Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data

Remotely sensed vegetation indices (RSVIs) can be used to efficiently estimate terrestrial primary productivity across space and time. Terrestrial productivity, however, has many facets (e.g., spatial and temporal variability, including seasonality, interannual variability, and trends), and different vegetation indices may not be equally good at predicting them. Their accuracy in monitoring productivity has been mostly tested in single-ecosystem studies, but their performance in different ecosystems distributed over large areas still needs to be fully explored. To fill this gap, we identified the facets of terrestrial gross primary production (GPP) that could be monitored using RSVIs. We compared the temporal and spatial patterns of four vegetation indices (NDVI, EVI, NIRV, and CCI), derived from the MODIS MAIAC data set and of GPP derived from data from 58 eddy-flux towers in eight ecosystems with different plant functional types (evergreen needle-leaved forest, evergreen broad-leaved forest, deciduous broad-leaved forest, mixed forest, open shrubland, grassland, cropland, and wetland) distributed throughout Europe, covering Mediterranean, temperate, and boreal regions. The RSVIs monitored temporal variability well in most of the ecosystem types, with grasslands and evergreen broad-leaved forests most strongly and weakly correlated with weekly and monthly RSVI data, respectively. The performance of the RSVIs monitoring temporal variability decreased sharply, however, when the seasonal component of the time series was removed, suggesting that the seasonal cycles of both the GPP and RSVI time series were the dominant drivers of their relationships. Removing winter values from the analyses did not affect the results. NDVI and CCI identified the spatial variability of average annual GPP, and all RSVIs identified GPP seasonality well. The RSVI estimates, however, could not estimate the interannual variability of GPP across sites or monitor the trends of GPP. Overall, our results indicate that RSVIs are suitable to track different facets of GPP variability at the local scale, therefore they are reliable sources of GPP monitoring at larger geographical scales.

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Temporal variability of the NPP-GPP ratio at seasonal and interannual time scales in a temperate beech forest

Biogeosciences ◽

10.5194/bg-8-2481-2011 ◽

2011 ◽

Vol 8 (9) ◽

pp. 2481-2492 ◽

Cited By ~ 33

Author(s):

M. Campioli ◽

B. Gielen ◽

M. Göckede ◽

D. Papale ◽

O. Bouriaud ◽

...

Keyword(s):

Primary Production ◽

Interannual Variability ◽

Temporal Variability ◽

Current Knowledge ◽

Net Primary Production ◽

Gross Primary Production ◽

Tree Canopy ◽

Data Uncertainty ◽

Severe Drought ◽

Beech Stand

Abstract. The allocation of carbon (C) taken up by the tree canopy for respiration and production of tree organs with different construction and maintenance costs, life span and decomposition rate, crucially affects the residence time of C in forests and their C cycling rate. The carbon-use efficiency, or ratio between net primary production (NPP) and gross primary production (GPP), represents a convenient way to analyse the C allocation at the stand level. In this study, we extend the current knowledge on the NPP-GPP ratio in forests by assessing the temporal variability of the NPP-GPP ratio at interannual (for 8 years) and seasonal (for 1 year) scales for a young temperate beech stand, reporting dynamics for both leaves and woody organs, in particular stems. NPP was determined with biometric methods/litter traps, whereas the GPP was estimated via the eddy covariance micrometeorological technique. The interannual variability of the proportion of C allocated to leaf NPP, wood NPP and leaf plus wood NPP (on average 11% yr−1, 29% yr−1 and 39% yr−1, respectively) was significant among years with up to 12% yr−1 variation in NPP-GPP ratio. Studies focusing on the comparison of NPP-GPP ratio among forests and models using fixed allocation schemes should take into account the possibility of such relevant interannual variability. Multiple linear regressions indicated that the NPP-GPP ratio of leaves and wood significantly correlated with environmental conditions. Previous year drought and air temperature explained about half of the NPP-GPP variability of leaves and wood, respectively, whereas the NPP-GPP ratio was not decreased by severe drought, with large NPP-GPP ratio on 2003 due mainly to low GPP. During the period between early May and mid June, the majority of GPP was allocated to leaf and stem NPP, whereas these sinks were of little importance later on. Improved estimation of seasonal GPP and of the contribution of previous-year reserves to stem growth, as well as reduction of data uncertainty, will be of relevance to increase the accuracy of the seasonal assessment of the NPP-GPP ratio in forests.

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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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Spatial and temporal variability in the net primary production of grassland in China and its relation to climate factors

Plant Ecology ◽

10.1007/s11258-017-0756-4 ◽

2017 ◽

Vol 218 (9) ◽

pp. 1117-1133 ◽

Cited By ~ 6

Author(s):

Meiling Zhang ◽

Rattan Lal ◽

Youyi Zhao ◽

Wenlan Jiang ◽

Quangong Chen

Keyword(s):

Primary Production ◽

Temporal Variability ◽

Net Primary Production ◽

Spatial And Temporal Variability ◽

Climate Factors

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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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