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A PROSPECT-guided multi-task learning hybrid framework for simultaneous retrieval of leaf chlorophyll and carotenoids

Hao, Weilin ; Sun, Jia LU orcid ; Zhang, Kan ; Yang, Shilin ; Qiu, Feng and Xu, Jin (2026) In International Journal of Applied Earth Observation and Geoinformation 152.
Abstract

Leaf chlorophyll (Chl) and carotenoids (Cxc) jointly govern light harvesting and photoprotection. However, concurrent retrieval from leaf reflectance remains challenging because Cxc absorption is weaker and strongly overlaps with Chl in the visible range. This spectral challenge, together with the physiological coordination between Chl and Cxc, motivates a multi-task learning (MTL) formulation for joint retrieval. Here, we propose a PROSPECT-guided MTL framework for the joint retrieval of leaf Chl and Cxc from 400–911 nm reflectance. The model is trained on PROSPECT-5 simulations and adopts a shared backbone with two pigment-specific branches. It is evaluated on five in-situ datasets (ANGERS, NX, BM, XS, and JTL; n = 680), benchmarked... (More)

Leaf chlorophyll (Chl) and carotenoids (Cxc) jointly govern light harvesting and photoprotection. However, concurrent retrieval from leaf reflectance remains challenging because Cxc absorption is weaker and strongly overlaps with Chl in the visible range. This spectral challenge, together with the physiological coordination between Chl and Cxc, motivates a multi-task learning (MTL) formulation for joint retrieval. Here, we propose a PROSPECT-guided MTL framework for the joint retrieval of leaf Chl and Cxc from 400–911 nm reflectance. The model is trained on PROSPECT-5 simulations and adopts a shared backbone with two pigment-specific branches. It is evaluated on five in-situ datasets (ANGERS, NX, BM, XS, and JTL; n = 680), benchmarked against separate single-task networks (STL), partial least squares regression (PLSR) and PROSPECT-based lookup-table inversion (PHY). Across datasets, the proposed MTL model consistently outperforms STL, PLSR and PHY for Chl and improves Cxc retrieval in most cases. In pooled evaluation, the RMSE for Chl is reduced by 32%, 13% and 28% compared with STL, PLSR and PHY, respectively. For Cxc, the corresponding reductions are 4%, 16% and 14%. On the public ANGERS benchmark, the model achieves an RMSE of 5.56 μg/cm2 for Chl (R2 = 0.93) and 2.39 μg/cm2 for Cxc (R2 = 0.78), yielding performance competitive with prior reflectance-based reports. PROSPECT-referenced occlusion analysis further suggests that, relative to STL, MTL induces more complementary wavelength reliance between the two tasks within pigment-sensitive regions. Overall, the PROSPECT–MTL framework enables accurate and robust reflectance-only joint retrieval of Chl and Cxc across datasets. These results provide a leaf-level basis for future canopy-scale and sensor-scale extensions in pigment-informed vegetation monitoring.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Hyperspectral reflectance, Leaf carotenoids, Leaf chlorophyll, Multi-task learning, Physics-guided machine learning, PROSPECT-5, Simultaneous retrieval
in
International Journal of Applied Earth Observation and Geoinformation
volume
152
article number
105467
publisher
Elsevier
external identifiers
  • scopus:105045142364
ISSN
1569-8432
DOI
10.1016/j.jag.2026.105467
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2026 The Author(s)
id
c96cfa8c-5828-43b4-9b15-efb1153376e5
date added to LUP
2026-10-02 15:25:25
date last changed
2026-10-05 09:14:19
@article{c96cfa8c-5828-43b4-9b15-efb1153376e5,
  abstract     = {{<p>Leaf chlorophyll (Chl) and carotenoids (Cxc) jointly govern light harvesting and photoprotection. However, concurrent retrieval from leaf reflectance remains challenging because Cxc absorption is weaker and strongly overlaps with Chl in the visible range. This spectral challenge, together with the physiological coordination between Chl and Cxc, motivates a multi-task learning (MTL) formulation for joint retrieval. Here, we propose a PROSPECT-guided MTL framework for the joint retrieval of leaf Chl and Cxc from 400–911 nm reflectance. The model is trained on PROSPECT-5 simulations and adopts a shared backbone with two pigment-specific branches. It is evaluated on five in-situ datasets (ANGERS, NX, BM, XS, and JTL; n = 680), benchmarked against separate single-task networks (STL), partial least squares regression (PLSR) and PROSPECT-based lookup-table inversion (PHY). Across datasets, the proposed MTL model consistently outperforms STL, PLSR and PHY for Chl and improves Cxc retrieval in most cases. In pooled evaluation, the RMSE for Chl is reduced by 32%, 13% and 28% compared with STL, PLSR and PHY, respectively. For Cxc, the corresponding reductions are 4%, 16% and 14%. On the public ANGERS benchmark, the model achieves an RMSE of 5.56 μg/cm<sup>2</sup> for Chl (R<sup>2</sup> = 0.93) and 2.39 μg/cm<sup>2</sup> for Cxc (R<sup>2</sup> = 0.78), yielding performance competitive with prior reflectance-based reports. PROSPECT-referenced occlusion analysis further suggests that, relative to STL, MTL induces more complementary wavelength reliance between the two tasks within pigment-sensitive regions. Overall, the PROSPECT–MTL framework enables accurate and robust reflectance-only joint retrieval of Chl and Cxc across datasets. These results provide a leaf-level basis for future canopy-scale and sensor-scale extensions in pigment-informed vegetation monitoring.</p>}},
  author       = {{Hao, Weilin and Sun, Jia and Zhang, Kan and Yang, Shilin and Qiu, Feng and Xu, Jin}},
  issn         = {{1569-8432}},
  keywords     = {{Hyperspectral reflectance; Leaf carotenoids; Leaf chlorophyll; Multi-task learning; Physics-guided machine learning; PROSPECT-5; Simultaneous retrieval}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{International Journal of Applied Earth Observation and Geoinformation}},
  title        = {{A PROSPECT-guided multi-task learning hybrid framework for simultaneous retrieval of leaf chlorophyll and carotenoids}},
  url          = {{http://dx.doi.org/10.1016/j.jag.2026.105467}},
  doi          = {{10.1016/j.jag.2026.105467}},
  volume       = {{152}},
  year         = {{2026}},
}