@article{Yang2026, 
author = {Jinpeng Yang and Zhaopeng Fu and Ke Zhang and Bohuai Shi and Jiang Wang and Weizhe Zhao and Qiang Cao and Yongchao Tian and Yan Zhu and Weixing Cao and Xiaojun Liu},
title = {Physiology-informed LSTM framework integrating crop model and Sentinel-2 time series for rice nitrogen status estimation},
year = {2026},
journal = {Plant Phenomics},
volume = {8},
number = {2},
pages = {100234},
keywords = {Physiology-informed LSTM, Precision nitrogen management, Plant dry matter, Plant N accumulation, Variable-rate fertilization},
url = {https://www.sciopen.com/article/10.1016/j.plaphe.2026.100234},
doi = {10.1016/j.plaphe.2026.100234},
abstract = {Accurate assessment of plant dry matter (PDM) and plant N accumulation (PNA) provides essential indicators for precision nitrogen (N) management in rice production. However, purely data-driven models struggle to generalize due to the spatial scarcity of ground-truth physiological data. To address this, a physiology-informed long short-term memory (PI-LSTM) framework was developed for robust regional N diagnosis and variable-rate fertilization. First, the model was pretrained to internalize crop growth dynamics using a DSSAT-based simulation library, which spanned 2000 representative fields and 700 management scenarios to provide physiologically consistent pseudo-labels. Subsequently, the framework was fine-tuned using multi-year field observations (2020, 2023, 2024), Sentinel-2 time-series data, and meteorological inputs. The proposed LSTM framework outperformed conventional machine learning approaches in estimating PDM and PNA, achieving five-fold cross-validation R2 values of 0.87 and 0.83, respectively. Based on these biophysical estimations, the N nutrition index (NNI) diagnosis achieved a 67.3% overall classification accuracy. Furthermore, by integrating the critical N dilution curve, the critical PNA and accumulated N deficiency (AND) were quantified, which served as the basis for developing the AND-based N recommendation algorithm (ANDA). Finally, variable-rate topdressing field experiments conducted across seven sites in 2024 and 2025 demonstrated that the ANDA reduced N input by 13.4% compared with farmers’ practices, while maintaining or increasing yield and improving N partial factor productivity by 18.6%. This study provides a reliable, physically consistent decision-support framework for regional-scale precision N management.}
}