Shi et al. (2026) Remote sensing of aquatic nutrients from proxy estimation to integration with process models

Identification

• Journal: Communications Earth & Environment
• Year: 2026
• Date: 2026-10-15
• Authors: Chenyi Shi, Xihua Wang, Y. Jun Xu, Xingyu Zhu, Shunqing Jia, Zejun Liu, Boyang Mao, Yan Dai, Yanxin Rong, Qinya Lv, Xuming Ji, Chengming Luo
• DOI: 10.1038/s43247-026-04152-0

Research Groups

• Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (CAS)
• Key Laboratory of Water Environment Simulation, CAS
• School of Environmental Science and Engineering, Nanjing University
• Department of Hydrology and Atmospheric Sciences, University of Arizona
• National Center for Atmospheric Research (NCAR)

Short Summary

This study reviews remote sensing approaches for estimating aquatic nutrients, focusing on their integration with process-based models. The review highlights the limitations of proxy-based estimation methods and emphasizes the need for direct mechanistic inference of nutrient cycling processes.

Objective

• Investigate the evolution of remote sensing approaches for estimating aquatic nutrients from proxy-based retrieval to integration with process-based models.
• Synthesize the applications, limitations, and future directions of remote sensing in supporting multiscale parameter constraints and dynamic simulations within process-based models.

Study Configuration

• Spatial Scale: Watershed, lake, field, and interface scales
• Temporal Scale: Short-term (days-weeks) to long-term (years-decades)

Methodology and Data

• Models used:
  • Empirical statistical models
  • Semi-analytical methods within a simplified radiative transfer framework
  • Machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forests (RF)
  • Deep learning techniques
• Data sources: Satellite remote sensing data from various platforms, including Landsat, Sentinel-2, MODIS, and hyperspectral sensors

Main Results

• The study highlights the limitations of proxy-based estimation methods, which rely on indirect covariance between nutrients and optically active constituents (OACs).
• Semi-analytical models provide enhanced physical interpretability but remain indirect approaches that rely on optical observables.
• Machine learning algorithms demonstrate strong performance in nutrient estimation, particularly when incorporating complementary spectral and environmental information.

Contributions

• This study provides a comprehensive review of remote sensing approaches for estimating aquatic nutrients, emphasizing the need for direct mechanistic inference of nutrient cycling processes.
• The review highlights the importance of integrating remote sensing with process-based models to constrain nutrient fluxes and improve mechanistic understanding of aquatic nutrient cycling.

Funding

• National Natural Science Foundation of China (NSFC)
• Chinese Academy of Sciences (CAS) Strategic Priority Research Program
• US National Oceanic and Atmospheric Administration (NOAA)
• European Union's Horizon 2020 research and innovation program

Citation

@article{Shi2026Remote,
  author = {Shi, Chenyi and Wang, Xihua and Xu, Y. Jun and Zhu, Xingyu and Jia, Shunqing and Liu, Zejun and Mao, Boyang and Dai, Yan and Rong, Yanxin and Lv, Qinya and Ji, Xuming and Luo, Chengming},
  title = {Remote sensing of aquatic nutrients from proxy estimation to integration with process models},
  journal = {Communications Earth & Environment},
  year = {2026},
  doi = {10.1038/s43247-026-04152-0},
  url = {https://doi.org/10.1038/s43247-026-04152-0}
}