A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production

By S. Running, R. Nemani, F. Heinsch, Maosheng Zhao, M. Reeves, H. Hashimoto. 2004

2,307 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/ef9b8640efb2f6a3f059a8a72fbac4d9e7da4251

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Martin Wild
2. Kaicun Wang
3. Xiaotong Zhang
4. Gustau Camps-Valls
5. Shunlin Liang
6. Dennis D. Baldocchi
7. Pablo J. Zarco-Tejada
8. Jerome K. Vanclay

In Ecology

1. Robert E. Kennedy
2. Zhiqiang Yang
3. Warren B. Cohen
4. Daniel T. Haydon
5. Gustau Camps-Valls
6. Caren S. Goldberg
7. Xiaotong Zhang
8. Martin Wild

In Environmental Engineering

1. Aaron Weiskittel
2. Robert E. Kennedy
3. Xiaotong Zhang
4. Martin Wild
5. Kaicun Wang
6. Gustau Camps-Valls
7. Warren B. Cohen
8. John A. Kershaw

Citations in the corpus

• Spectral Network (SpecNet)—What is it and why do we need it? (2006, background): “These systematic biases apparent in certain satellite products have implications for global carbon cycle modeling, since many of the global models are based on these satellite products (e.g. Running et al., 2004).”
• Forest Growth and Yield Modeling (2011, background): “In fact, estimates of gross primary production (GPP) by NASA’s Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) are driven entirely by satellites (Running et al., 2004).”
• Predicting species distributions: a critical comparison of the most common statistical models using artificial species (2007, neutral): “Available at http://modis.gsfc.nasa.gov, described by Running et al. (2004).”
• Late-summer carbon fluxes from Canadian forests and peatlands along an east-west continental transect (2006, background): “Light use efficiency is a critical parameter in many of the large-scale, bottom-up C cycle models that use remote sensing inputs (e.g., Heinsch et al. 2003; Running et al. 2004).”
• Temperature, but not productivity or geometry, predicts elevational diversity gradients in ants across spatial grains (2007, methodology): “We generated NPP values for each site by overlaying site localities on a GIS NPP data layer obtained from the NASA MODIS Land Algorithm (MOD17A3) at 1-km spatial resolution (data acquired in 2004 and 2005) (Running et al. , 2004).”
• Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence (2014, methodology): “3B) and the semiempirical moderate resolution imaging spectroradiometer (MODIS) MOD17 GPP model (31) (SI Appendix, Fig.”
• Review on Estimation of Land Surface Radiation and Energy Budgets From Ground Measurement, Remote Sensing and Model Simulations (2010, background): “Many ecosystem models calculate biomass accumulation linearly proportional to incident PAR [1], [2].”
• Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation (2010, background): “Understanding causes and consequences of biosphere change requires monitoring and modeling (Running et al., 2004).”
• Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes (2007, methodology): “The LUE model is built upon two fundamental assumptions (Running et al., 2004): (1) that ecosystem GPP is directly related to absorbed photosynthetically active radiation (APAR) through LUE, where LUE is defined as the amount of carbon produced per unit of APAR and (2) that realized LUE may be…”