Estimation of the gross primary production of an old‐growth temperate mixed forest using eddy covariance and remote sensing

2008 ◽  
Vol 30 (2) ◽  
pp. 463-479 ◽  
Author(s):  
J. B. Wu ◽  
X. M. Xiao ◽  
D. X. Guan ◽  
T. T. Shi ◽  
C. J. Jin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Eddy Covariance ◽  
Gross Primary Production ◽  
Mixed Forest ◽  
Old Growth ◽  
Temperate Mixed Forest

Combining remote sensing and eddy covariance data to monitor the gross primary production of an estuarine wetland ecosystem in East China

2015 ◽  
Vol 17 (4) ◽  
pp. 753-762
Author(s):  
Mingquan Wu ◽  
Shakir Muhammad ◽  
Fang Chen ◽  
Zheng Niu ◽  
Changyao Wang
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Eddy Covariance ◽  
Gross Primary Production ◽  
Model Performance ◽  
Wetland Ecosystem ◽  
Wetland Ecosystems ◽  
New Model ◽  
Estuarine Wetland ◽  
Better Than

A new model performance better than the MODIS GPP product for wetland ecosystems was proposed and validated.

Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data

2015 ◽  
Vol 168 ◽  
pp. 360-373 ◽  
Author(s):  
Gianluca Tramontana ◽  
Kazuito Ichii ◽  
Gustau Camps-Valls ◽  
Enrico Tomelleri ◽  
Dario Papale
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Uncertainty Analysis ◽  
Eddy Covariance ◽  
Random Forests ◽  
Gross Primary Production

Biometric and eddy-covariance-based estimates of carbon balance for a warm-temperate mixed forest in Japan

2008 ◽  
Vol 148 (5) ◽  
pp. 723-737 ◽  
Author(s):  
Yuji Kominami ◽  
Mayuko Jomura ◽  
Masako Dannoura ◽  
Yoshiaki Goto ◽  
Koji Tamai ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Carbon Balance ◽  
Mixed Forest ◽  
Temperate Mixed Forest ◽  
Warm Temperate

scholarly journals Gross Primary Production and False Spring: a spatio-temporal analysis

2020 ◽  
Author(s):  
Emma Izquierdo-Verdiguier ◽  
Raúl Zurita-Milla ◽  
Álvaro Moreno-Martinez ◽  
Gustau Camps-Valls ◽  
Anja Klisch ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Damage Index ◽  
Temporal Analysis ◽  
Environmental Stressors ◽  
Impact Of Climate Change ◽  
False Spring ◽  
Spatial Temporal Analysis

<p>Phenological information can be obtained from different sources of data. For instance, from remote sensing data or products and from models driven by weather variables. The former typically allows analyzing land surface phenology whereas the latter provide plant phenological information. Analyzing relationships between both sources of data allows us to understand the impact of climate change on vegetation over space and time. For example, the onset of spring is advanced or delayed by changes in the climate. These alterations affect plant productivity and animal migrations.</p><p>Spring onset monitoring is supported by the Extended Spring Index (SI-x), which are a suite of regression-based models for key indicator plant species. These models (Schwartz et al. in 2013) are based on daily maximum and minimum temperature from the first day of the year (January 1<sup>st</sup>). The primary products of these models are the timing of first leaf and first bloom, but they also provide derivative products such as the timing of last freeze day and the risk of frost damage day (damage index) for each year. This information helps to understand if vegetation could have suffered from environmental stressors such as droughts or a late frost events. The effects of environmental stressors in vegetation could be captured by the false spring index, which relates the first leaf day and the last freeze day. Moreover, this information could be used to understand plant productivity as well as to evaluate the economic impact of climate change.</p><p>Previous works studied the relationship between remote sensing and plant level products by means of spatial-temporal analysis between Gross Primary Production (GPP) and a spring onset index. However, they did not consider the possible impact of false spring effect in these relationships. Here, we present a spatial-temporal analysis between GPP and the damage index to better understand the effect of false springs (in annual gross photosynthesis data). The analysis is done for the period 2000 to 2015 over the contiguous US and at spatial resolution of 1 km. We used the MODIS annual sum of GPP and the damage and false spring indices derived from the SI-x models.</p>

scholarly journals Identification and Modeling Carbon and Energy Fluxes from Eddy Covariance Time Series Measurements in Rice and Rainfed Crops

2021 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Víctor Cicuéndez ◽  
Javier Litago ◽  
Víctor Sánchez-Girón ◽  
Laura Recuero ◽  
César Sáenz ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Primary Production ◽  
Eddy Covariance ◽  
Carbon Balance ◽  
Gross Primary Production ◽  
Meteorological Variables ◽  
Water Fluxes ◽  
Energy Fluxes ◽  
Global Carbon

Gross primary production (GPP) represents the carbon (C) uptake of ecosystems through photosynthesis and it is the largest flux of the global carbon balance. Our overall objective in this research is to identify and model GPP dynamics and its relationship with meteorological variables and energy fluxes based on time series analysis of eddy covariance (EC) data in two different agroecosystems, a Mediterranean rice crop in Spain and a rainfed cropland in Germany. Crops exerted an important influence on the energy and water fluxes dynamics existing a clear feedback between GPP, meteorological variables and energy fluxes in both type of crops.

scholarly journals Global Validation of a Process-Based Model on Vegetation Gross Primary Production Using Eddy Covariance Observations

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e110407 ◽  
Author(s):  
Dan Liu ◽  
Wenwen Cai ◽  
Jiangzhou Xia ◽  
Wenjie Dong ◽  
Guangsheng Zhou ◽  
...  
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Gross Primary Production

scholarly journals How drought severity constrains gross primary production(GPP) and its partitioning among carbon pools in a <i>Quercus ilex</i> coppice?

2014 ◽  
Vol 11 (23) ◽  
pp. 6855-6869 ◽  
Author(s):  
S. Rambal ◽  
M. Lempereur ◽  
J. M. Limousin ◽  
N. K. Martin-StPaul ◽  
J. M. Ourcival ◽  
...  
Keyword(s):  
Primary Production ◽  
Water Deficit ◽  
Eddy Covariance ◽  
Quercus Ilex ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Carbon Fluxes ◽  
Stem Growth ◽  
Drought Severity ◽  
Mediterranean Ecosystem

Abstract. The partitioning of photosynthates toward biomass compartments plays a crucial role in the carbon (C) sink function of forests. Few studies have examined how carbon is allocated toward plant compartments in drought-prone forests. We analyzed the fate of gross primary production (GPP) in relation to yearly water deficit in an old evergreen Mediterranean Quercus ilex coppice severely affected by water limitations. Carbon fluxes between the ecosystem and the atmosphere were measured with an eddy covariance flux tower running continuously since 2001. Discrete measurements of litterfall, stem growth and fAPAR allowed us to derive annual productions of leaves, wood, flowers and acorns, and an isometric relationship between stem and belowground biomass has been used to estimate perennial belowground growth. By combining eddy covariance fluxes with annual net primary productions (NPP), we managed to close a C budget and derive values of autotrophic, heterotrophic respirations and carbon-use efficiency (CUE; the ratio between NPP and GPP). Average values of yearly net ecosystem production (NEP), GPP and Reco were 282, 1259 and 977 g C m−2. The corresponding aboveground net primary production (ANPP) components were 142.5, 26.4 and 69.6 g C m−2 for leaves, reproductive effort (flowers and fruits) and stems, respectively. NEP, GPP and Reco were affected by annual water deficit. Partitioning to the different plant compartments was also impacted by drought, with a hierarchy of responses going from the most affected – the stem growth – to the least affected – the leaf production. The average CUE was 0.40, which is well in the range for Mediterranean-type forest ecosystems. CUE tended to decrease less drastically in response to drought than GPP and NPP did, probably due to drought acclimation of autotrophic respiration. Overall, our results provide a baseline for modeling the inter-annual variations of carbon fluxes and allocation in this widespread Mediterranean ecosystem, and they highlight the value of maintaining continuous experimental measurements over the long term.

scholarly journals Mapping the Intertidal Microphytobenthos Gross Primary Production, Part II: Merging Remote Sensing and Physical-Biological Coupled Modeling

2020 ◽  
Vol 7 ◽  
Author(s):  
Raphaël Savelli ◽  
Vona Méléder ◽  
Philippe Cugier ◽  
Pierre Polsenaere ◽  
Christine Dupuy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Coupled Modeling ◽  
Production Part
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