Comment on “Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009”

Samanta, A. K.; Costa, Marcos Heil; Nunes, Edson Luís; Vieira, Simone A.; Xu, Liang; Myneni, Ranga B.

doi:10.1126/science.1199048

Cited by 100 publications

(74 citation statements)

References 13 publications

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“…8) had no significant trend from 2000 to 2010 (slope of k = 0.07, P = 0.66), which was consistent with the result of MOD17A3 GPP (slope k = 0.02, P = 0.81) and satellite observations of vegetation activity (Samanta et al, 2011). Further comparison of trends of TEC and MOD17A3 GPP (Fig.…”

Section: Global Estimation Of Tec Gppsupporting

confidence: 86%

Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants

Yan

Wang

Billesbach

et al. 2015

Ecological Modelling

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A B S T R A C TAccurate simulation of terrestrial gross primary production (GPP), the largest global carbon flux, benefits our understanding of carbon cycle and its source of variation. This paper presents a novel light use efficiency-based GPP model called the terrestrial ecosystem carbon flux model (TEC) driven by MODIS FPAR and climate data coupled with a precipitation-driven evapotranspiration (E) model (Yan et al., 2012). TEC incorporated a new water stress factor, defined as the ratio of actual E to Priestley and Taylor (1972) potential evaporation (E PT ). A maximum light use efficiency (e*) of 1.8 gC MJ À1 and 2.76 gC MJ À1 was applied to C3 and C4 ecosystems, respectively. An evaluation at 18 eddy covariance flux towers representing various ecosystem types under various climates indicates that the TEC model predicted monthly average GPP for all sites with overall statistics of r = 0.85, RMSE = 2.20 gC m À2 day À1 , and bias = À0.05 gC m À2 day À1 . For comparison the MODIS GPP products (MOD17A2) had overall statistics of r = 0.73, RMSE = 2.82 gC m À2 day À1 , and bias = À0.31 gC m À2 day À1 for this same set of data. In this case, the TEC model performed better than MOD17A2 products, especially for C4 plants. We obtained an estimate of global mean annual GPP flux at 128.2 AE 1.5 Pg C yr À1 from monthly MODIS FPAR and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA reanalysis data at a 1.0 spatial resolution over 11 year period from 2000 to 2010. This falls in the range of published land GPP estimates that consider the effect of C4 and C3 species. The TEC model with its new definition of water stress factor and its parameterization of C4 and C3 plants should help better understand the coupled climate-carbon cycle processes.

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Section: Global Estimation Of Tec Gppsupporting

confidence: 86%

Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants

Yan

Wang

Billesbach

et al. 2015

Ecological Modelling

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“…These data (16-day LAI composite, from 2001-2015) were refined from the standard MODIS Collection 6 LAI product based on the quality flags of MOD15A2H (8-day LAI composite) and MOD13A2 (16-day vegetation index composite). The standard MODIS Collection 6 LAI product was widely used and well validated by the community [38][39][40] . The refined LAI data set follows the quality control scheme as described previously 40 and represents the highest quality MODIS LAI.…”

Section: Methodsmentioning

confidence: 99%

“…The standard MODIS Collection 6 LAI product was widely used and well validated by the community [38][39][40] . The refined LAI data set follows the quality control scheme as described previously 40 and represents the highest quality MODIS LAI. The refined LAI composite was aggregated to a 5 km × 5 km grid by averaging the available clean LAI values originally at 500-m resolution.…”

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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“…The maximum value composite (MVC) is applied to suppress atmospheric effects on NDVI (Holben 1986;Piao et al 2004). However, the residual atmospheric contamination may still exist in the MVC-processed time series of NDVI (Verhoef et al 2005;Julien and Sobrino 2010;Samanta et al 2011). Thus, the Harmonic Analysis of Time Series (HANTS) algorithm (Verhoef et al 1996;Roerink et al 2003) is used to remove cloud contamination and reconstruct vegetation indexes for a time series of 453 SPOT-VGT scenes between April 1998 and October 2010.…”

Section: Methodsmentioning

confidence: 99%

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Jiang

Zhang

et al. 2013

Theor Appl Climatol

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Based on the SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) data and daily precipitation data of 357 meteorological stations, the spatial and temporal variability of vegetation cover, measured by NDVI, and precipitation as well as their relationships are investigated in Eastern China, which is portioned into three subregions (regions I, II, and III), for the period 1998-2010. The results show that high NDVI values appear mainly in Northeastern China and in August while high precipitation (PRETOT) occurs in Southeastern China and in July (June for Southern China). Extreme precipitation days (RD95p) and amount (EPRETOT) coincide well with PRETOT. Extreme precipitation intensity (RINTEN) has a similar spatial variability to PRETOT but with a smaller seasonal variation than PRETOT. Growing season NDVI is positively correlated with PRETOT in 11.7 % of the study area (mostly in arid to subhumid regions of Northern China), where precipitation is a limiting factor for vegetation growth. In contrast, a negative correlation between growing season NDVI and PRETOT is found in 4.8 % of the study area, mostly in areas around the Yangtze River and deep Northeastern China. No significant correlations between these two variables are found for the other regions because vegetation response to precipitation is affected by other factors such as temperature, radiation, and human disturbance. On a monthly scale, there is a positive correlation between NDVI and PRETOT in May (for region II) and September (all subregions except region I). NDVI variations lag 1 month behind PRETOT in June (for region I) and October. Correlations between NDVI and RD95p, EPRETOT are similar to that with PRETOT, but the relationships between NDVI and RINTEN are relatively weaker than with PRETOT. This study provides the technical basis for agriculture development and ecological construction in Eastern China.

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Comment on “Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009”

Cited by 100 publications

References 13 publications

Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants

Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants

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

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

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