senSCOPE: modeling mixed canopies combining green and brown senesced leaves. Evaluation in a Mediterranean Grassland

Javier Pacheco-Labrador*, Tarek S. El-Madany, Christiaan van der Tol, M. Pilar Martin, Rosario Gonzalez-Cascon, Oscar Perez-Priego, Jinhong Guan, Gerardo Moreno, Arnaud Carrara, Markus Reichstein, Mirco Migliavacca

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)

    Abstract

    The coupling of radiative transfer, energy balance, and photosynthesis models has brought new opportunities to characterize vegetation functional properties from space. However, these models do not accurately represent processes in ecosystems characterized by mixtures of green vegetation and senescent plant material (SPM), in particular grasslands. These inaccuracies limit the retrieval of vegetation biophysical and functional properties. Green and senesced plants feature contrasting spectral properties and carry out different functions that current coupled models do not represent separately. Besides, senescent pigments' absorption features change as SPM decomposes, and neither is this process well parameterized in radiative transfer models. This manuscript aims at overcoming these limitations. On the one hand, we have developed senSCOPE, a version of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) that separately represents light interaction and physiology of green and senesced leaves. On the other, we have characterized new specific absorption coefficients of senescent pigments (Ks) from optical measurements of SPM from a Mediterranean grassland. Sensitivity analyses revealed that compared to SCOPE, senSCOPE 1) predicts variables that respond more linearly to the faction of green leaf area; and 2) keeps high levels of absorbed photosynthetically active radiation in the green leaves, which leads to significant differences in leaf photosynthesis, non-photochemical quenching, and transpiration. Moreover, we compared SCOPE vs. senSCOPE's capability to provide estimates of functional and biophysical parameters of vegetation. We assimilated different combinations of reflectance factors (R), chlorophyll sun-induced fluorescence radiance in the O2-A band (F760), gross primary production (GPP), and thermal radiance (Lt) measured in a Mediterranean grassland. Besides, we compared the role of three different sets of Ks coefficients in the inversion of senSCOPE, two estimated from SPM. The performance of the inversions was assessed using field data and a pattern-oriented model evaluation approach. Unlike SCOPE, senSCOPE provided unbiased estimates of chlorophyll content (Cab) during the dry season. The use of SPM-specific Ks improved the representation of R in the near-infrared wavelengths; and, consequently, the estimation of leaf area index (LAI). Compared with field LAI, the coefficient of determination R2 increased from ~0.4 to ~0.6, depending on the inversion constraints. Compared with SCOPE, the new model and coefficients together reduced the root mean squared error between observed and modeled R (~40%), F760 (~30%), and GPP (~5%). Both models failed to represent Lt; in this case, senSCOPE featured larger uncertainties. The modeling approach we propose improves the simulation and retrieval of vegetation properties and function in grasslands. Further work is needed to test the applicability of senSCOPE in different ecosystems, improve the simulation of the thermal spectral domain, and better characterize the optical parameters of SPM. To do so, new databases of SPM optical and biophysical properties should be produced.

    Original languageEnglish
    Article number112352
    Pages (from-to)1-18
    Number of pages18
    JournalRemote Sensing of Environment
    Volume257
    Early online date23 Feb 2021
    DOIs
    Publication statusPublished - May 2021

    Keywords

    • senSCOPE
    • SCOPE
    • Photosynthesis
    • Sun-induced fluorescence
    • Senesced leaves
    • Radiative transfer
    • Grassland
    • Chlorophyll
    • Plant functional traits
    • Hyperspectral
    • GPP

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