Urban Black and Odorous Water (BOW) has become an environmental problem in many cities in China. The use of satellite remote sensing technology to identify BOW is still in its infancy, and there are many problems that need further solutions. In order to monitor BOW by satellite, between 2016 and 2017, the reflectance of remote sensing and some other parameters of 173 samples were collected from multiple field water experiments first. The samples were located at the major BOW in the urban areas of four Chinese cities, and the differences in remote sensing reflectance of severe BOW (SBOW), moderate BOW (MBOW), and general water (GW) were analyzed. Based on field measurements of spectral data, six remote sensing classification or identification models of BOW were compared in terms of their correct identification rate and reliability. The results show that compared with the GW in the study area, the urban BOW has the lowest reflectance. The peaks and valleys were not obvious in the visible band, especially the remote sensing reflectance of heavy BOW, which fluctuated very little in the visible band. Compared with the other five models, the H Index model had the best identification correctness and reliability.

The need for an accurate model that can derive the absorption coefficient of optical activity constituents from both marine water and coastal water remains necessary. This study aimed to develop an algorithm for determining the absorption coefficients for both phytoplankton and non-phytoplankton pigments [a_ph(λ) and a_dg(λ), respectively]. This algorithm included two portions: (1) the total absorption coefficients at the blue and green bands were computed using a neural network technology-based, quasi-analytical algorithm; and (2) the relationship between the a_dg(λ) coefficient and the coefficient of total absorption was analyzed. This algorithm was evaluated with both in-situ observations and remote-sensed satellite data. The results showed that the algorithm could produce acceptable results in the retrievals of a_dg(λ) and a_ph(λ) in both turbid and clear waters. The results also indicated that the proposed algorithm was effective for distinguishing between a_dg(λ) and a_ph(λ) from the total coefficients of absorption, even though more independent assessments using in-situ and remote-sensed data are required to further improve the approach.