Articles | Volume 11, issue 14
https://doi.org/10.5194/bg-11-3871-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-11-3871-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
24 Jul 2014
Research article |
| 24 Jul 2014
Global cropland monthly gross primary production in the year 2000
T. Chen
Department of Earth Sciences, Faculty of Earth and Life Sciences, VU University Amsterdam, the Netherlands
International Center for Ecology, Meteorology and Environment (IceMe), School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, China
School of Atmospheric Sciences, Nanjing University, Nanjing, China
G. R. van der Werf
Department of Earth Sciences, Faculty of Earth and Life Sciences, VU University Amsterdam, the Netherlands
N. Gobron
Climate Risk Management Unit, Institute for Environment and Sustainability, European Commission Joint Research Center, Ispra, Italy
E. J. Moors
Earth System Science and Climate Change Group, Wageningen University and Research Centre, Wageningen, the Netherlands
A. J. Dolman
Department of Earth Sciences, Faculty of Earth and Life Sciences, VU University Amsterdam, the Netherlands
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Cited
24 citations as recorded by crossref.
- A Satellite-Based Method for National Winter Wheat Yield Estimating in China Y. Fu et al. 10.3390/rs13224680
- Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model C. Jin et al. 10.1016/j.agrformet.2015.07.003
- The 2012 Flash Drought Threatened US Midwest Agroecosystems C. Jin et al. 10.1007/s11769-019-1066-7
- Crop productivity estimation by integrating multisensor satellite, in situ, and eddy covariance data into efficiency-based model S. Kalra et al. 10.1007/s10661-023-12057-0
- Losses, inefficiencies and waste in the global food system P. Alexander et al. 10.1016/j.agsy.2017.01.014
- The effects of grazing and watering on ecosystem CO2 fluxes vary by community phenology J. Han et al. 10.1016/j.envres.2015.09.002
- Narrowing uncertainties in the effects of elevated CO2 on crops A. Toreti et al. 10.1038/s43016-020-00195-4
- Introducing a Farmer-Assisted Biomass Estimation (FABE) model using satellite images S. Hejazi & M. Mobasheri 10.1016/j.asr.2020.06.009
- Asymmetric NDVI trends of the two cropping seasons in the Huai River basin T. Chen et al. 10.1080/2150704X.2015.1109156
- A 2001–2022 global gross primary productivity dataset using an ensemble model based on the random forest method X. Chen et al. 10.5194/bg-21-4285-2024
- Vegetation Index‐Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity X. Chen et al. 10.1029/2023JG007499
- Estimating Global GPP From the Plant Functional Type Perspective Using a Machine Learning Approach R. Guo et al. 10.1029/2022JG007100
- Estimating crop yield using a satellite-based light use efficiency model W. Yuan et al. 10.1016/j.ecolind.2015.08.013
- Improving the global MODIS GPP model by optimizing parameters with FLUXNET data X. Huang et al. 10.1016/j.agrformet.2020.108314
- A Framework to Assess the Potential Uncertainties of Three FPAR Products X. Chen et al. 10.1029/2021JG006320
- The uncertainty analysis of the MODIS GPP product in global maize croplands X. Huang et al. 10.1007/s11707-018-0716-x
- Nutritional and developmental influences on components of rice crop light use efficiency W. Xue et al. 10.1016/j.agrformet.2016.03.018
- Supplement understanding of the relative importance of biophysical factors in determination of photosynthetic capacity and photosynthetic productivity in rice ecosystems W. Xue et al. 10.1016/j.agrformet.2016.10.009
- Crop productivity under heat stress: a structural analysis of light use efficiency models P. Lai et al. 10.1016/j.agrformet.2024.110376
- Estimation of Daily Maize Gross Primary Productivity by Considering Specific Leaf Nitrogen and Phenology via Machine Learning Methods C. Hu et al. 10.3390/rs16020341
- Modelling Within-Season Variation in Light Use Efficiency Enhances Productivity Estimates for Cropland M. Wellington et al. 10.3390/rs14061495
- Thermally derived evapotranspiration from the Surface Temperature Initiated Closure (STIC) model improves cropland GPP estimates under dry conditions Y. Bai et al. 10.1016/j.rse.2022.112901
- Estimating winter wheat yield based on a light use efficiency model and wheat variety data J. Dong et al. 10.1016/j.isprsjprs.2019.12.005
- Improving the Gross Primary Productivity Estimate by Simulating the Maximum Carboxylation Rate of the Crop Using Machine Learning Algorithms D. Yuan et al. 10.1109/TGRS.2022.3200988
24 citations as recorded by crossref.
- A Satellite-Based Method for National Winter Wheat Yield Estimating in China Y. Fu et al. 10.3390/rs13224680
- Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model C. Jin et al. 10.1016/j.agrformet.2015.07.003
- The 2012 Flash Drought Threatened US Midwest Agroecosystems C. Jin et al. 10.1007/s11769-019-1066-7
- Crop productivity estimation by integrating multisensor satellite, in situ, and eddy covariance data into efficiency-based model S. Kalra et al. 10.1007/s10661-023-12057-0
- Losses, inefficiencies and waste in the global food system P. Alexander et al. 10.1016/j.agsy.2017.01.014
- The effects of grazing and watering on ecosystem CO2 fluxes vary by community phenology J. Han et al. 10.1016/j.envres.2015.09.002
- Narrowing uncertainties in the effects of elevated CO2 on crops A. Toreti et al. 10.1038/s43016-020-00195-4
- Introducing a Farmer-Assisted Biomass Estimation (FABE) model using satellite images S. Hejazi & M. Mobasheri 10.1016/j.asr.2020.06.009
- Asymmetric NDVI trends of the two cropping seasons in the Huai River basin T. Chen et al. 10.1080/2150704X.2015.1109156
- A 2001–2022 global gross primary productivity dataset using an ensemble model based on the random forest method X. Chen et al. 10.5194/bg-21-4285-2024
- Vegetation Index‐Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity X. Chen et al. 10.1029/2023JG007499
- Estimating Global GPP From the Plant Functional Type Perspective Using a Machine Learning Approach R. Guo et al. 10.1029/2022JG007100
- Estimating crop yield using a satellite-based light use efficiency model W. Yuan et al. 10.1016/j.ecolind.2015.08.013
- Improving the global MODIS GPP model by optimizing parameters with FLUXNET data X. Huang et al. 10.1016/j.agrformet.2020.108314
- A Framework to Assess the Potential Uncertainties of Three FPAR Products X. Chen et al. 10.1029/2021JG006320
- The uncertainty analysis of the MODIS GPP product in global maize croplands X. Huang et al. 10.1007/s11707-018-0716-x
- Nutritional and developmental influences on components of rice crop light use efficiency W. Xue et al. 10.1016/j.agrformet.2016.03.018
- Supplement understanding of the relative importance of biophysical factors in determination of photosynthetic capacity and photosynthetic productivity in rice ecosystems W. Xue et al. 10.1016/j.agrformet.2016.10.009
- Crop productivity under heat stress: a structural analysis of light use efficiency models P. Lai et al. 10.1016/j.agrformet.2024.110376
- Estimation of Daily Maize Gross Primary Productivity by Considering Specific Leaf Nitrogen and Phenology via Machine Learning Methods C. Hu et al. 10.3390/rs16020341
- Modelling Within-Season Variation in Light Use Efficiency Enhances Productivity Estimates for Cropland M. Wellington et al. 10.3390/rs14061495
- Thermally derived evapotranspiration from the Surface Temperature Initiated Closure (STIC) model improves cropland GPP estimates under dry conditions Y. Bai et al. 10.1016/j.rse.2022.112901
- Estimating winter wheat yield based on a light use efficiency model and wheat variety data J. Dong et al. 10.1016/j.isprsjprs.2019.12.005
- Improving the Gross Primary Productivity Estimate by Simulating the Maximum Carboxylation Rate of the Crop Using Machine Learning Algorithms D. Yuan et al. 10.1109/TGRS.2022.3200988
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