To improve the ability of remote sensing technology in recognizing black-odorous water bodies in Hangzhou, this study analyzed the typical spectral characteristics of black-odorous water in Hangzhou based on measured spectral data and water quality parameters, including the transparency, dissolved oxygen, oxidation reduction potential, and ammonia nitrogen. The single-band threshold method, the normalized difference black-odorous water index (NDBWI) model, the black-odorous water index (BOI) model, and the color purity on a Commission Internationale de L’Eclairage (CIE) model were compared to analyze the spatial and temporal distribution characteristics of the black-odorous water in Hangzhou. The results showed that: (1) The remote sensing reflectance of black-odorous water was lower than that of ordinary water, the spectral curve was gentle, and the wave peak shifted toward the near-infrared direction in the wavelength range of 650–850 nm; (2) Among the aforementioned models, the normalized and improved normalized black-odorous water index methods had a higher accuracy, reaching 87.5%, and the threshold values for black-odorous water identification were 0.14 and 0.1, respectively; (3) From 2015 to 2018, the quantity of black-odorous water in the main urban area of Hangzhou showed a decreasing trend, and black-odorous water was mainly distributed in the Gongshu District and tended to appear in narrow rivers, densely populated areas, and factory construction sites. This study is expected to be of great practical value for the rapid tracking and monitoring of urban black-odorous water by using remote sensing technology for future work.

Due to the significant spatial variation of the performance of Moderate Resolution Imaging Spectrometer (MODIS) aerosol optical depth (AOD) retrievals, validation is very important for applications of MODIS AOD products at regional scales. This study presents a comparative analysis of Collection 6.1 MODIS AOD retrievals and ground measurements from five local sites and one Aerosol Robotic Network (AERONET) site in the Yangtze River Delta (YRD) region, which significantly complements the previous validation that utilized limited AERONET measurements. Generally, MODIS AOD retrievals showed a reasonable agreement with collocated ground measurements (R² > 0.7), with 66% of Dark Target (DT) 10 km retrievals, 56% of Deep Blue (DB) 10 km retrievals, and 69% of DT 3 km retrievals falling within the expected error (EE = ±(0.05 + 0.2 × AOD)). Nevertheless, it was found that the DT AOD retrievals tended to be overestimated over urbanized and lakeside sites, while the DB AOD retrievals tended to be underestimated over all ground sites except for lakeside sites. Such patterns appeared to be linked with the systematic biases of the single-scattering albedo estimation in the AOD retrieval algorithms. Another significant finding of this study is that the uncertainties of the MODIS AOD retrievals were highly correlated with the land cover proportions of urbanized features and water (LCP_UW) in the surrounding region, especially for the DT products. An empirical correction method based on these correlations could substantially reduce the uncertainties of DT AOD products over high LCP_UW areas. The results not only highlight the significant impacts of both urban and water areas on the MODIS AOD retrieval algorithms but also create new possibilities to correct such impacts once the universal correlations between LCP_UW and the uncertainty measures are established.

Suspended particulate matter is a crucial component in estuaries and coastal oceans, and a key parameter for evaluating their water quality. The Bohai Sea, a huge marginal sea covering an expanse of 77,000 km² and constantly fed by numerous sediment-laden rivers, has maintained a high level of total suspended particulate matter (TSM). Despite the widespread development and application of TSM retrieval algorithms using commonly available satellite data like Landsat, Sentinel, and MODIS, developing TSM retrieval algorithms for China’s Gaofen (GF) series (GF-6 and GF-1) in the Bohai Sea is still a great challenge, mainly due to the limited applicability of empirical algorithms. In this study, 259 in situ measured-TSM samples were collected for algorithm development. The remote sensing reflectance (R_rs) curve demonstrates prominent peaks between 550 and 580 nm. Through conversion to remote sensing reflectance, it was found that single-band data had a weak correlation with TSM, reaching a maximum correlation of 0.44. However, by combining bands of band ratio calculations, the correlation was enhanced. Particularly, the blue and green band equivalent R_rs ratio had a correlation coefficient of 0.81 with TSM, and the proposed TSM inversion exponential algorithm developed based on this factor obtained an R-squared (R²) value of 0.76 and a mean relative error (MRE) of 32.24%. Analysis results indicated that: (1) there are spatial variations in the TSM within the Bohai Sea, Laizhou Bay, and the Yellow River estuary, with higher levels near the coast and lower levels in open waters. The Yellow River estuary experiences seasonal fluctuations higher TSM during spring and winter, and lower variations during summer and autumn, and (2) the dynamics of TSM are affected by Yellow River runoff, with increased runoff leads to higher TSM levels and expanded turbid zones. This study proposes a new algorithm to quantify TSM evolutions and distributions in the Bohai Sea and adjacent regions using China’s Gaofen imageries.

In recent years, although the quality of the atmospheric environment has improved in some regions, monitoring air quality remains crucial. Aerosol optical thickness (aerosol optical depth, AOD) reflects the attenuation effect of atmospheric aerosol on light, which is an important parameter in aerosol research and plays an important role in the study of the atmosphere. Based on the CE-318 photometer data of 2015, 2016, and 2020 in Hangzhou and the MODIS satellite AOD data of 2015, 2016, and 2020, this paper first obtains the correlation between the MODIS AOD data and the ground-measured data by linear fitting. Then, the spatial and temporal distribution of the MODIS AOD data from 2012 to 2020 is analyzed. The analysis results indicate that the average correlation coefficient between the MODIS AOD data and ground-measured data is 0.77, and the average relative error is 30.53%. Thus, the MODIS AOD data can be used as an important basis for atmospheric research in Hangzhou. Based on this, the conclusions are as follows: (1) the AOD value in Hangzhou has been decreasing in recent years, from 0.43 in 2012 to 0.28 in 2020, with an average annual decrease of 0.017. (2) The AOD value in Hangzhou is large in spring and summer, and small in autumn and winter, with an average AOD value of 0.45 in spring, 0.39 in summer, 0.30 in autumn, and 0.33 in winter. (3) The AOD value in Hangzhou is large in the east and small in the west, and the AOD values in the Hangzhou urban area, Xiaoshan, and Yuhang are higher.

Hangzhou Bay (HZB) is the largest macro-tidal bay in China, where suspended sediment concentrations are significantly modulated by tidal oscillations. This makes it an ideal area for the study of the impact of tide on temporal–spatial variation in suspended sediment. The GaoFen-4 (GF-4) satellite is the first high-resolution geosynchronous orbiting satellite of China. It exhibits the unique advantages of capturing minute variations and finer details of total suspended matter (TSM) due to the enhancement in spatial resolution (50 m) and observation time interval (20 s). In this study, TSM concentration of the HZB was retrieved based on the GF-4 satellite. The spatial distribution and minute variations of TSM concentration under the ebb tide from 7:30 to 7:40 a.m. on 28 August 2017, were analyzed. The results showed that the average TSM concentration inside HZB was (371.8 ± 1.8) mg/L. There was a linearly increasing trend of TSM concentration at ebb tide, with an increment of (3.96 ± 0.31) (mg/L)/min, and a more significant increase was observed in the high TSM areas. This increase in TSM concentration was associated with both the bottom topography and tide processes. The tidal potential energy generated by the tidal range and the strong shear stress generated by the high current velocity both led to the re-suspension of the sedimentary particles, which affected the variation of TSM concentration. In addition, the influence of bottom topography changed the intensity of re-suspension and also affected the distribution of TSM concentration in HZB.

As coastal tidal flats—ecosystems of high ecological significance and socio-economic value—face accelerating degradation driven by climate change and intensified anthropogenic disturbances, there is an urgent need for efficient, automated, and scalable monitoring solutions. Traditional monitoring approaches are constrained by high implementation costs and limited spatial coverage, whereas remote sensing—particularly multispectral satellite imagery such as Sentinel-2—has emerged as a primary and widely adopted tool for large-scale environmental observation. Building upon recent advancements in cloud computing and WebGIS technologies, this study presents a web-based, interactive tidal flat extraction system implemented on Alibaba’s AI Earth platform. The system integrates multiple water indices (NDWI, mNDWI, and IWI) with a machine learning algorithm (Random Forest), and is deployed through a user-friendly interface developed using Vue.js and Leaflet, enabling flexible parameter configuration and real-time visualization of extraction results. Its front-end/back-end decoupled architecture enables non-programming users to conduct large-scale tidal flat mapping, thereby substantially lowering the technical barriers to coastal tidal flat monitoring and management in China.