scholarly journals Spatiotemporal patterns of terrestrial gross primary production: A review

2015 ◽  
Vol 53 (3) ◽  
pp. 785-818 ◽  
Author(s):  
Alessandro Anav ◽  
Pierre Friedlingstein ◽  
Christian Beer ◽  
Philippe Ciais ◽  
Anna Harper ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Spatiotemporal Patterns

scholarly journals Spatiotemporal Patterns of Ecosystem Restoration Activities and Their Effects on Changes in Terrestrial Gross Primary Production in Southwest China

2021 ◽  
Vol 13 (6) ◽  
pp. 1209
Author(s):  
Zhi Ding ◽  
Hui Zheng ◽  
Ying Liu ◽  
Sidong Zeng ◽  
Pujia Yu ◽  
...  
Keyword(s):  
Primary Production ◽  
Large Scale ◽  
Southwest China ◽  
Gross Primary Production ◽  
Ecosystem Restoration ◽  
Spatiotemporal Patterns ◽  
Observation Data ◽  
Entire Study ◽  
Field Observation Data ◽  
Moderate Resolution Imaging Spectroradiometer

Large-scale ecosystem restoration projects (ERPs) have been implemented since the beginning of the new millennium to restore vegetation and improve the ecosystem in Southwest China. However, quantifying the effects of specific restoration activities, such as afforestation and grass planting, on vegetation recovery is difficult due to their incommensurable spatiotemporal distribution. Long-term and successive ERP-driven land use/cover changes (LUCCs) were used to recognise the spatiotemporal patterns of major restoration activities, and a contribution index was defined to assess the effects of these activities on gross primary production (GPP) dynamics in Southwest China during the period of 2001–2015. The results were as follows. (1) Afforestation and grass planting were major restoration activities that accounted for more than 54% of all LUCCs in Southwest China. Approximately 96% of restoration activities involved afforestation, and these activities were mostly distributed around Yunnan Province. (2) The Breathing Earth System Simulator (BESS) GPP performed better than the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP validated by field observation data. Nevertheless, their annual GPP trends were similar and increased by 12,581 g C m−2 d−1 and 13,406 g C m−2 d−1 for MODIS and BESS GPPs, respectively. (3) Although the afforestation and grass planting areas accounted for less than 1% of the total area of Southwest China, they contributed to more than 1% of the annual GPP increase in the entire study area. Afforestation directly contributed 14.94% (BESS GPP) or 24.64% (MODIS GPP) to the annual GPP increase. Meanwhile, grass planting directly contributed only 0.41% (BESS GPP) or 0.03% (MODIS GPP) to the annual GPP increase.

scholarly journals Estimation of crop gross primary production (GPP): fAPARchl versus MOD15A2 FPAR

2014 ◽  
Vol 153 ◽  
pp. 1-6 ◽  
Author(s):  
Qingyuan Zhang ◽  
Yen-Ben Cheng ◽  
Alexei I. Lyapustin ◽  
Yujie Wang ◽  
Feng Gao ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production

scholarly journals Soil drying weakens the positive effect of climate factors on global gross primary production

2021 ◽  
Vol 129 ◽  
pp. 107953
Author(s):  
Huan Chen ◽  
Xiaoyong Bai ◽  
Yangbing Li ◽  
Qin Li ◽  
Luhua Wu ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Soil Drying ◽  
Climate Factors ◽  
Positive Effect

scholarly journals Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fan Liu ◽  
Chuankuan Wang ◽  
Xingchang Wang
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Gross Primary Production ◽  
Growing Season ◽  
Temperate Deciduous Forest ◽  
Near Infrared Reflectance ◽  
Modis Ndvi ◽  
Temperate Deciduous

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

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