As an environmental parameter, the chlorophyll-a concentration (Chl-a) is essential for monitoring water quality and managing the marine ecosystem. However, current mainstream Chl-a inversion algorithms have limited accuracy and poor spatial and temporal generalization in Case II waters. In this study, we constructed a quantitative model for retrieving the spatial and temporal distribution of Chl-a in the Bohai–Yellow Sea area using Geostationary Ocean Color Imager (GOCI) spectral remote sensing reflectance ($R_{rs}\left(\lambda \right)$) products. Firstly, the GOCI $R_{rs}\left(\lambda \right)$ correction model based on measured spectral data was proposed and evaluated. Then, the feature variables of the band combinations with the highest correlation with Chl-a were selected. Subsequently, Chl-a inversion models were developed using three empirical ocean color algorithms (OC4, OC5, and YOC) and four machine learning methods: BP neural network (BPNN), random forest (RF), AdaBoost, and support vector regression (SVR). The retrieval results showed that the machine learning methods were much more accurate than the empirical algorithms and that the RF model retrieved Chl-a with the best performance and the highest prediction accuracy, with a determination coefficient R² of 0.916, a root mean square error (RMSE) of 0.212 mg$·$m⁻³, and a mean absolute percentage error (MAPE) of 14.27%. Finally, the Chl-a distribution in the Bohai–Yellow Sea using the selected RF model was derived and analyzed. Spatially, Chl-a was high in the Bohai Sea, including in Laizhou Bay, Bohai Bay, and Liaodong Bay, with a value higher than 4 mg$·$m⁻³. Chl-a in the Bohai Strait and northern Yellow Sea was relatively low, with a value of less than 3 mg$·$m⁻³. Temporally, the inversion results showed that Chl-a was considerably higher in winter and spring compared to autumn and summer. Diurnal variation retrieval effectively demonstrated GOCI’s potential as a capable tool for monitoring intraday changes in chlorophyll-a concentrations.

The Taklamakan Desert Region (TDR) and the Gobi Desert Region (GDR) in East Asia significantly impact air quality, human health, and climate through dust aerosols. Utilizing the MERRA-2 dataset’s long-term dust aerosol optical depth (DAOD) at 550 nm from 2000 to 2022, we systematically monitored the spatiotemporal dynamics of DAOD. Our analysis covered annual, seasonal, and monthly scales, employing geographical detector analyses to investigate the impact of eight factors on DAOD distribution. Over the 23-year period, the interannual variability in DAOD across East Asia was not pronounced, but a discernible decreasing trend was observed, averaging an annual decrease of −0.0002. The TDR had higher DAOD values (0.337) than the GDR (0.103). The TDR showed an average annual increase of 0.004, while the GDR exhibited an average annual decrease of −0.0003. The spatial distribution displayed significant seasonal variations, with peak values in spring, although the peak months varied between the TDR and GDR. The driving factor analysis revealed that relative humidity and soil moisture significantly impacted the DAOD spatial distribution in East Asia, which were identified as common driving factors for both the region and the major dust sources. Complex mechanisms influenced the variation in DAOD, with interactions between variables having a greater impact than individual effects. The geodetector-derived interaction q-value identified the collective impact of soil temperature and relative humidity (0.896) as having the highest impact on the spatial and temporal DAOD distribution. The overall spatial pattern exhibited a nonlinear enhancement trend, with the TDR and GDR showing bilinear enhancement patterns. These findings contribute to a better understanding of the factors influencing DAOD, offering a theoretical basis for atmospheric pollution control in East Asia.

Dust storms are of great importance to climate change, air quality, and human health. In this study, a complete application frame of integrating hybrid methods based on multi-source data is proposed for remote sensing monitoring and process analysis of dust storms. In the frame, horizontal spatial distribution of dust intensity can be mapped by optical remote sensing products such as aerosol optical depth (AOD) from MODIS; the vertical spatial distribution of dust intensity by LIDAR satellite remote sensing products such as AOD profile from CALIPSO; geostationary satellite remote sensing products such as Chinese Fengyun or Japanese Himawari can achieve high-frequency temporal distribution information of dust storms. More detailed process analysis of dust storms includes air quality analysis supported by particulate matter (PM) data from ground stations and the dust emission trace and transport pathways from HYSPLIT back trajectory driven by meteorological data from the Global Data Assimilation System (GDAS). The dust storm outbreak condition of the source location can be proved by precipitation data from the WMO and soil moisture data from remote sensing products, which can be used to verify the deduced emission trace from HYSPLIT. The proposed application frame of integrating hybrid methods was applied to monitor and analyze a very heavy dust storm that occurred in northern China from 14–18 March 2021, which was one of the most severe dust storms in recent decades. Results showed that the dust storm event could be well monitored and analyzed dynamically. It was found that the dust originated in western Mongolia and northwestern China and was then transmitted along the northwest–southeast direction, consequently affected the air quality of most cities of northern China. The results are consistent with the prior research and showed the excellent potential of the integration of the hybrid methods in monitoring dust storms.