scholarly journals A Comparison between the MODIS Product (MOD17A2) and a Tide-Robust Empirical GPP Model Evaluated in a Georgia Wetland

2018 ◽  
Vol 10 (11) ◽  
pp. 1831 ◽  
Author(s):  
Jianbin Tao ◽  
Deepak Mishra ◽  
David Cotten ◽  
Jessica O’Connell ◽  
Monique Leclerc ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Training Data ◽  
Environmental Drivers ◽  
Canopy Reflectance ◽  
Co2 Fluxes ◽  
Moisture Index ◽  
Validation Data ◽  
Reflectance Data

Despite the importance of tidal ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these wetlands remain poorly understood. This limited understanding results from the challenges associated with in situ flux studies and their correlation with satellite imagery which can be affected by periodic tidal flooding. Carbon dioxide eddy covariance (EC) towers are installed in only a few wetlands worldwide, and the longest eddy-covariance record from Georgia (GA) wetlands contains only two continuous years of observations. The goals of the present study were to evaluate the performance of existing MODIS Gross Primary Production (GPP) products (MOD17A2) against EC derived GPP and develop a tide-robust Normalized Difference Moisture Index (NDMI) based model to predict GPP within a Spartina alterniflora salt marsh on Sapelo Island, GA. These EC tower-based observations represent a basis to associate CO2 fluxes with canopy reflectance and thus provide the means to use satellite-based reflectance data for broader scale investigations. We demonstrate that Light Use Efficiency (LUE)-based MOD17A2 does not accurately reflect tidal wetland GPP compared to a simple empirical vegetation index-based model where tidal influence was accounted for. The NDMI-based GPP model was capable of predicting changes in wetland CO2 fluxes and explained 46% of the variation in flux-estimated GPP within the training data, and a root mean square error of 6.96 g C m−2 in the validation data. Our investigation is the first to create a MODIS-based wetland GPP estimation procedure that demonstrates the importance of filtering tidal observations from satellite surface reflectance data.

scholarly journals Modeling light use efficiency in a subtropical mangrove forest equipped with CO<sub>2</sub> eddy covariance

2013 ◽  
Vol 10 (3) ◽  
pp. 2145-2158 ◽  
Author(s):  
J. G. Barr ◽  
V. Engel ◽  
J. D. Fuentes ◽  
D. O. Fuller ◽  
H. Kwon
Keyword(s):  
Eddy Covariance ◽  
Environmental Conditions ◽  
Mangrove Forest ◽  
Environmental Drivers ◽  
Model Parameters ◽  
Co2 Uptake ◽  
Light Use Efficiency ◽  
Co2 Fluxes ◽  
Use Efficiency ◽  
Reflectance Data

Abstract. Despite the importance of mangrove ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these forests remain poorly understood. This limited understanding is partly a result of the challenges associated with in situ flux studies. Tower-based CO2 eddy covariance (EC) systems are installed in only a few mangrove forests worldwide, and the longest EC record from the Florida Everglades contains less than 9 years of observations. A primary goal of the present study was to develop a methodology to estimate canopy-scale photosynthetic light use efficiency in this forest. These tower-based observations represent a basis for associating CO2 fluxes with canopy light use properties, and thus provide the means for utilizing satellite-based reflectance data for larger scale investigations. We present a model for mangrove canopy light use efficiency utilizing the enhanced green vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) that is capable of predicting changes in mangrove forest CO2 fluxes caused by a hurricane disturbance and changes in regional environmental conditions, including temperature and salinity. Model parameters are solved for in a Bayesian framework. The model structure requires estimates of ecosystem respiration (RE), and we present the first ever tower-based estimates of mangrove forest RE derived from nighttime CO2 fluxes. Our investigation is also the first to show the effects of salinity on mangrove forest CO2 uptake, which declines 5% per each 10 parts per thousand (ppt) increase in salinity. Light use efficiency in this forest declines with increasing daily photosynthetic active radiation, which is an important departure from the assumption of constant light use efficiency typically applied in satellite-driven models. The model developed here provides a framework for estimating CO2 uptake by these forests from reflectance data and information about environmental conditions.

scholarly journals Satellite-based estimates of light-use efficiency in a subtropical mangrove forest equipped with CO<sub>2</sub> eddy covariance

2012 ◽  
Vol 9 (11) ◽  
pp. 16457-16492 ◽  
Author(s):  
J. G. Barr ◽  
V. Engel ◽  
J. D. Fuentes ◽  
D. O. Fuller ◽  
H. Kwon
Keyword(s):  
Eddy Covariance ◽  
Environmental Conditions ◽  
Mangrove Forest ◽  
Environmental Drivers ◽  
Model Parameters ◽  
Co2 Uptake ◽  
Light Use Efficiency ◽  
Co2 Fluxes ◽  
Use Efficiency ◽  
Reflectance Data

Abstract. Despite the importance of mangrove ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these forests remain poorly understood. This limited understanding is partly a result of the challenges associated with in situ flux studies. Tower-based carbon dioxide eddy covariance (EC) systems are installed in only a few mangrove forests worldwide and the longest EC record from the Florida Everglades contains less than 9 yr of observations. A primary goal of the present study was to develop a methodology to estimate canopy-scale photosynthetic light use efficiency in this forest. These tower-based observations represent a basis for associating CO2 fluxes with canopy light use properties, and thus provide the means for utilizing satellite-based reflectance data for larger-scale investigations. We present a model for mangrove canopy light use efficiency utilizing the enhanced green vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) that is capable of predicting changes in mangrove forest CO2 fluxes caused by a hurricane disturbance and changes in regional environmental conditions, including temperature and salinity. Model parameters are solved for in a Bayesian framework. The model structure requires estimates of ecosystem respiration (RE) and we present the first-ever tower-based estimates of mangrove forest RE derived from night-time CO2 fluxes. Our investigation is also the first to show the effects of salinity on mangrove forest CO2 uptake, which declines 5% per each 10 parts per thousand (ppt) increases in salinity. Light use efficiency in this forest declines with increasing daily photosynthetic active radiation, which is an important departure from the assumption of constant light use efficiency typically applied in satellite-driven models. The model developed here provides a framework for estimating CO2 uptake by these forests from reflectance data and information about environmental conditions.

scholarly journals Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)

2019 ◽  
Vol 11 (6) ◽  
pp. 649 ◽  
Author(s):  
Koffi Noumonvi ◽  
Mitja Ferlan ◽  
Klemen Eler ◽  
Giorgio Alberti ◽  
Alessandro Peressotti ◽  
...  
Keyword(s):  
Linear Relationship ◽  
Eddy Covariance ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Information Criterion ◽  
Growing Season ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Eddy Covariance Method

The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands.

scholarly journals Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

2019 ◽  
Vol 11 (13) ◽  
pp. 1589 ◽  
Author(s):  
Duie Tien Bui ◽  
Khabat Khosravi ◽  
Himan Shahabi ◽  
Prasad Daggupati ◽  
Jan F. Adamowski ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Vegetation Index ◽  
Slope Angle ◽  
Training Data ◽  
Multivariate Logistic Regression Model ◽  
Success Rates ◽  
Topographic Wetness Index ◽  
Validation Data ◽  
Flood Susceptibility ◽  
Susceptibility Maps

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.

scholarly journals Eddy covariance carbon flux in a scrub in the Mexican highland

2020 ◽  
Author(s):  
Aurelio Guevara-Escobar ◽  
Enrique González-Sosa ◽  
Mónica Cervantes-Jiménez ◽  
Humberto Suzán-Azpiri ◽  
Mónica Elisa Queijeiro-Bolaños ◽  
...  
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Vegetation Index ◽  
Carbon Sink ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Net Ecosystem Exchange ◽  
R Package ◽  
Dark Cycle ◽  
Remote Sensors

Abstract. Vegetation fixes C in its biomass through photosynthesis or might release it into the atmosphere through respiration. Measurements of these fluxes would help us understand ecosystem functioning. The eddy covariance technique (EC) is widely used to measure the net ecosystem exchange of C (NEE) which is the balance between gross primary production (GPP) and ecosystem respiration (Reco). Orbital satellites such as MODIS can also provide estimates of GPP. In this study, we measured NEE with the EC in a scrub at Bernal in Mexico, and then partitioned into gross primary production (GPP-EC) and Reco using the recent R package Reddyproc. Measurements of GPP-EC were related to the estimates from the MODIS satellite provided in product MOD17A2H, which contains data of the gross primary productivity (GPP-MODIS). The Bernal site was a carbon sink despite it was an overgrazed site, the average NEE during fifteen months of 2017 and 2018 was −0.78 g C m−2 d−1 and the flux was negative in all measured months. The GPP-MODIS underestimated the ground data when representing the relation with a Theil-Sen regression: GPP-EC = 1.866 + 1.861 GPP-MODIS; an ordinary less squares regression had similar coefficients and the R2 was 0.6. Although cacti (CAM), legume shrubs (C3) and herbs (C3) had a similar vegetation index, the nighttime flux was characterized by positive NEE suggesting that the photosynthetic dark-cycle flux of cacti was lower than Reco. The discrepancy among the GPP flux estimates stresses the need to understand the limitations of EC and remote sensors, while incorporating complementary monitoring and modelling schemes of nighttime Reco, particularly in the presence of species with different photosynthetic cycles.

scholarly journals Combining Phenological Camera Photos and MODIS Reflectance Data to Predict GPP Daily Dynamics for Alpine Meadows on the Tibetan Plateau

2020 ◽  
Vol 12 (22) ◽  
pp. 3735
Author(s):  
Xuqiang Zhou ◽  
Xufeng Wang ◽  
Songlin Zhang ◽  
Yang Zhang ◽  
Xuejie Bai
Keyword(s):  
Tibetan Plateau ◽  
Eddy Covariance ◽  
Vegetation Index ◽  
Learning Algorithm ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Estimation Accuracy ◽  
The Tibetan Plateau ◽  
Unit Space

Gross primary production (GPP) is the overall photosynthetic fixation of carbon per unit space and time. Due to uncertainties resulting from clouds, snow, aerosol, and topography, it is a challenging task to accurately estimate daily GPP. Daily digital photos from a phenological camera record vegetation daily greenness dynamics with little cloud or aerosol disturbance. It can be fused with satellite remote sensing data to improve daily GPP prediction accuracy. In this study, we combine the two types of datasets to improve the estimation accuracy of GPP for alpine meadow on the Tibetan Plateau. To examine the performance of different methods and vegetation indices (VIs), three experiments were designed. First, GPP was estimated with the light use efficiency (LUE) model with the green chromatic coordinate (GCC) from the phenological camera and vegetation index from MODIS, respectively. Second, GPP was estimated with the Backpropagation neural network machine learning algorithm (BNNA) method with GCC from the phenological camera and vegetation index from MODIS, respectively. Finally, GPP was estimated with the BNNA method using GCC and vegetation index as inputs at the same time. Compared with eddy covariance GPP, GPP predicted by the BNNA method with GCC and vegetation indices as inputs at the same time showed the highest accuracy of all the experiments. The results indicated that GCC had a higher accuracy than NDVI and EVI when only one vegetation index data was used in the LUE model or the BNNA method. The R2 of GPP estimated by BNNA and GPP from eddy covariance increased by 0.12 on average, RMSE decreased by 1.13 g C·m−2·day−1 on average, and MAD decreased by 0.87 g C·m−2·day−1 on average compared with GPP estimated by the traditional LUE model and GPP from eddy covariance. This study puts forth a new way to improve the estimation accuracy of GPP on the Tibetan Plateau. With the emergence of a large number of phenological cameras, this method has great potential for use on the Tibetan Plateau, which is heavily affected by clouds and snow.

scholarly journals Sun-induced fluorescence and near-infrared reflectance of vegetation track the seasonal dynamics of gross primary production over Africa

2021 ◽  
Vol 18 (9) ◽  
pp. 2843-2857
Author(s):  
Anteneh Getachew Mengistu ◽  
Gizaw Mengistu Tsidu ◽  
Gerbrand Koren ◽  
Maurits L. Kooreman ◽  
K. Folkert Boersma ◽  
...  
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Numerical Models ◽  
Gross Primary Production ◽  
Infrared Reflectance ◽  
Terrestrial Vegetation ◽  
Max Planck ◽  
Near Infrared Reflectance ◽  
Validation Data

Abstract. The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems of Africa (savannas and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa's vast land area, a lack of sufficient ground-based observation networks and validation data for CO2, and incomplete representation of important processes in numerical models. In this study, we therefore turn to two remotely sensed vegetation products that have been shown to correlate highly with gross primary production (GPP): sun-induced fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv). The former is available from an updated product that we recently published (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval – SIFTER v2), which specifically improves retrievals in tropical environments. A comparison against flux tower observations of daytime-partitioned net ecosystem exchange from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of >0.9 (N=12 months, four sites) over savannas in the Northern and Southern hemispheres. These coefficients are slightly higher than for the widely used Max Planck Institute for Biogeochemistry (MPI-BGC) GPP products and enhanced vegetation index (EVI). Similarly to SIF signals in the neighboring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture and is followed by an initial decline during the early dry season, which reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R>0.9; N≥250 pixels) with multi-year MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and the EVI show saturation relative to peak NIRv and SIF signals during high-productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested.

scholarly journals Spatial and temporal CO<sub>2</sub> exchanges measured by Eddy Covariance over a temperate intertidal flat and their relationships to net ecosystem production

2012 ◽  
Vol 9 (1) ◽  
pp. 249-268 ◽  
Author(s):  
P. Polsenaere ◽  
E. Lamaud ◽  
V. Lafon ◽  
J.-M. Bonnefond ◽  
P. Bretel ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Tidal Flat ◽  
Gross Primary Production ◽  
Co2 Uptake ◽  
Intertidal Mudflat ◽  
Intertidal Flat ◽  
Co2 Fluxes ◽  
Zostera Noltii ◽  
Night Time ◽  
Central Station

Abstract. Measurements of carbon dioxide fluxes were performed over a temperate intertidal mudflat in southwestern France using the micrometeorological Eddy Covariance (EC) technique. EC measurements were carried out in two contrasting sites of the Arcachon flat during four periods and in three different seasons (autumn 2007, summer 2008, autumn 2008 and spring 2009). In addition, satellite images of the tidal flat at low tide were used to link the net ecosystem CO2 exchange (NEE) with the occupation of the mudflat by primary producers, particularly by Zostera noltii meadows. CO2 fluxes during the four deployments showed important spatial and temporal variations, with the flat rapidly shifting from sink to source with the tide. Absolute CO2 fluxes showed generally small negative (influx) and positive (efflux) values, with larger values up to −13 μmol m−2 s−1 for influxes and 19 μmol m−2 s−1 for effluxes. Low tide during the day was mostly associated with a net uptake of atmospheric CO2. In contrast, during immersion and during low tide at night, CO2 fluxes where positive, negative or close to zero, depending on the season and the site. During the autumn of 2007, at the innermost station with a patchy Zostera noltii bed (cover of 22 ± 14% in the wind direction of measurements), CO2 influx was −1.7 ± 1.7 μmol m−2 s−1 at low tide during the day, and the efflux was 2.7 ± 3.7 μmol m−2 s−1 at low tide during the night. A gross primary production (GPP) of 4.4 ± 4.1 μmol m−2 s−1 during emersion could be attributed to microphytobenthic communities. During the summer and autumn of 2008, at the central station with a dense eelgrass bed (92 ± 10%), CO2 uptakes at low tide during the day were −1.5 ± 1.2 and −0.9 ± 1.7 μmol m−2 s−1, respectively. Night time effluxes of CO2 were 1.0 ± 0.9 and 0.2 ± 1.1 μmol m−2 s−1 in summer and autumn, respectively, resulting in a GPP during emersion of 2.5 ± 1.5 and 1.1 ± 2.0 μmol m−2 s−1, respectively, attributed primarily to the seagrass community. At the same station in April 2009, before Zostera noltii started to grow, the CO2 uptake at low tide during the day was the highest (−2.7 ± 2.0 μmol m−2 s−1). Influxes of CO2 were also observed during immersion at the central station in spring and early autumn and were apparently related to phytoplankton blooms occurring at the mouth of the flat, followed by the advection of CO2-depleted water with the flooding tide. Although winter data as well as water carbon measurements would be necessary to determine a precise CO2 budget for the flat, our results suggest that tidal flat ecosystems are a modest contributor to the CO2 budget of the coastal ocean.

scholarly journals Sun-induced Fluorescence and Near Infrared Reflectance of vegetation track the seasonal dynamics of gross primary production over Africa

2020 ◽  
Author(s):  
Anteneh Getachew Mengistu ◽  
Gizaw Mengistu Tsidu ◽  
Gerbrand Koren ◽  
Maurits L. Kooreman ◽  
K. Folkert Boersma ◽  
...  
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Numerical Models ◽  
Gross Primary Production ◽  
Infrared Reflectance ◽  
Terrestrial Vegetation ◽  
Wet Season ◽  
Near Infrared Reflectance ◽  
Validation Data

Abstract. The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems of Africa (savannas, and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa's vast land area, a lack of sufficient ground-based observation networks and validation data for CO2, and incomplete representation of important processes in numerical models. In this study, we, therefore, turn to two remotely-sensed vegetation products that have shown to correlate highly with Gross Primary Production (GPP): Sun-Induced Fluorescence (SIF) and Near-Infrared Reflectance of vegetation (NIRv). The former is available from an updated product that we recently published (SIFTER v2), which specifically improves retrievals in tropical environments. A comparison against flux tower observations of daytime-partitioned Net Ecosystem Exchange from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of > 0.9 (N = 12 months, 4 sites) over savannas in the northern and southern hemispheres. These coefficients are slightly higher than for the widely used MPI-BGC GPP products and Enhanced Vegetation Index (EVI). Similar to SIF signals in the neighboring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture, and is followed by an initial decline during the early dry season, that reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R > 0.9, N = 250 + pixels) with multi-year MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and EVI show saturation relative to peak NIRv and SIF signals during high productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested.

Predictions of Gross Primary Production from a suite of Terrestrial Biome Models display divergent relationships with key environmental variables

2021 ◽  
Author(s):  
Keith Bloomfield ◽  
Benjamin Stocker ◽  
Trevor Keenan ◽  
Colin Prentice
Keyword(s):  
Soil Moisture ◽  
Primary Production ◽  
Eddy Covariance ◽  
Empirical Analysis ◽  
Gross Primary Production ◽  
Additive Models ◽  
Theoretical Modelling ◽  
Environmental Drivers ◽  
Model Structure ◽  
Wide Range

&lt;p&gt;Accurate simulations of gross primary production (GPP) are vital for Earth System Models that must inform public policy decisions. &amp;#160;The instantaneous controls of leaf-level photosynthesis, which can be measured in manipulative experiments, are well established.&amp;#160; At the canopy scale, however, there is no consensus on how GPP depends on (a) light or (b) other aspects of the physical environment such as temperature and CO&lt;sub&gt;2&lt;/sub&gt;. &amp;#160;Models of GPP make a variety of different assumptions when &amp;#8216;scaling-up&amp;#8217; the standard model of photosynthesis. &amp;#160;As a troublesome consequence, they make a variety of different predictions about how GPP responds to projected environmental change.&lt;/p&gt;&lt;p&gt;This problem can be tackled by theoretical modelling and by empirical analysis of GPP as reconstructed from eddy-covariance flux measurements. &amp;#160;Theoretical modelling has provided an explanation for why &amp;#8216;light-use efficiency&amp;#8217; (LUE) models work well at time scales of a week or longer. &amp;#160;The same logic provides a justification for the use of LUE as a key metric in an empirical analysis. &amp;#160;By focusing on LUE, we can isolate the drivers of GPP independent of its over-riding control by absorbed light. &amp;#160;We have used open-access eddy covariance data from over 100 sites, collated over 20 years (the number of sites has grown with time).&amp;#160; These sites, located in a wide range of biomes and climate zones, form part of the FLUXNET network.&amp;#160; We have combined the flux data with a satellite product (EVI from MODIS) that allows spatial estimates of the fraction of incident light absorbed by green vegetation.&amp;#160; Matching soil moisture data were estimated using the SPLASH model, with appropriate meteorological inputs, and soil water-holding capacity derived using SoilGrids. &amp;#160;LUE was then calculated as the amount of carbon fixed per unit of absorbed light.&amp;#160; We then explored additive models (incorporating multiple explanatory factors) that support non-linear responses.&amp;#160; Recognising that our longitudinal data lack independence, we controlled for the hierarchical nature of the dataset through a variance structure that nests measurement year within site location.&lt;/p&gt;&lt;p&gt;In arriving at a preferred parsimonious model, we show that daytime air temperature and vapour pressure deficit, and soil moisture content are all salient predictors of LUE.&amp;#160; The same explanatory terms are retained in iterations of this analysis run at timescales from weeks to months. &amp;#160;As a model-comparison exercise, we used that portion of our dataset which overlaps the North American Carbon Program to apply our empirical model structure to site-based estimates of GPP generated by 19 discrete Terrestrial Biosphere Models (TBMs).&amp;#160; The comparative analysis reveals wide variation between the TBMs in the shape, strength and even sign of the environmental effects on modelled GPP.&lt;/p&gt;&lt;p&gt;This empirical analysis suggests it is feasible to predict GPP using a single model structure, common across vegetation categories.&amp;#160; And the appeal of such universal approaches is highlighted by inconsistent relationships with key environmental drivers within extant terrestrial models.&lt;/p&gt;

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