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Phosphorus is the most important nutrient associated with lake eutrophication and changes in cyanobacterial blooms, and particulate phosphorus (PP) is the main form of phosphorus found in highly turbid inland waters. Therefore, it is urgent to monitor PP concentrations in inland water bodies. In this study, we take Hongze Lake as the research area and establish a semianalytical model to estimate PP concentrations based on the total particle absorption coefficient (ap); the mean absolute percentage error (MAPE) and root-mean-square error (RMSE) values, which indicate the model accuracy, were 14.90% and 0.009 mg/L, respectively. In addition, the construction process and parameter selection criteria of the remote sensing-based PP concentration estimation model were derived using remote sensing data obtained at different spectral resolutions. Sentinel 3 Ocean and Land Color Instrument (OLCI) and Landsat 9 Operational Land Imager version 2 (OLI-2) data were selected as representatives to verify the accuracy of the model; compared to these two datasets, the MAPE values of the models were 16.32% and 26.84%, respectively, while the RMSE values were 0.010 mg/L and 0.014 mg/L, respectively. Finally, the models were applied to Sentinel 3 OLCI and Landsat 9 OLI-2 images obtained on 16 January 2022. The results show that the spatiotemporal distributions of PP concentrations in Hongze Lake estimated from these two images were relatively consistent, but the OLI data reflected overestimations and underestimations in some areas. These research results provide a new methodology for estimating PP concentrations through remote sensing methods and help to further improve the accuracy of remotely sensed PP concentration estimations in inland water bodies.

The Hongze Lake is the fourth largest freshwater lake in China and an important lake for the East Route of the South-to-North Water Diversion Project. The water quality of the lake affects social development and the lives of residents. To assess the impacts of environmental changes and human activities on the distribution of the total suspended matter (TSM) in the Hongze Lake, we developed an algorithm that utilizes the near-infrared (NIR) band to estimate TSM based on in situ measurements. The algorithm was applied to Landsat images to derive TSM distribution maps from 1984 to 2019, revealing significant inter-annual, seasonal, and spatial variability. The relationship between TSM, precipitation, and wind speed was analyzed, and we found that: (1) The estimation model of TSM concentration in the Hongze Lake constructed for TM and OLI has a high accuracy, and it can be used to jointly monitor TSM concentration in the Hongze Lake for long-term series; (2) From 1984 to 2019, the concentration of TSM in the Hongze Lake showed a trend of first rising and then falling, with the maximum value in 2010 at 100.18 mg/L mainly caused by sand mining activities. Precipitation and wind speed weakly influence the inter-annual variation of TSM concentration; (3) The concentration of TSM in the Hongze Lake in summer is easily affected by flooding in the Huai River, and the concentration of TSM in other seasons is significantly negatively correlated with precipitation; (4) TSM is highest in the Huaihe Bay, followed by the Lihe Bay and Chengzi Bay. The main reason for this is that the input of the Huaihe Bay flows directly into this lake area and is also the main navigation channel. The results of this study are of great significance for the protection and management of the water environment of the Hongze Lake.

The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth observation systems, it has become crucial to assess hyperspectral satellite imagery capabilities on inland water monitoring. The Orbita hyperspectral (OHS) satellite is the latest hyperspectral sensor with both high spectral and spatial resolution (2.5 nm and 10 m, respectively), which could provide great potential for remotely estimating the concentration of Chl-a for inland waters. However, there are still some deficiencies that are mainly manifested in the Chl-a concentration remote sensing retrieval model assessment and accuracy validation, as well as signal-to-noise ratio (SNR) estimation of OHS imagery for inland waters. Therefore, the radiometric performance of OHS imagery for water quality monitoring is evaluated in this study by comparing different atmospheric correction models and the SNR with several remote sensing images. Several crucial findings can be drawn: (1) the three-band model ((1/B15-1/B17)B19) developed by OHS imagery is most suitable for estimating the Chl-a concentration in Dianchi Lake, with the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of 15.55 µg/L and 16.31%, respectively; (2) the applicability of the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model for OHS imagery in a eutrophic plateau lake (Dianchi Lake) was better than the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model, and QUAC (Quick Atmospheric Correction) model, as well as the dark pixel method; (3) the SNR of the OHS imagery was similar to that of Hyperion imagery and was significantly higher than SNR of the HSI imagery; (4) the spatial resolution showed slight influence on the SNR of the OHS imagery. The results show that OHS imagery could be applied to remote sensing retrieval of Chl-a in eutrophic plateau lakes and presents a new tool for dynamic hyperspectral monitoring of water quality.

Assessing the impacts of environmental change and anthropogenic activities on the historical and current total suspended matter (TSM) pattern in Dongting Lake, China, is a large challenge. We addressed this challenge by using more than three decades of Landsat data. Based on in situ measurements, we developed an algorithm based on the near-infrared (NIR) band to estimate TSM in Dongting Lake. The algorithm was applied to Landsat images to derive TSM distribution maps from 1978 to 2013 in the wet season, revealing significant inter-annual and spatial variability. The relationship of TSM to water level, precipitation, and wind speed was analyzed, and we found that: (1) sand mining areas usually coincide with regions that have high TSM levels in Dongting Lake; (2) water level and seven-day precipitation were both important to TSM variation, but no significant relationship was found between TSM and wind speed or other meteorological data; (3) the increased level of sand mining in response to rapid economic growth has deeply influenced the TSM pattern since 2000 due to the resuspension of sediment; and (4) TSM variation might be associated with policy changes regarding the management of sand mining; it might also be affected by lower water levels caused by the impoundment of the Three Gorges Dam since 2000.

Although remote sensing technology has been widely used to monitor inland water bodies; the lack of suitable data with high spatial and spectral resolution has severely obstructed its practical development. The objective of this study is to improve the unmixing-based fusion (UBF) method to produce fused images that maintain both spectral and spatial information from the original images. Images from Environmental Satellite 1 (HJ1) and Medium Resolution Imaging Spectrometer (MERIS) were used in this study to validate the method. An improved UBF (IUBF) algorithm is established by selecting a proper HJ1-CCD image band for each MERIS band and thereafter applying an unsupervised classification method in each sliding window. Viewing in the visual sense—the radiance and the spectrum—the results show that the improved method effectively yields images with the spatial resolution of the HJ1-CCD image and the spectrum resolution of the MERIS image. When validated using two datasets; the ERGAS index (Relative Dimensionless Global Error) indicates that IUBF is more robust than UBF. Finally, the fused data were applied to evaluate the chlorophyll a concentrations (Cchla) in Taihu Lake. The result shows that the Cchla map obtained by IUBF fusion captures more detailed information than that of MERIS.

Atmospheric correction is an essential prerequisite for obtaining accurate inland water color information. An inland water atmospheric correction algorithm, ACbTC (Atmospheric Correction based on Turbidity Classification), was proposed in this study by using OLCI (Ocean and Land Color Instrument) and SLSTR (Sea and Land Surface Temperature Radiometer) synergistic observations for the first time. This method includes two main steps: (1) water turbidity classification by the GRA index (GRAdient of the spectrum index); and (2) atmospheric correction by synergistic use of OLCI and SLSTR images. The algorithm was validated with 72 in situ sampling sites in Lake Erhai, Lake Hongze, and Lake Taihu, and compared with other atmospheric correction methods, i.e., C2RCC (Case 2 Regional Coast Colour processor), MUMM (Management Unit of the North Seas Mathematical Models), FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes), POLYMER (POLYnomial based algorithm applied to MERIS), and BPAC (Bright Pixel Atmospheric Correction). The results show that (1) the GRA index performed better than the proposed turbidity classification indices, i.e., the Diff (spectral difference index) and the Tind (turbid index), in inland lakes by using the reflectance peak at 1020 nm in clean water; (2) the synergistic use of OLCI and SLSTR performed feasibly for atmospheric correction, and the ACbTC algorithm achieved full-band average values of the mean absolute percentage error (MAPE) = 29.55%, mean relative percentage error (MRPE) = 13.98%, and the root mean square of error (RMSE) = 0.0039 sr−1, which were more reliable than C2RCC, MUMM, FLAASH, POLYMER, and BPAC; and (3) the synergistic use of the 17th band (865 nm) on OLCI and the 5th band (1613 nm) on SLSTR are suitable for clean inland lakes, while both the 5th band (1613 nm) and 6th band (2250 nm) on SLSTR are advisable for the turbidity.