ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1612430
This article is part of the Research TopicMachine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture, Volume IIView all 36 articles
Integrating PROSPECT-D Physics and Adversarial Domain Adaptation Resnet for Robust Cross-Ecosystem Plant Traits Estimation
Provisionally accepted- 1Guizhou University of Finance and Economics, Guiyang, Guizhou Province, China
- 2Guizhou Normal University, Guiyang, Guizhou Province, China
- 3China Agricultural University, Beijing, China
- 4Guizhou University, Guiyang, Guizhou Province, China
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Plant functional traits, including chlorophyll content (CHL), equivalent water thickness (EWT), and leaf mass per area (LMA), are critical indicators for assessing ecosystem functioning, functional diversity, and their roles in the Earth system. Hyperspectral remote sensing serves as a pivotal tool for multi-trait mapping; however, existing methods exhibit limited generalizability across ecosystems, land cover types, and sensor modalities. Challenges such as data heterogeneity, domain shifts, and sparse in situ measurements further hinder model generalization. To address these limitations, this study developed PPADA-Net, a novel framework integrating PROSPECT-D radiative transfer modeling with adversarial domain adaptation for robust cross-ecosystem plant trait prediction. In a two-stage process, a residual network is pretrained on synthetic spectra from PROSPECT-D to capture biophysical links between leaf traits and spectral signatures, followed by adversarial learning to align source and target domain features, reducing domain shifts. The model's performance is validated on four public datasets and one field-measured dataset. PPADA-Net outperforms traditional partial least squares regression (PLSR) and purely data-driven models (e.g., ResNet), achieving mean R² values of 0.72 (CHL),0.77 (EWT), and 0.86 (LMA). Additionally, PPADA-Net demonstrates practical utility in a real-world farmland dataset (D5), achieving highprecision spatial mapping with an nRMSE of 0.07 for LMA. By merging physical priors with adaptive learning, PPADA-Net enhances spectral-trait modeling under data scarcity, offering a scalable tool for ecosystem monitoring, precision agriculture, and climate adaptation.
Keywords: hyperspectral1, deep learning2, Plant phenotyping3, Adversarial domain adaptation4, Plant functional traits5
Received: 15 Apr 2025; Accepted: 03 Jul 2025.
Copyright: © 2025 Zhang, Su, Shen, Sun and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Qi Wang, Guizhou University, Guiyang, 550025, Guizhou Province, China
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