Assessing MODIS carbon and water fluxes in grasslands and shrublands in semiarid regions using eddy covariance tower data

References

• Alvarez-Taboada, F., D. Tammadge, M. Schlerf, and A. Skidmore. 2015. “Assessing MODIS GPP in Non-Forested Biomes in Water Limited Areas Using EC Tower Data.” Remote Sensing 7 (3): 3274–3292. doi:10.3390/rs70303274.
   Web of Science ®Google Scholar
• Bastrikov, V., N. MacBean, C. Bacour, D. Santaren, S. Kuppel, and P. Peylin. 2018. “Land Surface Model Parameter Optimisation Using in Situ Flux Data: Comparison of Gradient-Based versus Random Search Algorithms (A Case Study Using ORCHIDEE V1.9.5.2).” Geoscientific Model Development 11 (12): 4739–4754. doi:10.5194/gmd-11-4739-2018.
   Web of Science ®Google Scholar
• Chang, X., W. Zhao, Z. Zhang, and Y. Su. 2006. “Sap Flow and Tree Conductance of Shelter-belt in and Region of China.” Agricultural and Forest Meteorology 138 (1–4): 132–141. doi:10.1016/j.agrformet.2006.04.003.
   Web of Science ®Google Scholar
• Cramer, W., A. Bondeau, F. I. Woodward, I. C. Prentice, R. A. Betts, V. Brovkin, P. M. Cox, et al. 2001. “Global Response of Terrestrial Ecosystem Structure and Function to CO2 and Climate Change: Results from Six Dynamic Global Vegetation Models.” Global Change Biology 7 (4): 357–373. doi:10.1046/j.1365-2486.2001.00383.x.
   Web of Science ®Google Scholar
• Dong, J., L. Li, H. Shi, X. Chen, G. Luo, and Q. Yu. 2017. “Robustness and Uncertainties of the “Temperature and Greenness” Model for Estimating Terrestrial Gross Primary Production.” Scientific Reports 7. doi:10.1038/srep44046.
   Web of Science ®Google Scholar
• Dong, J., X. Xiao, P. Wagle, G. Zhang, Y. Zhou, C. Jin, M. S. Torn, et al. 2015. “Comparison of Four EVI-Based Models for Estimating Gross Primary Production of Maize and Soybean Croplands and Tallgrass Prairie under Severe Drought.” Remote Sensing of Environment 162: 154–168. doi:10.1016/j.rse.2015.02.022.
   Web of Science ®Google Scholar
• Doughty, R., X. Xiao, X. Wu, Y. Zhang, R. Bajgain, Y. Zhou, Y. Qin, et al. 2018. “Responses of Gross Primary Production of Grasslands and Croplands under Drought, Pluvial, and Irrigation Conditions during 2010–2016, Oklahoma, USA.” Agricultural Water Management 204: 47–59. doi:10.1016/j.agwat.2018.04.001.
   Web of Science ®Google Scholar
• Gelybo, G., Z. Barcza, A. Kern, and N. Kljun. 2013. “Effect of Spatial Heterogeneity on the Validation of Remote Sensing Based GPP Estimations.” Agricultural and Forest Meteorology 174: 43–53. doi:10.1016/j.agrformet.2013.02.003.
   Web of Science ®Google Scholar
• Gong, W., Q. Duan, J. Li, C. Wang, Z. Di, Y. Dai, A. Ye, and C. Miao. 2015. “Multi-Objective Parameter Optimization of Common Land Model Using Adaptive Surrogate Modeling.” Hydrology and Earth System Sciences 19 (5): 2409–2425. doi:10.5194/hess-19-2409-2015.
   Web of Science ®Google Scholar
• Heinsch, F. A., M. Zhao, S. W. Running, J. S. Kimball, R. R. Nemani, K. J. Davis, P. V. Bolstad, et al. 2006. “Evaluation of Remote Sensing Based Terrestrial Productivity from MODIS Using Regional Tower Eddy Flux Network Observations.” IEEE Transactions on Geoscience and Remote Sensing 44 (7): 1908–1925. doi:10.1109/tgrs.2005.853936.
   Web of Science ®Google Scholar
• Herman, M. R., A. P. Nejadhashemi, M. Abouali, J. S. Hernandez-Suarez, F. Daneshvar, Z. Zhang, M. C. Anderson, A. M. Sadeghi, C. R. Hain, and A. Sharifi. 2018. “Evaluating the Role of Evapotranspiration Remote Sensing Data in Improving Hydrological Modeling Predictability.” Journal of Hydrology 556: 39–49. doi:10.1016/j.jhydrol.2017.11.009.
   Web of Science ®Google Scholar
• Hilker, T., F. G. Hall, N. C. Coops, A. Lyapustin, Y. Wang, Z. Nesic, N. Grant, et al. 2010. “Remote Sensing of Photosynthetic Light-Use Efficiency across Two Forested Biomes: Spatial Scaling.” Remote Sensing of Environment 114 (12): 2863–2874. doi:10.1016/j.rse.2010.07.004.
   Web of Science ®Google Scholar
• Hinojo-Hinojo, C., A. E. Castellanos, T. Huxman, J. C. Rodriguez, R. Vargas, J. R. Romo-Leon, and J. A. Biederman. 2019. “Native Shrubland and Managed Buffelgrass Savanna in Drylands: Implications for Ecosystem Carbon and Water Fluxes.” Agricultural and Forest Meteorology 268: 269–278. doi:10.1016/j.agrformet.2019.01.030.
   Web of Science ®Google Scholar
• Hu, G., L. Jia, and M. Menenti. 2015. “Comparison of MOD16 and LSA-SAF MSG Evapotranspiration Products over Europe for 2011.” Remote Sensing of Environment 156: 510–526. doi:10.1016/j.rse.2014.10.017.
   Web of Science ®Google Scholar
• Hu, Z., S. Li, G. Yu, X. Sun, L. Zhang, S. Han, and Y. Li. 2013. “Modeling Evapotranspiration by Combing a Two-Source Model, a Leaf Stomatal Model, and a Light-Use Efficiency Model.” Journal of Hydrology 501: 186–192. doi:10.1016/j.jhydrol.2013.08.006.
   Web of Science ®Google Scholar
• Huang, Q., F. Qiu, W. Fan, Y. Liu, and Q. Zhang. 2019a. “Evaluation of Different Methods for Estimating the Fraction of Sunlit Leaves and Its Contribution for Photochemical Reflectance Index Utilization in a Coniferous Forest.” Remote Sensing 11 (14): 1643. doi:10.3390/rs11141643.
   Web of Science ®Google Scholar
• Huang, X., J. Xiao, and M. Ma. 2019. “Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe.” Remote Sensing 11 (15): 1823. doi:10.3390/rs11151823.
   Web of Science ®Google Scholar
• Jia, X., T. Zha, J. Gong, B. Wang, Y. Zhang, B. Wu, S. Qin, and H. Peltola. 2016. “Carbon and Water Exchange over a Temperate Semi-Arid Shrubland during Three Years of Contrasting Precipitation and Soil Moisture Patterns.” Agricultural and Forest Meteorology 228: 120–129. doi:10.1016/j.agrformet.2016.07.007.
   Web of Science ®Google Scholar
• Jing, C., L. Li, X. Chen, and G. Luo. 2014. “Comparison of Root Water Uptake Functions to Simulate Surface Energy Fluxes within a Deep-Rooted Desert Shrub Ecosystem.” Hydrological Processes 28 (21): 5436–5449. doi:10.1002/hyp.10047.
   Web of Science ®Google Scholar
• Jung, M., M. Reichstein, P. Ciais, S. I. Seneviratne, J. Sheffield, M. L. Goulden, G. Bonan, et al. 2010. “Recent Decline in the Global Land Evapotranspiration Trend Due to Limited Moisture Supply.” Nature 467 (7318): 951–954. doi:10.1038/nature09396.
   PubMed Web of Science ®Google Scholar
• Kang, X., L. Yan, X. Zhang, Y. Li, D. Tian, C. Peng, H. Wu, J. Wang, and L. Zhong. 2018. “Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data.” Remote Sensing 10 (5): 708. doi:10.3390/rs10050708.
   Web of Science ®Google Scholar
• Kato, T., W. Knorr, M. Scholze, E. Veenendaal, T. Kaminski, J. Kattge, and N. Gobron. 2013. “Simultaneous Assimilation of Satellite and Eddy Covariance Data for Improving Terrestrial Water and Carbon Simulations at a Semi-Arid Woodland Site in Botswana.” Biogeosciences 10 (2): 789–802. doi:10.5194/bg-10-789-2013.
   Web of Science ®Google Scholar
• Lasslop, G., M. Reichstein, D. Papale, A. D. Richardson, A. Arneth, A. Barr, P. Stoy, and G. Wohlfahrt. 2010. “Separation of Net Ecosystem Exchange into Assimilation and Respiration Using a Light Response Curve Approach: Critical Issues and Global Evaluation.” Global Change Biology 16 (1): 187–208. doi:10.1111/j.1365-2486.2009.02041.x.
   Web of Science ®Google Scholar
• Li, L., C. van der Tol, X. Chen, C. Jing, B. Su, G. Luo, and X. Tian. 2013. “Representing the Root Water Uptake Process in the Common Land Model for Better Simulating the Energy and Water Vapour Fluxes in a Central Asian Desert Ecosystem.” Journal of Hydrology 502: 145–155. doi:10.1016/j.jhydrol.2013.08.026.
   Web of Science ®Google Scholar
• Liu, Y., J. Xiao, W. Ju, G. Zhu, X. Wu, W. Fan, D. Li, and Y. Zhou. 2018. “Satellite-Derived LAI Products Exhibit Large Discrepancies and Can Lead to Substantial Uncertainty in Simulated Carbon and Water Fluxes.” Remote Sensing of Environment 206: 174–188. doi:10.1016/j.rse.2017.12.024.
   Web of Science ®Google Scholar
• Long, D., L. Longuevergne, and B. R. Scanlon. 2014. “Uncertainty in Evapotranspiration from Land Surface Modeling, Remote Sensing, and GRACE Satellites.” Water Resources Research 50 (2): 1131–1151. doi:10.1002/2013wr014581.
   Web of Science ®Google Scholar
• Ma, S., R. Lardy, A.-I. Graux, H. B. Touhami, K. Klumpp, R. Martin, and G. Bellocchi. 2015. “Regional-Scale Analysis of Carbon and Water Cycles on Managed Grassland Systems.” Environmental Modelling and Software 72: 356–371. doi:10.1016/j.envsoft.2015.03.007.
   Web of Science ®Google Scholar
• Melaas, E. K., M. A. Friedl, and Z. Zhu. 2013. “Detecting Interannual Variation in Deciduous Broadleaf Forest Phenology Using Landsat TM/ETM Plus Data.” Remote Sensing of Environment 132: 176–185. doi:10.1016/j.rse.2013.01.011.
   Web of Science ®Google Scholar
• Monteith, J. L. 1972. “Solar Radiation and Productivity in Tropical Ecosystems.” Journal of Applied Ecology 9 (3): 747–766. doi:10.2307/2401901.
   Web of Science ®Google Scholar
• Mu, Q., F. A. Heinsch, M. Zhao, and S. W. Running. 2007. “Development of a Global Evapotranspiration Algorithm Based on MODIS and Global Meteorology Data.” Remote Sensing of Environment 111 (4): 519–536. doi:10.1016/j.rse.2007.04.015.
   Web of Science ®Google Scholar
• Perez-Quezada, J. F., H. E. Bown, J. P. Fuentes, F. A. Alfaro, and N. Franck. 2012. “Effects of Afforestation on Soil Respiration in an Arid Shrubland in Chile.” Journal of Arid Environments 83: 45–53. doi:10.1016/j.jaridenv.2012.03.015.
   Web of Science ®Google Scholar
• Potter, C. S., J. T. Randerson, C. B. Field, P. A. Matson, P. M. Vitousek, H. A. Mooney, and S. A. Klooster. 1993. “Terrestrial Ecosystem Production: A Process Model -based on Global Satellite and Surface Data.” Global Biogeochemical Cycles 7 (4): 811–841. doi:10.1029/93gb02725.
   Web of Science ®Google Scholar
• Prentice, I. C., X. Liang, B. E. Medlyn, and Y. Wang. 2015. “Reliable, Robust and Realistic: The Three R’s of Next-Generation Land-Surface Modelling.” Atmospheric Chemistry and Physics 15 (10): 5987–6005. doi:10.5194/acp-15-5987-2015.
   Web of Science ®Google Scholar
• Rahman, A. F., D. A. Sims, V. D. Cordova, and B. Z. El-Masri. 2005. “Potential of MODIS EVI and Surface Temperature for Directly Estimating Per-Pixel Ecosystem C Fluxes.” Geophysical Research Letters 32 (19). doi:10.1029/2005gl024127.
   Web of Science ®Google Scholar
• Raoult, N. M., T. E. Jupp, P. M. Cox, and C. M. Luke. 2016. “Land-Surface Parameter Optimisation Using Data Assimilation Techniques: The adJULES System V1.0.” Geoscientific Model Development 9 (8): 2833–2852. doi:10.5194/gmd-9-2833-2016.
   Web of Science ®Google Scholar
• Reichstein, M., E. Falge, D. Baldocchi, D. Papale, M. Aubinet, P. Berbigier, C. Bernhofer, et al. 2005. “On the Separation of Net Ecosystem Exchange into Assimilation and Ecosystem Respiration: Review and Improved Algorithm.” Global Change Biology 11 (9): 1424–1439. doi:10.1111/j.1365-2486.2005.001002.x.
   Web of Science ®Google Scholar
• Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto. 2004. “A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production.” BioScience 54 (6): 547–560. doi:10.1641/0006-3568(2004)054[0547:Acsmog]2.0.CO;2.
   Web of Science ®Google Scholar
• Ryu, Y., J. A. Berry, and D. D. Baldocchi. 2019. “What Is Global Photosynthesis? History, Uncertainties and Opportunities.” Remote Sensing of Environment 223: 95–114. doi:10.1016/j.rse.2019.01.016.
   Web of Science ®Google Scholar
• Sims, D. A., A. F. Rahman, V. D. Cordova, B. Z. El-Masri, D. D. Baldocchi, P. V. Bolstad, L. B. Flanagan, et al. 2008. “A New Model of Gross Primary Productivity for North American Ecosystems Based Solely on the Enhanced Vegetation Index and Land Surface Temperature from MODIS.” Remote Sensing of Environment 112 (4): 1633–1646. doi:10.1016/j.rse.2007.08.004.
   Web of Science ®Google Scholar
• Song, C., M. P. Dannenberg, and T. Hwang. 2013. “Optical Remote Sensing of Terrestrial Ecosystem Primary Productivity.” Progress in Physical Geography- Earth and Environment 37 (6): 834–854. doi:10.1177/0309133313507944.
   Web of Science ®Google Scholar
• Stockli, R., T. Rutishauser, D. Dragoni, J. O’Keefe, P. E. Thornton, M. Jolly, L. Lu, and A. S. Denning. 2008. “Remote Sensing Data Assimilation for a Prognostic Phenology Model.” Journal of Geophysical Research-Biogeosciences 113 (G4). doi:10.1029/2008jg000781.
   Web of Science ®Google Scholar
• Tang, X., M. Ma, Z. Ding, X. Xu, L. Yao, X. Huang, Q. Gu, and L. Song. 2017. “Remotely Monitoring Ecosystem Water Use Efficiency of Grassland and Cropland in China’s Arid and Semi-Arid Regions with MODIS Data.” Remote Sensing 9 (6): 616. doi:10.3390/rs9060616.
   Web of Science ®Google Scholar
• Tian, H., G. Chen, M. Liu, C. Zhang, G. Sun, C. Lu, X. Xu, W. Ren, S. Pan, and A. Chappelka. 2010. “Model Estimates of Net Primary Productivity, Evapotranspiration, and Water Use Efficiency in the Terrestrial Ecosystems of the Southern United States during 1895–2007.” Forest Ecology and Management 259 (7): 1311–1327. doi:10.1016/j.foreco.2009.10.009.
   Web of Science ®Google Scholar
• Turner, D. P., S. Urbanski, D. Bremer, S. C. Wofsy, T. Meyers, S. T. Gower, and M. Gregoy. 2003. “A Cross-Biome Comparison of Daily Light Use Efficiency for Gross Primary Production.” Global Change Biology 9 (3): 383–395. doi:10.1046/j.1365-2486.2003.00573.x.
   Web of Science ®Google Scholar
• Van Dijk, A., and A. J. Dolman. 2004. “Estimates of CO2 Uptake and Release among European Forests Based on Eddy Covariance Data.” Global Change Biology 10 (9): 1445–1459. doi:10.1111/j.1365-2486.2004.00831.x.
   Web of Science ®Google Scholar
• Verma, M., M. A. Friedl, A. D. Richardson, G. Kiely, A. Cescatti, B. E. Law, G. Wohlfahrt, et al. 2014. “Remote Sensing of Annual Terrestrial Gross Primary Productivity from MODIS: An Assessment Using the FLUXNET La Thuile Data Set.” Biogeosciences 11 (8): 2185–2200. doi:10.5194/bg-11-2185-2014.
   Web of Science ®Google Scholar
• Vickers, D., and L. Mahrt. 1997. “Quality Control and Flux Sampling Problems for Tower and Aircraft Data.” Journal of Atmospheric and Oceanic Technology 14 (3): 512–526. doi:10.1175/1520-0426(1997)014<0512:Qcafsp>2.0.Co;2.
   Web of Science ®Google Scholar
• Wang, K., and R. E. Dickinson. 2012. “A Review of Global Terrestrial Evapotranspiration: Observation, Modeling, Climatology, and Climatic Variability.” Reviews of Geophysics 50. doi:10.1029/2011rg000373.
   Web of Science ®Google Scholar
• Wang, L., H. Zhu, A. Lin, L. Zou, W. Qin, and Q. Du. 2017. “Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data.” Remote Sensing 9 (5). doi:10.3390/rs9050418.
   Web of Science ®Google Scholar
• Wang, X., M. Ma, G. Huang, F. Veroustraete, Z. Zhang, Y. Song, and J. Tan. 2012. “Vegetation Primary Production Estimation at Maize and Alpine Meadow over the Heihe River Basin, China.” International Journal of Applied Earth Observation and Geoinformation 17: 94–101. doi:10.1016/j.jag.2011.09.009.
   Web of Science ®Google Scholar
• Wang, X., M. Ma, X. Li, Y. Song, J. Tan, G. Huang, Z. Zhang, et al. 2013. “Validation of MODIS-GPP Product at 10 Flux Sites in Northern China.” International Journal of Remote Sensing 34 (2): 587–599. doi:10.1080/01431161.2012.715774.
   Web of Science ®Google Scholar
• Watham, T., N. R. Patel, S. P. S. Kushwaha, V. K. Dadhwal, and A. S. Kumar. 2017. “Evaluation of Remote-Sensing-Based Models of Gross Primary Productivity over Indian Sal Forest Using Flux Tower and MODIS Satellite Data.” International Journal of Remote Sensing 38 (18): 5069–5090. doi:10.1080/01431161.2017.1333653.
   Web of Science ®Google Scholar
• Wu, C., J. Chen, and N. Huang. 2011. “Predicting Gross Primary Production from the Enhanced Vegetation Index and Photosynthetically Active Radiation: Evaluation and Calibration.” Remote Sensing of Environment 115 (12): 3424–3435. doi:10.1016/j.rse.2011.08.006.
   Web of Science ®Google Scholar
• Wu, D., X. Zhao, S. Liang, T. Zhou, K. Huang, B. Tang, and W. Zhao. 2015. “Time-Lag Effects of Global Vegetation Responses to Climate Change.” Global Change Biology 21 (9): 3520–3531. doi:10.1111/gcb.12945.
   PubMed Web of Science ®Google Scholar
• Wu, M., Y. Ran, P.-E. Jansson, P. Chen, X. Tan, and W. Zhang. 2019. “Global Parameters Sensitivity Analysis of Modeling Water, Energy and Carbon Exchange of an Arid Agricultural Ecosystem.” Agricultural and Forest Meteorology 271: 295–306. doi:10.1016/j.agrformet.2019.03.007.
   Web of Science ®Google Scholar
• Wu, W., S. Wang, X. Xiao, G. Yu, Y. Fu, and Y. Hao. 2008. “Modeling Gross Primary Production of a Temperate Grassland Ecosystem in Inner Mongolia, China, Using MODIS Imagery and Climate Data.” Science in China Series D-Earth Sciences 51 (10): 1501–1512. doi:10.1007/s11430-008-0113-5.
   Google Scholar
• Xiao, J., Q. Zhuang, B. E. Law, J. Chen, D. D. Baldocchi, D. R. Cook, R. Oren, et al. 2010. “A Continuous Measure of Gross Primary Production for the Conterminous United States Derived from MODIS and AmeriFlux Data.” Remote Sensing of Environment 114 (3): 576–591. doi:10.1016/j.rse.2009.10.013.
   Web of Science ®Google Scholar
• Xiao, X., Q. Zhang, B. Braswell, S. Urbanski, S. Boles, S. Wofsy, M. Berrien, and D. Ojima. 2004. “Modeling Gross Primary Production of Temperate Deciduous Broadleaf Forest Using Satellite Images and Climate Data.” Remote Sensing of Environment 91 (2): 256–270. doi:10.1016/j.rse.2004.03.010.
   Web of Science ®Google Scholar
• Xie, X., A. Li, H. Jin, J. Tan, C. Wang, G. Lei, Z. Zhang, J. Bian, and X. Nan. 2019. “Assessment of Five Satellite-Derived LAI Datasets for GPP Estimations through Ecosystem Models.” Science of the Total Environment 690: 1120–1130. doi:10.1016/j.scitotenv.2019.06.516.
   PubMed Web of Science ®Google Scholar
• Xie, X., A. Li, J. Tan, H. Jin, X. Nan, Z. Zhang, J. Bian, and G. Lei. 2020. “Assessments of Gross Primary Productivity Estimations with Satellite Data-Driven Models Using Eddy Covariance Observation Sites over the Northern Hemisphere.” Agricultural and Forest Meteorology 280: 107771. doi:10.1016/j.agrformet.2019.107771.
   Web of Science ®Google Scholar
• Xin, F., X. Xiao, B. Zhao, A. Miyata, D. Baldocchi, S. Knox, M. Kang, et al. 2017. “Modeling Gross Primary Production of Paddy Rice Cropland through Analyses of Data from CO2 Eddy Flux Tower Sites and MODIS Images.” Remote Sensing of Environment 190: 42–55. doi:10.1016/j.rse.2016.11.025.
   Web of Science ®Google Scholar
• Yang, Y., D. Long, and S. Shang. 2013. “Remote Estimation of Terrestrial Evapotranspiration without Using Meteorological Data.” Geophysical Research Letters 40 (12): 3026–3030. doi:10.1002/grl.50450.
   Web of Science ®Google Scholar
• Yuan, X., J. Bai, L. Li, A. Kurban, P. De Maeyer, and D. Hui. 2017. “The Dominant Role of Climate Change in Determining Changes in Evapotranspiration in Xinjiang, China from 2001 to 2012.” PLoS One 12 (8): e0183071. doi:10.1371/journal.pone.0183071.
   Web of Science ®Google Scholar
• Zhang, J., Y. Hu, X. Xiao, P. Chen, S. Han, G. Song, and G. Yu. 2009. “Satellite-Based Estimation of Evapotranspiration of an Old-Growth Temperate Mixed Forest.” Agricultural and Forest Meteorology 149 (6–7): 976–984. doi:10.1016/j.agrformet.2008.12.002.
   Web of Science ®Google Scholar
• Zhang, L., B. Wylie, T. Loveland, E. Fosnight, L. L. Tieszen, L. Ji, and T. Gilmanov. 2007. “Evaluation and Comparison of Gross Primary Production Estimates for the Northern Great Plains Grasslands.” Remote Sensing of Environment 106 (2): 173–189. doi:10.1016/j.rse.2006.08.012.
   Web of Science ®Google Scholar
• Zhang, L., D. Zhou, J. Fan, Q. Guo, S. Chen, R. Wang, and Y. Li. 2019a. “Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems.” Remote Sensing 11 (11). doi:10.3390/rs11111333.
   PubMed Web of Science ®Google Scholar
• Zhang, Y., C. Song, G. Sun, L. E. Band, S. McNulty, A. Noormets, Q. Zhang, and Z. Zhang. 2016b. “Development of a Coupled Carbon and Water Model for Estimating Global Gross Primary Productivity and Evapotranspiration Based on Eddy Flux and Remote Sensing Data.” Agricultural and Forest Meteorology 223: 116–131. doi:10.1016/j.agrformet.2016.04.003.
   Web of Science ®Google Scholar
• Zhang, Y., D. Kong, R. Gan, F. H. S. Chiew, T. R. McVicar, Q. Zhang, and Y. Yang. 2019b. “Coupled Estimation of 500 M and 8-Day Resolution Global Evapotranspiration and Gross Primary Production in 2002–2017.” Remote Sensing of Environment 222: 165–182. doi:10.1016/j.rse.2018.12.031.
   Web of Science ®Google Scholar
• Zhang, Y., J. L. Pena-Arancibia, T. R. McVicar, F. H. S. Chiew, J. Vaze, C. Liu, X. Lu, et al. 2016a. “Multi-Decadal Trends in Global Terrestrial Evapotranspiration and Its Components.” Scientific Reports 6. doi:10.1038/srep19124.
   Web of Science ®Google Scholar
• Zhang, Y., Q. Yu, J. Jiang, and Y. Tang. 2008. “Calibration of Terra/MODIS Gross Primary Production over an Irrigated Cropland on the North China Plain and an Alpine Meadow on the Tibetan Plateau.” Global Change Biology 14 (4): 757–767. doi:10.1111/j.1365-2486.2008.01538.x.
   Web of Science ®Google Scholar
• Zhang, Y., R. Leuning, L. B. Hutley, J. Beringer, I. McHugh, and J. P. Walker. 2010. “Using Long-Term Water Balances to Parameterize Surface Conductances and Calculate Evaporation at 0.05 Degrees Spatial Resolution.” Water Resources Research 46. doi:10.1029/2009wr008716.
   PubMed Web of Science ®Google Scholar
• Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running. 2005. “Improvements of the MODIS Terrestrial Gross and Net Primary Production Global Data Set.” Remote Sensing of Environment 95 (2): 164–176. doi:10.1016/j.rse.2004.12.011.
   Web of Science ®Google Scholar
• Zhao, M., and S. W. Running. 2010. “Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 through 2009.” Science 329 (5994): 940–943. doi:10.1126/science.1192666.
   PubMed Web of Science ®Google Scholar
• Zhao, M., S. W. Running, and R. R. Nemani. 2006. “Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) Terrestrial Primary Production to the Accuracy of Meteorological Reanalyses.” Journal of Geophysical Research-Biogeosciences 111 (G1). doi:10.1029/2004jg000004.
   Web of Science ®Google Scholar
• Zheng, Y., L. Zhang, J. Xiao, W. Yuan, M. Yan, T. Li, and Z. Zhang. 2018. “Sources of Uncertainty in Gross Primary Productivity Simulated by Light Use Efficiency Models: Model Structure, Parameters, Input Data, and Spatial Resolution.” Agricultural and Forest Meteorology 263: 242–257. doi:10.1016/j.agrformet.2018.08.003.
   Web of Science ®Google Scholar
• Zhou, W., L. Huang, H. Yang, W. Ju, and T. Yue. 2019. “Interannual Variation in Grassland Net Ecosystem Productivity and Its Coupling Relation to Climatic Factors in China.” Environment Geochemistry and Health 41 (3): 1583–1597. doi:10.1007/s10653-018-0236-3.
   PubMed Web of Science ®Google Scholar
• Zhu, H., A. Lin, L. Wang, Y. Xia, and L. Zou. 2016. “Evaluation of MODIS Gross Primary Production across Multiple Biomes in China Using Eddy Covariance Flux Data.” Remote Sensing 8 (5): 395. doi:10.3390/rs8050395.
   Web of Science ®Google Scholar
• Zhu, X., Y. Pei, Z. Zheng, J. Dong, Y. Zhang, J. Wang, L. Chen, R. B. Doughty, G. Zhang, and X. Xiao. 2018. “Underestimates of Grassland Gross Primary Production in MODIS Standard Products.” Remote Sensing 10 (11): 1771. doi:10.3390/rs10111771.
   Web of Science ®Google Scholar