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Decoupling of greenness and gross primary productivity as aridity decreases

Journal Article · · Remote Sensing of Environment
 [1];  [2];  [3];  [2];  [2];  [4];  [5];  [6];  [6];  [7];  [8];  [9];  [10]
  1. South China Normal University (SCNU), Guangzhou (China); Hainan University, Haikou (China); Southern Marine Science and Engineering Guangdong Laboratory (China)
  2. Peking Univ., Beijing (China)
  3. Colorado State Univ., Fort Collins, CO (United States)
  4. Sun Yat-Sen Univ., Guangzhou (China); Southern Marine Science and Engineering Guangdong Laboratory (China)
  5. Univ. of Montana, Missoula, MT (United States)
  6. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  7. Laboratoire des Sciences du Climatet de l’Environnement, Gif-sur-Yvette (France)
  8. Northern Arizona Univ., Flagstaff, AZ (United States)
  9. Oklahoma State Univ., Stillwater, OK (United States)
  10. Chinese Academy of Sciences (CAS), Beijing (China)
+ Show Author Affiliations

Ecosystem primary productivity is a key ecological process influencing many ecosystem services, including carbon storage. Thus, clarifying how primary productivity in terrestrial ecosystems responds to climatic variability can reveal key mechanisms that will drive future changes in the global carbon budget. Satellite products of canopy greenness are widely used as proxies for vegetation productivity to evaluate how ecosystems respond to climate variability. However, to what degree inter-annual variations in productivity are consistent with greenness and how this relationship varies spatially remains unclear. Here we investigated the strength of the coupling between inter-annual variations in leaf area index (LAI, a measure of greenness) and ecosystem gross primary productivity (GPP) derived from eddy covariance towers, i.e., the r2 of the LAI-GPP relationship. Overall, inter-annual GPP and LAI were highly coupled (i.e., high r2) in arid grasslands, but were fully decoupled in mesic evergreen broadleaf forests, indicating that this relationship varies strongly along aridity gradients. A possible mechanism of the spatial variation in the LAI-GPP relationship is that the tradeoff between ecosystem structure (LAI) and physiology (photosynthesis per unit leaf area) becomes stronger in more humid climates. Land models overestimated the r2 of LAI-GPP correlation for most ecosystem types and failed to capture the spatial pattern along aridity gradients. We conclude that relying on greenness products for evaluating inter-annual changes in vegetation productivity may bias assessments, especially in tropical rainforest ecosystems. Our findings may also reconcile observed disparities between responses in greenness and GPP during drought in Amazon forests.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER); National Natural Science Foundation of China; National Key R&D Program of China; Second Tibetan Plateau Scientific Expedition and Research Program
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1876255
Journal Information:
Remote Sensing of Environment, Journal Name: Remote Sensing of Environment Journal Issue: 1 Vol. 279; ISSN 0034-4257
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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