Aquatic plants are vital for lake ecosystem functioning and water-quality stability, yet their community dynamics and phenology rhythms remain insufficiently understood, due to the lack of effective strategies for fine-scale species mapping and phenology extraction. In this study, based on Sentinel-2 MSI imagery, we developed an integrated framework combining machine learning and a priori ecological knowledge to quantify the spatiotemporal changes in aquatic plant distribution, species composition and phenological dynamics for eight dominant species in five regulating lakes along the Eastern Route of the South-to-North Water Diversion Project in China. Results showed that the proposed framework enabled accurate aquatic plant identification, achieving an overall classification accuracy of 96.16% and over 90% accuracy for each species. Since 2016, aquatic vegetation coverage has substantially declined in most lakes, mainly due to the retreat of submerged vegetation. Community structure has shifted from submerged-plant dominance to emergent and floating-leaved dominance in two of them. Phenologically, we found that most aquatic vegetation exhibited a longer growing season, characterized by earlier growth onset (-0.28 days/year) and peak timing (-0.67 days/year) and delayed senescence (0.54 days/year). Correlation analysis indicated that aquatic vegetation dynamics was associated with climate variation, nutrient enrichment, turbidity, and water diversion, with warming and solar radiation likely promoting the growth of some emergent species, while nutrient enrichment and turbidity could be linked with submerged vegetation decline and earlier phenological shifts. Overall, this study provides an effective framework for species-level mapping and phenological monitoring of aquatic vegetation, offering valuable support for the management and conservation of lake ecosystems.

The correlation between the spatial differentiation of pond-related ecosystem services value (PRESV) and geographic environments is essential for management decisions of ponds. However, the types of ponds and geographic environments are more diverse across a larger scale region, necessitating a systematic approach on PRESV assessment. Therefore, we conducted targeted research on PRESV across a large-scale region to reveal the factors influencing the spatial differentiation of PRESV. To address the lack of a systematic approach on PRESV, this study proposes an optimization method for the aquatic production (AP) through a refined index structure and differentiated yield calculations based on the InVEST model. Notably, the optimized AP evaluation method significantly achieved an R2 exceeding 0.6 and demonstrated a marked improvement over the existing method. Consequently, we generated spatial distribution data for the PRESV in China. The results indicate that: (1) The AP exhibits significant spatial differentiation corresponding to the surrounding geographic environments, summarized as “more in the south and less in the north, more in the east and less in the west”. (2) The differentiated surrounding water bodies, elevation and precipitation are potential drivers of the AP function to be disentangled in a way not possible at small watershed scale. (3) Since areas with high PRESV are all associated with high-density AP, ponds should be incorporated into watershed management.

Ponds can act as either sources or sinks for phosphorus (P) export within a watershed. However, most existing studies treat ponds as homogeneous units, lacking a quantitative framework to identify these opposing functions. To address this, we develop a source–sink diagnostic framework that couples index of connectivity (IC) for the pond–river network with P export simulated by an optimized SWAT+ model to identify P-source and sink ponds. Using the Taihu Lake Basin as a representative eutrophic watershed, the results indicate that: (1) Ponds play a small but critical role in P export. Although ponds account for only 0.78% of the watershed area, they contribute 22.76% of P export. (2) Correlations between IC and P export differ markedly across pond functional types. IC positively correlates with P export from aquaculture ponds (r = 0.74, p < 0.05) but negatively with landscape ponds (r = -0.75, p < 0.05); (3) An operational connectivity boundary at IC = − 2 helped distinguish source and sink zones: above this threshold, ponds predominantly act as P sources, dominated by strongly connected aquaculture ponds and moderately connected natural ponds. Below it, ponds shift to P sinks, mainly weakly connected natural and landscape ponds. This new framework provides the first quantitative basis for differentiated pond management, prioritizing source control in strongly connected aquaculture ponds while conserving sink functions in weakly connected natural and landscape ponds

Variations in the mass ratio of nitrogen to phosphorus (TN/TP) are closely linked to algal bloom outbreaks in lakes. Traditional studies predominantly rely on laboratory or in situ observations, lacking long-term, whole-scene monitoring. This study, based on remote sensing and intelligent algorithms, optimized direct derivation, indirect derivation, and various machine learning methods to, for the first time through satellite-enabled panoramic observation, obtain the spatiotemporal distribution of TN/TP in 28 lakes of Yangtze-Huaihe region (YHR) over 21 years and analyze its effects on phytoplankton. The results show that the Extreme Gradient Boosting-based direct derivation method yielded the best validation performance, with an R² of 0.59 and RMSE of 11.34. Spatially and temporally, TN/TP predominantly ranged between 15-25, with a multi-year average of 20.49. 61% of the lakes exhibited declining trends in TN/TP ratios, with all small lakes (<100 km²) showing consistent decreases. Seasonally, TN/TP ratios demonstrated lower values in summer-autumn and higher values in winter-spring. Leveraging the spatiotemporal observational advantages of remote sensing, it was found that TN/TP at downstream outlets was 2.9% higher than upstream inflow areas in 71% of the lakes, with the most pronounced difference in autumn (6.6%). Based on reported nutrient limitation thresholds, 63% of the lake areas were co-limited by nitrogen and phosphorus (P), while 36% were P-limited, with P-limited areas being higher in winter and spring, reaching 49%. By screening continuous observation cases, we found that Floating Algae Index became less sensitive to TN/TP when TN/TP exceeded 15 in all seasons, suggesting TN/TP = 15 as a feasible threshold for eutrophication control. Remote sensing-derived TN/TP patterns reveal the need for enhanced small lake monitoring, with the proposed locally adapted P-limitation threshold offering science-based guidance for eutrophication control.

Taihu Lake is one of the most severely eutrophic large lakes worldwide, characterized by a complex ecosystem where algal blooms and aquatic vegetation coexist, and it serves as a critical regional water source. Identifying the channels through which algal blooms invade vegetated areas and understanding their underlying mechanisms is essential for effective lake management. In this study, multi-source remote-sensing data were used to extract the spatial patterns of algal bloom frequency and aquatic vegetation over the past 22 years. We detected the major encroachment routes of algal blooms into vegetated zones and revealed the long-term interaction mechanisms between algae and vegetation. Algal blooms were persistently concentrated in the western and northern bays, exhibiting an overall fluctuating upward trend. Aquatic vegetation was mainly distributed in four eastern bays, remained relatively stable before 2015, declined markedly in 2016 due to dredging and shoreline modifications, and then gradually recovered. A stable negative response relationship was observed between algal blooms and aquatic vegetation, jointly regulated by seasonal climate and nutrient structure. High temperatures and intense precipitation in summer facilitated bloom expansion, whereas low temperatures in winter favored vegetation recovery. Meanwhile, lower TN to TP ratios suppressed vegetation growth and enhanced bloom encroachment during summer. Three primary channels of algal bloom encroachment into vegetated areas were identified: the northern shoreline of Gonghu Bay, the surroundings of Dongshan Island, and the southern marginal zone. Among them, the water intake in northern Gonghu Bay represents the most vulnerable zone requiring priority control. Although bloom risk is relatively low for the central and southern water intakes, maintaining the existing hydrodynamic regime and vegetation structure remains essential to prevent vegetation degradation, and enhanced monitoring is needed to identify in situ bloom occurrences within vegetated regions.

Eutrophication in shallow plain lakes is highly dynamic and often characterized by short-term trophic fluctuations that are difficult to resolve using annual or single-sensor satellite observations. The Eastern Plain Lake Zone (EPL) of China contains numerous shallow lakes and reservoirs embedded in densely populated and intensively cultivated lowland catchments, making it a representative region for high-frequency eutrophication monitoring. Here, we developed a monthly Trophic State Index (TSI) monitoring framework for lakes and reservoirs in the EPL using Harmonized Landsat–Sentinel (HLS) observations. The XGBoost model using combined spectral features achieved the best validation performance (R2 = 0.90, RMSE = 5.23), and provided the highest trophic-state classification accuracy, with an overall accuracy of 0.73 and a Kappa coefficient of 0.66. Lakes showed substantially higher trophic levels than reservoirs, with mean TSI values of 57.32 ± 6.89 and 45.00 ± 9.22, respectively. Temporally, significantly decreasing and increasing TSI trends accounted for about 15% and 5%, respectively. Natural lake TSI reflects integrated climatic, anthropogenic, and hydro-morphological influences, whereas reservoir TSI variability may be more closely associated with hydrological regulation and morphometric conditions. Our results show that HLS observations provide a robust basis for monthly eutrophication monitoring in optically complex shallow lake regions. The EPL-focused framework highlights the value of virtual satellite constellations for detecting short-term trophic deterioration and supporting region-specific lake and reservoir management.

Total nitrogen (TN) and total phosphorus (TP) serve as key indicators of aquatic eutrophication. While the monitoring stations are continuous in time, there are too few points in some lakes, and many lakes with low attention do not have monitoring stations, making it difficult to reflect the overall pattern of the lake area and multi-lake regions. This study integrated Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data with machine learning techniques to estimate TN and TP concentrations across 26 lakes in the Yangtze-Huaihe Region (YHR) of China over a 20-year period (2003-2023). The Extreme Gradient Boosting (XGB) algorithm was identified as the top performer, achieving coefficient of determination (R2) values of 0.56 (RMSE = 1.14 mg/L) for TN and 0.58 (RMSE = 0.06 mg/L) for TP estimation. The long-term reconstruction revealed that TN levels consistently ranging from 1.2 to 2.0 mg/L, with 70% of the retrieved values exceeding the 1.5 mg/L threshold after 2015. TP concentrations showed a progressive increase, generally fluctuating between 0.10 and 0.16 mg/L levels throughout the study period. Notably, the lakes exhibited a higher trophic state index for phosphorus (mean TSITP = 74.30) was demonstrated compared to nitrogen (mean TSITN = 61.13), with spatial analysis identifying external riverine inputs as the primary phosphorus source. These results align with environmental parameters derived from other remote sensing observations, providing reliable data to support water management decisions in the YHR and offering a transferable framework for global eutrophication monitoring.

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.

Column-integrated algal biomass has been recognized as a more logical proxy for the evaluation of lake eutrophication. Here, an algorithm with a 3-step framework is put forward for algal biomass mapping in 3 lakes of China (Lake Hongze, Lake Taihu, and Lake Chaohu). It can be summarized in step 1: inversion of surface chlorophyll a (Chl a ), step 2: inversion of diffuse attenuation coefficient of the photosynthetic active radiation [ K d (PAR)], and step 3: estimation of algal biomass with a pretrained generalized additive model. The proposed algorithm outperforms the result-oriented and process-oriented methods in terms of accuracy in 3 lakes (the root mean square error [RMSE] values for datasets of Lake Hongze, Lake Taihu, and Lake Chaohu were 5.09, 8.21, and 3.90 mg/m 2 , respectively). Validated with match-up satellite data, the algorithm generates acceptable results (RMSE = 5.69 mg/m 2 , mean absolute percentage error = 30.9%, N = 16). Another important discovery is that the extremum of algal biomass of the entire lake (B tot ) does not always coincide with that of total surface Chl a . For example, the maximum total surface Chl a was recorded in 2016, whereas the maximum B tot of Lake Hongze was observed in 2020. For Lake Taihu, 3 peaks of B tot appearing in 2017, 2019, and 2021, respectively, did not coincide with those of total surface Chl a . For Lake Chaohu, the interannual B tot followed a bimodal pattern that differed from the pattern of interannual total surface Chl a . The proposed algorithm plays an indispensable role in broadening the horizon for algal biomass inversion.

Land salinisation in the Songnen Plain is a serious constraint to agricultural development. Assessing and identifying saline land as having advantages for improvement is of great significance for improving the efficiency of saline croplands improvement. Taking the western Songnen Plain as the study area, an evaluation framework for identifying saline-alkali croplands for improvement was proposed. Compared with traditional research models that evaluate the degree of saline-alkali land suitable for farming, the purpose of this study is to identify land that is easier to improve and has more or more value for farming. On the basis of evaluating the suitability of salinealkali land using the Analytic Hierarchy Process-Geographic Information System (AHP-GIS) model, this framework incorporates the degree of soil salinisation, quantified by electrical conductivity and exchangeable sodium percentage, and hydrological connectivity, calculated based on terrain factors and vegetation cover., constructs a saline-alkali land improvement index, and divides suitable improvement areas at different levels. The results indicate that the northern part of the study area exhibits low cropland suitability value, primarily due to higher pH values, unfavorable topography, and lower temperatures. Extreme salinity area are concentrated in the central part of the study area, mainly due to elevated soil conductivity and exchangeable sodium percentages. Areas with high hydrological connectivity value are distributed in the northern, southern, and eastern parts, owing to the presence of numerous lakes and dense vegetation cover. Using the Suitable Improvement Index (SII), 39.42 % of the croplands were identified as suitable for improvement. Among these, low-value, moderatevalue, and high-value accounted for approximately 5.43 %, 33.36 %, and 61.21 % of SII subareas. The low SII values are found in Qianan and Tongyu in the southern and central parts of the study area, while high SII values are distributed in Tongyu, Daan, and Taobei in the central part of the study area. The evaluation framework of this study identifies saline-alkali croplands that is more suitable for improvement, providing valuable insights into the restoration of saline-alkali croplands in Northeast China. We provides valuable insights into saline-alkali land restoration from a management and strategic perspective.

To address the issues of short flight duration and the inability to carry high‐computation resources in small observation unmanned aerial vehicles (UAVs) due to limited energy and payload capacities, this paper proposes a deployment framework for an air–water surface collaborative observation system based on energy‐replenishment and computation offloading. In this framework, UAVs serve as platforms for observation tools, while unmanned surface vehicles (USVs) function as platforms for energy replenishment and edge computing nodes. The edge computing nodes are capable of processing, analyzing, and distributing observation data received from the UAVs. UAVs can perform coordinated landing and recharging on the USVs using high‐precision BeiDou positioning. Experimental results indicate that the application of this framework allows small observation UAVs to avoid the burden of carrying heavy computational loads during flight and enables cyclic recharging and operation using the USV platform. The findings of this study have broad applicability in various scenarios, including environmental monitoring, disaster patrol, marine mapping, and marine aquaculture.

Water salinity characterizes the physicochemical properties of natural water, serving as an essential parameter for assessing lake water quality. However, the efficiency of remote sensing inversion of water salinity is limited as salinity is a non-optically active parameter, leading to the lack of a pixel-scale lake salinity dataset. Conventional function models based on salinity tracers or single lakes have low regional applicability, while machine learning algorithms can effectively capture the nonlinear relationship between radiance and salinity, providing large-scale inversion opportunities. Our study constructed an extreme gradient boosting (XGB) salinity model, which was used to generate the Inner Mongolia lake salinity (IMSAL) dataset with Sentinel-2 remote sensing reflectance. The IMSAL dataset contains 928 raster scenes with 10-meter spatial resolution for eight lakes from 2016 to 2024. Cross-validation and independent validation with measured and published literature-recorded salinities confirmed the good consistency and reliability. This dataset provides invaluable information on spatial patterns and long-term variations in lake salinity useful to prevent lake salinization and facilitate the lake management for sustainable ecosystem development.

Unmanned surface vehicles (USVs) are intelligent platforms for unmanned surface navigation based on artificial intelligence, motion control, environmental awareness, and other professional technologies. Obstacle avoidance is an important part of its autonomous navigation. Although the USV works in the water environment (e.g. monitoring and tracking, search and rescue scenarios), the dynamic and complex operating environment makes the traditional methods not suitable for solving the obstacle avoidance problem of the USV. In this paper, to address the issue of poor convergence of the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm of Deep Reinforcement Learning (DRL) in an unstructured environment and wave current interference, random walk policy is proposed to deposit the pre-exploration policy of the algorithm into the experience pool to accelerate the convergence of the algorithm and thus achieve USV obstacle avoidance, which can achieve collision-free navigation from any start point to a given end point in a dynamic and complex environment without offline trajectory and track point generation. We design a pre-exploration policy for the environment and a virtual simulation environment for training and testing the algorithm and give the reward function and training method. The simulation results show that our proposed algorithm is more manageable to converge than the original algorithm and can perform better in complex environments in terms of obstacle avoidance behavior, reflecting the algorithm's feasibility and effectiveness.

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.