Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R² = 0.74, RMSE = 0.42 mg/m³, MAE = 0.33 mg/m³, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km²) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m³ for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.

Using Deep Bay in China as an example, an effective method for the retrieval of total suspended sediment (TSS) concentration using HJ-1A/1B satellite images is proposed. The factors driving the variation of the TSS spatial distribution are also discussed. Two field surveys, conducted on August 29 and October 26, 2012, showed that there was a strong linear relationship (R² = 0.9623) between field-surveyed OBS (optical backscatter) measurements (5-31NTU) and laboratory-analyzed TSS concentrations (9.89–35.58 mg/L). The COST image-based atmospheric correction procedure and the pseudo-invariant features (PIF) method were combined to remove the atmospheric effects from the total radiance measurements obtained with different CCDs onboard the HJ-1A/1B satellites. Then, a simple and practical retrieval model was established based on the relationship between the satellite-corrected reflectance band ratio of band 3 and band 2 (Rrs3/Rrs2) and in-situ TSS measurements. The R² of the regression relationship was 0.807, and the mean relative error (MRE) was 12.78%, as determined through in-situ data validation. Finally, the influences of tide cycles, wind factors (direction and speed) and other factors on the variation of the TSS spatial pattern observed from HJ-1A/1B satellite images from September through November of 2008 are discussed. The results show that HJ-1A/1B satellite CCD images can be used to estimate TSS concentrations under different tides in the study area over synoptic scales without using simultaneous in-situ atmospheric parameters and spectrum data. These findings provide strong informational support for numerical simulation studies on the combined influence of tide cycles and other associated hydrologic elements in Deep Bay.

Suspended particulate matter plays a significant role in the studies of sediment fluxes, phytoplankton dynamics, and water optical properties. This study focuses on the relationships between particle size distribution (PSD), water’s inherent optical properties (IOPs), and water constituents. We investigated the complex waters of Poyang Lake, the largest freshwater lake in China, in wet and dry seasons during 2008–2011. Because of the distinct temporal–spatial variation of Poyang Lake, these parameters and relationships also demonstrate seasonal and regional variability. The variation range of the concentration of suspended particulate matter is $0.32–69.08\mathrm{mg}/\mathrm{l}$, with a mean value of $22.21\mathrm{mg}/\mathrm{l}$. The median particle size in the dry season is much larger than that of the wet season. The Junge distribution fits the PSD of Poyang Lake very well in the scope of 6.21–331 μm. Furthermore, the slopes of the PSD range from 3.54 to 4.69, with a mean value of 4.11, with the steepest slopes ($>4.5$) occurring in the waters around Songmen Mountain Island and the northern waterway. A negative correlation was found between median particle size ($D_{v50}$) and the mass-specific absorption coefficient at 443 nm $[a_p^m(443)]$ for both wet and dry seasons. Identical to analogous waters, the spectral slopes of the PSD correlate well with the spectral slopes of the attenuation coefficient, but with different fitted formulas. In the dry season, the particle size can better explain the variability of the scattering coefficient, while the mass-specific scattering coefficient is better explained by the apparent density. However, no similar results were found for the wet season. In addition, the spectral slopes of the backscattering coefficient correlated well with the PSD slope, and the bulk refractive index calculated from the backscattering ratio and PSD slope can indicate the particle composition of Poyang Lake. Overall, the knowledge on the PSD and IOPs gained in this study broadens our understanding of water optics in highly turbid water columns.

The six largest Arctic rivers (Yenisey, Lena, Ob’, Kolyma, Yukon, and Mackenzie) drain the organic-rich Arctic watersheds and serve as important pools in the global carbon cycle. Satellite remote sensing data are considered to be a necessary supplement to the ground-based monitoring of riverine organic matter circulation, especially for the ice-free periods in high-latitudes. In this study, we propose a remote sensing retrieval algorithm to obtain the chromophoric dissolved organic matter (CDOM) levels of the six largest Arctic rivers using Sentinel-2 images from 2016 to 2018. These CDOM results are converted to dissolved organic carbon (DOC) concentrations using the strong relationship (R² = 0.89) between the field measurements of these two water constituents. The temporal-spatial distributions of the DOC in the six largest Arctic rivers during ice-free conditions are depicted. The performance of the retrieval algorithm verifies the capacity of using Sentinel-2 data to monitor riverine DOC variations due to its improved spatial resolution, better band placement, and increased observation frequency. River discharge, watershed slopes, human activities, and land use/land cover change drove much of the variation in the satellite-derived DOC. The seasonality, geography, and scale would affect the correlation between DOC concentration and these influence factors. Our results could improve the ability to monitor DOC fluxes in Arctic rivers and advance our understanding of the Earth’s carbon cycle.