The quantitative retrieval of the chlorophyll-a concentration is an important remote sensing method that is used to monitor the nutritional status of water bodies. The high spatial resolution of the Sentinel-2 MSI and its subdivision in the red-edge band highlight the characteristics of water chlorophyll-a, which is an important detection tool for assessing water quality parameters in plateau lakes. In this study, the Nine Plateau Lakes in the Yunnan-Kweichow Plateau of China were selected as the study area. Using Sentinel-2 MSI transit images and in situ measured chlorophyll-a concentration as the data source, the chlorophyll-a concentrations of plateau lakes (CCAPLs) were investigated, and the surface temperatures of plateau lakes (STPLs) were retrieved to verify the hypothesis that the lake surface temperature could increase the chlorophyll-a concentration. By comparing feature importance using a random forest (RF), the Sentinel-2 MSI surface reflectance and in situ data were linearly fitted using four retrieval spectral indices with high feature importance, and the accuracy of the estimated concentration of chlorophyll-a was evaluated by monitoring station data in the same period. Then, Landsat-8 TIRS Band 10 data were used to retrieve the STPL with a single-channel temperature retrieval algorithm and to verify the correlation between the STPL and the CCAPL. The results showed that the retrievals of the CCAPL and the STPL were consistent with the actual situation. The root-mean-square error (RMSE) of the fifteenth normalized difference chlorophyll-a index (NDCI15) was 0.0249. When the CCAPL was greater than 0.05 mg/L and the STPL was within 28–34 °C, there was a positive linear correlation between the CCAPL and the STPL. These results will provide support for the remote sensing monitoring of eutrophication in plateau lakes and will contribute to the scientific and effective management of plateau lakes.

The lake water surface temperature (LWST) is a critical parameter influencing lake ecosystem dynamics and addressing challenges posed by climate change. Traditional point measurement techniques exhibit limitations in providing comprehensive LWST data. However, the emergence of satellite remote sensing and unmanned aerial vehicle (UAV) Thermal Infrared (TIR) technology has opened new possibilities. This study presents an approach for retrieving plateau lake LWST (p-LWST) from UAV TIR data. The UAV TIR dataset, obtained from the DJI Zenmuse H20T sensor, was stitched together to form an image of brightness temperature (BT). Atmospheric parameters for atmospheric correction were acquired by combining the UAV dataset with the ERA5 reanalysis data and MODTRAN5.2. Lake Water Surface Emissivity (LWSE) spectral curves were derived using 102 hand-portable FT-IR spectrometer (102F) measurements, along with the sensor’s spectral response function, to obtain the corresponding LWSE. Using estimated atmospheric parameters, LWSE, and UAV BT, the un-calibrated LWST was calculated through the TIR radiative transfer model. To validate the LWST retrieval accuracy, the FLIR Infrared Thermal Imager T610 and the Fluke 51-II contact thermometer were utilized to estimate on-point LWST. This on-point data was employed for cross-calibration and verification. In the study area, the p-LWST method retrieved LWST ranging from 288 K to 295 K over Erhai Lake in the plateau region, with a final retrieval accuracy of 0.89 K. Results demonstrate that the proposed p-LWST method is effective for LWST retrieval, offering technical and theoretical support for monitoring climate change in plateau lakes.

Remote sensing-based retrieval of lake water quality parameters has become an increasingly effective approach for monitoring the environmental status of water bodies and maintaining the ecological balance of lakes. However, due to the intricate non-linear correlation between the water quality parameters and the optical properties of water bodies, existing remote sensing retrieval methods still struggle to simultaneously resolve the multi-component parameters of water bodies from their spectral data. This study proposes a robust and versatile deep learning framework (DLMWQR) that leverages hyperspectral data to facilitate the simultaneous and independent estimation of multiple water quality parameters in lake waters, such as Chlorophyll a (Chl-a), Total Suspended Solids (TSS), and Secchi Disk Depth (SDD). Based on hyperspectral reflectance measurements data as an example, the results demonstrate that the proposed framework is capable of effectively retrieving multiple water quality parameters simultaneously, with R² values of 0.77, 0.85, and 0.94 for Chl-a, TSS, and SDD, respectively. When individually retrieving each parameter, the framework achieved an R² of 0.89 for Chl-a with RMSE of 1.70 mg/m³, an R² of 0.76 for TSS with an RMSE of 35.28 mg/L, and an R² of 0.90 for SDD with an RMSE of 0.53 m. The results demonstrate a significant improvement in R² values compared to traditional machine learning (SVR, XGBoost) and deep learning (CNN, RNN) retrieval methods. when simultaneously retrieving these water quality parameters. The proposed method is also applicable to hyperspectral remote sensing imagery, achieving R² values of 0.81 and 0.85 for the independent retrieval of SDD and TSS. The proposed DLMWQR method maintains robust applicability with R² = 0.45 and RMSE = 2.33 mg/m³ in marine environments. This multi-parameter retrieval framework offers excellent applicability and robustness, providing a reliable approach for both the retrieval and analysis of water quality parameters and for studying the spatial and temporal dynamics of lake and inland water bodies.

The frequency and spatial extent of cyanobacterial bloom outbreaks have increased in recent years due to climate warming and human activities, causing significant harm to inland water ecosystems. Phycocyanin (PC), a characteristic pigment of cyanobacteria, plays a crucial role in detecting cyanobacterial blooms and providing early warnings. However, accurately estimating PC in turbid waters using remote sensing is challenging due to the optical complexity of most inland waters and the relatively weak optical signal of PC. To address this issue, an enhanced three-band algorithm (ETBA) is proposed for retrieving PC concentration in inland waters. Since the PC absorption feature at 620 nm is prone to interference from non-phycocyanin pigments, the algorithm incorporates the quantification of the absorption coefficient of non-phycocyanin pigments at 620 nm (a _ph-pc (620)) to improve the accurate of estimating a _pc (620) . Using in-situ observation data from Erhai Lake as an example, the results show that ETBA significantly outperforms the original three-band algorithm (TBA) in mitigating interference from non-phycocyanin pigments. Among all PC algorithms considered in this study, the ETBA achieved the lowest mean relative error (12.76%) and root-mean-square error (0.37 μg L ^-1 ), with a goodness-of-fit of R2 = 0.73. Therefore, the introduction of the ETBA has a direct positive impact on monitoring harmful algal blooms caused by cyanobacteria in inland and coastal water.

Chromophoric dissolved organic matter (CDOM) is a crucial component of aquatic environments. Accurately quantifying the content of CDOM is essential for supporting lake water quality monitoring and management. In this study, we utilized quasi-analytical algorithms (QAA_v6 and QAA_CDOM) to estimate the CDOM concentration in the intricate freshwater environment of Erhai Lake. This was accomplished by utilizing in-situ hyperspectral data and water sample measurements. A quasi-analytical algorithm, named QAA_CDOM_680, was proposed to retrieve CDOM absorption coefficients from Sentinel-3 satellite images by optimizing the reference wavelength based on these two algorithms. The root mean square errors (RMSE) for QAA_V6, QAA_CDOM algorithm, and the new algorithm QAA_CDOM_680 are 0.615, 0.2235, and 0.1935, respectively. The calibration determination coefficients (R ² ) of the three algorithms are 0.44, 0.51, and 0.63, respectively. The new algorithm demonstrates a significant improvement in accuracy. The results of two satellite-ground synchronous inversions indicate that the calibration determination coefficients (R ² ) of CDOM absorption coefficients in the Erhai Lake research area are 0.42 and 0.48, respectively. This study has successfully completed the dynamic quantification monitoring of CDOM in Erhai Lake, contributing to the preservation of Erhai Lake’s water quality.