scholarly journals Joint control of terrestrial gross primary productivity by plant phenology and physiology

2015 ◽  
Vol 112 (9) ◽  
pp. 2788-2793 ◽  
Author(s):  
Jianyang Xia ◽  
Shuli Niu ◽  
Philippe Ciais ◽  
Ivan A. Janssens ◽  
Jiquan Chen ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Primary Productivity ◽  
Abiotic Factors ◽  
Plant Phenology ◽  
Gross Primary Productivity ◽  
Fire Disturbance ◽  
Spatiotemporal Variability ◽  
Joint Control ◽  
Time And Space ◽  
Over Time

Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate–carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy–covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO2 uptake period (CUP) and the seasonal maximal capacity of CO2 uptake (GPPmax). The product of CUP and GPPmax explained >90% of the temporal GPP variability in most areas of North America during 2000–2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 (r2 = 0.90) and GPP recovery after a fire disturbance in South Dakota (r2 = 0.88). Additional analysis of the eddy–covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPPmax than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPPmax and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space.

scholarly journals Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations

2021 ◽  
Author(s):  
Trina Merrick ◽  
Stephanie Pau ◽  
Matteo Detto ◽  
Eben North Broadbent ◽  
Stephanie Bohlman ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Primary Productivity ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Canopy Structure ◽  
Gross Primary Productivity ◽  
Spatiotemporal Variability ◽  
Individual Tree ◽  
Tropical Forest Canopy

Abstract. Presented here for the first time are emerging vegetation indicators: near-infrared reflectance (NIRv) of vegetation, the fluorescence correction vegetation index (FCVI), and radiance (NIRvrad) of vegetation, for a tropical forest canopy calculated using UAS-based hyperspectral data. Fine-scale tropical forest heterogeneity represented by NIRv, FCVI, and NIRvrad, is investigated using unmanned aerial vehicle data and eddy covariance-based gross primary productivity estimates. By exploiting near-infrared signals, emerging vegetation indicators captured the greatest spatiotemporal variability, followed by the enhanced vegetation index (EVI), then the normalized difference vegetation index (NDVI), which saturates. Wavelet analyses showed the dominant spatial variability of all indicators is driven by tree clusters and larger-than-tree-crown size gaps (not individual tree crowns or leaf clumps), but emerging indices and EVI captured structural information at smaller spatial scales (~50 m) than NDVI (~90 m) and lidar (~70 m). As predicted in previous studies, we confirm that NIRv and FCVI are virtually identical for a dense green canopy despite the differences in how these indices were derived. Furthermore, we show that NIRvrad, which does not require separate irradiance measurements, correlated most strongly with gross primary productivity and photosynthetically active radiation. These emerging indicators, which are related to canopy structure and the radiation regime of vegetation canopies are promising tools to improve understanding of tropical forest canopy structure and function.

Evaluation of MODIS gross primary productivity for Africa using eddy covariance data

2013 ◽  
Vol 131 ◽  
pp. 275-286 ◽  
Author(s):  
M. Sjöström ◽  
M. Zhao ◽  
S. Archibald ◽  
A. Arneth ◽  
B. Cappelaere ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Primary Productivity ◽  
Gross Primary Productivity

Legacy effects of drought on gross primary productivity from eddy-covariance measurements

2021 ◽  
Author(s):  
Xin Yu ◽  
René Orth ◽  
Markus Reichstein ◽  
Ana Bastos
Keyword(s):  
Eddy Covariance ◽  
Primary Productivity ◽  
Carbon Balance ◽  
Climatic Conditions ◽  
Gross Primary Productivity ◽  
Legacy Effects ◽  
Ecosystem Carbon ◽  
Ecosystem Carbon Balance ◽  
Eddy Covariance Measurements ◽  
Effects Of Drought

<p>The frequency and severity of droughts are expected to increase in the wake of climate change. Drought events not only cause direct impacts on the ecosystem carbon balance but also result in legacy effects during the following years. These legacies result from, for example, drought damage to the xylem or the crown which causes impaired growth, or from higher vulnerability to pests and diseases. To understand how droughts might affect the carbon cycle in the future, it is important to consider both direct and legacy effects. Such effects likely affect interannual variability in C fluxes but are challenging to detect in observations, and poorly represented in models. Therefore, the patterns and mechanisms inducing the legacy effects of drought on ecosystem carbon balance are necessarily needed to improve.</p><p>In this study, we analyze gross primary productivity (GPP) from eddy-covariance measurements in Germany to detect legacy effects from recent droughts. We follow a data-driven modeling approach using a random forest model trained in different sets of drought and non-drought periods. This approach allows quantifying legacy effects as deviations of observed GPP from modeled GPP in legacy years, which indicates a change in the vegetation response to hydro-climatic conditions as compared with the training period.</p>

scholarly journals Explaining inter-annual variability of gross primary productivity from plant phenology and physiology

2016 ◽  
Vol 226-227 ◽  
pp. 246-256 ◽  
Author(s):  
Sha Zhou ◽  
Yao Zhang ◽  
Kelly K. Caylor ◽  
Yiqi Luo ◽  
Xiangming Xiao ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Plant Phenology ◽  
Gross Primary Productivity ◽  
Annual Variability

scholarly journals Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

2014 ◽  
Vol 11 (8) ◽  
pp. 2185-2200 ◽  
Author(s):  
M. Verma ◽  
M. A. Friedl ◽  
A. D. Richardson ◽  
G. Kiely ◽  
A. Cescatti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
Primary Productivity ◽  
Remotely Sensed ◽  
Gross Primary Productivity ◽  
Spatiotemporal Variability ◽  
Variable Component ◽  
Carbon Budgets ◽  
Data Set ◽  
Flux Tower

Abstract. 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 studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10–80%). The ability of remotely sensed proxies and models to explain interannual variability in 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, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux tower GPP, and difference between the footprints of MODIS pixels and flux tower measurements are acknowledged as unresolved challenges.

Evapotranspiration and gross primary productivity of secondary vegetation in Amazonia inferred by eddy covariance

2020 ◽  
Vol 294 ◽  
pp. 108141
Author(s):  
Rita de Cassia Silva von Randow ◽  
Javier Tomasella ◽  
Celso von Randow ◽  
Alessandro Carioca de Araújo ◽  
Antonio Ocimar Manzi ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Primary Productivity ◽  
Gross Primary Productivity ◽  
Secondary Vegetation

Estimation of Gross Primary Productivity of a raised bog ecosystem using satellite models and eddy covariance techniques under exceptional climatic conditions  

2021 ◽  
Author(s):  
Ruchita Ingle ◽  
Saheba Bhatnagar ◽  
Bidisha Ghosh ◽  
Laurence Gill ◽  
Matthew Saunders
Keyword(s):  
Eddy Covariance ◽  
Satellite Imagery ◽  
Primary Productivity ◽  
Vegetation Indices ◽  
Weather Conditions ◽  
Gross Primary Productivity ◽  
Detailed Knowledge ◽  
Hybrid Index ◽  
Raised Bog ◽  
C Cycle

<p>Peatlands are vital to the global carbon (C) cycle as they act as a significant C store and these systems in Ireland store between 1064 –1503 Gt C on ~20% of the land area. However, around 90% of this area has been drained and degraded by various anthropogenic activities and the emissions from these activities are approximately 3 million t C per year. A better understanding of the land-atmosphere C and greenhouse gas (GHG) dynamics is vital to halt these emissions and enhance the C sink strength of these ecosystems. Gross Primary Productivity (GPP) is a major part of the peatland carbon cycle and detailed knowledge of the spatial and temporal extent of GPP is imperative for improving our predictions of peatland ecology, biogeochemistry and carbon balance in response to global change. Eddy covariance (EC) techniques are widely used to measure carbon fluxes but can only account for fluxes within the flux footprint of the tower, and it is challenging to scale up data from EC towers to regional and global scales due to the limited number of towers and their geographic locations. This research assesses the relationship between remote sensing and ground-based measurements for a near-natural raised bog in Ireland using EC techniques and high-resolution Sentinel 2A satellite imagery. Vegetation indices (VIs) are one of the key input parameters for satellite-based GPP and most of the existing VIs have been developed for grassland, agriculture, and forest ecosystems. This study developed a hybrid index for raised bogs using multiple linear regression and six widely practiced conventional vegetation indices. Two approaches have been used in this study for estimating GPP using the LUE model. Initially, all the individual indices have been used to model the GPP, which was subsequently compared with the EC GPP to determine the performance of each index against the EC data. The model was run with meteorological data and satellite-derived vegetation indices. During the 2018 study period, the weather was exceptionally dry which made it challenging and rewarding at the same time as the hybrid index was developed for an exceptional year. It was crucial to test the performance of the hybrid index under more normal weather conditions with limited clear sky satellite imagery. Therefore, the hybrid index was validated for the year 2019 which had normal weather conditions. The hybrid index based modelled GPP showed a significant correlation with the EC GPP for both the years with an R<sup>2</sup> > 0.95. Overall, this research has demonstrated the potential of combining EC techniques and the hybrid index along with satellite-derived models to better understand and monitor key drivers and patterns of GPP of raised bog ecosystems under different climate scenarios.</p>

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