Water transparency, measured with Secchi disk depth (SDD), is an important parameter for describing the optical properties of a water body. This study evaluates variations of SDD and related impact factors in the Bohai and Yellow Seas (BYS). Based on a new mechanistic model proposed by Lee et al. (2015) applied to MODIS remote sensing reflectance data, climatological SDD variation from 2003 to 2019 was estimated. The annual mean images showed an increasing trend from the coastal zone to the deep ocean. Lower values were found in the Bohai Sea (BHS), while higher values observed in the center of the southern Yellow Sea (SYS). Additionally, the entire sea has shown a decreasing temporal tend, with the variation rate lowest in the BHS at 0.003 m y⁻¹, and highest in the SYS at 0.015 m y⁻¹. However, the weak increasing trend that appeared since 2017 suggests that water quality seems to have improved. Further, it displayed seasonal patterns of low in winter and spring and high in summer and autumn. The empirical orthogonal function (EOF) analysis of SDD variations over the BYS, shows that the first SDD EOF mode is the highest, strongly correlated with total suspended matter. With the high correlation coefficients of chromophoric dissolved organic matter, it illustrates that the SDD variation is mainly dominated by the optical components in the seawater, although correlation with chlorophyll-a is the weakest. The second and third EOF modes show that photosynthetically available radiation, sea surface temperature, sea surface salinity, and wind speed are the main covariates that cause SDD changes. Water transparency evaluation on a long-term scale is essential for water quality monitoring and marine ecosystem protection.

Chlorophyll-a (Chl-a) is an objective biological indicator, which reflects the nutritional status of coastal waters. However, the turbid coastal waters pose challenges to the application of existing Chl-a remote sensing models of case II waters. Based on the bio-optical models, we analyzed the suppression of coastal total suspended matter (TSM) on the Chl-a optical characteristics and developed an improved model using the imagery from a hyper-spectrometer mounted on an unmanned aerial vehicle (UAV). The new model was applied to estimate the spatiotemporal distribution of Chl-a concentration in coastal waters of Qingdao on 17 December 2018, 22 March 2019, and 20 July 2019. Compared with the previous models, the correlation coefficients (${\mathrm{R}}^2$) of Chl-a concentrations retrieved by the new model and in situ measurements were greatly improved, proving that the new model shows a better performance in retrieving coastal Chl-a concentration. On this basis, the spatiotemporal variations of Chl-a in Qingdao coastal waters were analyzed, showing that the spatial variation is mainly related to the TSM concentration, wind waves, and aquaculture, and the temporal variation is mainly influenced by the sea surface temperature (SST), sea surface salinity (SSS), and human activities.

A bridge over water is typically an artificial object that can be damaged by changes in landform, geology, hydrology, and other surrounding conditions. Natural disaster scenarios must be studied using high-spatial-resolution remote-sensing images to detect and assess damage to bridges over water. Such studies are important for emergency rescue operations, disaster relief, and rapid disaster assessment. This article describes a method of damage detection and assessment for bridges over water using high-spatial-resolution remote-sensing images based on research regarding the selection and expression of characteristic knowledge.

Error estimation of precipitation products is an important procedure in the data quality evaluation. It is a challenging task due to the lack of the in situ ground observations and the variations of the geophysical characteristics in regions with complex terrain. Compared with the traditional methods, the double instrumental variable (DIV) method has the merits of being able to estimate the errors between two products. In this study, the DIV method for data error estimation is applied and validated on precipitation products in regions with complex terrain. The DIV-based errors for two state-of-the-art precipitation products Integrated Multisatellite Retrievals for Global Precipitation Measurement Mission (IMERG) and Soil Moisture to Rain (SM2RAIN) are being further verified by using another high-accuracy ground-based precipitation products China Merged Precipitation Analysis (CMPA). The results indicate that the DIV-based errors of IMERG and SM2RAIN range from 0 to 25 mm per day and from 0 to 15 mm per day, respectively. The root-mean-square errors (RMSEs) of IMERG and SM2RAIN compared with CMPA, which are defined as CMPA-based errors, are ranging from 0 to 23 and 0 to 22 mm, respectively. It is concluded that the spatial distribution of the DIV-based errors shows the consistency with the CMPA-based errors, which further demonstrates the potential of using the DIV method for precipitation products fusion.

To overcome the drawback that spectral matching is time consuming because of the large volume of remote sensing data, a new parallel algorithm of sea surface oil spill identification based on Graphic Process Unit (GPU) is presented in this paper by taking advantage of HJ-1 CCD remote sensing data. Taking minimum distance classification as an example, we reorganized the pre-processed image data, selected reference spectral data and stored in constant memory, and then the algorithm core code of the host was transplanted to the device so as to realize parallelization on NVIDIA GeForce GTX 550Ti device. Results showed that, the maximum speed-up ratio is up to 102. Finally, the advantage of GPU parallel computing for computationally intensive problems has also been validated through experiments. When the image size is 2048x2048, speed-up ratio is up to 89.