China’s northern Hulun Lake is a significant body of water internationally. The issue of eutrophication has gained prominence in recent years. The achievement of precise chlorophyll-a (Chl-a) monitoring is crucial for safeguarding Hulun Lake’s ecosystem. The machine learning-based remote sensing inversion method has been shown to be effective in capturing the intricate relationship between independent and dependent variables; however, it lacks a priori knowledge and is limited by the quality of remote sensing data sources. The relationship between independent and dependent variables can be more accurately simulated with the use of suitable auxiliary variables. Therefore, three machine learning models—random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost)—were established in this study using meteorological observation parameters as auxiliary variables combined with Sentinel-2 satellite image remote sensing band combinations as independent variables and measured Chl-a data as dependent variables. The estimation effects before and after the fusion of meteorological ground observation data were compared, and the best model was used to estimate the spatial–temporal variation trend of Chl-a in the regional water body. The results show that (1) the addition of meteorological parameters as auxiliary variables improved the precision of the three machine models; the decision coefficient (R2) rose by 7.25%, 5.71%, and 7.20%, respectively, to 0.76, 0.66, and 0.73. (2) The concentration of Chl-a in the lake region was projected from June to October 2019 to October 2021 using the RF optimal estimating model of meteorological fusion. The northeast, southwest, and south of the lake were where the comparatively high concentration values of Chl-a were located, whereas the lake’s center had a generally low concentration of the substance. Chromatically, Chl-a typically peaked in August after initially increasing and then declining. (3) The three rivers that feed into the river have varying levels of water pollution, with chemical oxygen demand (COD) and total nitrogen (TN) pollution being the most severe. This is what primarily caused the higher levels of Chl-a in the northeast, southwest, and south. This study is crucial for the preservation and restoration of Hulun Lake’s natural ecosystem and offers some technical support for the monitoring of the lake’s concentration of Chl-a.

Managing water resources amidst the pressures of climate change and human activities is a significant challenge, especially in regions experiencing shrinking lakes, deteriorating water quality, and ecological degradation. This review focuses on achieving integrated river basin management by learning from the governance experiences of typical watersheds globally, using the Hulun Lake Basin as a case study. Hulun Lake, China’s fifth-largest lake, experienced severe ecological problems from 2000 to 2009 but saw improvements after comprehensive management efforts from 2012 onward. This review systematically explores methods to address water resource, environment, and ecological challenges through the lenses of data acquisition, mechanism identification, model simulation, and regulation and management. Drawing lessons from successful basins such as the Rhine, Ganges, Mississippi, and Murray–Darling, the review proposes key goals for comprehensive management, including establishing extensive monitoring networks, developing predictive models, and creating contingency plans for routine and emergency management. Leveraging advanced technologies like satellite imagery and IoT sensors, alongside continuous improvement mechanisms, will ensure the sustainable use and protection of river basins. This review provides a detailed roadmap for achieving comprehensive watershed management in Hulun Lake, summarizing effective strategies and outcomes from data acquisition to regulation, thus serving as a model for similar regions globally.

The latest UN environmental report warns that the world is facing three interconnected crises: climate change, environmental pollution, and biodiversity loss. Notably, under the dual pressures of extreme weather events and long-term environmental pollution, both the rate of species extinction and the extent of habitat degradation have reached unprecedented levels. Efficient monitoring and prediction of high-risk areas and critical periods are essential to mitigating biodiversity loss. Traditional methods, which rely heavily on in-situ ground observations, often provide only localized snapshots of ecosystem change. These approaches fall short in addressing the need for global, cross-scale identification and early warning of biodiversity degradation risks. A multi-scale remote sensing network offers a powerful solution, enabling early detection and timely mitigation by integrating satellite observations, aerial drones, ground-based monitoring stations, and oceanic sensors. The fusion of complementary data sources enhances both spatial resolution and coverage, allowing for dynamic tracking of biodiversity across scales. To operationalize such a system globally, international collaboration, open data access, standardized indicators, and strategic technological investments are crucial. Taken together, this integrated framework will strengthen real-time ecological forecasting and support proactive policy and conservation responses.