The research presented in this paper explores a method for identifying pollution sources in urban black-odor rivers using UAV hyperspectral imaging technology.Excessive Dissolved Organic Matter(DOM)is a primary cause of black-odor water bodies,and fluorescence spectroscopy can identify the fluorescence characteristics and composition of DOM,providing insights into pollution sources.The study focuses on developing a model based on the fluorescence characteristics and optical properties of DOM to distinguish between different types of pollution sources,such as domestic sewage,industrial wastewater,and mixed waste water. Key findings of the research include the identification of the fluorescence peak integral ratio(IA∶IT)as a reliable indicator for tracking dynamic changes in DOM components in urban rivers.This ratio proved to be more effective than other fluorescence indices.By determining the threshold values for IA∶IT,the study categorizes pollution sources in heavily polluted water bodies into three types.The developed model,which incorporates remote sensing reflectance and the absorption coefficient of CDOM at 275 nm(aCDOM(275)),is validated using UAV hyperspectral images from the Tunliang River in Nanjing.The results show that the identified pollution sources in the river section are consistent with actual field investigations. The methodology involves extensive data collection from several industrialized cities in Jiangsu Province,China,including Nanjing,Wuxi,Changzhou,and Yangzhou.Parameters such as dissolved oxygen,oxidation-reduction potential,and remote sensing reflectance were measured.UAV hyperspectral data was collected using a hyperspectral imager with a spectral range of 400-1000 nm and 270 spectral channels.The study employed three-dimensional fluorescence spectroscopy to analyze the fluorescence characteristics of DOM and used Parallel Factor Analysis(PARAFAC)to decompose the fluorescence data into individual components. Statistical methods were utilized to establish relationships between fluorescence indices and water optical properties.Linear models were developed to predict IA:IT based on the absorption coefficient aCDOM(275)and remote sensing reflectance ratios.The models were validated and applied to UAV hyperspectral imagery to classify sections of the river based on their primary pollution sources. In conclusion,the study demonstrates the effectiveness of combining UAV hyperspectral imagery with fluorescence spectroscopy to identify pollution sources in urban black-odor rivers.The developed models provide a robust method for monitoring and managing water quality in urban environments,offering a promising approach for pollution source identification and water body management.The findings emphasize the potential of this technology to aid in the effective management and remediation of polluted urban waterways,highlighting the importance of accurate pollution source identification for sustainable urban development and water quality improvement.

As one of the optically active components, phytoplankton are common photosynthetic organisms in oceans, nearshore, and inland water bodies. The variations in phytoplankton algal density play a crucial role in understanding primary productivity, carbon cycling, and early warning of algal blooms. In this study, three typical eutrophic lakes in China, Lake Taihu, Lake Chaohu, and Lake Dianchi, were taken as the research area. Algorithms for estimating algal density of cyanobacteria-dominated and non-cyanobacteria-dominated water types were developed based on Mie theory. The results demonstrated that the developed algorithm had favorable estimation performance for inland eutrophic lakes, with a determination coefficient (R2) of 0.88, a mean absolute percentage error (MAPE) of 51%, an unbiased mean absolute percentage error (UMAPE) of 39%, and a root mean square error (RMSE) of 23.99 × 106 cells/L. Furthermore, comparison with other algorithms for estimating algal density showed that the developed algorithm had the lowest MAPE of 60% and UMAPE of 43%, with the RMSE of 23.42 × 106cells/L. Extensive evaluation based on satellite-ground synchronous data demonstrated the applicability of the developed algorithm to the Sentinel-3 OLCI sensor, enabling the determination of spatial and temporal distribution characteristics of algal density in the three lakes from 2016 to 2022 using Sentinel-3 OLCI images. The results of algal density inversion revealed a continuous decreasing trend in algal density in Lake Dianchi from 2016 to 2022, while the algal density in Lake Taihu and Lake Chaohu both decreased after 2019.

The global mining sector generates billions of tons of tailings stored in thousands of tailing ponds. The occasional spills of tailings resulting from dam failures or pipe damage can have devastating consequences, threatening nearby human populations and ecosystems, particularly those located along river corridors. Satellite remote sensing technology is a vital supplementary method to traditional field methods for monitoring and evaluating the water pollution caused by spilled tailings. The researchers have developed workflows to evaluate the effect of tailing spills on water quality using low and medium-spatial satellite imagery from satellite sensors. Due to insufficient spatial resolution, these workflows were hard to apply to monitor the water pollution caused by spilled tailing in small rivers. Using valuable on-site data from a river water pollution incident caused by spilled tailing, a workflow utilizing high-resolution satellite imagery (GF1) was developed. This workflow incorporates a machine learning algorithm (improved DeepLabV3+) +) to extract water masks first and a novel spectral index method to determine TSM concentrations. The improved DeepLabV3+ + algorithm can obtain an accurate water mask no matter the water pixels, whether influenced by the tailing spills from GF1 imagery with IoU of 82.66%, Precision of 93.21%, Recall of 87.96%, and F1-score of 90.51%. A new spectral index combination algorithm that provides reliable TSM products for an extensive TSM magnitude range was presented to assess the level of water contamination. The strong correlation (R2 2 = 0.97) between in situ TSM and Mo concentrations suggests that the retrieved TSM products are suitable for assessing the water pollution caused by the spilled tailing. This workflow provides a method for monitoring and evaluating water pollution resulting from spilled tailings in small rivers. It utilizes high-resolution satellite data to observe and analyze the pollution levels.

Many chlorophyll-a (Chl-a) remote sensing estimation algorithms have been developed for inland water, and they are proposed always based on some ideal assumptions, which are difficult to meet in complex inland waters. Based on MIE scattering theory, this study calculated the optical properties of mineral particles under different size distribution and refractive index conditions, and the Hydrolight software was employed to simulate remote sensing reflectance in the presence of different mineral particles. The findings indicated that the reflectance is significantly influenced by the slope (j) of particle size distribution function and the imaginary part (n’) of the refractive index, with the real part (n) having a comparatively minor impact. Through both a simulated dataset containing 18,000 entries and an in situ measured dataset encompassing 2183 data from hundreds of lakes worldwide, the sensitivities of band ratio (BR), fluorescence baseline height (FLH), and three-band algorithms (TBA) to mineral particles were explored. It can be found that BR showed the best tolerance to mineral particles, followed by TBA. However, when the ISM concentration is less than 30 g m-3, the influence of CDOM cannot be ignored. Additionally, a dataset of over 400 entries is necessary for developing the BR algorithm to mitigate the incidental errors arising from differences in data magnitude. And if the amount of developing datasets is less than 400 but greater than 200, the TBA algorithm is more likely to obtain more stable accuracy.

Accurate understanding of vertical patterns of canopy structure characteristics and solar radiation distribution patterns of aquatic vegetation is pivotal in formulating a bidirectional reflection model and comprehending the ecological dynamics of wetlands. Further, physiological and biochemical stratified structural properties of aquatic vegetation in wetlands remain unexplored due to more inherent investigation challenges than terrestrial vegetation. This study evaluated the structural characteristics of vegetation communities and the regulation of direct solar radiation variations within the canopy across seasons of Phragmites australis (P. australis) and Typha orientalis (T. orientalis), 2 typical emergent aquatic vegetations (EAVs), based on radiative transfer theory. Observations revealed that physiological and biochemical metrics varied at different growth stages with canopy height, the stratified leaf area index in the middle being higher than at the top and bottom of the P. australis cluster. Moreover, the vertical profiles of direct solar radiation decrease with depth, showing a bowl-shaped and V-shaped curve in the P. australis and T. orientalis clusters, respectively. Interestingly, the sensitivity of layered solar direct radiation transmittance to canopy structural parameters is obviously higher than that of canopy pigments, suggesting considerable potential for estimating layered structural parameters. The transmittance of direct solar radiation decreases with increasing leaf area index at different heights, and stratified transmittance in the cluster can be accurately described by a negative binomial function with a deviation of less than 2%.

Total nitrogen concentration (CTN) enrichment is the primary cause of natural water eutrophication. Accurately estimating CTN and its spatiotemporal dynamics is crucial for formulating monitoring and control measures to alleviate lake eutrophication. A hybrid model was proposed for estimating CTN in optically complex inland waters by incorporating the relationship between CTN and water optical active components for Zhuhai-1 Orbita hyperspectral (OHS) imagery. Compared with other semi-analytical algorithms, the re-adjusted reference wavelength QAA716 shows the best performance in aph(lambda) retrieval. The hybrid model for CTN estimation achieves an root mean square deviation (RMSD) of 0.20 mg/L, a mean absolute percentage deviation (MAPD) of 6.96 %, and a unbiased mean absolute percentage deviation (UMAPD) of 6.96 %, with aph(5 6 9) accuracy exerting the greatest influence. Ground-satellite synchronous validation demonstrates robust performance, with an RMSD of 0.28 mg/L, a MAPD of 14.49 %, and a UMAPD of 14.92 %. The hybrid model was applied to OHS observations of Lake Dianchi from April 2019 to September 2021. The analysis revealed a generally decreasing trend in CTN during this timeframe. The above results demonstrate that the robustness and applicability of the proposed CTN hybrid model for inland waters with complex optical properties. Furthermore, satellite-based data products provide valuable information for formulating lake management strategies.

为弄清滇池外海蓝藻水华暴发时空变化规律及其影响因素,将滇池外海分为北、中、南3个区域,基于2002—2018年期间中分辨率成像光谱仪(MODIS)反演的水华面积,分析了上述3个区域蓝藻水华的时空变化特征.基于2007—2018年水文、气象和出入流数据,构建了外海三维水动力生态模型(AEM3D),并计算了各区域的水力滞留时间.通过冗余分析(RDA)、随机森林(RF)和斯皮尔曼相关分析方法,分析了影响以上区域蓝藻水华暴发的主要驱动因子.结果表明:2002—2018年期间,整个滇池外海区域年平均水华面积比(水华面积占该区域总面积比例)呈缓慢下降趋势,空间上由北向南依次递减,整个外海水华暴发面积最大主要发生在秋季.在外海北部区域,其东部水华较西部更为严重,而在中部和南部区域,呈现西部水华较东部更为严重的空间分布模式.通过对各影响因子的统计分析发现,风速、水温和日照时长是上述各区域中蓝藻水华暴发的主要决定性因素.水华暴发期间以西南风为主导风向,且上述区域的水华面积比随风速增加呈下降趋势.在外海各区域,水力滞留时间与水华暴发面积均呈显著正相关,空间上水力滞留时间由北向南逐渐增大,风速和风向是影响蓝藻水华空间分布规律和严重程度的主要因素.

The proliferation of algal blooms can lead to environmental issues. The phytoplankton responsible for these blooms are diverse. Different species of bloom-forming algae have distinct characteristics and hazards, and therefore need different treatment methods. An accurate and quick determination of the spatial and temporal distribution characteristics of different algal species is crucial for lake ecological restoration. Based on the differences in remote sensing reflectance (Rrs) of various typical algae species in eutrophic lakes (including Microcystis aeruginosa, Aphanizomenon sp., and Pseudanabaena sp. in Cyanobacteria and Chlorella sp. and Scenedesmus quadricauda in Chlorophytes), difference index and algae distinguishing index were developed to differentiate algae species. A validation, using an independent dataset from an indoor experiment and in-situ-measured and satellite-image-derived Rrs, showed that the algorithm can provide reliable results (overall accuracies of 81.97%, 81.25%, and 60.42%, respectively). According to Ocean and Land Color Instrument images of Lake Taihu in the period of 2016 to 2020, Microcystis was the dominant algae, followed by Pseudanabaena and Aphanizomenon. The dominance of the two types of Chlorophytes was less pronounced. The proportion of Microcystis as the dominant algae was highest in summer, while the proportion of Pseudanabaena peaked in winter. The proportion of Aphanizomenon varied slightly throughout the year, while the proportion of the two Chlorophytes peaked in winter. In terms of spatial distribution, the patterns in spring and autumn were relatively similar. In summer, approximately 80% of the lake was dominated by Microcystis. In winter, Chlorella and Scenedesmus were more prevalent along the southeastern shore of Lake Taihu. The construction and application of this model can provide a technical support for prediction and prevention of blooms in inland lakes.

Owing to accelerated urbanisation, increased pollutants have degraded urban water quality. Timely identifica-tion and control of pollution sources enable relevant departments to effectively perform water treatment and restoration. To achieve this goal, a remote sensing identification method for urban water pollution sources applicable to unmanned aerial vehicle (UAV) hyperspectral images was established. First, seven fluorescent components were obtained through three-dimensional excitation-emission matrix fluorescence spectroscopy of dissolved organic matter (DOM) combined with parallel factor analysis. Based on the hierarchical cluster analysis of the seven fluorescence components and three spectral indices, four pollution source (PS) types were deter-mined, namely, domestic sewage, terrestrial input, agricultural and algal, and industrial wastewater sources. Second, several water colour and optical parameters, including the absorption coefficient of chromophoric DOM at 254 nm, humification index, chlorophyll-a concentration, and hue angle, were utilised to develop an identi-fication method with a recognition accuracy exceeding 70% for the four PSs that is suitable for UAV hyper -spectral data. This study demonstrated the potential of identifying PSs by combining the fluorescence characteristics of DOM with the optical properties of water, thus expanding the application of remote sensing technologies and providing more comprehensive and reliable information for urban water quality management.

Leaf normal inclination angle distribution (LAD) determines the interception of radiation by leaves and the transmission of radiation by the vegetation canopy, which affects the distribution of incident photosynthetically active radiation on plant leaves and ultimately directly affects the net primary productivity of vegetation. However, the contact measurement method for leaf inclination angle is difficult to perform due to the complex growing environment of wetland aquatic vegetation. In this study, the method of measuring normal leaf inclination angles was improved for wetland aquatic vegetation based on horizontal digital photography combined with the application of a protractor. Phragmites australis and Typha orientalis, two typical emergent aquatic vegetation (EAV) species growing in wetlands, were used to evaluate the robustness of leaf inclination measurement methods. In addition, LAD values were measured and analyzed for their variations across seasonal and vertical gradients within the canopy. Finally, the applicability of the two simulation algorithms for leaf normal inclination was evaluated and compared. Analysis revealed that Phragmites australis LAD exhibited a variation with seasonal changes and vertical canopy height, which is beneficial for light transmission within the canopy. The LAD of Typha orientalis was always an erectophile pattern and changed only slightly with the seasons. The new findings provided additional insights and evidence on aquatic plant resistance and environmental adaptation. Comparing simulation models for EAV, the trigonometric algorithm outperforms other algorithms in accuracy and deviation. The results of this research provide a valuable reference for exploring radiation transfer within aquatic vegetation canopies and understanding vegetation growth.

Water clarity is a critical parameter of water, it is typically measured using the setter disc depth (SDD). The accurate estimation of SDD for optically varying waters using remote sensing remains challenging. In this study, a water classification algorithm based on the Landsat 5 TM/Landsat 8 OLI satellite was used to distinguish different water types, in which the waters were divided into two types by using the ad(443)/ap(443) ratio. Water type 1 refers to waters dominated by phytoplankton, while water type 2 refers to waters dominated by non-algal particles. For the different water types, a specific algorithm was developed based on 994 in situ water samples collected from Chinese inland lakes during 42 cruises. First, the Rrs(443)/Rrs(655) ratio was used for water type 1 SDD estimation, and the band combination of (Rrs(443)/Rrs(655) - Rrs(443)/Rrs(560)) was proposed for water type 2. The accuracy assessment based on an independent validation dataset proved that the proposed algorithm performed well, with an R2 of 0.85, mean absolute percentage error (MAPE) of 25.98%, and root mean square error (RMSE) of 0.23 m. To demonstrate the applicability of the algorithm, it was extensively evaluated using data collected from Lake Erie and Lake Huron, and the estimation accuracy remained satisfactory (R2 = 0.87, MAPE = 28.04%, RMSE = 0.76 m). Furthermore, compared with existing empirical and semi-analytical SDD estimation algorithms, the algorithm proposed in this paper showed the best performance, and could be applied to other satellite sensors with similar band settings. Finally, this algorithm was successfully applied to map SDD levels of 107 lakes and reservoirs located in the Middle-Lower Yangtze Plain (MLYP) from 1984 to 2020 at a 30 m spatial resolution, and it was found that 53.27% of the lakes and reservoirs in the MLYP generally show an upward trend in SDD. This research provides a new technological approach for water environment monitoring in regional and even global lakes, and offers a scientific reference for water environment management of lakes in the MLYP.

我国淡水湖库频发水华,不同类群形成的水华特征、危害及其治理方法差异显著,因此,如何区分不同藻种的遥感反射率特征,获取湖泊优势种信息是一个亟待解决的科学问题.研究基于室内藻种培养实验,培养了富营养化湖泊中的典型蓝藻和绿藻藻种,其中,蓝藻包括铜绿微囊藻(Microcystis aeruginosa)、假鱼腥藻(Pseudanabaena sp.)和束丝藻(Aphi-anizomenon sp.),绿藻包括小球藻(Chlorella sp.)以及四尾栅藻(Scenedesmnus quadricauda);基于实测的遥感反射率,经归一化处理后,分析了不同藻种的遥感反射率特征,构建了 DI(difference index)指数以及ADI(algae distinguish index)指数,建立了藻种分类模型,利用验证集数据进行检验,整体识别精度达77.55％,Kappa系数为0.7178.将分类方法应用于太湖野外实测遥感反射率数据集中,结果与实测的生物量数据有较好的匹配;将模型应用于太湖OLCI(ocean and land colour instrument)影像数据,获得了 2019年12月和2020年8月太湖冬季和夏季的优势藻种分布,总体而言,太湖蓝藻占比在夏季及冬季均高于绿藻,其中微囊藻显示出较明显的优势,且夏季优势地位明显高于冬季.从季节及空间分布上看,冬季太湖微囊藻主导区域分布在北部及南部湖湾,假鱼腥藻主导区域主要位于湖心,极少部分束丝藻及栅藻零散分布;夏季,太湖大部分水域的优势藻种为微囊藻,假鱼腥藻主导区域分布于湖心及南部,其余3种藻种的主导区域仍只占一小部分并零散分布.优势藻种遥感识别模型的构建可为湖泊水环境遥感监测和水华的预测预防提供技术支持.

Particulate organic matter (POM) plays a major role in freshwater ecosystems by serving as a bridge for the conversion of various nutrients. The composition and sources of POM in inland lakes are complex, making it difficult to estimate its concentration accurately via remote sensing. Therefore, a classification-based method based on the sources and composition of POM is proposed for estimating POM concentrations in inland lakes. In this study, 379 samples were collected from ten lakes in the Yangtze River Delta (YRD) at different times. A water-type classification method based on OLCI Rrs(560) , Rrs(620) , Rrs(681) , and Rrs(709) was developed for POM estimation based on biological and optical characteristics. Water type 1 is relatively clear, and POM may originate from aquatic vegetation or sediment. Water type 2 was dominated by inorganic suspended matter, and POM mainly originated from the attachment and entrainment of inorganic minerals. Water type 3 is an algae-dominated water body, and POM is mainly derived from fresh algal particles and the microbial degradation of phytoplankton. Therefore, specific POM estimation algorithms were developed for each water type. OLCI Rrs(490) , Rrs(665) , Rrs(673) , and Rrs(754) were used for water type 1; Rrs(620) , Rrs(865) , and Rrs(885) were adopted for water type 2; and Rrs(620) , Rrs(665) , and Rrs(709) were selected for water type 3. Using an independent dataset to evaluate the estimation accuracy of the developed algorithm, the results show that the estimation performance of this algorithm is significantly improved compared to the two other algorithms used; the mean absolute percentage errors (MAPE) decreased from 72.56

Under the influence of intensive human activities and global climate change, the sources and compositions of dissolved organic matter (DOM) in the eastern plain lake (EPL) region in China have fluctuated sharply. It has been successfully proven that the humification index (HIX), which can be derived from three-dimensional excitation-emission matrix fluorescence spectroscopy, can be an effective proxy for the sources and compositions of DOM. Therefore, combined with remote sensing technology, the sources and compositions of DOM can be tracked on a large scale by associating the HIX with optically active components. Here, we proposed a novel HIX remote sensing retrieval (IRHIX) model suitable for Landsat series sensors based on the comprehensive analysis of the covariation mechanism between HIX and optically active components in different water types. The validation results showed that the model runs well on the independent validation dataset and the satellite -ground synchronous sampling dataset, with an uncertainty ranging from 30.85 % to 36.92 % (average +/- standard deviation = 33.6 % +/- 3.07 %). The image-derived HIX revealed substantial spatiotemporal variations in the sources and compositions of DOM in 474 lakes in the EPL during 1986-2021. Subsequently, we obtained three long-term change modes of the HIX trend, namely, significant decline, gentle change, and significant rise, accounting for 74.68 %, 17.09 %, and 8.23 % of the lake number, respectively. The driving factor analysis showed that human activities had the most extensive influence on the DOM humification level. In addition, we also found that the HIX increased slightly with increasing lake area (R-2 = 0.07, P < 0.05) or significantly with decreasing trophic state (R-2 = 0.83, P < 0.05). Our results provide a new exploration for the effective acquisition of long-term dynamic information about the sources and compositions of DOM in inland lakes and provide important support for lake water quality management and restoration.

It can be challenging to accurately estimate the Chlorophyll-a (Chl-a) concentration in inland eutrophic lakes due to lakes' extremely complex optical properties. The Orbita Hyperspectral (OHS) satellite, with its high spatial resolution (10 m), high spectral resolution (2.5 nm), and high temporal resolution (2.5 d), has great potential for estimating the Chl-a concentration in inland eutrophic waters. However, the estimation capability and radiometric performance of OHS have received limited examination. In this study, we developed a new quasi-analytical algorithm (QAA716) for estimating Chl-a using OHS images. Based on the optical properties in Dianchi Lake, the ability of OHS to remotely estimate Chl-a was evaluated by comparing the signal-to-noise ratio (SNR) and the noise equivalent of Chl-a (NEChl-a). The main findings are as follows: (1) QAA716 achieved significantly better results than those of the other three QAA models, and the Chl-a estimation model, using QAA716, produced robust results with a mean absolute percentage difference (MAPD) of 11.54 %, which was better than existing Chl-a estimation models; (2) The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model (MAPD = 22.22 %) was more suitable for OHS image compared to the other three atmospheric correction models we tested; (3) OHS had relatively moderate SNR and NEChl-a, improving its ability to accurately detect Chl-a concentration and resulting in an average SNR of 59.47 and average NEChl-a of 72.86 μg/L; (4) The increased Chl-a concentration in Dianchi Lake was primarily related to the nutrients input, and this had a significant positive correlation with total nitrogen. These findings expand existing knowledge of the capabilities and limitations of OHS in remotely estimating Chl-a, thereby facilitating effective water quality management in eutrophic lake environments.

Cyanobacterial blooms are one of the most severe ecological problems affecting lakes. The vertical migration of cyanobacteria in the water column increases the uncertainty in the formation and disappearance of blooms, which may be closely associated with light, temperature, and wind speed. However, it is difficult to quantitatively evaluate the influencing factors of cyanobacteria vertical movement in natural environment compared to the laboratory experimental environment. Besides, both field survey and laboratory experiment method have the difficulties in determining the diurnal vertical migration of cyanobacteria at the synoptic lake scale. In this study, based on the diurnal dynamics of cyanobacterial bloom intensity (CBI) observed by the Geostationary Ocean Color Imager (GOCI) from 2011 to 2019, the daily variations, floating rate, and sinking rate of Microcystis aeruginosa were calculated in the natural environment. Then, the effects of light, temperature, and wind speed on the vertical migration of M. aeruginosa were analysed from the perspectives of day, night, and season. The results are as follows: the records of three typical patterns of diurnal CBI exhibited strong seasonal variability from the 9-year statistics; at night, the buoyancy recovery rate of cyanobacterial colonies increased with temperature, so that at temperature > 15 ? and wind speed < 3 m s(-1), CBI reached the maximum of the whole day at 08:16; the sinking rate of M. aeruginosa was positively correlated with the cumulated light energy at both synoptic and pixel scale; the upward migration speed of M. aeruginosa was positively correlated with the maximum wind speed of the day before cyanobacterial bloom. Therefore, the severer cyanobacterial blooms were often observed by satellite images after strong winds. The analysis of diurnal variation, floating rate, and sinking rate of M. aeruginosa will expand our knowledge for further understanding the formation mechanism of cyanobacterial blooms and for improving the accuracy of model simulation to predict the hourly changes in cyanobacterial blooms in Lake Taihu.

二类水体主要包括内陆及近岸水体,受浮游植物、悬浮颗粒、有色可溶性有机物等多种因素影响,光学特性复杂多变,难以建立统一的水环境参数遥感定量估算模型。针对水体的光学特征,进行水体光学分类,进而反演水环境参数的方法,不仅能够提高参数估算精度,而且便于模型在同类水体中推广应用。水体光学分类方法主要包括基于固有光学特征的光学分类、基于遥感反射率波形特征的光学分类和以参数反演为目标的光学分类等方法。在分类反演的策略中,包括分类与模型算法融合、基于水体光学类型优选算法、优选多模型混合计算等方法。具体应用时,需要根据研究区水体光学特征的复杂程度和研究目的,选取不同的分类方法及参数遥感估算策略。

Particulate composition provides important information for understanding the changes in underwater light fields and primary productivity. In this study, a semianalytical algorithm, based on Rayleigh-corrected reflectance at 678 nm and 748 nm on Moderate Resolution Imaging Spectroradiometer (MODIS) images was used to estimate the ratio of chlorophyll a to total suspended solids (Chla/TSS), which characterizes the particulate composition of the Great Lakes. The long-term spatial and temporal characteristics of Chla/TSS in the Great Lakes from 2000 to 2020 were obtained. The results demonstrated that Lake Superior had the highest average Chla/TSS values (5.79±0.76 µg/mg), while Lake Erie had the lowest average Chla/TSS values (2.93±0.76 µg/mg). The Mann-Kendall test showed that the Chla/TSS of the Great Lakes all showed an increasing trend, notably in Lake Michigan, with 88.23% pixels showing significant increasing trend. Climatic and hydrological factors dominated the intra-annual variation of Chla/TSS, with contribution rates ranging from 71.47% to 92.54%. Through the annual Chla/TSS change pattern analysis, it was found that the contribution of wind speed to the annual variation in Chla/TSS was slight. Changes in temperature played a major role in the interannual variability of Chla/TSS in Lake Superior and Ontario; runoff and settlement were the major contributors in Lake Huron and Michigan, while cropland dominated the Chla/TSS interannual variability in Lake Erie. Furthermore, the significantly low values of Chla/TSS in spring had the potential to predict the occurrence of blooms in western Lake Erie, and the spatial distribution of Chla/TSS could help predict the location of blooms in the next few days.

Serious cyanobacterial blooms (CBs) caused by lake eutrophication have become a global ecological and environmental problem and have adversely affected the production, life, and health of human beings. Lake Chaohu and Lake Taihu are two large closed shallow eutrophication lakes in the Yangtze River Delta in China with frequent CBs. In this study, the floating algae index (FAI) algorithm was applied to detect a long-time CBs sequence using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2019. The common characteristics and differences of the CBs patterns were further explored in both lakes over the last 20 years. The results showed that the severity of CBs in Lakes Chaohu and Taihu presented a similar trend of decreasing and then increasing during the period of 2000–2004 and 2005–2007, respectively. Although the severity of CBs in the two lakes was alleviated after 2008, CBs in Lake Taihu has gradually increased since 2011 and severe CBs broke out again in 2017 and 2019. Meanwhile, the CBs in Lake Chaohu have varied significantly in different years, and severe CBs were observed in 2012, 2014–2015, and 2018–2019, while in other years, CBs remained relatively low level. The high-frequency regions of CBs were mainly concentrated in the western part in Lake Chaohu and in Zhushan Bay and Meilian Bay in Lake Taihu in the initial years of 2000. However, since 2005, the CBs in Lake Chaohu gradually expanded to the central and eastern parts, and to the northwestern and western shore in Lake Taihu. Furthermore, the relationship between the monthly mean area of CBs (CBsmean) and environmental factors based on principal component analysis (PCA) indicated that temperature was the most important driving factor affecting CBs patterns. Compared to the period from 2001 to 2007, TP played a more important role in both lakes from 2008 to 2019. Various management measures have been adopted to reduce CBs in both lakes and these methods can effectively remove cyanobacteria in a short time, but they do not change CBs patterns in the long period.

The composition of suspended particles is a key factor in determining the underwater light field, which is of great significance for understanding the variability in the optical properties of water bodies. In this study, the ratio of the phytoplankton absorption coefficient to the backscattering coefficient at wavelength of 681 nm (a(ph)(681)/ b(b)(681)) was found to be an optimal optical indicator of the ratio of chlorophyll-a to the total suspended matter concentration (Chla/TSM), a parameter indicating the particulate composition. Therefore, a semianalytical algorithm was proposed to estimate Chla/TSM from remote sensing reflectance (Rrs(lambda)) at 681 nm and 754 nm on Sentinel-3 Ocean and Land Color Instrument (OLCI) images. The validation dataset collected from 11 inland lakes and 3 reservoirs in China and 2 inland lakes in America was used to evaluate the algorithm's performance. The evaluation results demonstrated that the proposed algorithm could have favorable performance in inland waters. Furthermore, comparison with two other state-of-the-art algorithms (Sun_13 and NTD675) showed that this proposed algorithm had higher estimation accuracy, with an overall winning rate (OWR) of 60%, an unbiased mean absolute percentage error (UMAPE) reduction from 72.95% to 46.44%, a root mean square error (RMSE) decline from 1.42 mu g/mg to 0.83 mu g/mg and a normalized root mean square error (NRMSE) decline from 11.36% to 6.62%. This algorithm was successfully applied to acquire the Chla/TSM tempo-spatial variation using the OLCI images of Lake Taihu from 2016 to 2019. It was found that the algorithm developed based on OLCI images can be applied to satellite sensors with similar bands, such as Medium-Resolution Imaging Spectrometer (MERIS), and Sentinel-2 multispectral instrument (MSI), etc. As a simple and effective algorithm, the proposed algorithm has the potential to monitor changes in Chla/TSM in inland waters on a global scale.