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<p>Leaf hyperspectral reflectance (HSR) data have gained increasing attention due to their usage in predicting a range of leaf physiological, biochemical, structural, and photosynthetic traits using machine learning (ML) models. The PROSPECT family of models offers a complementary, mechanistic means to estimate leaf traits from HSR data using model inversion. However, a comprehensive evaluation of the accuracy and transferability of the PROSPECT model across a large set of species is hindered by the limited availability of ground truth data sets. Here, we employed a combination of inversion and forward simulation of the PROSPECT-D model across a broad range of species and identified four narrow wavebands linked to environmental effects. We also introduced a novel framework using partial least squares regression to enable the analysis of the transferability of the machine learning models trained base on the PROSPECT-D across species. This analysis revealed trait-specific patterns of transferability for the machine learning surrogate based on the PROSPECT-D forward model. We then extended this analysis to PROSPECT-D inversion using neural networks and developed a fast, accurate deep-learning-based surrogate inversion approach to estimate leaf traits from measured HSR data. Our data-driven framework paves the way for improving the accuracy of PROSPECT and similar mechanistic models.</p>

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Machine learning surrogate for the leaf PROSPECT-D model and its applications across plant species

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月19日

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  • Milad Rahimi-Majd,
  • Rudan Xu,
  • Stefan Bauermeister,
  • Zoran Nikoloski

摘要

Leaf hyperspectral reflectance (HSR) data have gained increasing attention due to their usage in predicting a range of leaf physiological, biochemical, structural, and photosynthetic traits using machine learning (ML) models. The PROSPECT family of models offers a complementary, mechanistic means to estimate leaf traits from HSR data using model inversion. However, a comprehensive evaluation of the accuracy and transferability of the PROSPECT model across a large set of species is hindered by the limited availability of ground truth data sets. Here, we employed a combination of inversion and forward simulation of the PROSPECT-D model across a broad range of species and identified four narrow wavebands linked to environmental effects. We also introduced a novel framework using partial least squares regression to enable the analysis of the transferability of the machine learning models trained base on the PROSPECT-D across species. This analysis revealed trait-specific patterns of transferability for the machine learning surrogate based on the PROSPECT-D forward model. We then extended this analysis to PROSPECT-D inversion using neural networks and developed a fast, accurate deep-learning-based surrogate inversion approach to estimate leaf traits from measured HSR data. Our data-driven framework paves the way for improving the accuracy of PROSPECT and similar mechanistic models.