senSCOPE: Modeling radiative transfer and biochemical processes in mixed canopies combining green and senescent leaves with SCOPE

Abstract

Semi-arid grasslands and other ecosystems combine green and senescent leaves featuring different biochemical and optical properties, as well as functional traits. Knowing how these properties vary is necessary to understand the functioning of these ecosystems. However, differences between green and senescent leaves are not considered in recent models representing radiative transfer, heat, water and CO2 exchange such as the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE). Neglecting the contribution of senescent leaves to the optical and thermal signal of vegetation limits the possibilities to use remote sensing information for studying these ecosystems; as well as neglecting their lack of photosynthetic activity increases uncertainty in the representation of ecosystem fluxes. In this manuscript we present senSCOPE as a step towards a more realistic representation of mixed green and senescent canopies. senSCOPE is a modified version of SCOPE model that describes a canopy combining green and senescent leaves with different properties and function. The model relies on the same numerical solutions than SCOPE, but exploits the linear nature of the scattering coefficients to combine optical properties of both types of leaf. Photosynthesis and transpiration only take place in green leaves; and different green and senescent leaf temperatures are used to close the energy balance. Radiative transfer of sun-induced fluorescence (SIF) and absorptance changes induced by the xanthophyll cycle action are also simulated. senSCOPE is evaluated against SCOPE both using synthetic simulations, forward simulations based on observations in a Mediterranean tree-grass ecosystem, and inverting dataset of ground measurements of reflectance factors, SIF, thermal radiance and gross primary production on a heterogeneous and partly senescent Mediterranean grassland. Results show that senSCOPE outputs vary quite linearly with the fraction of green leaf area, whereas SCOPE does not respond linearly to the effective leaf properties, calculated as the weighted average of green and senescent leaf parameters. Inversion results and pattern-oriented model evaluation show that senSCOPE improves the estimation of some parameters, especially chlorophyll content, with respect SCOPE retrievals during the dry season. Nonetheless, inaccurate knowledge of the optical properties of senescent matter still complicates model inversion. senSCOPE brings new opportunities for the monitoring of canopies mixing green and senescent leaves, and for improving the characterization of the optical properties of senescent material.

Combining hyperspectral remote sensing and eddy covariance data streams for estimation of vegetation functional traits

“…Also, the misrepresentation of optical properties of the senescent material biases the estimates of some parameters in an attempt to compensate low NIR 540 scattering. At grass plot scale LAI, NIR R λ were underestimated and f green was overestimated (Pacheco-Labrador et al, 2020); whereas at ecosystem scale LAI and NIR R λ were not so strongly biased, and f green was underestimated in the dry period. In C ab and V cmax estimates and their relationship with nitrogen are consistent with field data.…”

“…The application of the multiple-constrained inversion of a coupled SVAT-RTM model at ecosystem scale implies facing sources of error and uncertainty that were not present in previous works carried out at close range over grassland plots (Pacheco-Labrador et al, 2020;Pacheco-Labrador et al, 2019). These can be summarized as: 1) the spatial mismatch between optical, thermal and EC footprints, 2) uncertainties in the estimation of GPP and EC energy balance closure, 3) the 450 atmospheric correction of airborne imagery and the addition of uncertainties in the EnMAP End-to-End simulator, and 4) errors induced by the representation of a Mediterranean tree-grass ecosystem featuring species with very different function (grasses and trees) when using a 1-D model to represent light interaction and photosynthesis.…”

“…senSCOPE (Pacheco-Labrador et al, 2020) is an extended version of the model SCOPE that separates radiative transfer, energy balance and photosynthesis for green and senescent leaves randomly mixed within a homogeneous canopy. While 255 SCOPE parametrizes a single leaf type, senSCOPE requires leaf parameters for both senescent and green leaves; where senescent leaves only present senescent pigments and green ones feature any pigment but senescent.…”

“…While 255 SCOPE parametrizes a single leaf type, senSCOPE requires leaf parameters for both senescent and green leaves; where senescent leaves only present senescent pigments and green ones feature any pigment but senescent. Like in Pacheco-Labrador et al, (2020), during inversion we assumed the same C dm for both leaf types, but that C w of green leaves is four times higher than in senescent leaves (Kidnie et al, 2015). These assumptions allowed us to later invert the model optimizing averaged leaf parameters rather than the individual parameters of each leaf type.…”

“…The state-of-the-art model coupling optical, SIF and TIR RTM with energy balance and photosynthesis models is SCOPE (van der Tol et al, 2009). Further improvements of this model are mSCOPE , which allows representing vertically heterogeneous canopies, and senSCOPE (Pacheco-Labrador et al, 2020), which improves the 105 representation of canopies featuring mixed green and senescent leaves (Pacheco-Labrador et al, 2020). Using satellite imagery, SCOPE has been used to obtain estimates of physiological parameters of vegetation such as V cmax and / or m exploiting reflectance factors (R λ , where λ denotes spectral) and SIF (Zhang et al, 2014), R λ , SIF and EC fluxes (Zhang et al, 2018), R λ and TIR data (Bayat et al, 2018), or R λ and EC fluxes (Dutta et al, 2019).…”