Transfer Learning in Junction With a Light Use Efficiency Model for Estimating Grassland Gross Primary Production

Authors

Ruiyang Yu, Beijing Normal University
Yunjun Yao, Beijing Normal University
Qingxin Tang, Liaocheng University
Xueyi Zhang, Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions
Changliang Shao, Chinese Academy of Agricultural Sciences
Joshua B. Fisher, Chapman UniversityFollow
Jiquan Chen, Michigan State University
Xiaotong Zhang, Beijing Normal University
Yufu Li, Jincheng Meteorological Administration
Jia Xu, University of Melbourne
Lu Liu, Beijing Normal University
Zijing Xie, Beijing Normal University
Jing Ning, Beijing Normal University
Jiahui Fan, Beijing Normal University
Luna Zhang, Beijing Normal University

Document Type

Article

Publication Date

3-12-2025

Abstract

It is significant to simulate grassland gross primary production (GPP) to understand the terrestrial carbon budget over Inner Mongolia (IMG), China. Nevertheless, there is not sufficient in situ GPP data over this region. In this study, we proposed a novel model-based transfer learning (MTL) approach with generative adversarial networks-long short-term memory (GAN-LSTM) and light use efficiency (LUE) models to derive grassland GPP over IMG, China. We first used 25 grassland eddy covariance sites over the conterminous United States to establish the GAN-LSTM model and then fine-tuned it with six sites over IMG to estimate water constraints that were embedded into the LUE model to predict GPP. We then compared it with instance-based transfer learning and nontransfer learning approaches. Against the six IMG EC sites, the GPP estimates of MTL-LUE outperformed the other approaches with a lower root-mean-square error median (1.35 g C m−2 d−1) and a higher Kling-Gupta efficiency of 0.54. An innovation of this approach is that MTL-LUE mitigates the effect of limited training samples on the machine learning-based LUE hybrid model for GPP estimates over IMG.

Comments

This article was originally published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, volume 18, in 2025. https://doi.org/10.1109/JSTARS.2025.3549373

Recommended Citation

R. Yu et al., "Transfer Learning in Junction With a Light Use Efficiency Model for Estimating Grassland Gross Primary Production," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 9738-9754, 2025, https://doi.org/10.1109/JSTARS.2025.3549373

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.