 57%) to 0.19 m (MAPE = 21%), and from 0.18 m (MAPE = 35%) to 0.09 m (MAPE = 17%), Z_SD estimations over optically complex waters were improved based on this water classification. Due to its low cost and ease of operation, the SAM – derived classification applied in this paper provides a possibility for dynamic and high-precision monitoring for water management.