## 4 Discussion  
Traditional water quality cross-section monitoring is limited to detecting in small-scale with high time consumption and high cost. Considering the advantage of Markov and remote sensing, a novel water quality evaluation method is proposed, which breaks through the traditional water quality cross-section point sampling and detecting. This Markov-based water quality remote sensing evaluation method can directly associate the TSS concentration level with the degree of progress to quantify the dynamic change process of the TSS in multiple time series, which has great advantages especially in evaluating the implementation of the river chief system compared with the traditional water quality evaluation method. It can potentially be implemented in other developing countries in the future. The study reveals the dynamic continuous trend of TSS in the five years. Especially in 2018, the degree of progress is 10.232 with the relatively largest improvement. Additionally, the progress degree produced from the Markov model can be related directly to the change in TSS levels, which further indicates the meanings or effects of water quality status or change for corresponding management measures taken. At the same time, this method constructs a continuous, integrated and dynamic spatial-temporal pattern in the analysis of TSS, and driving factors were studied to effectively find the problems of point and non-point source pollution in the reservoir area. This is of great significance for the real-time monitoring, future prediction and management of the aquatic environment in the reservoir or other areas. The method presented will have great potential for dynamic analysis of TSS in big data scenarios of remote sensing images. The spectral curve of the water body of Hedi Reservoir shows a typical four-peak pattern that probably results in the unsuitability of the previous band ratio model in coastal and inland waters [10; 18; 24; 29; 43]. Part of the reason can be attributed to the abundant phytoplankton and the majority of cyanobacteria in the water body of Hedi Reservoir [49; 50]. A semi-empirical three-band TSS retrieval model (Equation (11)) was developed under the consideration of the special four-peak optical characteristics of water bodies (\(R^{2}=0.816,N=34\)). The three-band model has better validation accuracies (\(RMSE=6.24\text{mg}/\text{L},\,MRE=18.02\%,N=15\)), which can be compared to the accuracies of previous Landsat-based models in Table 4 (\(RMSE:8.78\text{--}20.57\text{mg}/\text{L},MRE:27.36\%\text{--}63.54\%\), [18; 22]). It has also obtained reasonable validation accuracy (\(RMSE=3.55\text{mg}/\text{L},MRE=27.55\%,N=19\)) through 19 in situ data and synchronized Landsat OLI images. Simultaneously, the TSS retrieval models in nearby coastal waters in South China commonly studied by the previous researchers do not apply to inland water bodies, as the phytoplankton types in estuaries and coasts are different from reservoirs such as diatoms or cyanobacteria. Additionally, the TSS three-band model proposed in this study also includes the 20 pairs of field data in Poyang Lake, which increases the applicability of the model. To a large extent, the good performance of the three-band model developed here can provide a scientific reference for water quality remote sensing in South China. There are some uncertainties for this study in the inversion of water quality parameters, which are mainly reflected in the following aspects. On the one hand, the accuracy of atmospheric correction for remote sensing imageries plays a decisive role in the reliable retrieval of optical parameters of inland waters. There are differences in the spectral reflectance resulting from the different parameter settings in the same atmospheric correction model or from using different atmospheric correction models, which may affect the accuracy of the retrieval optical parameters of inland water bodies.