Qin et al. (2026) Pear tree growth assessment across phenological stages via remote sensing data assimilation

Identification

• Journal: Smart Agricultural Technology
• Year: 2026
• Date: 2026-09-07
• Authors: Yasen Qin, Jianan Chi, Ning Yan, Yuwei Wu, Sumin Lv, Zehua Fan
• DOI: 10.1016/j.atech.2026.102555

Research Groups

• College of Information Engineering, Tarim University, Aral, 843300, China
• Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, Tarim University, Alar, 843300, China
• College of Cyber Security, Tarim University, Alar, 843300, China
• Corps Cybersecurity Collaborative Innovation Center, Tarim University, Alar, 843300, China

Short Summary

This study proposes a comprehensive evaluation method for pear tree growth that is adaptable to multiple phenological stages, based on the World Food Studies (WOFOST) model and data assimilation techniques. The method effectively quantifies temporal dynamics and spatial heterogeneity of pear growth vigor.

Objective

• To develop a localized calibration of WOFOST model parameters tailored to local pear trees.
• To construct an LAI inversion model based on Sentinel-2 time-series images, and achieve data assimilation between remote sensing LAI and the WOFOST model through the EnKF algorithm.
• To establish a comprehensive growth evaluation method for pear tree growth across multiple phenological periods.

Study Configuration

• Spatial Scale: Regional scale (Alar region of Xinjiang)
• Temporal Scale: Daily time step, with data collected over four key phenological stages: flowering, young fruit development, fruit enlargement, and maturity.

Methodology and Data

• Models used: WOFOST model, Ensemble Kalman Filter (EnKF) algorithm.
• Data sources: Field observation data, remote sensing data from Sentinel-2 satellite.

Main Results

• The calibrated WOFOST model significantly improved simulation accuracy for pear trees (R²=0.62, 0.65, and 0.66 for the three aspects).
• After LAI assimilation, R² for all six parameters exceeded 0.75.
• The proposed method effectively quantified temporal dynamics and spatial heterogeneity of pear growth vigor.

Contributions

• This study provides a comprehensive evaluation method for pear tree growth that is adaptable to multiple phenological stages.
• The method combines the spatial advantages of remote sensing with the mechanistic advantages of the WOFOST model, achieving a scale leap from single-point simulation to continuous regional simulation.

Funding

• Not specified.

Citation

@article{Qin2026Pear,
  author = {Qin, Yasen and Chi, Jianan and Yan, Ning and Wu, Yuwei and Lv, Sumin and Fan, Zehua},
  title = {Pear tree growth assessment across phenological stages via remote sensing data assimilation},
  journal = {Smart Agricultural Technology},
  year = {2026},
  doi = {10.1016/j.atech.2026.102555},
  url = {https://doi.org/10.1016/j.atech.2026.102555}
}