Modelling gross primary production in semi-arid Inner Mongolia using MODIS imagery and eddy covariance data

Abstract. It has been shown that vegetation growth in semi-arid regions is important to the global terrestrial CO2 sink, which indicates the strong need for improved understanding and spatially explicit estimates of CO2 uptake (gross primary production; GPP) in semi-arid ecosystems. This study has three aims: (1) to evaluate the MOD17A2H GPP (collection 6) product against GPP based on eddy covariance (EC) for six sites across the Sahel; (2) to characterize relationships between spatial and temporal variability in EC-based photosynthetic capacity (Fopt) and quantum efficiency (α) and vegetation indices based on earth observation (EO) (normalized difference vegetation index (NDVI), renormalized difference vegetation index (RDVI), enhanced vegetation index (EVI) and shortwave infrared water stress index (SIWSI)); and (3) to study the applicability of EO upscaled Fopt and α for GPP modelling purposes. MOD17A2H GPP (collection 6) drastically underestimated GPP, most likely because maximum light use efficiency is set too low for semi-arid ecosystems in the MODIS algorithm. Intra-annual dynamics in Fopt were closely related to SIWSI being sensitive to equivalent water thickness, whereas α was closely related to RDVI being affected by chlorophyll abundance. Spatial and inter-annual dynamics in Fopt and α were closely coupled to NDVI and RDVI, respectively. Modelled GPP based on Fopt and α upscaled using EO-based indices reproduced in situ GPP well for all except a cropped site that was strongly impacted by anthropogenic land use. Upscaled GPP for the Sahel 2001–2014 was 736 ± 39 g C m−2 yr−1. This study indicates the strong applicability of EO as a tool for spatially explicit estimates of GPP, Fopt and α; incorporating EO-based Fopt and α in dynamic global vegetation models could improve estimates of vegetation production and simulations of ecosystem processes and hydro-biochemical cycles.

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

Engineering Proceedings ◽

10.3390/engproc2021009009 ◽

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.

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

PLoS ONE ◽

10.1371/journal.pone.0110407 ◽

2014 ◽

Vol 9 (11) ◽

pp. e110407 ◽

Cited By ~ 4

Author(s):

Dan Liu ◽

Wenwen Cai ◽

Jiangzhou Xia ◽

Wenjie Dong ◽

Guangsheng Zhou ◽

...

Keyword(s):

Primary Production ◽

Eddy Covariance ◽

Gross Primary Production

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

Biogeosciences ◽

10.5194/bg-11-6855-2014 ◽

2014 ◽

Vol 11 (23) ◽

pp. 6855-6869 ◽

Cited By ~ 23

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.

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

Environmental Science Processes & Impacts ◽

10.1039/c5em00061k ◽

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.

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Modeling spatially explicit fire impact on gross primary production in interior Alaska using satellite images coupled with eddy covariance

Remote Sensing of Environment ◽

10.1016/j.rse.2013.04.003 ◽

2013 ◽

Vol 135 ◽

pp. 178-188 ◽

Cited By ~ 7

Author(s):

Shengli Huang ◽

Heping Liu ◽

Devendra Dahal ◽

Suming Jin ◽

Lisa R. Welp ◽

...

Keyword(s):

Primary Production ◽

Eddy Covariance ◽

Satellite Images ◽

Gross Primary Production ◽

Spatially Explicit ◽

Interior Alaska ◽

Fire Impact

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Responses in gross primary production of Stipa krylovii and Allium polyrhizum to a temporal rainfall in a temperate grassland of Inner Mongolia, China

Journal of Arid Land ◽

10.1007/s40333-019-0127-1 ◽

2019 ◽

Vol 11 (6) ◽

pp. 824-836 ◽

Cited By ~ 2

Author(s):

Xiaoxing Hu ◽

Mitsuru Hirota ◽

Wuyunna ◽

Kiyokazu Kawada ◽

Hao Li ◽

...

Keyword(s):

Primary Production ◽

Inner Mongolia ◽

Gross Primary Production ◽

Temperate Grassland ◽

Stipa Krylovii

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Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

Remote Sensing ◽

10.3390/rs10091346 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1346 ◽

Cited By ~ 32

Author(s):

Joanna Joiner ◽

Yasuko Yoshida ◽

Yao Zhang ◽

Gregory Duveiller ◽

Martin Jung ◽

...

Keyword(s):

Primary Production ◽

Eddy Covariance ◽

Satellite Data ◽

Gross Primary Production ◽

Satellite Observation ◽

Data Driven ◽

Spatial And Temporal Variability ◽

Light Use Efficiency ◽

High Productivity ◽

Use Efficiency

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.

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