Hydrological processes drive the transport of phosphorus (P) from soil to surface water. This study is the first to use space-based observations to examine P storage in watershed lakes. Based on the vertical distribution characteristics of the total P (TP) concentration in multiple eutrophic lakes, a remote sensing estimation method for water column integrated P storage in eutrophic lakes was proposed using machine learning. The results showed that the TP profile followed a quadratic distribution that was primarily influenced by chlorophyll-a in shallow water and suspended particulate matter (SPM) in deep water. Based on this observation, a water column TP mass estimation algorithm was developed using extreme gradient boosting (XGBoost) to estimate surface TP, combined with an adjusted floating algae index (FAI) and near-infrared band. The algorithm achieved R-2 >= 0.6, with the error increasing with depth. Then, water depth and lake spatial information were added to the algorithm, the average P storage of 35 large lakes in the Jianghuai region was calculated as 5347 t, and the lake area explains 85% of the P storage. The modeled P storage in Lake Taihu and Chaohu exhibited increasing trends that were mainly driven by the water level. This study is the first to observe lake P storage from space and to help elicidate the P cycle in shallow eutrophic lakes. At present, the Yangtze River Basin exports large amounts of P, lakes reduce P loss in the basin and enrich, and there is still great potential for the recycling and utilization of P resources.

Lake Taihu has a history of recurrent harmful cyanobacterial blooms. There is a need to better understand the aquatic ecosystem of Lake Taihu in order to improve methods for controlling the cyanobacterial blooms. Based on the field measurement and satellite remote sensing, we produced and collected a time-series dataset, including the water quality, bio-optics, climate, and anthropogenic data of Lake Taihu (THQBCA), which could provide comprehensive information regarding cyanobacterial blooms. The THQBCA dataset contains 26 variables organized into four categories: water quality, bio-optics, climate, and anthropogenic data. The water quality and climate data are field measured data with sampling frequency from daily to quarterly, and bio-optics and anthropogenic data are satellite-derived annual data. The dataset spans more than 15 years (8 of which cover approximately 35 years, 4 of which cover 20 years), and the spatial resolutions of the satellite-derived data range from 30 m to 500 m. This dataset is expected to advance research on evaluating and predicting cyanobacterial blooms, and support science-based management decisions for sustainable ecological development.

Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (Rrs) and the inversion of surface chlorophyll-a concentration (Chla). In this study, vertical profiles of Chla(z) (where z is the water depth) and field Rrs (Rrs_F) were collected and utilized to retrieve the vertical profiles of Chla in Lake Chaohu in China. Chla(z) was categorized into vertically uniform (Type 1: N = 166) and vertically non-uniform (Type 2: N = 58) types. Based on the validation of the atmospheric correction performance of the Geostationary Ocean Color Imager (GOCI), a Chla(z) inversion model was developed for Lake Chaohu from 2011 to 2020 using GOCI Rrs data (Rrs_G). (1) Five functions of non-uniform Chla(z) were compared, and the best result was found for Chla(z) = a × exp(b × z) + c (R2 = 0.98, RMSE = 38.15 μg/L). (2) A decision tree of Chla(z) was established with the alternative floating algae index (AFAIRrs), the fluorescence line height (FLH), and wind speed (WIN), where the overall accuracy was 89% and the Kappa coefficient was 0.79. The Chla(z) inversion model for Type 1 was established using the empirical relationship between Chla (z = surface) and AFAIRrs (R2 = 0.58, RMSE = 10.17 μg/L). For Type 2, multivariate regression models were established to estimate the structural parameters of Chla(z) combined with Rrs_G and environmental parameters (R2 = 0.75, RMSE = 72.80 μg/L). (3) There are obvious spatial variations in Chla(z), especially from the water surface to a depth of 0.1 m; the largest diurnal variations were observed at 12:16 and 13:16 local time. The Chla(z) inversion method can determine Chla in different layers of each pixel, which is important for the scientific assessment of phytoplankton biomass and lake carbon and can provide vertical information for the short-term prediction of algal blooms (and the generation of corresponding warnings) in lake management.

Column-integrated algal biomass (CAB), represented by integrated Chlorophyll-a concentrations (Chla) throughout the water column, is indicative of overall algal biomass for the entire lake. The performance of biomass retrieval algorithms is limited since previous research on CAB underestimate the impact of subsurface information. To enhance the CAB estimation in shallow lakes, an upper-to-lower method and a bivariate model were combined and a novel algorithm was applied to Lake Chaohu. Maximum Chlorophyll Index (MCI) algorithm outperformed the other three surface Chla retrieval algorithms for surface Chla inversion (R2 = 0.73, RMSE = 10.06 μg/L, MAPE = 35.22 %). The upper-to-lower method was used for subsurface Chla retrieval, depending on the empirical regression between two adjacent layers. The performance of different surface–subsurface combinations was validated to determine the optimal one for algorithm development. The algorithm was tested in two scenarios (no-bloom scenario and no-bloom + bloom scenario), displaying superior performance compared to conventional ones (R2 = 0.83, RMSE = 16.77 μg/L, MAPE = 25.68 %, N = 36, in no-bloom scenario; R2 = 0.88, RMSE = 32.34 μg/L, MAPE = 38.15 %, N = 50, in no-bloom + bloom scenario). Furthermore, the application of the algorithm was further extended to OLCI imageries, exhibiting a spatial consistency with RGB composites. The enhanced stability and reliability of total biomass estimations can provide a more comprehensive understanding of aquatic ecosystem.

The lake eutrophication is highly variable in both time and location, and greatly restricts the sustainable development of water resources. The lack of national eutrophication evaluation for multi-scale lakes limits the pertinent governance and sustainable management of water quality. In this study, a remote sensing approach was developed to capture 40-year dynamics of trophic state index (TSI) for nationwide lakes in China. 32% of lakes (N = 1925) in China were eutrophic and 26% were oligotrophic, and a longitudinal pattern was discovered, with the 40-year average TSI of 62.26 in the eastern plain compared to 23.72 in the Tibetan Plateau. A decreasing trend was further observed in the past four decades with a correlation of -0.16, which was mainly discovered in the Tibetan Plateau lakes (r > -0.90, p < 0.01). The contribution of climate change and human activities was quantified and varied between lake zones, with anthropogenic factors playing a dominant role in the east plain lakes (88%, N = 473) and large lakes are subject to a more complex driving mechanism (≥ 3 driving factors). The study expands the spatiotemporal scale for eutrophication monitoring and provides an important base for strengthening lake management and ecological services.

Chlorophyll-a (Chl-a) is one of the optically active constituents in waters, and its concentration is frequently utilized as a proxy for lake trophic levels. However, generating a large-scale, long-term, and consistent data record of Chl-a in lakes from satellite images has been a challenging undertaking due to the limitations of conventional algorithms in monitoring inland waters spanning various optical properties. Here, we develop a practical deep neural network (DNN) model to generate a long-term Chl-a series (2012-2021) in 217 large lakes (> 50 km(2)) across China from the Visible Infrared Imaging Radiometer Suite (VIIRS) imagery. The assessment showed that the NOAA operational VIIRS remote sensing reflectance (R-rs(lambda)) products were reliable over 28 of China's examined lakes (N = 340, bias = -12%, mean absolute percentage error [MAPE] = 38%), particularly at bands ranging from the green to near-infrared domain. The DNN model performed satisfactorily on Chl-a retrievals (bias = 5%, MAPE = 32%) in 79 lakes over three orders of magnitude (0.1-300 mu g L-1) spanning clear/deep to turbid/shallow waters, with significant improvements compared with the existing algorithms and other machine learning algorithms. The algorithm was applied to VIIRS images to produce a data record of spatial and temporal variations in Chl-a for China's large lakes over the past decade. The VIIRS-derived data record showed that China's lakes have an average Chl-a of 9.5 mu g L-1 and are to 45.5% eutrophic. The results revealed a spatial trend of lower Chl-a in the western deep lakes than that in the eastern shallow lakes. In addition, we observed a significant increase in Chl-a in the lakes of the China East Plain but a decreasing trend of Chl-a in the Tibetan Plateau. This study highlights the feasibility of a machine learning approach based on synchronous matchups to derive Chl-a data in various lakes from satellite images. Our results provide a comprehensive understanding of overall changes to the optical conditions of China's lakes and enable scientists to elucidate the roles of climate and human activities in regulating lake productivity.

Salinity is an essential parameter for evaluating water quality and plays a crucial role in maintaining the stability of lake ecosystems, particularly in arid and semi-arid climates. Salinity responds to changes in climate and human activity, with significant impacts on water quality and ecosystem services. In this study, Sentinel-2A/B Multi-Spectral Instrument (MSI) images and quasi-synchronous field data were utilized to estimate lake salinity using machine learning approaches (i.e., XGB, CNN, DNN, and RFR). Atmospheric correction for MSI images was tested using six processors (ACOLITE, C2RCC, POLYMER, MUMM, iCOR, and Sen2Cor). The most accurate model and atmospheric correction method were found to be the extreme gradient boosting tree combined with the ACOLITE correction algorithm. These were used to develop a salinity model (N = 70, mean absolute percentage error = 9.95%) and applied to eight lakes in Inner Mongolia from 2016 to 2024. Seasonal and interannual variations were explored, along with an examination of potential drivers of salinity changes over time. Average salinities in the autumn and spring were higher than in the summer. The highest salinities were observed in the lake centers and tended to be consistent and homogeneous. Interannual trends in salinity were evident in several lakes, influenced by evaporation and precipitation. Climate factors were the primary drivers of interannual salinity trends in most lakes.

Trophic state index (TSI) serves as a key indicator for quantifying and understanding the lake eutrophication, which has not been fully explored for long-term water quality monitoring, especially for small and medium inland waters. Landsat satellites offer an effective complement to facilitate the temporal and spatial monitoring of multi-scale lakes. Landsat surface reflectance products were utilized to retrieve the annual average TSI for 2693 lakes over 1 km2 in China from 1984 to 2023. Our method first distinguishes lake types by pixels with a decision tree and then derives relationships between trophic state and algal biomass index. Validation with public reports and existing datasets confirmed the good consistency and reliability. The dataset provides reliable annual TSI results and credible trends for lakes under different area scales, which can serve as a reference for further research and provide convenience for lake sustainable management.

The Trophic state index (TSI) stands as one of the most direct and effective indices for characterizing the trophic state of water bodies, and the long-time TSI series data are important for monitoring the dynamic shifts in water quality in lakes. In this dataset, we used Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI as data sources, and crafted an Algal biomass index (ABI) to investigate its responsiveness to TSI. And through rigorous analysis, we estimated the TSI time series of Hulun Lake for 35 years from 1986 to 2020. Furthermore, we generated spatial maps depicting the mean TSI values in summer. The results revealed a distinctive spatial pattern, characterized by elevated TSI levels in the southwestern region and lower levels in the northeast of Hulun Lake during the summer season. Moreover, there was a noticeable discrepancy between the shoreline and the central area of the lake, with higher TSI values near the shore and lower values in the lake's center. To ensure the reliability and accuracy of the results, we compared the TSI derived from the OLI and the TSI based on measured reflectance ratios, which shows that the root mean square error (RMSE) of less than 2.39, and the mean absolute percentage error (MAPE) of less than 3.53%. The dataset is provided in *.tif format with a spatial resolution of 30 m. Users can leverage professional GIS software to view and edit, or use the relevant python packages or R languages for advanced data processing. The spatial and temporal variation characteristics of TSI reflected in this dataset can provide effective data support for water quality monitoring and ecological protection in Hulun Lake area.

Under the variations of natural conditions (temperature, wind speed, light, et al.) and self-regulation of buoyancy, cyanobacterial blooms can change rapidly in a short time. The Geostationary Ocean Color Imager (GOCI) can provide hourly monitoring of the dynamics of algal blooms (eight times per day), and has potential in observing the horizontal and vertical movement of cyanobacterial blooms. Based on the fractional floating algae cover (FAC), the diurnal dynamics and migration of floating algal blooms were evaluated, and the horizontal and vertical migration speed of phytoplankton was estimated from the proposed algorithm in two eutrophic lakes, Lake Taihu and Lake Chaohu in China. The locations, number, and area of algal bloom patches showed the hotspots and horizontal movement of bloom patches. The spatial and seasonal variations of the vertical velocities indicated that both the rising and sinking speed were higher in summer and autumn than those in spring and winter. The factors affecting diurnal horizontal and vertical migrations of phytoplankton were analyzed. Diffuse horizontal irradiance (DHI), direct normal irradiance (DNI), and temperature had significant positive relationships with FAC in the morning. Wind speed contributed 18.3 and 15.1% to the horizontal movement speed in Lake Taihu and Lake Chaohu, respectively. The rising speed was more related to DNI and DHI in Lake Taihu and Lake Chaohu with contribution of 18.1 and 16.6%. The horizontal and vertical movement of algae provide important information for understanding phytoplankton dynamics and the prediction and warning of algal blooms in lake management.

Analysing the vertical distribution of nutrient salts and estimating the total mass of lake nutrients is helpful for the management of lake nutrient status and the formulation of drainage standards in basins. However, studies on nitrogen (N) and phosphorus (P) in lakes have focused on obtaining measures of N and P concentrations, but no understanding exists on the vertical distribution of N and P in the entire water column. The present study proposes algorithms for estimating the total masses of N/P per unit water column (ALGO-TNmass/ALGO-TPmass) for shallow eutrophic lakes. Using Lake Taihu as an example, the total masses of nutrients in Lake Taihu in the historical period were obtained, and the algorithm performance was discussed. The results showed that the vertical distribution of nutrients decreased with increasing depth and exhibited a quadratic distribution. Surface nutrients and chlorophyll-a concentrations play important roles in the vertical distribution of nutrients. Based on conventional surface water quality indicators, algorithms for the vertical nutrient concentration in Lake Taihu were proposed. Both algorithms had good accuracy (ALGO-TNmass R2 > 0.75, RMSE <0.57; ALGO-TPmass R2 > 0.80, RMSE ≤0.50), the ALGO-TPmass had better applicability than the ALGO-TNmass, and had good accuracy in other shallow lakes. Therefore, deducing the TPmass using conventional water quality indicators in surface water, which not only simplifies the sampling process but also provides an opportunity for remote sensing technology to monitor the total masses of nutrients, is feasible. The long-term average total mass of N was 11,727 t, showing a gradual downward trend before 2010, after which it stabilised. The maximum and minimum intra-annual total N masses were observed in May and November, respectively. The long-term average total mass of P was 512 t, showing a gradual downward trend before 2010, and a slow upward trend thereafter. The maximum and minimum intra-annual total masses of P occurred in August and February or May, respectively. The correlation between the total mass of N and meteorological conditions was not obvious, whereas some influence on the total mass of P was evident, particularly water level and wind speed.

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Eutrophication of the eastern plain lake (EPL) region has a significant impact on the sustainable economic development and is closely related to the shortage of water resources in China. Remote sensing provides an effective tool for quantifying the trophic state of inland waters by associating the trophic state index (TSI) with optically active water quality parameters. However, limited by the satellite coverage range and operation time, the long-term changes in the trophic state of the EPL region have not been thoroughly investigated. This study aims to fill this gap by generating a 35-year (1986-2020) TSI dataset of lakes in the eastern plain based on Landsat images. The TSI inversion algorithm based on the algal biomass index (ABI) was designed for Landsat series after consistency analysis. The seasonal variations of the TSI showed the highest TSI (62.0 +/- 11.4) in summer and the lowest TSI (51.6 +/- 8.0) in winter, with uncertainties caused by the limitation of ABI for extremely turbid waters and the number of Landsat seasonal images. The TSI of the EPLs increased over the past 35 years by about 8.2%. Four change patterns were defined for the long-term interannual TSI variations: increasing trend less than 50% (Mode 1) or more than 50% (Mode 2), breaking points that show a surge trend (Mode 3), and decreasing trend (Mode 4). The contribution of meteorological and anthropogenic factors was calculated using a generalized linear model, which revealed that the eutrophication of inland lakes in the EPL region is mainly affected by industrial wastewater discharge and urban expansion. The influence of these explanatory variables becomes more complex with an increase in lake area. Our research provides an estimation of the TSI for the first 35-year basin-scale in the EPL region and a comprehensive evaluation of the driving factors of inland water eutrophication. The results can be used for the effective management and restoration of lakes.

Water is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources is becoming increasingly serious worldwide. Therefore, the monitoring of water bodies is indispensable. Remote sensing has the advantages of real time, wide coverage, and rich information and has become a brand-new technical means to quickly obtain water information. This study summarizes the current common methods of water extraction based on optical and radar images, including the threshold method, support vector machine, decision tree, object-oriented extraction, and deep learning, as well as the advantages and disadvantages of each method. These methods were applied to the Huai River Basin in China and Nam Co on the Qinghai-Tibet Plateau. The extraction results show that all the aforementioned approaches can obtain reliable results. Among them, the threshold segmentation method based on normalized difference water index is more robust than others. In the water extraction process, there are still many problems that restrict the accuracy of the results. In the future, researchers will continue to search for more automatic, extensive, and high-precision water extraction methods.

Coarse-resolution sensors have been used operationally to monitor floating algal blooms with a near daily revisit in coastal and inland waters. Most of the current methods in estimating fractional floating algae cover (FAC) were based on the linear pixel un-mixing assumption. In this study, a new FAC model following a logistic curve was developed and applied to multisensor satellite data in two large shallow eutrophic lakes, Lake Taihu and Lake Chaohu, in China. The results indicated that after resampling to 250 m, match-up pairs of Moderate Resolution Imaging Spectroradiometer (MODIS), The Visible and Infrared Imager/Radiometer Suite (VIIRS), Geostationary Ocean Color Imager (GOCI), and Ocean and Land Color Instrument (OLCI) possessed consistent Rayleigh corrected reflectance ( $R_{\mathrm {rc}}$ ) and floating algae index (FAI) or AFAI (alternative FAI). The FAC model was developed based on the simulated AFAI data of GOCI using point spread function (PSF) and bloom percent derived from Operational Land Imager (OLI) and then was applied to MODIS, VIIRS, and OLCI. Compared with the linear pixel un-mixing method, the FAC model reflects the asymptotic reflectance saturation in the near-infrared (NIR) band with the accumulation of blooms. Besides, the equivalent bloom area (EBA) of GOCI was validated using OLI-matched pairs with UPD 37.6% ( $N=39$ , $R^{2}=0.96$ ). The spatial-temporal dataset of FAC (2002–2020) shows that Lake Taihu and Lake Chaohu experienced severe algal blooms after 2010, partly resulting from the higher frequency of multisensor data. This study provides a method for building a lasting and comparable FAC dataset using multisensors.

Phosphorus is a limiting nutrient in freshwater ecosystems. Therefore, the estimation of total phosphorus (TP) concentration in eutrophic water using remote sensing technology is of great significance for lake environmental management. However, there is no TP remote sensing model for lake groups, and thus far, specific models have been used for specific lakes. To address this issue, this study proposes a framework for TP estimation. First, three algorithm development frameworks were compared and applied to the development of an algorithm for Lake Taihu, which has complex water environment characteristics and is a representative of eutrophic lakes. An Extremely Gradient Boosting (BST) machine learning framework was proposed for developing the Taihu TP algorithm. The machine learning algorithm could mine the relationship between FAI and TP in Lake Taihu, where the optical properties of the water body are dominated by phytoplankton. The algorithm exhibited robust performance with an R-2 value of 0.6 (RMSE = 0.07 mg/L, MRE = 43.33%). Then, a general TP algorithm (R-2 = 0.64, RMSE = 0.06 mg/L, MRE = 34.13%) was developed using the proposed framework and tested in seven other lakes using synchronous image data. The algorithm accuracy was found to be affected by aquatic vegetation and enclosure aquaculture. Third, compared with field investigations in other studies on Lake Taihu, the Taihu TP algorithm showed good performance for long-term TP estimation. Therefore, the machine learning framework developed in this study has application potential in large-scale spatio-temporal TP estimation in eutrophic lakes.

Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland waters. First, the turbid water index was applied for the determination of optical water types on each pixel, and water bodies are divided into two categories: algae-dominated water (Type I) and turbid water (Type II). The algal biomass index (ABI) was then established based on water classification to derive the trophic state index (TSI) proposed by Carlson (1977). The results showed a considerable precision in Type I water (R2 = 0.62, N = 282) and Type II water (R2 = 0.57, N = 132). The ABI-derived TSI outperformed several band-ratio algorithms and a machine learning method (RMSE = 4.08, MRE = 5.46%, MAE = 3.14, NSE = 0.64). Such a model was employed to generate the trophic state index of 146 lakes (> 10 km2) in eastern China from 2013 to 2020 using Landsat-8 surface reflectance data. The number of hypertrophic and oligotrophic lakes decreased from 45.89% to 21.92% and 4.11% to 1.37%, respectively, while the number of mesotrophic and eutrophic lakes increased from 12.33% to 23.97% and 37.67% to 52.74%. The annual mean TSI for the lakes in the lower reaches of the Yangtze River basin was higher than that in the middle reaches of the Yangtze River and Huai River basin. The retrieval algorithm illustrated the applicability to other sensors with an overall accuracy of 83.27% for moderate-resolution imaging spectroradiometer (MODIS) and 82.92% for Sentinel-3 OLCI sensor, demonstrating the potential for high-frequency observation and large-scale simulation capability. Our study can provide an effective trophic state assessment and support inland water management.

蓝藻水华是湖泊水体富营养化的重要特征之一,不同水华蓝藻类群形成的水华特征、危害及其治理方法差异显著.因此,如何快速、准确地掌握不同蓝藻类群的时空分布特征成为实施富营养化湖泊污染治理与生态恢复、蓝藻生态灾害预测预警中一个亟待解决的科学问题.本研究基于纯藻种实验室培养和室内光学控制实验,在微囊藻(Microcystis)、鱼腥藻(Dolichospermum)、束丝藻(Aphanizomenon)3种主要水华蓝藻固有光学特性的基础上,通过甄别不同水华蓝藻的吸收、散射和后向散射光谱的特征波段,构建了基于吸收和散射特性的5种水华蓝藻类群的非线性最优化定量识别模型,其中,基于440、620和675 nm 3个波段吸收的a-CIM440,620,675具有较为稳定的定量识别能力;并基于野外实测光学特性数据,实现了巢湖主要水华蓝藻类群的定量监测,初步分析了巢湖主要水华蓝藻类群的时空分布特性.研究表明,巢湖的水华蓝藻以鱼腥藻、微囊藻为主,束丝藻较少,鱼腥藻主要出现在温度较低的季节,微囊藻在夏季的西部湖区占优势;巢湖水华主要为微囊藻藻华和鱼腥藻藻华,且浓度较高的蓝藻主要存在于水体表面以下20 cm范围内;微囊藻和鱼腥藻在非藻华断面垂向上均匀分布.本研究可为富营养化湖泊蓝藻水华预测预警以及相关管理部门决策提供重要的理论依据和科学支撑.

Due to the difference of vertical distribution of algae in lakes, it is necessary to carry out remote sensing estimation of algal biomass based on the vertically heterogeneous distribution of chlorophyll in order to improve the accuracy of biomass inversion. A new algorithm is proposed and validated to measure algal biomass in Lake Chaohu based on the Moderate Resolution Imaging Spectrometer (MODIS) images. The algal biomass index (ABI) is defined as the difference in remote-sensing reflectance (R-rs, sr(-1)) at 555 nm normalized against two baselines with one formed linearly between R-rs(859) and R-rs(469) and another formed linearly between R-rs(645) and R-rs(469). Both theory and model simulations show that ABI has a good relation with the algal biomass in the euphotic zone (R-2 = 0.88, p < 0.01, N = 50). Field data were further used to estimate the biomass outside the euphotic layer through an empirical algorithm. The ABI algorithm was applied to MODIS Rayleigh-corrected reflectance (R-rc) data after testing the sensitivity to sun glint and thickness of aerosols, which showed an acceptable precision (root mean square error < 21.31 mg and mean relative error < 16.08%). Spectral analyses showed that ABI algorithm was immune to concentration of colored dissolved organic matter (CDOM) but relatively sensitive to suspended particulate inorganic matter (SPIM), which can be solved by using Turbid Water Index (TWI) though in such a challenging environment. A long-term (2012-2017) estimation of algal biomass was further calculated based on the robust algorithm, which shows both seasonal and spatial variations in Lake Chaohu. Tests of ABI algorithm on Sentinel-3 OLCI demonstrates the potential for application in other remote sensors, which meets the need of observation using multi-sensor remote sensing in the future. (C) 2021 Elsevier B.V. All rights reserved.

There are some uncertainties of using chlorophyll a (Chla) concentrations in water surface to address phytoplankton dynamics, especially in large shallow lakes, because of the dramatic vertical migration of phytoplankton. The column-integrated algal biomass (CAB) can reflect the whole water column information, so it is considered as a better indicator for phytoplankton total biomass. An algal biomass index (ABI) and an empirical algorithm were proposed previously to measure algal biomass inside and outside euphotic zone from the Moderate Resolution Imaging Spectrometer (MODIS) data. A long-term CAB time series was generated in this study to clarify the temporal and spatial changes in phytoplankton and address its sensitivity to climatic factors in Lake Chaohu, a shallow eutrophic lake in China, from 2000 to 2018. Overall, the CAB for Lake Chaohu showed significant temporal and spatial dynamics. Temporally, the annual average CAB (total CBA within the whole lake) was increased at rate of 0.569 t Chla/y, ranging from 62.06±8.89 t Chla to 76.03±10.01 t Chla during the 19-year period. Seasonal and periodic variations in total CAB presented a bimodal annual cycle every year, the total CAB was highest in summer, followed by that in autumn, and it was the lowest in winter. The pixel-based CAB (total CAB of a unit water column), ranging from 112.42 to 166.85 mg Chla, was the highest in the western segment, especially its northern part, and was the lowest in the central parts of eastern and central segments. The sensitivity of CAB dynamics to climatic conditions was found to vary by region and time scale. Specifically, the change of pixel-based algal biomass was more sensitive to the temperature change on the monthly and annual scales, while wind speed impacted directly on the short-term spatial-temporal redistribution of algal biomass. High temperature and low wind speed could prompt the growth of total CAB for the whole lake, and the hydrodynamic situations affected by wind and so on determined the spatial details. It also indicated that Lake Chaohu may face more severe challenges with the future climatic warming. This study may serve as a reference to support algal bloom forecasting and early warning management for other large eutrophic lakes with similar problems.