References

1. J. E. Campbell, et al. , Large historical growth in global terrestrial gross primary production. Nature 544 , 84–87 (2017).
2. Y. Zheng, et al. , Improved estimate of global gross primary production for reproducing its long-term variation, 1982-2017.Earth Syst. Sci. Data Discuss. (2019).
3. G. Lasslop, et al. , Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate. Science (80-.).329 , 834–838 (2010).
4. J. Xia, et al. , Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl. Acad. Sci. U. S. A. 112 , 2788–2793 (2015).
5. Wang, H., Prentice, I.C., Keenan, T.F. et al. Towards a universal model for carbon dioxide uptake by plants. Nature Plants 3 , 734–741 (2017).
6. G. B. Bonan, et al. , Model Structure and Climate Data Uncertainty in Historical Simulations of the Terrestrial Carbon Cycle (1850–2014). Global Biogeochem. Cycles 33 , 1310–1326 (2019).
7. X. Xiao, et al. , Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens. Environ. 89 , 519–534 (2004).
8. W. Yuan, et al. , Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agric. For. Meteorol. 143 , 189–207 (2007).
9. W. Yuan, et al. , Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. 143 , 189–207 (2007).
10. Z. Xiao, et al. , Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance. 1–18 (2016).
11. S. W. Running, M. Zhao, Daily GPP and Annual NPP (MOD17A2/A3) products NASA Earth Observing System MODIS Land Algorithm - User’s guide V3. 28 (2015).
12. P. Wagle, P. H. Gowda, X. Xiao, K. C. Anup, Parameterizing ecosystem light use efficiency and water use efficiency to estimate maize gross primary production and evapotranspiration using MODIS EVI. Agric. For. Meteorol. 222 , 87–97 (2016).
13. Y. Zheng, et al. , Agricultural and Forest Meteorology Sources of uncertainty in gross primary productivity simulated by light use efficiency models: Model structure, parameters, input data, and spatial resolution. Agric. For. Meteorol. 263 , 242–257 (2018).
14. W. Yuan, et al. , Estimating crop yield using a satellite-based light use efficiency model. Ecol. Indic.60 , 702–709 (2016).
15. S. W. RUNNING, et al. , A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. Bioscience 54 , 547 (2006).
16. X. Xie, et al. , Assessments of gross primary productivity estimations with satellite data-driven models using eddy covariance observation sites over the northern hemisphere. Agric. For. Meteorol. 280 , 107771 (2020).
17. W. Yuan, et al. , Agricultural and Forest Meteorology Uncertainty in simulating gross primary production of cropland ecosystem from satellite-based models. Agric. For. Meteorol. 207 , 48–57 (2015).
18. H. Wang, et al. , Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling. Remote Sens. Environ. 114 , 2248–2258 (2010).
19. J. Xiao, K. J. Davis, N. M. Urban, K. Keller, N. Z. Saliendra, Upscaling carbon fluxes from towers to the regional scale: Influence of parameter variability and land cover representation on regional flux estimates. 116 , 1–15 (2011).
20. G. Badgley, L. D. L. Anderegg, J. A. Berry, C. B. Field, Terrestrial gross primary production: Using NIRV to scale from site to globe.Glob. Chang. Biol. 25 , 3731–3740 (2019).
21. S. Kang, et al. , A regional phenology model for detecting onset of greenness in temperate mixed forests, Korea: an application of MODIS leaf area index. 86 , 232–242 (2003).
22. T. Sasai, K. Okamoto, T. Hiyama, Y. Yamaguchi, Comparing terrestrial carbon fluxes from the scale of a flux tower to the global scale.Ecol. Modell. 208 , 135–144 (2007).
23. J. Gomis-cebolla, J. C. Jimenez, J. A. Sobrino, Remote Sensing of Environment LST retrieval algorithm adapted to the Amazon evergreen forests using MODIS data. Remote Sens. Environ. , 0–1 (2017).
24. S. Foga, et al. , Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 194 , 379–390 (2017).
25. Stillinger, T., Roberts, D. A., Collar, N. M., & Dozier, J. Cloud masking for Landsat 8 and MODIS Terra over snow‐covered terrain: Error analysis and spectral similarity between snow and cloud. Water Resources Research, 55 , 6169 – 6184 (2019).
26. S. J. Cheng, Z. Butterfield, G. Keppel-aleks, A. L. Steiner, The Global Influence of Cloud Optical Thickness on Terrestrial Carbon Uptake. 23 (2019).
27. L. Gu, et al. , Response of a Deciduous Forest to the Mount Pinatubo Eruption: Enhanced Photosynthesis. 299 , 2035–2038 (2003).
28. Keppel‐Aleks, G., and Washenfelder, R. A., The effect of atmospheric sulfate reductions on diffuse radiation and photosynthesis in the United States during 1995–2013, Geophys. Res. Lett., 43 , 9984– 9993, (2016).
29. M. S. Lee, D. Y. Hollinger, T. F. Keenan, A. P. Ouimette, S. V Ollinger, Agricultural and Forest Meteorology Model-based analysis of the impact of di ff use radiation on CO 2 exchange in a temperate deciduous forest. Agric. For. Meteorol. 249 , 377–389 (2018).
30. V. Haverd, et al. , Higher than expected CO 2 fertilization inferred from leaf to global observations. 1–13 (2020).
31. Z. Sun, et al. , Science of the Total Environment Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO 2 trends. Sci. Total Environ.668 , 696–713 (2019).
32. A. Bastos, P. Ciais, F. Chevallier, C. Rödenbeck, A. P. Ballantyne, Contrasting effects of CO 2 fertilization, land-use change and warming on seasonal amplitude of Northern Hemisphere CO 2 exchange. 12361–12375 (2019).
33. W. Yuan, et al. , Agricultural and Forest Meteorology Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database.Agric. For. Meteorol. 192193 , 108–120 (2014).
34. S. A. Kurc, E. E. Small, Dynamics of evapotranspiration in semiarid grassland and shrubland ecosystems during the summer monsoon season, central New Mexico. 40 , 1–15 (2004).
35. Y. Chen, et al. , Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China.Remote Sens. Environ. 140 , 279–293 (2014).
36. H. Y. Ma, et al. , CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States. J. Geophys. Res. Atmos. 123 , 2888–2909 (2018).
37. B. Gao, NDWI-A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. Remote Sens. Environ.266 , 257–266 (1996).
38. Z. Liu, L. Wang, S. Wang, Comparison of different GPP models in China using MODIS image and ChinaFLUX data. Remote Sens.6 , 10215–10231 (2014).
39. C. Wu, J. W. Munger, Z. Niu, D. Kuang, Remote Sensing of Environment Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest. Remote Sens. Environ. 114 , 2925–2939 (2010).
40. Morton, D., Nagol, J., Carabajal, C. et al. Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature506 , 221–224 (2014).
41. E. Borbas, G. Hulley, R. Knuteson, M. Feltz, S. Science, MEaSUREs Unified and Coherent Land Surface Temperature and Emissivity (LST & E) Earth System Data Record ( ESDR ): The Combined ASTER and MODIS Emissivity database over Land ( CAMEL ) Version 2 Users ’ Guide (2019).
42. C. Jin, et al. , Agricultural and Forest Meteorology Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model.Agric. For. Meteorol. 213 , 240–250 (2015).
43. X. Wang, J. M. Chen, W. Ju, Photochemical reflectance index (PRI) can be used to improve the relationship between gross primary productivity (GPP) and sun-induced chlorophyll fluorescence (SIF).Remote Sens. Environ. 246 , 111888 (2020).
44. M. Chiesi, et al. , Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset. Ann. For. Sci. 73 , 713–727 (2016).
45. M. M. Rienecker, et al. , MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Clim.24 , 3624–3648 (2011).
46. David W. Kicklighter, Michele Bruno, Silke DZönges et al. , A first-order analysis of the potential rôle of CO2 fertilization to affect the global carbon budget: a comparison of four terrestrial biosphere models, Tellus B: Chemical and Physical Meteorology,51 :2, 343-366 (1999).
47. A. J. W. Raich, et al. , Potential Net Primary Productivity in South America: Application of a Global Model Published by : Ecological Society of America Stable URL : http://www.jstor.org/stable/1941899 . POTENTIAL NET PRIMARY PRODUCTIVITY IN SOUTH AMERICA: APPLICATION OF A GLOB. 1 , 399–429 (2013).
48. R. J. Murphy, B. Whelan, A. Chlingaryan, S. Sukkarieh, Quantifying leaf ‑ scale variations in water absorption in lettuce from hyperspectral imagery: a laboratory study with implications for measuring leaf water content in the context of precision agriculture.Precis. Agric. (2018).
49. C. Ding, X. Liu, F. Huang, Y. Li, X. Zou, Onset of drying and dormancy in relation to water dynamics of semi-arid grasslands from MODIS NDWI. Agric. For. Meteorol. 234235 , 22–30 (2017).
50. Y. Zhang, N. C. Parazoo, A. P. Williams, S. Zhou, P. Gentine, Large and projected strengthening moisture limitation on end-of-season photosynthesis. Proc. Natl. Acad. Sci. , 201914436 (2020).