Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011

Zhu, Zaichun; Bi, Jian; Pan, Yaozhong; Ganguly, Sangram; Anav, Alessandro; Xu, Liang; Samanta, A. K.; Piao, Shilong; Nemani, Ramakrishna R.; Myneni, Ranga B.

doi:10.3390/rs5020927

Cited by 837 publications

(688 citation statements)

References 81 publications

(112 reference statements)

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“…The prescribed LAI in ACCESS-ESM1 is based on MODIS observations (Yang et al, 2006) with no IAV. If compared with the observation-based estimates of Zhu et al (2013), which uses a combination of MODIS and AVHRR data, over the last 20 years of the historical period (mean of 1.4), we notice that our current prescribed LAI is somewhat smaller (mean of 1.3), but agrees well in terms of its seasonal cycle (Fig. 10).…”

Section: Laimentioning

confidence: 87%

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

et al. 2017

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Abstract. Over the last decade many climate models have evolved into Earth system models (ESMs), which are able to simulate both physical and biogeochemical processes through the inclusion of additional components such as the carbon cycle. The Australian Community Climate and Earth System Simulator (ACCESS) has been recently extended to include land and ocean carbon cycle components in its ACCESS-ESM1 version. A detailed description of ACCESS-ESM1 components including results from pre-industrial simulations is provided in Part 1. Here, we focus on the evaluation of ACCESS-ESM1 over the historical period in terms of its capability to reproduce climate and carbon-related variables. Comparisons are performed with observations, if available, but also with other ESMs to highlight common weaknesses. We find that climate variables controlling the exchange of carbon are well reproduced. However, the aerosol forcing in ACCESS-ESM1 is somewhat larger than in other models, which leads to an overly strong cooling response in the land from about 1960 onwards. The land carbon cycle is evaluated for two scenarios: running with a prescribed leaf area index (LAI) and running with a prognostic LAI. We overestimate the seasonal mean (1.7 vs. 1.4) and peak amplitude (2.0 vs. 1.8) of the prognostic LAI at the global scale, which is common amongst CMIP5 ESMs. However, the prognostic LAI is our preferred choice, because it allows for the vegetation feedback through the coupling between LAI and the leaf carbon pool. Our globally integrated land-atmosphere flux over the historical period is 98 PgC for prescribed LAI and 137 PgC for prognostic LAI, which is in line with estimates of land use emissions (ACCESS-ESM1 does not include land use change).The integrated ocean-atmosphere flux is 83 PgC, which is in agreement with a recent estimate of 82 PgC from the Global Carbon Project for the period 1959-2005. The seasonal cycle of simulated atmospheric CO 2 is close to the observed seasonal cycle (up to 1 ppm difference for the station at Mace Head and up to 2 ppm for the station at Mauna Loa), but shows a larger amplitude (up to 6 ppm) in the high northern latitudes. Overall, ACCESS-ESM1 performs well over the historical period, making it a useful tool to explore the change in land and oceanic carbon uptake in the future.

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Section: Laimentioning

confidence: 87%

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

et al. 2017

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“…The latest version of GIMMS-3g LAI was generated by training the GIMMS-3gV1 NDVI with LAI modis using an artificial neural network 41 . Compared to the previous version of GIMMS-3g LAI, the new version is trained by LAI modis instead of the Beijing Normal University LAI, which was used for previous versions.…”

Section: Methodsmentioning

confidence: 99%

Increased vegetation growth and carbon stock in China karst via ecological engineering

et al. 2018

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T he impacts of climate change and anthropogenic activities on Earth's vegetation and ecosystems have been in the spotlight of science in the past decades [1][2][3][4][5][6] . With increasing climate variability and more frequent occurrences of extreme events expected in the future 7 , research has targeted the sensitivity of ecosystems 8,9 . At the same time, recent studies have shown globally increasing leaf area index (LAI; a proxy for green vegetation cover) 10 , and aboveground biomass carbon (ABC) 11 , also known as the greening Earth 10,12 . Dynamic vegetation models and Earth observation studies reveal climatic and atmospheric changes as the main drivers of large scale increases in LAI 10,13 . On the contrary, the anthropogenic footprint is usually found to cause land degradation and deforestation 13,14 , and only a few studies find a direct positive effect of management on vegetation cover and biomass trends 2,11,15 . Although ecological conservation projects aim at increasing biodiversity, carbon sequestration and vegetation cover 16,17 , the success of such conservation efforts is not easily quantifiable, and the spatial footprint of projects is not always commensurable with contemporary satellite-and modelling-based monitoring methods. Adaptation and mitigation strategies to climate change should be anchored in knowledge on how ecosystems respond to climatic and anthropogenic disturbances, but at present it is not known whether conservation projects impact on the ability of vegetation to alleviate the effects of climate change at large scales.China's ecological restoration projects (for example, the Natural Forest Protection Project, the Grain to Green Project, and the Karst Rocky Desertification Restoration Project) are considered 'megaengineering' activities and the most ambitious afforestation and conservation projects in human history [16][17][18][19] . The highly sensitive and vulnerable karst ecosystem in southwest China is one of the largest exposed carbonate rock areas (more than 0.54 million km 2 ) in the world. This area hosts 220 million people 20,21 and has been selected as a major target of restoration projects. Descriptions as early as the seventeenth century reported the rocky karst mountains as an area of sparse forest or vegetation cover 22 , and accelerating desertification has been reported during the past half century, caused by the increasing intensity of human exploitation of natural resources [21][22][23][24] . As a result, approximately 0.13 million km 2 of karst areas previously covered by vegetation and soil were turned into a rocky landscape. To combat this severe form of land degradation and to relieve poverty, more than 130 billion yuan (~19 billion USD) have been invested in mitigation initiatives since the end of the 1990s 24 . The largest programme implemented, the Grain to Green Project, offers grain, cash and free seedlings as compensation for rural households to re-establish forests, shrub and/or grassland 24 . The costs of ecological engineering projects as a clima...

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“…1a), annual precipitation from previous November to current October is used instead of GS precipitation only ( Supplementary Fig. 1b), or satellite-observed leaf area index 16 is used instead of NDVI as a proxy of vegetation productivity (Supplementary Fig. 1c).…”

Section: Satellite Observationmentioning

confidence: 99%

Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity

et al. 2014

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Satellite-derived Normalized Difference Vegetation Index (NDVI), a proxy of vegetation productivity, is known to be correlated with temperature in northern ecosystems. This relationship, however, may change over time following alternations in other environmental factors. Here we show that above 30°N, the strength of the relationship between the interannual variability of growing season NDVI and temperature (partial correlation coefficient R NDVI-GT ) declined substantially between 1982 and 2011. This decrease in R NDVI-GT is mainly observed in temperate and arctic ecosystems, and is also partly reproduced by process-based ecosystem model results. In the temperate ecosystem, the decrease in R NDVI-GT coincides with an increase in drought. In the arctic ecosystem, it may be related to a nonlinear response of photosynthesis to temperature, increase of hot extreme days and shrub expansion over grass-dominated tundra. Our results caution the use of results from interannual time scales to constrain the decadal response of plants to ongoing warming.

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Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011

Cited by 837 publications

References 81 publications

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

Increased vegetation growth and carbon stock in China karst via ecological engineering

Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity

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