Vegetation-specific model parameters are not required for estimating gross primary production

Yuan, Wenping; Cai, Wenju; Liu, Shuguang; Dong, Wenjie; Chen, Jiquan; Arain, M. Altaf; Blanken, Peter D.; Cescatti, Alessandro; Wohlfahrt, Georg; Georgiadis, T.; Genesio, L.; Gianelle, Damiano; Grelle, Achim; Kiely, Gerard; Knohl, Alexander; Liú, Dan; Marek, Michal V.; Merbold, Lutz; Montagnani, Leonardo; Panferov, O.; Peltoniemi, Mikko; Rambal, Serge; Raschi, A.; Varlagin, Andrej; Xia, Jiangzhou

doi:10.1016/j.ecolmodel.2014.08.017

Cited by 38 publications

(27 citation statements)

References 48 publications

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“…Remote sensing models (e.g., CASA, EC-LUE, GLO-PEM, MOD17) have shown the uncertainty in estimating NPP in major ecosystem types [22,[101][102][103][104] where the lowest global NPP (39.9 Pg C·year −1 ) was almost 50% smaller than the maximum estimate (80.5 Pg C·year −1 ) [81,103,104]. Except for the MOD17 model, these remote sensing models have comparable consistency and correctly estimate spatiotemporal NPP [22].…”

Section: Comparison With Results Derived By Remote Sensing Estimationsmentioning

confidence: 99%

Analysis of Spatiotemporal Dynamics of the Chinese Vegetation Net Primary Productivity from the 1960s to the 2000s

Shang

Zhang

et al. 2018

Remote Sensing

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Abstract:Field net primary productivity (NPP) is useful in research modeling of regional and global carbon cycles and for validating results by remote sensing or process-based models. In this study, we used multiple models of NPP estimation and vegetation classification methods to study Chinese vegetation NPP characteristics, trends, and drivers using 7618 field measurements from the 1960s, 1980s, and 2000s. The values of other relevant NPP models, as well as process-based simulation and remote sensing models, were compared. Our results showed that NPP ranged from 3 to 12,407 gC·m −2 ·year −1 with a mean value of 571 gC·m −2 ·year −1 . Vegetation NPP gradually decreased from the southeast to the northwest. Forest, farmland, and grassland NPP was 1152, 294, and 518 gC·m −2 ·year −1 , respectively. Total NPP of grassland was higher than that of farmland. Total terrestrial NPP decreased from 3.58 to 3.41 Pg C·year −1 from the 1960s to the 2000s, a decadal decrease of 4.7%. Total NPP in forests and grasslands consistently showed a decreasing trend and decreased by 0.46 Pg C·year −1 and 0.16 Pg C·year −1 , respectively, whereas NPP for farmland showed an opposite trend, with a growth of 0.45 Pg C·year −1 . Our research findings filled gaps in the information regarding NPP for the entire landmass of China based on field data from a long-term time series and provide valuable information and a basis for validation analyses by remote sensing models, as well as a robust quantification of carbon estimation to anticipate future development at the national and global scale.

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Section: Comparison With Results Derived By Remote Sensing Estimationsmentioning

confidence: 99%

Analysis of Spatiotemporal Dynamics of the Chinese Vegetation Net Primary Productivity from the 1960s to the 2000s

Shang

Zhang

et al. 2018

Remote Sensing

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“…This could be achieved by using and further developing model inventories such as those created as part of the MACSUR project , in order to match models to the systems and regions they were designed for, or could potentially be suitable for. Assessments of the potential for widening model applicability can draw on the findings of investigations that have used generic approaches to model biophysical processes across a variety of regions (Yuan et al, 2014). Recent work comparing models from different regions, such as carried out within the FP7 project MultiSward (http://www.multisward.eu/multisward_ eng/) the MACSUR project (Sándor et al, 2015(Sándor et al, , 2016 and the Agricultural Model Inter-comparison and Improvement Programme (AgMIP) (http://www.agmip.org) can provide further evidence about the applicability of models to different environments and systems.…”

Section: Modelling Different Regions and Production Systemsmentioning

confidence: 99%

Key challenges and priorities for modelling European grasslands under climate change

Kipling

Virkajärvi

Breitsameter

et al. 2016

Science of The Total Environment

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Grassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change.

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“…There is still a vivid debate on the relevance of using vegetation specific LUE in remote sensing studies of productivity (Yuan et al, 2014;Chen et al, 2009). Following Yuan et al (2014) I have assumed that variations in light-use efficiency are primarily captured by variations in NDVI because this vegetation index correlates with structural and physiological properties of canopies (e.g., leaf area index, chlorophyll, and nitrogen content). Multiple sources of uncertainty affect remotely sensed estimates of productivity and it is questionable whether the product NDVI times PAR is a robust predictor of GPP in alpine pastures.…”

Section: P Choler: Growth Response Of Grasslands To Snow Cover Durationmentioning

confidence: 99%

Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration

Choler

2015

Biogeosciences

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Abstract.A remote sensing approach is used to examine the direct and indirect effects of snow cover duration and weather conditions on the growth response of mountain grasslands located above the tree line in the French Alps. Time-integrated Normalized Difference Vegetation Index (NDVI int ), used as a surrogate for aboveground primary productivity, and snow cover duration were derived from a 13-year long time series of the Moderate-resolution Imaging Spectroradiometer (MODIS). A regional-scale meteorological forcing that accounted for topographical effects was provided by the SAFRAN-CROCUS-MEPRA model chain. A hierarchical path analysis was developed to analyze the multivariate causal relationships between forcing variables and proxies of primary productivity. Inter-annual variations in primary productivity were primarily governed by year-to-year variations in the length of the snow-free period and to a much lesser extent by temperature and precipitation during the growing season. A prolonged snow cover reduces the number and magnitude of frost events during the initial growth period but this has a negligible impact on NDVI int as compared to the strong negative effect of a delayed snow melting. The maximum NDVI slightly responded to increased summer precipitation and temperature but the impact on productivity was weak. The period spanning from peak standing biomass to the first snowfall accounted for two-thirds of NDVI int and this explained the high sensitivity of NDVI int to autumn temperature and autumn rainfall that control the timing of the first snowfall. The ability of mountain plants to maintain green tissues during the whole snowfree period along with the relatively low responsiveness of peak standing biomass to summer meteorological conditions led to the conclusion that the length of the snow-free period is the primary driver of the inter-annual variations in primary productivity of mountain grasslands.

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Vegetation-specific model parameters are not required for estimating gross primary production

Cited by 38 publications

References 48 publications

Analysis of Spatiotemporal Dynamics of the Chinese Vegetation Net Primary Productivity from the 1960s to the 2000s

Analysis of Spatiotemporal Dynamics of the Chinese Vegetation Net Primary Productivity from the 1960s to the 2000s

Key challenges and priorities for modelling European grasslands under climate change

Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration

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