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.

On 15 January 2022, a violent eruption and tsunami of the Hunga Tonga-Hunga Ha’apai (HTHH) volcano in Tonga, South Pacific, caused widespread international concern. In order to detect the anomalous environmental response caused by the HTHH volcanic eruption based on GNSS ionospheric data, GNSS tropospheric data and GNSS coordinate time series, a new method combining the zenith non-hydrostatic delay difference method and the extreme-point symmetric mode decomposition (ESMD) method, was proposed to detect tropospheric anomalies. The moving interquartile range method and the ESMD method were introduced to detect ionospheric anomalous and coordinate time series anomalies, respectively. The results showed that 9–10 h before the eruption of the Tonga volcano and 11–12 h after the eruption of the Tonga volcano, obvious total electron content (TEC) anomalies occurred in the volcanic eruption center and its northeast and southeast, with the maximum abnormal value of 15 TECU. Significant tropospheric anomalies were observed on the day of the HTHH volcano eruption as well as 1–3 days and 16–17 days after the eruption, and the abnormal intensity was more than 10 times that of normal. The coordinate time series in direction E showed very significant anomalies at approximately 2:45 p.m. on 14 January, at approximately 4:30 a.m.–5:40 a.m. on 15 January, and at approximately 3:45 a.m. on 16 January, with anomalies reaching a maximum of 7–8 times daily. The abnormality in the direction north (N) is not obvious. Very prominent anomalies can be observed in the direction up (U) at approximately 4:30 a.m.–5:40 a.m., with the intensity of the anomalies exceeding the normal by more than 10 times. In this study, GNSS was successfully used to detect the anomalous environmental response during this HTHH volcano eruption.

Water transparency is one of the important parameters to describe the optical properties of seawater. It's a visual reflection of the degree of turbidity of seawater and the degree of absorption and scattering of light by seawater. Firstly, this paper verified and analyzed the suitability of the water transparency retrieval method proposed by Lee in 2015 (denoted as Zsd_lee_2015) for Jiaozhou Bay and Qingdao offshore waters by using the in-situ data. Secondly, the water transparency estimation results retrieved from GOCI (Geostationary Ocean Color Imager) using four atmospheric correction algorithms were compared and analyzed with in-situ data in study area. The four atmospheric correction algorithms are the standard atmospheric correction algorithms of KOSC GOCI GDPS1.3 and GDPS2.0, the standard near-infrared atmospheric correction algorithm of NASA, and MUMM (Management Unit of the North Sea Mathematical Models). At last, GOCI data on Mar 09, 2018 were used to analyze the differences of hourly changes of transparency caused by different atmospheric correction algorithms in detail. The results show that the Zsd_lee_2015 model is suitable for the study area (R ² >0.8, MAPE = 16.4%). The difference of transparency value caused by atmospheric correction algorithm cannot be ignored completely. Moreover, there are great differences in the moment when the maximum value of transparency presented based on different atmospheric correction algorithms, while the moment when the minimum value of transparency presented is basically the same, mostly occurring at 00:16. The results cast doubt on the previous conclusions about the hourly variations of biochemical parameters using GOCI because the hourly variations obtained under different atmospheric correction algorithms are not completely consistent.

Secchi disk depth (SDD) is a crucial indicator for assessing changes in the water environment. Coastal water transparency varies due to weather, climate, and human activities. Systematic observations are crucial for assessing water quality and ecosystem health. This study used Landsat 8 imagery to investigate the transparency of Jiaozhou Bay (JZB) from 2013 to 2022 and to explore the primary factors influencing transparency changes. Among natural factors, precipitation plays a dominant role in transparency changes. After 2019, there was an increase in rainfall in JZB, which usually leads to reduced transparency. However, the significant improvement in transparency, attributed to a sharp reduction in human activities due to the coronavirus disease 2019 (COVID-19), highlights the substantial impact of human activities on JZB's transparency.

The current manual spectra acquisition for monitoring water constituents has resulted in discontinuous data acquisition, insufficient amount of data, and small ocean coverage. This article presents the design of a novel fully automatic ocean spectra acquisition and control system based on the real-time solar angle analyzing and tracking. To ensure that the requirements for spectra acquisition are met, the system is capable of accurately calculating the solar angle by collecting the information of latitude, longitude, date, time and direction, and automatically adjusting the position of instrument observation plane and the pointing angle of fiber optic probe in real-time. It achieves full automation of collecting the downward radiance of skylight, the upward radiance from reference panel and seawater separately through controlling the rotation of fiber optic probe. A 188-day observation experiment was carried out at the coastal ocean experimental station in Qingdao from September 11, 2018 to March 17, 2019. After that, the system was conducted onboard the Dongfanghong 3 scientific research vessel for a one-month demonstration and sea trial in June 2019. Comparative experiments including manual spectra collection, chlorophyll-a sensor measurement, and water samples collection were carried out. The experimental results show that the relative error of the spectra between the system and manual collection is less than 5%, and the relative error of the remote sensing reflectance calculated by the spectra is less than 4%. Considering the chlorophyll-a concentration obtained by the sensor and the water samples as the true value, the relative error of the chlorophyll-a concentration obtained by the system is 10% and 25% respectively. The results show its full and reliable capacity in collecting spectra of seawater automatically and continuously in real-time, with satisfactory accuracy and timeliness.