Representativeness of global climate and vegetation by carbon-monitoring networks; implications for estimates of gross and net primary productivity at biome and global levels-Reference-Cited by-同舟云学术

Representativeness of global climate and vegetation by carbon-monitoring networks; implications for estimates of gross and net primary productivity at biome and global levels

Published:2020-08 Issue: Volume:290 Page:108017
ISSN:0168-1923
Container-title:Agricultural and Forest Meteorology
language:en
Short-container-title:Agricultural and Forest Meteorology

Author:

Alton Paul B.

Publisher

Elsevier BV

Subject

Atmospheric Science,Agronomy and Crop Science,Global and Planetary Change,Forestry

Reference96 articles.

1. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2;Ainsworth;New Phytol.,2005

2. From site-level to global simulation: reconciling carbon, water and energy fluxes over different spatial scales using a process-based ecophysiological land-surface model;Alton;Agric. For. Meteorol.,2013

3. The sensitivity of models of gross primary productivity to meteorological and leaf area forcing: a comparison between a Penman-Monteith ecophysiogical approach and the MODIS light-use efficiency algorithm;Alton;Agric. For. Meteorol.,2016

4. Retrieval of seasonal rubisco-limited photosynthetic capacity at global FLUXNET sites from hyperspectral satellite remote sensing:impact on carbon modelling;Alton;Agric. For. Meteorol.,2017

5. A comparative study of a multilayer and a productivity (light-use) efficiency land-surface model over different temporal scales;Alton;Agric. For. Meteorol.,2010

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Integrating machine learning with thermal-driven analytical energy balance model improved terrestrial evapotranspiration estimation through enhanced surface conductance;Remote Sensing of Environment;2024-09

2. TVDI-based water stress coefficient to estimate net primary productivity in soybean areas;Ecological Modelling;2024-04

3. Integrating machine learning with analytical surface energy balance model improved terrestrial evaporation through biophysical regulation;2024-01-22

4. Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO₂ exchange;Biogeosciences;2023-03-02

5. Contributions of Climatic and Anthropogenic Drivers to Net Primary Productivity of Vegetation in the Mongolian Plateau;Remote Sensing;2022-07-14