Sensitivity of near real-time MODIS gross primary productivity in terrestrial forest based on eddy covariance measurements

2015 ◽  
Vol 25 (5) ◽  
pp. 537-548 ◽  
Author(s):  
Xuguang Tang ◽  
Hengpeng Li ◽  
Guihua Liu ◽  
Xinyan Li ◽  
Li Yao ◽  
...  
Keyword(s):  
Real Time ◽  
Eddy Covariance ◽  
Primary Productivity ◽  
Gross Primary Productivity ◽  
Eddy Covariance Measurements

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 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.

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

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

scholarly journals A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States

2021 ◽  
Vol 13 (2) ◽  
pp. 281-298
Author(s):  
Chongya Jiang ◽  
Kaiyu Guan ◽  
Genghong Wu ◽  
Bin Peng ◽  
Sheng Wang
Keyword(s):  
United States ◽  
Real Time ◽  
Temporal Resolution ◽  
Primary Productivity ◽  
Vegetation Index ◽  
Regional Scale ◽  
Gross Primary Productivity ◽  
Environmental Research ◽  
Near Infrared Reflectance ◽  
Spatiotemporal Resolution

Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near-infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product for the contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus >500 m), temporal resolution (daily versus 8 d), instantaneity (latency of 1 d versus >2 weeks) and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Cropland Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.

scholarly journals Comprehensive description of the carbon cycle of an ancient temperate broadleaved woodland

2010 ◽  
Vol 7 (3) ◽  
pp. 3735-3763 ◽  
Author(s):  
K. Fenn ◽  
Y. Malhi ◽  
M. Morecroft ◽  
C. Lloyd ◽  
M. Thomas
Keyword(s):  
Carbon Cycle ◽  
Eddy Covariance ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Ecosystem Respiration ◽  
Stem Respiration ◽  
Ecosystem Carbon ◽  
Deciduous Woodland ◽  
Below Ground ◽  
Eddy Covariance Measurements

Abstract. There exist very few comprehensive descriptions of the productivity and carbon cycling of forest ecosystems. Here we present a description of the components of annual Net Primary Productivity (NPP), Gross Primary Productivity (GPP), autotrophic and heterotrophic respiration, and ecosystem respiration (RECO) for a temperate mixed deciduous woodland at Wytham Woods in southern Britain, calculated using "bottom-up" biometric and chamber measurements (leaf and wood production and soil and stem respiration). These are compared with estimates of these parameters from eddy-covariance measurements made at the same site. NPP was estimated as 7.0±0.8 Mg C ha−1 yr−1, and GPP as 20.3+1.0 Mg C ha−1 yr−1, a value which closely matched to eddy covariance-derived GPP value of 21.1 Mg C ha−1 yr−1. Annual RECO was calculated as 18.9±1.7 Mg C ha−1 yr−1, close to the eddy covariance value of 19.8 Mg C ha−1 yr−1; the seasonal cycle of biometric and eddy covariance RECO estimates also closely matched. The consistency between eddy covariance and biometric measurements substantially strengthens the confidence we attach to each as alternative indicators of site carbon dynamics, and permits an integrated perspective of the ecosystem carbon cycle. 37% of NPP was allocated below ground, and the ecosystem carbon use efficiency (CUE, = NPP/GPP) calculated to be 0.35±0.05, lower than reported for many temperate broadleaved sites.

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