Environment-sensitivity functions for gross primary productivity in light use efficiency models

Bao, Shanning; Wutzler, Thomas; Koirala, Sujan; Cuntz, Matthias; Ibrom, Andreas; Besnard, Simon; Walther, Sophia; Šigut, Ladislav; Moreno, A.; Weber, Ulrich; Wohlfahrt, Georg; Cleverly, Jamie; Migliavacca, Mirco; Woodgate, William; Merbold, Lutz; Veenendaal, E.M.

doi:10.1016/j.agrformet.2021.108708

Cited by 41 publications

(42 citation statements)

References 131 publications

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“…Besides that, an enhancement of LUE g under diffuse conditions has been linked to a smaller fraction of the canopy in deep shade (Williams et al, 2014) and previous research showed that coniferous forests can also be larger CO 2 sinks under diffuse conditions (Law et al, 2002;Urban et al, 2007Urban et al, , 2012. A linear increase between cloudiness and LUE was hence included in LUE-models (S. Wang et al, 2018), though Bao et al (2022) found an exponential increase more suitable that also fits better with our results. As the R/FR ratio was always shifted to FR after passing the canopy but less so during diffuse conditions, obscured parts of the canopy received not only a higher light quantity, but also a higher light quality than under clear skies.…”

Section: Environmental Driverssupporting

confidence: 86%

“…Likewise, as summarized by Bao et al. (2022), the temperature sensitivity has been represented by bell‐shaped functions many times in LUE‐models though with differing optimum ranges (e.g., Horn & Schulz, 2011; Stocker et al., 2020; Xiao et al., 2004). Otherwise, it was also modeled by a linearly increasing function that reaches a plateau at ca.…”

Section: Discussionmentioning

confidence: 99%

“…The resulting biomass further is important for human demands of food, energy, and construction materials (Taye et al., 2021). The assimilation of atmospheric CO 2 via photosynthesis is primarily driven by photosynthetically active radiation (PAR), though it is also sensitive to intertwined environmental and physiological variables, such as temperature, water, and nutrient availability, or chlorophyll content of the canopy (Anav et al., 2015; Bao et al., 2022; Keenan et al., 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Under optimal conditions, a linear relation between GPP and APAR is assumed (Monteith, 1972), and LUE models utilize this logic for estimating GPP based on APAR and sensitivity functions for environmental conditions limiting LUE (e.g., Horn & Schulz, 2011; Stocker et al., 2020; S. Wang et al., 2018). The shape of these functions representing the response of LUE to meteorological variables, however, varies widely between approaches (Bao et al., 2022). Although LUE models are widely used to predict GPP, such as for the MODIS GPP product (Running & Zhao, 2015), they rely on accurate APAR measurements.…”

Section: Introductionmentioning

confidence: 99%

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Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest

Reitz

Bogena

Neuwirth³

et al. 2023

JGR Biogeosciences

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The gross primary productivity (GPP) of terrestrial ecosystems, of which forests are the dominant factor (Pan et al., 2011), is a key element of the global carbon cycle (Canadell et al., 2021). The resulting biomass further is important for human demands of food, energy, and construction materials (Taye et al., 2021). The assimilation of atmospheric CO 2 via photosynthesis is primarily driven by photosynthetically active radiation (PAR), though it is also sensitive to intertwined environmental and physiological variables, such as temperature, water, and nutrient availability, or chlorophyll content of the canopy (

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Section: Environmental Driverssupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest

Reitz

Bogena

Neuwirth³

et al. 2023

JGR Biogeosciences

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“…Complementary information from satellites or digital cameras (phenocams, Wingate et al, 2015) aids and refines studies of local land-atmosphere interactions as they relate to ecosystem structure, phenology, and functioning and the state of the land surface (e.g. Bao et al, 2022). Earth observation (EO) data for varying regional sizes around the sites can represent the actual area that contributes to the flux measurements -partly even more accurately than similar ground-based measurements can (Gamon, 2015) -provided sufficiently high spatial resolution and temporal overlap with the site-level records.…”

mentioning

confidence: 99%

Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set

et al. 2022

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Abstract. The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, AmeriFlux, and Terrestrial Ecosystem Research Network (TERN)/OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the benchmarking of terrestrial biosphere models. However, insufficient quality and frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices, and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40∘ off-nadir. Finally, we offer the community living data sets of pre-processed Earth observation data, where version 1.0 features the MCD43A4/A2 and MxD11A1 MODIS products and Landsat Collection 1 Tier 1 and Tier 2 products in a radius of 2 km around 338 flux sites. The data sets we provide can widely facilitate the integration of activities in the eddy-covariance, remote sensing, and modelling fields.

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A Robust Light Use Efficiency Model Parameterization Method Based on Ecosystem Properties

Bao

Alonso

Wang

et al. 2022

Preprint

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In a model simulating the dynamics of a system, parameters can represent system properties and unresolved processes, therefore affecting the model accuracy and uncertainties. For a light use efficiency (LUE) model, which is a typical tool to estimate gross primary productivity (GPP), the plant-functional-type (PFT)-dependent parameterization method was widely used to extrapolate parameters to larger spatial scales. However, the method cannot capture the spatial variability within PFT and introduces misclassification errors. To overcome the shortage, here we proposed an ecosystem-property-based parameterization method (mNN-GPP) for an LUE model. This method refers to predicting model parameters using the multi-output artificial neural network based on collected variables including PFT, climate types, bioclimatic variables, vegetation features, atmospheric deposition and soil properties at 196 FLLUXNET eddy covariance flux sites. The neural network was optimized according to GPP errors and constraints on sensitivity functions of the LUE model. We compared mNN-GPP with eleven other typical parameter extrapolating methods, including PFT-, climate-specific parameterization, global and PFT-based parameter optimization, site-similarity-based, and regression methods. These twelve methods were assessed using Nash-Sutcliffe model efficiency (NSE), determination coefficient and normalized root mean squared error of the simulated GPP. The simulated results were also contrasted with those of site-specific calibration based on full-time-series GPP estimated from observational net ecosystem exchange. The N-fold cross-validated results showed that mNN-GPP had the best performance across various temporal and spatial scales (e.g., NSE=0.62 at the daily scale). No extrapolated parameters reached the same performance as the calibrated parameters (NSE=0.82), but the ranges of predicted parameters were constrained. Furthermore, the Shapley values, layer-wise relevance and partial dependence of the input features showed that bioclimatic variables, PFT, and vegetation features are the key variables determining parameters. We recommend using the parameterization method considering both ecosystem properties and prediction errors to other GPP models and across spatio-temporal scales.

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Environment-sensitivity functions for gross primary productivity in light use efficiency models

Cited by 41 publications

References 131 publications

Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest

Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest

Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set

A Robust Light Use Efficiency Model Parameterization Method Based on Ecosystem Properties

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