Anticipating global terrestrial ecosystem state change using FLUXNET

Agriculture & Biology

• air temperature 31%
• Biological Sciences 8%
• carbon 15%
• climate change 19%
• deserts 10%
• ecological models 16%
• ecosystems 60%
• eddy covariance 16%
• elasticity (mechanics) 85%
• energy 6%
• global change 13%
• micrometeorology 21%
• neural networks 12%
• savannas 11%
• solar radiation 9%
• synthesis 6%
• terrestrial ecosystems 100%
• tropical forests 12%
• water 4%

Earth & Environmental Sciences

• air temperature 27%
• artificial neural network 10%
• carbon flux 24%
• climate change 13%
• desert 9%
• ecological status 11%
• ecosystem 47%
• ecosystem dynamics 13%
• eddy covariance 12%
• elasticity 79%
• energy flux 12%
• global change 11%
• in situ 7%
• micrometeorology 17%
• resource 6%
• savanna 12%
• solar radiation 10%
• terrestrial ecosystem 80%
• train 10%
• transition 14%
• tropical forest 11%
• water flux 12%

Chemical Compounds

• Amount 3%
• Carbon Atom 8%
• Ecosystem 95%
• Energy 4%
• Solar Radiation 11%
• Strength 11%
• Transition State 14%