Total phosphorus (TP) concentration is one of the indicators for surface water quality evaluation. In this study, an indirect algorithm was proposed to retrieve TP concentration. This algorithm retrieves the TP concentration in urban waters based on Gaofen-1 (GF-1) remote sensing data. The algorithm uses the correlation between remote-sensing reflectance, optically significant constituents of water (chlorophyll, suspended sediment, and organic matter (excluding algae)), and TP to establish a retrieval model. First, the concentrations of optically active components are retrieved using a semi-analytical model. Second, the correlation between TP and optically active components is used to retrieve the TP concentration in waters. The GF-1 remote sensing data for 7 August 2015 were used to perform remote sensing retrieval of TP concentration in the Pearl River channels in Guangzhou, China. The results show that the TP concentration in most areas of the Front Channel, Western Channel, Guangzhou Channel, and the western part of the Back Channel was higher than 0.2 mg/L, while the TP concentration in the middle and eastern parts of the Back Channel was generally lower than 0.2 mg/L. The mean absolute percentage error of the retrieval is 24.18%. The experimental results show that the model is suitable for remote sensing retrieval of TP in urban waters in Guangzhou.

Optimization-based semi-analytical methods (OSMs) and empirical methods (EMs) have been developed to derive bathymetry maps from satellite-based multispectral data of coral reefs, allowing for the management, monitoring, and protection of coral reefs. However, OSMs are often criticized due to the time-consuming requirements of iterative computations, yet they are praised for working without the need for in situ bathymetry data. EMs are praised for their time-saving characteristics and criticized for their need for in situ measurements. To estimate the water depth from multispectral data quickly without in situ bathymetry data, we provide a new EM that combines our previously developed OSM called the unmixing-based multispectral optimization process exemplar method (UMOPE) and an EM called Stumpf’s ratio method (SRM). In the new method, reflectance values from a small number of sampled pixels and the corresponding water depths estimated by UMOPE are used to determine the regression parameters for SRM. Thus, SRM determines the upper limit of accuracy for the new method, and UMOPE determines the possibility of reaching the upper limit. The new method was evaluated using three types of imagery of Xisha Islands, namely, WorldView-2 imagery with three traditional visible bands (WV-2a), Landsat 8 imagery with four visible bands, and WV-2 imagery with six visible bands (WV-2b). The results show that the new method can perform as well as SRM for Landsat 8 data and WV-2b data with similar root mean square error values at different depths. The lack of a coastal band in WV-2a imagery may cause large errors for the new method in deep water regions, especially when the water-leaving reflectance is noise perturbed. We found that even though the depths estimated by UMOPE are not error free at different ranges of water depth, if the regression line between the depths estimated by UMOPE and the measured depths is near the 1:1 line, the new method can perform as well as SRM. The new method may facilitate the rapid estimation of bathymetry from free Landsat 8 data of optically shallow waters around the world without in situ bathymetry data.

Ship wakes are more distinct than the hulls and can be visually observed in optical images. In this paper the wakes of 2836 ships in 32 optical images with different resolutions are observed and summarized. The ships are divided into four types according to the hull and wake features: fishing vessels, motorboats, cargo ships, and warships. The results show that each ship type has characteristic wakes, and there are significant differences among the categories. The probabilities of occurrence of different types of wakes and their components are shown. Turbulent wakes are inevitable. The probability of occurrence of Kelvin wakes is small and less than 40%. The visibilities of internal waves that are generated by only cargo ships are very low as a result of the harsh formation conditions. Turbulent wakes should be preferentially detected. Low-resolution images are more suitable for the detection and positioning of hulls and wakes, while high-resolution images with more details are convenient for further analysis of the size, velocity, and draft of ships. The study on the cause of the formation of the features of ship wakes in optical images proves that the classification of the wakes is reasonable and that the features of wakes can be used to initially identify the type of ship.

Surface water extraction from remote sensing imagery has been a very active research topic in recent years, as this problem is essential for monitoring the environment, ecosystems, climate, and so on. In order to extract surface water accurately, we developed a new subpixel surface water extraction (SSWE) method, which includes three steps. Firstly, a new all bands water index (ABWI) was developed for pure water pixel extraction. Secondly, the mixed water–land pixels were extracted by a morphological dilation operation. Thirdly, the water fractions within the mixed water–land pixels were estimated by local multiple endmember spectral mixture analysis (MESMA). The proposed ABWI and SSWE have been evaluated by using three data sets collected by the Landsat 8 Operational Land Imager (OLI). Results show that the accuracy of ABWI is higher than that of the normalized difference water index (NDWI). According to the obtained surface water maps, the proposed SSWE shows better performance than the automated subpixel water mapping method (ASWM). Specifically, the root-mean-square error (RMSE) obtained by our SSWE for the data sets considered in experiments is 0.117, which is better than that obtained by ASWM (0.143). In conclusion, the SSWE can be used to extract surface water with high accuracy, especially in areas with optically complex aquatic environments.

Bathymetry is important to shallow coral reef management. Remote sensing is one of the most important techniques for mapping bathymetry. For instance, multispectral remote sensing images have been widely used to map bathymetry of coral reefs. These physically based methods, which are able to retrieve bathymetry, water column inherent optical properties (IOPs), and benthic reflectance simultaneously, generally can achieve good bathymetry results. However, there are strong limitations when facing the scenarios of mixing benthic reflectance for bathymetry with multispectral images. In this study, aiming at handling the mixing phenomenon on the use of multispectral remote sensing images for mapping bathymetry, we propose a new unmixing-based method, namely unmixing-based multispectral optimization process exemplar method (UMOPE). The new method incorporates endmember variability with a linear combination of three fixed endmembers and the relaxation of the sum-to-one constrained. UMOPE was validated with two existing methods with in situ data and a WorldView-2 image in the South China Sea. Results from in situ data show that the proposed method performs the best, with the smallest absolute root mean square error ${\rm{(RMSE)}}$ (2.26 m) and the best agreement ( $R^{2}= {\text{0.91}}$ ) between the measured and estimated water depth. Moreover, results from WV-2 imagery demonstrate the superior performance for the UMOPE at depth range from about 9 to 26 m. Furthermore, since the errors from IOPs can propagate to bathymetry, we further take this issue into account by analyzing the rule of influence of bottom on IOPs. Finally, the result of benthic classification map from UMOPE is shown.

The log-ratio method (LRM) proposed by Stumpf et al. has been widely used to map bathymetry from multispectral imagery for oligotrophic waters, while the selection criteria of bands for the LRM have been subject to tradeoffs between maximum detectable depth and sensitivity. In this study, we first applied a method for global sensitivity analysis to a semi-analytical forward model of optically shallow waters with the WorldView-2 band-set. The results show that the sensitive wavelength band in water-leaving reflectance for water depth varies from the longer wavelength band to the shorter wavelength band with increasing water depth. Then, we developed an adaptive blended algorithm approach (ABAA) to seamlessly map bathymetry from the shallower region to the deeper region. The LRM with different band combinations was selected for the sub-algorithms of the ABAA. The sub-algorithms and depth range used for each sub-algorithm of the ABAA were automatically determined by the proposed applicable depth range analysis that considers logarithmic regression for the LRM. The ABAA was applied to WorldView-2 and Landsat-8 imagery of the Xisha Qundao. When the in-situ bathymetry data are available, compared with the LRM with the blue and green bands, the ABAA significantly improves the accuracy of the estimated depth, especially for waters shallower than 6 m (root mean square error (RMSE)=0.31 to 0.94 m for WorldView-2 data, RMSE=0.25 to 1.42 m for Landsat-8 data). When the in-situ bathymetry data are absent, the ABAA performs better than the LRM with a single band ratio and an optimization-based method overall.