Improving Estimates of Gross Primary Productivity by Assimilating Solar-Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process-Based SIF Model

Abstract Over the last few years, solar-induced chlorophyll fluorescence (SIF) observations from space have emerged as a promising resource for evaluating the spatio-temporal distribution of gross primary productivity (GPP) simulated by global terrestrial biosphere models. SIF can be used to improve GPP simulations by optimizing critical model parameters through statistical Bayesian data assimilation techniques. A prerequisite is the availability of a functional link between GPP and SIF in terrestrial biosphere models. Here we present the development of a mechanistic SIF observation operator in the ORCHIDEE (Organizing Carbon and Hydrology In Dynamic Ecosystems) terrestrial biosphere model. It simulates the regulation of photosystem II fluorescence quantum yield at the leaf level thanks to a novel parameterization of non-photochemical quenching as a function of temperature, photosynthetically active radiation, and normalized quantum yield of photochemistry. It emulates the radiative transfer of chlorophyll fluorescence to the top of the canopy using a parametric simplification of the SCOPE (Soil Canopy Observation Photosynthesis Energy) model. We assimilate two years of monthly OCO-2 (Orbiting Carbon Observatory-2) SIF product at 0.5° (2015?2016) to optimize ORCHIDEE photosynthesis and phenological parameters over an ensemble of grid points for all plant functional types. The impact on the simulated GPP is considerable with a large decrease of the global scale budget by 28 GtC/year over the period 1990?2009. The optimized GPP budget (134/136 GtC/year over 1990?2009/2001?2009) remarkably agrees with independent GPP estimates, FLUXSAT (137 GtC/year over 2001?2009) in particular and FLUXCOM (121 GtC/year over 1990?2009). Our results also suggest a biome dependency of the SIF-GPP relationship that needs to be improved for some plant functional types.Peer reviewe

Incorporating leaf chlorophyll content into a two-leaf terrestrial biosphere model for estimating carbon and water fluxes at a forest site

Chlorophyll is the main light-harvesting pigment in leaves, facilitating photosynthesis and indicating the supply of nitrogen for photosynthetic enzymes. In this study, we explore the feasibility of integrating leaf chlorophyll content (Chlleaf) into a Terrestrial Biosphere Model (TBM), as a proxy for the leaf maximum carboxylation rate at 25°C (Vmax25), for the purpose of improving carbon and water flux estimation. Measurements of Chlleaf and Vmax25 were made in a deciduous forest stand at the Borden Forest Research Station in southern Ontario, Canada, where carbon and water fluxes were measured by the eddy covariance method. The use of Chlleaf-based Vmax25 in the TBM significantly reduces the bias of estimated gross primary productivity (GPP) and evapotranspiration (ET) and improves the temporal correlations between the simulated and the measured fluxes, relative to the commonly employed cases of using specified constant Vmax25, leaf area index (LAI)-based Vmax25 or specific leaf area (SLA)-based Vmax25. The biggest improvements are found in spring and fall, when the mean absolute errors (MAEs) between modelled and measured GPP are reduced from between 2.2–3.2 to 1.8gCm−2d−1 in spring and from between 2.1–2.8 to 1.8gCm−2 d−1 in fall. The MAEs in ET estimates are reduced from 0.7–0.8mmd−1 to 0.6mmd−1 in spring, but no significant improvement is noted in autumn. A two-leaf upscaling scheme is used to account for the uneven distribution of incoming solar radiation inside canopies and the associated physiological differences between leaves. We found that modelled Vmax25 in sunlit leaves is 34% larger than in the shaded leaves of the same Chlleaf, which echoes previous physiological studies on light acclimation of plants. This study represents the first case of the incorporation of chlorophyll as a proxy for Vmax25 in a two-leaf TBM at a forest stand and demonstrates the efficacy of using chlorophyll to constrain Vmax25 and reduce the uncertainties in GPP and ET simulations

Reviews and syntheses:Remotely sensed optical time series for monitoring vegetation productivity

International audienceAbstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring

Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery

Spatial information on crop nutrient status is central for monitoring vegetation health, plant productivity and managing nutrient optimization programs in agricultural systems. This study maps the spatial variability of leaf chlorophyll content within felds with differing quantities of nitrogen fertilizer application, using multispectral Landsat-8 OLI data (30 m). Leaf chlorophyll content and leaf area index measurements were collected at 15 wheat (Triticum aestivum) sites and 13 corn (Zea mays) sites approximately every 10 days during the growing season between May and September 2013 near Stratford, Ontario. Of the 28 sites, 9 sites were within controlled areas of zero nitrogen fertilizer application. Hyperspectral leaf refectance measurements were also sampled using an Analytical Spectral Devices FieldSpecPro spectroradiometer (400–2500 nm). A two-step inversion process was developed to estimate leaf chlorophyll content from Landsat-8 satellite data at the subfeld scale, using linked canopy and leaf radiative transfer models. Firstly, at the leaf-level, leaf chlorophyll content was modelled using the PROSPECT model, using both hyperspectral and simulated mulitspectral Landsat-8 bands from the same leaf sample. Hyperspectral and multispectral validation results were both strong (R2=0.79, RMSE=13.62 μg/cm2 and R2=0.81, RMSE=9.45 μg/cm2, respectively). Secondly, leaf chlorophyll content was estimated from Landsat-8 satellite imagery for 7 dates within the growing season, using PROSPECT linked to the 4-Scale canopy model. The Landsat-8 derived estimates of leaf chlorophyll content demonstrated a strong relationship with measured leaf chlorophyll values (R2=0.64, RMSE=16.18 μg/cm2), and compared favourably to correlations between leaf chlorophyll and the best performing tested spectral vegetation index (Green Normalised Diference Vegetation Index, GNDVI; R2=0.59). This research provides an operational basis for modelling within-feld variations in leaf chlorophyll content as an indicator of plant nitrogen stress, using a physically-based modelling approach, and opens up the possibility of exploiting a wealth of multispectral satellite data and UAV-mounted multispectral imaging systems

Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

Leaf area index (LAI) and leaf chlorophyll content (Chll) represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and Chll provide critical information on vegetation density, vitality and photosynthetic potentials.However, simultaneous retrieval of LAI and Chll fromspace observations is extremely challenging. Regularization strategies are required to increase the robustness and accuracy of retrieved properties and enable more reliable separation of soil, leaf and canopy parameters. To address these challenges, the REGularized canopy reFLECtance model (REGFLEC) inversion system was refined to incorporate enhanced techniques for exploiting ancillary LAI and temporal information derived from multiple satellite scenes. In this current analysis, REGFLEC is applied to a time-series of Landsat data. A novel aspect of the REGFLEC approach is the fact that no site-specific data are required to calibrate the model, which may be run in a largely automated fashion using information extracted entirely from image-based and other widely available datasets. Validation results, based upon in-situ LAI and Chll observations collected over maize and soybean fields in centralNebraska for the period 2001–2005, demonstrate Chll retrievalwith a relative root-mean-square-deviation (RMSD) on the order of 19% (RMSD = 8.42 μg cm−2). While Chll retrievals were clearly influenced by the version of the leaf optical properties model used (PROSPECT), the application of spatio-temporal regularization constraints was shown to be critical for estimating Chll with sufficient accuracy. REGFLEC also reproduced the dynamics of in-situ measured LAI well (r2 = 0.85), but estimates were biased low, particularly over maize (LAI was underestimated by ~36 %). This disparity may be attributed to differences between effective and true LAI caused by significant foliage clumping not being properly accounted for in the canopy reflectance model (SAIL). Additional advances in the retrieval of canopy biophysical and leaf biochemical constituents will require innovative use of existing remote sensing data within physically realistic canopy reflectancemodels along with the ability to exploit the enhanced spectral and spatial capabilities of upcoming satellite systems

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring

The impact of alternative trait-scaling hypotheses for the maximum photosynthetic carboxylation rate (Vcmax) on global gross primary production

The maximum photosynthetic carboxylation rate (Vcmax) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global Vcmax distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.1 to 128.2 PgC yr−1, 65% of the range of a recent model intercomparison of global GPP. The variation in GPP propagated through to a 27% coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r = 0.85–0.91) with three proxies of global GPP. Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand Vcmax variation in the field, particularly in northern latitudes