## 1 Introduction
The study by [13] showed that the red band reflectance is positively correlated with TSS, but when TSS reach a certain level, the reflectance will tend to converge or remain unchanged. Besides, the difference in optical characteristics of different component water bodies makes the TSS-sensitive bands different among open ocean, coastal and inland water bodies. Compared to open ocean and estuary coastal waters, inland waters are optically more heterogeneous and complex [31]. The reason is probably because the different phytoplankton types in estuaries and coasts are different from those in Hedi Reservoir, such as diatoms or cyanobacteria. The different types of phytoplankton produce different spectral properties in the water. Therefore, it is necessary todevelop a reliable model based on the specific spectral properties of water constituents for retrieving the temporal and spatial distribution of TSS by remote sensing. No doubt analyzing the spatial-temporal trends of TSS is important for studying the evolution process and effect of lake and reservoir water environment systems. Some related research works have made important achievements in their respective fields, such as Taihu Lake [8; 12], Dongting Lake [32; 33], reservoirs [5; 22] and so on. [PERSON] et al. [32] conducted remote sensing assessments of environmental changes and human economic activities on the variation patterns of the concentration of TSS, revealing their significant inter-annual and spatial variations. Unfortunately, although studies have been able to use large amounts of data to analyze the changes in TSS [15; 19; 31], few studies can be combined with local policy strategies to comprehensively analyze TSS changes [7; 34]. Moreover, fewer studies focus on cross-border areas where the water quality change is more complicated from upstream runoff. To achieve the goal of the development of Green Water and Green Hills that China's government put forward, it has become key to systematically study the long time series dynamic changes in the water quality of reservoirs and understand the impact mechanism for water environment management and pollution prevention by different river chiefs. A new method of combining the Markov process and remote sensing technology is used to reveal the spatiotemporal heterogeneity of TSS, which is based on the advantage of a Markov model that reflects the stochastic dynamic trends of events during a certain period. Large-scale and long-term monitoring has replaced the traditional cross-section in situ sampling [35], which makes the monitoring process faster and more concise and can more intuitively reflect the dynamic and continuous change process of the TSS concentrations. In the study, based on the field spectra and remote sensing experiments carried out in the cross-border watershed Hedi Reservoir of western Guangdong in South China, we built the TSS three-band retrieval model, analyzed the spatial and temporal distribution characteristics of TSS concentration and then explored the driving factors. The dynamic changes in TSS in the Hedi Reservoir were analyzed from 2014 to 2018 based on the Markov model. The quantitative assessments of TSS variations and their driving force factors were produced for the effect analysis of the river and lake chief system in China.