Andrej Varlagin | Severtsov Institute of ecology and evolution (original) (raw)

Papers by Andrej Varlagin

Izvestiâ Rossijskoj akademii nauk. Seriâ geografičeskaâ, Jul 1, 2023

Gross primary productivity (GPP) is the largest and most variable component of the global terrest... more Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET "La Thuile" data set, which includes several times more sites (144) and site years (422) than previous efforts have used. Our results show that remotely sensed proxies and modeled GPP are able to capture statistically significant amounts of spatial variation in mean annual GPP in every biome except croplands, but that the total variance explained differed substantially across biomes (R 2 ≈ 0.1 − 0.8). The ability of remotely sensed proxies and models to explain interannual variability GPP was even more limited. Remotely sensed proxies explained 40-60 % of interannual variance in annual GPP in moisture-limited biomes including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, and deciduous broadleaf forests. Because important factors that affect year-to-year variation in GPP are not explicitly captured or included in the remote sensing proxies and models we examined (e.g., interactions between biotic and abiotic conditions, and lagged ecosystems responses to environmental process), our results are not surprising. Nevertheless, robust and repeatable characterization of interannual variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. As larger and more comprehensive data sets derived from the FLUXNET community become available,

Global Change Biology, Jan 24, 2019

He B, et al. Solar-induced chlorophyll fluorescence exhibits a universal relationship with gross ... more He B, et al. Solar-induced chlorophyll fluorescence exhibits a universal relationship with gross primary productivity across a wide variety of biomes.

AGU Fall Meeting Abstracts, Dec 1, 2018

The aim of this research is to assess the feasibility of retrieving plant functional traits from ... more The aim of this research is to assess the feasibility of retrieving plant functional traits from Sentinel-3 data by using combined vegetation-atmosphere inversions of radiative transfer models. Sentinel-3 is a constellation of ESA’s satellites (Sentinel-3A and 3B) that have roughly 30 bands on two sensors (Ocean and Land Colour Instrument, OLCI and Sea and Land Surface Temperature Radiometer, SLSTR) in the visible, near- and mid-infrared regions. The revisit time of about one day makes Sentinel-3 suitable for producing time series and monitoring the ecosystem state. However, the measurements provided by Sentinel-3 are strongly affected by the atmosphere. Atmospheric correction is usually carried out before surface parameter retrievals, but we attempted to retrieve properties from OLCI and SLSTR from the radiative transfer models SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) and 6S so the vegetation and atmospheric parameters are simultaneously modelled. SCOPE is a radiative transfer and micro-meteorological model that simulates reflectance spectra at leaf and canopy levels as well as photosynthesis and the components of the energy balance (net radiation, sensible and latent heat flux) for soil an canopy. We developed an algorithm based on the coupling of the optical module of SCOPE with the atmospheric radiative transfer model 6S to retrieve plant traits (chlorophyll content, leaf area index, water content) from Sentinel-3 measurements. Clouded images, based on a cloud band of SLSTR product, and images without vegetation, based on a low value of the normalized difference vegetation index (NDVI), were excluded from the analysis. The retrieved plant traits were further used as an input for the complete SCOPE model to produce ecosystem fluxes. Ground measurements from a number of European FLUXNET eddy-covariance towers were used for the validation of the simulated ecosystem fluxes. The plant functional types represented by those towers included savannahs, croplands, grasslands, evergreen broadleaf and needleleaf forests and deciduous forests. To reduce the noise in the data, we compared 3-days mean fluxes. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995.

AGU Fall Meeting Abstracts, Dec 1, 2018

Solar-induced chlorophyll fluorescence exhibits a universal relationship with gross primary produ... more Solar-induced chlorophyll fluorescence exhibits a universal relationship with gross primary productivity across a wide variety of biomes In our recent study (Li et al., 2018a), we examined the relationship between solar-induced chlorophyll fluorescence (SIF) measured from the Orbiting Carbon Observatory-2 (OCO-2) and gross primary productivity (GPP) derived from eddy covariance flux towers across the globe, and we discovered that there is a nearly universal relationship between SIF and GPP across a wide variety of biomes. This finding reveals the tremendous potential of SIF for accurately mapping terrestrial photosynthesis globally. In a letter to the Editor, Zhang et al. (2018) argued that different viewing zenith angles (VZA) of the OCO-2 instrument could impact the SIF-GPP relationship revealed by our recent study. We need to clarify four over-or misinterpretations. Zhang et al. (2018) first explained the measurement modes of OCO-2 (i.e., nadir, glint, and target) and called for attention to the effects of VZA (Frankenberg et al. 2014; He et al. 2017) on SIF magnitude. We recognized the effects of viewing geometries and also demonstrated that with all observations from 64 sites grouped together, there was no significant difference in the mean SIF between the nadir mode and the combined modes (glint/target) (Li et al. 2018a).

Oecologia, Oct 12, 1999

... 1963; Westoby 1984), or from sporadic disturbances like forest fires, pests, insect infestati... more ... 1963; Westoby 1984), or from sporadic disturbances like forest fires, pests, insect infestations and wind-throw (Peet and Christensen 1987). ... Fire weather conditions as characterised by the monthly se-verity rating (MSR) (Stocks and Lynham 1996) indicate high fire risk in our ...

Boreal Eurasia is a region where the interaction between droughts and the carbon cycle may have s... more Boreal Eurasia is a region where the interaction between droughts and the carbon cycle may have significant impacts on the global carbon cycle. Yet the region is extremely data sparse with respect to meteorology, soil moisture and carbon fluxes as compared to e.g. Europe. To better constrain our vegetation model SiBCASA, we increase data usage by assimilating two streams of satellite derived soil moisture. We study if the assimilation improved SiBCASA's soil moisture and its effect on the simulated carbon fluxes. By comparing to unique in situ soil moisture observations, we show that the passive microwave soil moisture product did not improve the soil moisture simulated by SiBCASA, but the active data seem promising in some aspects. The match between SiBCASA and ASCAT soil moisture is best in the summer months over low vegetation. Nevertheless, ASCAT failed to detect the major droughts occurring between 2007 and 2013. The performance of ASCAT soil moisture seems to be particularly sensitive to ponding, rather than to biomass. The effect on the simulated carbon fluxes is large, 5-10 % on annual GPP and TER, and tens of percent on local NEE, and 2 % on areaintegrated NEE, which is the same order of magnitude as the inter-annual variations. Consequently, this study shows that assimilation of satellite derived soil moisture has potentially large impacts, while at the same time further research is needed to understand under which conditions the satellite derived soil moisture improves the simulated soil moisture.

EGU General Assembly Conference Abstracts, Apr 1, 2017

Forests

This paper reports on the location of sources contributing to a point flux measurement in the sou... more This paper reports on the location of sources contributing to a point flux measurement in the southern taiga, Russia. The measurement tower is surrounded by a coniferous forest with a mean aerodynamically active height of 27 m (h). Aerodynamical parameters of the forest, such as displacement height d and aerodynamic roughness z0, derived from wind speed profile measurements for 2017–2019, were used to estimate the seasonal and daily behavior of the flux footprint. Two analytical footprint models driven by d and z0 were used to estimate the footprint for canopy sources. The Lagrangian simulation (LS) approach driven by flow statistics from measurements and modeling was used to estimate the footprint for ground-located sources. The Flux Footprint Prediction (FFP) tool for assessing canopy flux footprint applied as the option in the EddyPro v.7 software was inspected against analytical and LS methods. For model comparisons, two parameters from estimated footprint functions were used: t...

Agricultural and Forest Meteorology

Global Change Biology, 2021

Understanding the critical soil moisture (SM) threshold (θcrit) of plant water stress and land su... more Understanding the critical soil moisture (SM) threshold (θcrit) of plant water stress and land surface energy partitioning is a basis to evaluate drought impacts and improve models for predicting future ecosystem condition and climate. Quantifying the θcrit across biomes and climates is challenging because observations of surface energy fluxes and SM remain sparse. Here, we used the latest database of eddy covariance measurements to estimate θcrit across Europe by evaluating evaporative fraction (EF)‐SM relationships and investigating the covariance between vapor pressure deficit (VPD) and gross primary production (GPP) during SM dry‐down periods. We found that the θcrit and soil matric potential threshold in Europe are 16.5% and −0.7 MPa, respectively. Surface energy partitioning characteristics varied among different vegetation types; EF in savannas had the highest sensitivities to SM in water‐limited stage, and the lowest in forests. The sign of the covariance between daily VPD a...

Nature Ecology & Evolution, 2021

Global Ecology and Biogeography, 2019

AimThe mechanisms of plant trait adaptation and acclimation are still poorly understood and, cons... more AimThe mechanisms of plant trait adaptation and acclimation are still poorly understood and, consequently, lack a consistent representation in terrestrial biosphere models (TBMs). Despite the increasing availability of geo‐referenced trait observations, current databases are still insufficient to cover all vegetation types and environmental conditions. In parallel, the growing number of continuous eddy‐covariance observations of energy and CO2 fluxes has enabled modellers to optimize TBMs with these data. Past attempts to optimize TBM parameters mostly focused on model performance, overlooking the ecological properties of ecosystems. The aim of this study was to assess the ecological consistency of optimized trait‐related parameters while improving the model performances for gross primary productivity (GPP) at sites.LocationWorldwide.Time period1992–2012.Major taxa studiedTrees and C3 grasses.MethodsWe optimized parameters of the ORCHIDEE model against 371 site‐years of GPP estimate...

Izvestiya Rossiiskoi akademii nauk. Seriya geograficheskaya, 2019

The technology of allocation of order parameters (invariants) of the spatial structure of the the... more The technology of allocation of order parameters (invariants) of the spatial structure of the thermal field of the southern taiga landscape (Central Forest Nature Reserve) obtained on the basis of the analysis of the time series of measurements in the long-wave channel of Landsat satellites from 1986 to 2017 and reflecting its stationary state is considered. It is shown that the heat flux is measured by the satellite not directly from the forest crowns, but from the ground layer of the atmosphere, the state of which is determined by the parameters of the landscape. It is found that the invariant component of the spatiotemporal variation of the thermal field is displayed by two order parameters: the first mainly reflects the temperature of winter months, the second – of summer. The contribution of relief and vegetation to the determination of invariants and the autochthonous components of the thermal field determined by the transition zones between the landscape elements contrasting ...

Agricultural and Forest Meteorology, 2015

Accurate and reliable estimates of gross primary productivity (GPP) are required for monitoring t... more Accurate and reliable estimates of gross primary productivity (GPP) are required for monitoring the global carbon cycle at different spatial and temporal scales. Because GPP displays high spatial and temporal variation, remote sensing plays a major role in producing gridded estimates of GPP across spatiotemporal scales. In this context, understanding the strengths and weaknesses of remote sensing-based models of GPP and improving their performance is a key contemporary scientific activity. We used measurements from 157 research sites (~470 site-years) in the FLUXNET "La Thuile" data and compared the skills of 11 different remote sensing models in capturing intra-and inter-annual variations in daily GPP in seven different biomes. Results show that the models were able to capture significant intra-annual variation in GPP (Index of Agreement = 0.4 to 0.80) in all biomes. However, the models" ability to track inter-annual variation in daily GPP was significantly weaker (IoA<0.45). We examined whether the inclusion of different mechanisms that are missing in the models could improve their predictive power. The mechanisms included the effect of sub-daily variation in environmental variables on daily GPP, factoring-in differential rates of GPP conversion efficiency for direct and diffuse incident radiation, lagged effects of environmental variables, better representation of soil-moisture dynamics, and allowing spatial variation in model parameters. Our analyses suggest that the next generation remote sensing models need better representation of soil-moisture, but other mechanisms that have been found to influence GPP in site-level studies may not have significant bearing on model performance at continental and global scale. Understanding the relative controls of biotic visa -vis abiotic factors on GPP and accurately scaling up leaf level processes to the ecosystem scale are likely to be important for recognizing the limitations of remote sensing model and improving their formulation.

Environmental Research Letters, 2009

Izvestiâ Rossijskoj akademii nauk. Seriâ geografičeskaâ, Jul 1, 2023

Gross primary productivity (GPP) is the largest and most variable component of the global terrest... more Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET "La Thuile" data set, which includes several times more sites (144) and site years (422) than previous efforts have used. Our results show that remotely sensed proxies and modeled GPP are able to capture statistically significant amounts of spatial variation in mean annual GPP in every biome except croplands, but that the total variance explained differed substantially across biomes (R 2 ≈ 0.1 − 0.8). The ability of remotely sensed proxies and models to explain interannual variability GPP was even more limited. Remotely sensed proxies explained 40-60 % of interannual variance in annual GPP in moisture-limited biomes including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, and deciduous broadleaf forests. Because important factors that affect year-to-year variation in GPP are not explicitly captured or included in the remote sensing proxies and models we examined (e.g., interactions between biotic and abiotic conditions, and lagged ecosystems responses to environmental process), our results are not surprising. Nevertheless, robust and repeatable characterization of interannual variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. As larger and more comprehensive data sets derived from the FLUXNET community become available,

Global Change Biology, Jan 24, 2019

He B, et al. Solar-induced chlorophyll fluorescence exhibits a universal relationship with gross ... more He B, et al. Solar-induced chlorophyll fluorescence exhibits a universal relationship with gross primary productivity across a wide variety of biomes.

AGU Fall Meeting Abstracts, Dec 1, 2018

The aim of this research is to assess the feasibility of retrieving plant functional traits from ... more The aim of this research is to assess the feasibility of retrieving plant functional traits from Sentinel-3 data by using combined vegetation-atmosphere inversions of radiative transfer models. Sentinel-3 is a constellation of ESA’s satellites (Sentinel-3A and 3B) that have roughly 30 bands on two sensors (Ocean and Land Colour Instrument, OLCI and Sea and Land Surface Temperature Radiometer, SLSTR) in the visible, near- and mid-infrared regions. The revisit time of about one day makes Sentinel-3 suitable for producing time series and monitoring the ecosystem state. However, the measurements provided by Sentinel-3 are strongly affected by the atmosphere. Atmospheric correction is usually carried out before surface parameter retrievals, but we attempted to retrieve properties from OLCI and SLSTR from the radiative transfer models SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) and 6S so the vegetation and atmospheric parameters are simultaneously modelled. SCOPE is a radiative transfer and micro-meteorological model that simulates reflectance spectra at leaf and canopy levels as well as photosynthesis and the components of the energy balance (net radiation, sensible and latent heat flux) for soil an canopy. We developed an algorithm based on the coupling of the optical module of SCOPE with the atmospheric radiative transfer model 6S to retrieve plant traits (chlorophyll content, leaf area index, water content) from Sentinel-3 measurements. Clouded images, based on a cloud band of SLSTR product, and images without vegetation, based on a low value of the normalized difference vegetation index (NDVI), were excluded from the analysis. The retrieved plant traits were further used as an input for the complete SCOPE model to produce ecosystem fluxes. Ground measurements from a number of European FLUXNET eddy-covariance towers were used for the validation of the simulated ecosystem fluxes. The plant functional types represented by those towers included savannahs, croplands, grasslands, evergreen broadleaf and needleleaf forests and deciduous forests. To reduce the noise in the data, we compared 3-days mean fluxes. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995.

AGU Fall Meeting Abstracts, Dec 1, 2018

Solar-induced chlorophyll fluorescence exhibits a universal relationship with gross primary produ... more Solar-induced chlorophyll fluorescence exhibits a universal relationship with gross primary productivity across a wide variety of biomes In our recent study (Li et al., 2018a), we examined the relationship between solar-induced chlorophyll fluorescence (SIF) measured from the Orbiting Carbon Observatory-2 (OCO-2) and gross primary productivity (GPP) derived from eddy covariance flux towers across the globe, and we discovered that there is a nearly universal relationship between SIF and GPP across a wide variety of biomes. This finding reveals the tremendous potential of SIF for accurately mapping terrestrial photosynthesis globally. In a letter to the Editor, Zhang et al. (2018) argued that different viewing zenith angles (VZA) of the OCO-2 instrument could impact the SIF-GPP relationship revealed by our recent study. We need to clarify four over-or misinterpretations. Zhang et al. (2018) first explained the measurement modes of OCO-2 (i.e., nadir, glint, and target) and called for attention to the effects of VZA (Frankenberg et al. 2014; He et al. 2017) on SIF magnitude. We recognized the effects of viewing geometries and also demonstrated that with all observations from 64 sites grouped together, there was no significant difference in the mean SIF between the nadir mode and the combined modes (glint/target) (Li et al. 2018a).

Oecologia, Oct 12, 1999

... 1963; Westoby 1984), or from sporadic disturbances like forest fires, pests, insect infestati... more ... 1963; Westoby 1984), or from sporadic disturbances like forest fires, pests, insect infestations and wind-throw (Peet and Christensen 1987). ... Fire weather conditions as characterised by the monthly se-verity rating (MSR) (Stocks and Lynham 1996) indicate high fire risk in our ...

Boreal Eurasia is a region where the interaction between droughts and the carbon cycle may have s... more Boreal Eurasia is a region where the interaction between droughts and the carbon cycle may have significant impacts on the global carbon cycle. Yet the region is extremely data sparse with respect to meteorology, soil moisture and carbon fluxes as compared to e.g. Europe. To better constrain our vegetation model SiBCASA, we increase data usage by assimilating two streams of satellite derived soil moisture. We study if the assimilation improved SiBCASA's soil moisture and its effect on the simulated carbon fluxes. By comparing to unique in situ soil moisture observations, we show that the passive microwave soil moisture product did not improve the soil moisture simulated by SiBCASA, but the active data seem promising in some aspects. The match between SiBCASA and ASCAT soil moisture is best in the summer months over low vegetation. Nevertheless, ASCAT failed to detect the major droughts occurring between 2007 and 2013. The performance of ASCAT soil moisture seems to be particularly sensitive to ponding, rather than to biomass. The effect on the simulated carbon fluxes is large, 5-10 % on annual GPP and TER, and tens of percent on local NEE, and 2 % on areaintegrated NEE, which is the same order of magnitude as the inter-annual variations. Consequently, this study shows that assimilation of satellite derived soil moisture has potentially large impacts, while at the same time further research is needed to understand under which conditions the satellite derived soil moisture improves the simulated soil moisture.

EGU General Assembly Conference Abstracts, Apr 1, 2017

Forests

This paper reports on the location of sources contributing to a point flux measurement in the sou... more This paper reports on the location of sources contributing to a point flux measurement in the southern taiga, Russia. The measurement tower is surrounded by a coniferous forest with a mean aerodynamically active height of 27 m (h). Aerodynamical parameters of the forest, such as displacement height d and aerodynamic roughness z0, derived from wind speed profile measurements for 2017–2019, were used to estimate the seasonal and daily behavior of the flux footprint. Two analytical footprint models driven by d and z0 were used to estimate the footprint for canopy sources. The Lagrangian simulation (LS) approach driven by flow statistics from measurements and modeling was used to estimate the footprint for ground-located sources. The Flux Footprint Prediction (FFP) tool for assessing canopy flux footprint applied as the option in the EddyPro v.7 software was inspected against analytical and LS methods. For model comparisons, two parameters from estimated footprint functions were used: t...

Agricultural and Forest Meteorology

Global Change Biology, 2021

Understanding the critical soil moisture (SM) threshold (θcrit) of plant water stress and land su... more Understanding the critical soil moisture (SM) threshold (θcrit) of plant water stress and land surface energy partitioning is a basis to evaluate drought impacts and improve models for predicting future ecosystem condition and climate. Quantifying the θcrit across biomes and climates is challenging because observations of surface energy fluxes and SM remain sparse. Here, we used the latest database of eddy covariance measurements to estimate θcrit across Europe by evaluating evaporative fraction (EF)‐SM relationships and investigating the covariance between vapor pressure deficit (VPD) and gross primary production (GPP) during SM dry‐down periods. We found that the θcrit and soil matric potential threshold in Europe are 16.5% and −0.7 MPa, respectively. Surface energy partitioning characteristics varied among different vegetation types; EF in savannas had the highest sensitivities to SM in water‐limited stage, and the lowest in forests. The sign of the covariance between daily VPD a...

Nature Ecology & Evolution, 2021

Global Ecology and Biogeography, 2019

AimThe mechanisms of plant trait adaptation and acclimation are still poorly understood and, cons... more AimThe mechanisms of plant trait adaptation and acclimation are still poorly understood and, consequently, lack a consistent representation in terrestrial biosphere models (TBMs). Despite the increasing availability of geo‐referenced trait observations, current databases are still insufficient to cover all vegetation types and environmental conditions. In parallel, the growing number of continuous eddy‐covariance observations of energy and CO2 fluxes has enabled modellers to optimize TBMs with these data. Past attempts to optimize TBM parameters mostly focused on model performance, overlooking the ecological properties of ecosystems. The aim of this study was to assess the ecological consistency of optimized trait‐related parameters while improving the model performances for gross primary productivity (GPP) at sites.LocationWorldwide.Time period1992–2012.Major taxa studiedTrees and C3 grasses.MethodsWe optimized parameters of the ORCHIDEE model against 371 site‐years of GPP estimate...

Izvestiya Rossiiskoi akademii nauk. Seriya geograficheskaya, 2019

The technology of allocation of order parameters (invariants) of the spatial structure of the the... more The technology of allocation of order parameters (invariants) of the spatial structure of the thermal field of the southern taiga landscape (Central Forest Nature Reserve) obtained on the basis of the analysis of the time series of measurements in the long-wave channel of Landsat satellites from 1986 to 2017 and reflecting its stationary state is considered. It is shown that the heat flux is measured by the satellite not directly from the forest crowns, but from the ground layer of the atmosphere, the state of which is determined by the parameters of the landscape. It is found that the invariant component of the spatiotemporal variation of the thermal field is displayed by two order parameters: the first mainly reflects the temperature of winter months, the second – of summer. The contribution of relief and vegetation to the determination of invariants and the autochthonous components of the thermal field determined by the transition zones between the landscape elements contrasting ...

Agricultural and Forest Meteorology, 2015

Accurate and reliable estimates of gross primary productivity (GPP) are required for monitoring t... more Accurate and reliable estimates of gross primary productivity (GPP) are required for monitoring the global carbon cycle at different spatial and temporal scales. Because GPP displays high spatial and temporal variation, remote sensing plays a major role in producing gridded estimates of GPP across spatiotemporal scales. In this context, understanding the strengths and weaknesses of remote sensing-based models of GPP and improving their performance is a key contemporary scientific activity. We used measurements from 157 research sites (~470 site-years) in the FLUXNET "La Thuile" data and compared the skills of 11 different remote sensing models in capturing intra-and inter-annual variations in daily GPP in seven different biomes. Results show that the models were able to capture significant intra-annual variation in GPP (Index of Agreement = 0.4 to 0.80) in all biomes. However, the models" ability to track inter-annual variation in daily GPP was significantly weaker (IoA<0.45). We examined whether the inclusion of different mechanisms that are missing in the models could improve their predictive power. The mechanisms included the effect of sub-daily variation in environmental variables on daily GPP, factoring-in differential rates of GPP conversion efficiency for direct and diffuse incident radiation, lagged effects of environmental variables, better representation of soil-moisture dynamics, and allowing spatial variation in model parameters. Our analyses suggest that the next generation remote sensing models need better representation of soil-moisture, but other mechanisms that have been found to influence GPP in site-level studies may not have significant bearing on model performance at continental and global scale. Understanding the relative controls of biotic visa -vis abiotic factors on GPP and accurately scaling up leaf level processes to the ecosystem scale are likely to be important for recognizing the limitations of remote sensing model and improving their formulation.

Environmental Research Letters, 2009