中国湖泊水色遥感：迈向智能新时代的变革与突破

马荣华; 胡旻琪; 薛坤; 曹志刚

doi:10.11834/jrs.20255238

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中国湖泊水色遥感：迈向智能新时代的变革与突破
Remote sensing of lake water color in China： Transformation and breakthrough towards a new intelligent era
2026年30卷第1期页码：1-10
收稿：2025-07-08，

纸质出版：2026-01-07
DOI： 10.11834/jrs.20255238
稿件说明：

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马荣华，胡旻琪，薛坤，曹志刚.2026.中国湖泊水色遥感：迈向智能新时代的变革与突破.遥感学报，30（1）： 1-10 DOI： 10.11834/jrs.20255238.

Ma R H，Hu M Q，Xue K and Cao Z G. 2026. Remote sensing of lake water color in China： Transformation and breakthrough towards a new intelligent era. National Remote Sensing Bulletin， 30（1）：1-10 DOI： 10.11834/jrs.20255238.

摘要

水色遥感历经六十余年发展，已从光学遥感知识积累阶段，逐步跃迁至智能驱动的机理建模新阶段。以“技术演进—问题驱动—智能变革”为主线，系统梳理了中国湖泊水色遥感的发展历程；以水体辐射传输理论为基础、湖泊环境治理为现实驱动，重点分析了湖泊水色遥感的核心挑战，提出了智能技术的突破路径。智能水色遥感已实现自动化水平提升和反演精度突破，但面临模型泛化性不足、物理可解释性弱等问题。当前，中国湖泊水色遥感技术正经历从经验建模、机理建模到人工智能驱动的范式革新，逐步构建起“基础光学建模—国产卫星研发—智能算法创新—行业业务应用”的技术体系，分析了其面临的挑战与机遇，对智能水色遥感的未来发展提出了若干思考。

Abstract

Lake water color remote sensing has undergone a significant transformation over the past six decades

evolving from an initial phase of knowledge discovery and accumulation in optical remote sensing into a new

intelligently driven era focused on mechanistic modeling. The development in China began in the 1980s

with its industrialization marked by the launch of the HY-1A satellite in 2002. Since the 1990s

extensive national efforts in lake environmental restoration and management have provided continuous impetus for its advancement. The period after 2020 witnessed the large-scale integration of Artificial Intelligence (AI)

propelling the field into an intelligent epoch characterized by dramatically enhanced automation and intelligence

thereby enabling comprehensive

large-scale regional monitoring applications.

Remote sensing technology is currently undergoing a paradigm shift from empirical and mechanistic modeling toward AI-driven approaches

progressively establishing a technical framework integrating “basic optical modeling–domestic satellite development–intelligent algorithm innovation–operational industry application.” The theoretical cornerstone remains the water radiative transfer theory

which quantitatively links the apparent and inherent optical properties of water bodies with the concentrations of optically active constituents. However

lake water color remote sensing faces unique complexities not encountered in traditional marine applications. These complexities include challenges stemming from the generally small size and rapid dynamic changes of lakes

which demand high spatial and temporal resolution from sensors. Furthermore

atmospheric correction is particularly difficult because of the prevalence of absorbing aerosols near human settlements

often rendering marine algorithms ineffective. Other complications involve discriminating between algal blooms and aquatic vegetation with similar spectral features

correcting for significant land adjacency effects in enclosed basins

and developing universally applicable inversion algorithms owing to the high spatial and temporal heterogeneity of inland waters.

The rise of intelligent water color remote sensing represents a pivotal breakthrough. Machine and deep learning techniques are now being deployed to tackle the persistent challenge of atmospheric correction over diverse lakes and to achieve the simultaneous retrieval of multiple water quality parameters. These data-driven methods overcome the limitations of region-specific empirical algorithms and the sensitivity of semianalytical models to parameterization

offering a promising path toward robust

generalizable models. This intelligent approach signifies a profound paradigm shift from retrieving single elements to constructing integrated

AI-powered systems. Future trajectories point toward the deep integration of physics and AI

such as embedding differentiable radiative transfer equations into neural networks with physical constraints. The development of a dual-stage framework involving a “global foundational model” trained on multisource satellite data and “regional adaptation modules” fine-tuned with local measurements is envisioned. Ultimately

the integration of remote sensing with hydrodynamic models to create lake ecological digital twins will enable probabilistic forecasting of events like algal blooms and provide powerful decision-support capabilities for sustainable water management.

In conclusion

lake water color remote sensing has firmly entered an intelligent era defined by substantially improved automation

scalability

efficiency

and accuracy. This evolution

from empirical to physical to AI-driven modeling paradigms

provides a solid technical foundation for advancing water environment monitoring

management

and ecological restoration on a global scale.

关键词

Keywords

references

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