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Article Dans Une Revue Remote Sensing of Environment Année : 2023

Estimating leaf mass per area with leaf radiative transfer model

Résumé

Leaf mass per area (LMA) is an important leaf trait but challenging to be accurately estimated. This article proposes a simple leaf radiative transfer model called ISPECT. It explains the difference in optical properties observed on the adaxial (upper) and abaxial (lower) sides of leaves, i.e., their dorsiventrality, with a limited number of structural parameters. The performance of ISPECT in estimating LMA is compared to that of five other leaf radiative transfer models (FASPECT, DLM, PROSPECT-D, PROSPECT-5B, and Leaf-SIP). We tested six experimental datasets with 962 leaf samples and two spectral ranges: the solar domain (0.4–2.5 μm) and the shortwave infrared (1.7–2.4 μm). Results show that PROSPECT-D and PROSPECT-5B accurately estimate LMA using the shortwave infrared spectra, while ISPECT and FASPECT perform well in both spectral ranges. Further analysis demonstrates that leaf dorsiventrality is likely to be an influential factor for LMA estimation: thus ISPECT can accurately estimate LMA in the solar and shortwave infrared domains, with NRMSE of 26.0% and 28.8%, respectively. This motivates further studies on LMA mapping from spaceborne imaging spectrometers (e.g., PRISMA, GaoFen-5, EnMAP) by coupling ISPECT with canopy radiative transfer models.
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Dates et versions

hal-03945777 , version 1 (18-01-2023)

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Hanyu Shi, Jingyi Jiang, Stéphane Jacquemoud, Zhiqiang Xiao, Mingguo Ma. Estimating leaf mass per area with leaf radiative transfer model. Remote Sensing of Environment, 2023, 286, pp.113444. ⟨10.1016/j.rse.2022.113444⟩. ⟨hal-03945777⟩
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