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Review

Remote sensing of terrestrial gross primary productivity: a review of advances in theoretical foundation, key parameters and methods

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Article: 2318846 | Received 07 Jul 2023, Accepted 09 Feb 2024, Published online: 20 Feb 2024

References

  • Ahmad, N., A. Irfan, and H. R. Ahmad. 2023. “Impact of Changing Abiotic Environment on Photosynthetic Adaptation in Plants.” In New Frontiers in Plant-Environment Interactions: Innovative Technologies and Developments, edited by T. Aftab, 385–32. Cham: Springer Nature Switzerland.
  • Almeida, C. T. D., R. C. Delgado, L. S. Galvão, L. E. D. O. C. E. Aragão, and M. C. Ramos. 2018. “Improvements of the MODIS Gross Primary Productivity Model Based on a Comprehensive Uncertainty Assessment Over the Brazilian Amazonia.” ISPRS Journal of Photogrammetry and Remote Sensing 145:268–283. https://doi.org/10.1016/j.isprsjprs.2018.07.016.
  • Alonso, L., L. Gomez-Chova, J. Vila-Frances, J. Amoros-Lopez, L. Guanter, J. Calpe, J. Moreno, et al. 2008. “Improved Fraunhofer Line Discrimination Method for Vegetation Fluorescence Quantification.” IEEE Geoscience and Remote Sensing Letters 5 (4): 620–624. https://doi.org/10.1109/LGRS.2008.2001180.
  • Alton, P. B. 2017. “Retrieval of Seasonal Rubisco-Limited Photosynthetic Capacity at Global FLUXNET Sites from Hyperspectral Satellite Remote Sensing: Impact on Carbon Modelling.” Agricultural and Forest Meteorology 232:74–88. https://doi.org/10.1016/j.agrformet.2016.08.001.
  • Alton, P. B. 2018. “Decadal Trends in Photosynthetic Capacity and Leaf Area Index Inferred from Satellite Remote Sensing for Global Vegetation Types.” Agricultural and Forest Meteorology 250-251:361–375. https://doi.org/10.1016/j.agrformet.2017.11.020.
  • Anav, A., P. Friedlingstein, C. Beer, P. Ciais, A. Harper, C. Jones, G. Murray‐Tortarolo, et al. 2015. “Spatiotemporal Patterns of Terrestrial Gross Primary Production: A Review.” Reviews of Geophysics 53 (3): 785–818. https://doi.org/10.1002/2015RG000483.
  • Bacour, C., F. Maignan, N. MacBean, A. Porcar‐Castell, J. Flexas, C. Frankenberg, P. Peylin, et al. 2019. “Improving Estimates of Gross Primary Productivity by Assimilating Solar-Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process-Based SIF Model.” Journal of Geophysical Research: Biogeosciences 124 (11): 3281–3306. https://doi.org/10.1029/2019JG005040.
  • Badgley, G., B. Field Christopher, and A. Berry Joseph. 2017. “Canopy Near-Infrared Reflectance and Terrestrial Photosynthesis.” Science Advances 3 (3): e1602244. https://doi.org/10.1126/sciadv.1602244.
  • Bai, Y., S. Liang, and W. Yuan. 2021. “Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods, Remote Sensing.” Remote Sensing 13 (5): 963. https://doi.org/10.3390/rs13050963.
  • Baker, N. R. 2008. “Chlorophyll Fluorescence: A Probe of Photosynthesis in vivo.” Annual Review of Plant Biology 59 (1): 89–113. https://doi.org/10.1146/annurev.arplant.59.032607.092759.
  • Baker, I., A. S. Denning, N. Hanan, L. Prihodko, M. Uliasz, P.-L. Vidale, K. Davis, et al. 2003. “Simulated and Observed Fluxes of Sensible and Latent Heat and CO2 at the WLEF-TV Tower Using SiB2.5.” Global Change Biology 9 (9): 1262–1277. https://doi.org/10.1046/j.1365-2486.2003.00671.x.
  • Baker, I. T., L. Prihodko, A. S. Denning, M. Goulden, S. Miller, and H. R. da Rocha. 2008. “Seasonal Drought Stress in the Amazon: Reconciling Models and Observations.” Journal of Geophysical Research: Biogeosciences 113 (G1): G00B01. https://doi.org/10.1029/2007JG000644.
  • Baldocchi, D., E. Falge, L. Gu, R. Olson, D. Hollinger, S. Running, P. Anthoni, et al. 2001. “FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem–Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities.” Bulletin of the American Meteorological Society 82 (11): 2415–2434. https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2.
  • Bao, S., T. Wutzler, S. Koirala, M. Cuntz, A. Ibrom, S. Besnard, S. Walther, et al. 2022. “Environment-Sensitivity Functions for Gross Primary Productivity in Light Use Efficiency Models.” Agricultural and Forest Meteorology 312:108708. https://doi.org/10.1016/j.agrformet.2021.108708.
  • Barton, C. V. M., and P. R. J. North. 2001. “Remote Sensing of Canopy Light Use Efficiency Using the Photochemical Reflectance Index: Model and Sensitivity Analysis.” Remote Sensing of Environment 78 (3): 264–273. https://doi.org/10.1016/S0034-4257(01)00224-3.
  • Beerling, D. J., and W. P. Quick. 1995. “A New Technique for Estimating Rates of Carboxylation and Electron Transport in Leaves of C3 Plants for Use in Dynamic Global Vegetation Models.” Global Change Biology 1 (4): 289–294. https://doi.org/10.1111/j.1365-2486.1995.tb00027.x.
  • Beer, C., M. Reichstein, E. Tomelleri, P. Ciais, M. Jung, N. Carvalhais, C. Rödenbeck, et al. 2010. “Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate.” Science 329 (5993): 834–838. https://doi.org/10.1126/science.1184984.
  • Berger, K., J. Verrelst, J.-B. Féret, Z. Wang, M. Wocher, M. Strathmann, M. Danner, et al. 2020. “Crop Nitrogen Monitoring: Recent Progress and Principal Developments in the Context of Imaging Spectroscopy Missions.” Remote Sensing of Environment 242:111758. https://doi.org/10.1016/j.rse.2020.111758.
  • Bodesheim, P., M. Jung, F. Gans, M. D. Mahecha, and M. Reichstein. 2018. “Upscaled Diurnal Cycles of Land–Atmosphere Fluxes: A New Global Half-Hourly Data Product.” Earth System Science Data 10 (3): 1327–1365. https://doi.org/10.5194/essd-10-1327-2018.
  • Box, E. O., B. N. Holben, and V. Kalb. 1989. “Accuracy of the AVHRR Vegetation Index as a Predictor of Biomass, Primary Productivity and Net CO2 Flux.” Vegetatio 80 (2): 71–89. https://doi.org/10.1007/BF00048034.
  • Boyd, D. S., S. Almond, J. Dash, P. J. Curran, R. A. Hill, and G. M. Foody. 2012. “Evaluation of Envisat Meris Terrestrial Chlorophyll Index-Based Models for the Estimation of Terrestrial Gross Primary Productivity.” IEEE Geoscience and Remote Sensing Letters 9 (3): 457–461. https://doi.org/10.1109/LGRS.2011.2170810.
  • Braswell, B. H., D. S. Schimel, J. L. Privette, B. Moore, W. J. Emery, E. W. Sulzman, A. T. Hudak, et al. 1996. “Extracting Ecological and Biophysical Information from AVHRR Optical Data: An Integrated Algorithm Based on Inverse Modeling.” Journal of Geophysical Research: Atmospheres 101 (D18): 23335–23348. https://doi.org/10.1029/96JD02181.
  • Bruhn, D., F. Newman, M. Hancock, P. Povlsen, M. Slot, S. Sitch, J. Drake, et al. 2022. “Nocturnal Plant Respiration is Under Strong Non-Temperature Control.” Nature Communications 13 (1): 5650. https://doi.org/10.1038/s41467-022-33370-1.
  • Camps-Valls, G., M. Campos-Taberner, Á. Moreno-Martínez, S. Walther, G. Duveiller, A. Cescatti, M. D. Mahecha, et al. 2021. “A Unified Vegetation Index for Quantifying the Terrestrial Biosphere.” Science Advances 7 (9): eabc7447. https://doi.org/10.1126/sciadv.abc7447.
  • Carlson, T. 2007. “An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery.” Sensors 7 (8): 1612–1629. https://doi.org/10.3390/s7081612.
  • Celesti, M., C. van der Tol, S. Cogliati, C. Panigada, P. Yang, F. Pinto, U. Rascher, et al. 2018. “Exploring the Physiological Information of Sun-Induced Chlorophyll Fluorescence Through Radiative Transfer Model Inversion.” Remote Sensing of Environment 215:97–108. https://doi.org/10.1016/j.rse.2018.05.013.
  • Chapin, F. S., P. A. Matson, and P. M. Vitousek. 2011. “Carbon Inputs to Ecosystems.” In Principles of Terrestrial Ecosystem Ecology, edited by F. S. Chapin, P. A. Matson, and P. M. Vitousek, 123–156. New York: Springer.
  • Chapin, F. S., G. M. Woodwell, J. T. Randerson, E. B. Rastetter, G. M. Lovett, D. D. Baldocchi, D. A. Clark, et al. 2006. “Reconciling Carbon-Cycle Concepts, Terminology, and Methods.” Ecosystems (New York, NY) 9 (7): 1041–1050. https://doi.org/10.1007/s10021-005-0105-7.
  • Chen, J. M. 2018. “Remote Sensing of Leaf Area Index and Clumping Index.” In Comprehensive Remote Sensing, edited by S. Liang, 53–77. Oxford: Elsevier.
  • Chen, J. M., and T. A. Black. 1992. “Defining leaf area index for non-flat leaves.” Plant, Cell & Environment 15 (4): 421–429. https://doi.org/10.1111/j.1365-3040.1992.tb00992.x.
  • Chen, B., J. M. Chen, and W. Ju. 2007. “Remote Sensing-Based Ecosystem–Atmosphere Simulation Scheme (EASS)—Model Formulation and Test with Multiple-Year Data.” Ecological Modelling 209 (2): 277–300. https://doi.org/10.1016/j.ecolmodel.2007.06.032.
  • Cheng, J., S. Liang, and X. Meng. 2020. “Chapter 7 – Land Surface Temperature and Thermal Infrared Emissivity.” In Advanced Remote Sensing, edited by S. Liang and J. Wang, 251–295. 2nd ed. United States: Academic Press.
  • Cheng, Y.-B., E. M. Middleton, Q. Zhang, K. Huemmrich, P. Campbell, L. Corp, B. Cook, et al. 2013. “Integrating Solar Induced Fluorescence and the Photochemical Reflectance Index for Estimating Gross Primary Production in a Cornfield.” Remote Sensing 5 (12): 6857–6879. https://doi.org/10.3390/rs5126857.
  • Chen, J. M., C. H. Menges, and S. G. Leblanc. 2005. “Global Mapping of Foliage Clumping Index Using Multi-Angular Satellite Data.” Remote Sensing of Environment 97 (4): 447–457. https://doi.org/10.1016/j.rse.2005.05.003.
  • Chen, J. M., G. Mo, J. Pisek, J. Liu, F. Deng, M. Ishizawa, D. Chan, et al. 2012. “Effects of Foliage Clumping on the Estimation of Global Terrestrial Gross Primary Productivity.” Global Biogeochemical Cycles 26 (1). https://doi.org/10.1029/2010GB003996.
  • Chen, J. M., Wang, R., Liu, Y., He, L., Croft, H., Luo, X., Wang, H. et al. 2022. “Global Datasets of Leaf Photosynthetic Capacity for Ecological and Earth System Research.” Earth System Science Data Discussions 14 (9): 4077–4093. https://doi.org/10.5194/essd-14-4077-2022.
  • Chi, M., A. Plaza, J. A. Benediktsson, Z. Sun, J. Shen, and Y. Zhu. 2016. “Big Data for Remote Sensing: Challenges and Opportunities.” Proceedings of the IEEE 104 (11): 2207–2219. https://doi.org/10.1109/JPROC.2016.2598228.
  • Cogliati, S., W. Verhoef, S. Kraft, N. Sabater, L. Alonso, J. Vicent, J. Moreno, et al. 2015. “Retrieval of Sun-Induced Fluorescence Using Advanced Spectral Fitting Methods.” Remote Sensing of Environment 169:344–357. https://doi.org/10.1016/j.rse.2015.08.022.
  • Collatz, G. J., J. T. Ball, C. Grivet, and J. A. Berry. 1991. “Physiological and Environmental Regulation of Stomatal Conductance, Photosynthesis and Transpiration: A Model That Includes a Laminar Boundary Layer.” Agricultural and Forest Meteorology 54 (2): 107–136. https://doi.org/10.1016/0168-1923(91)90002-8.
  • Collatz, G. J., M. Ribas-Carbo, and J. A. Berry. 1992. “Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants.” Functional Plant Biology 19 (5): 519–538. https://doi.org/10.1071/PP9920519.
  • Damm, A., J. A. N. Elbers, A. Erler, B. Gioli, K. Hamdi, R. Hutjes, M. Kosvancova, et al. 2010. “Remote Sensing of Sun-Induced Fluorescence to Improve Modeling of Diurnal Courses of Gross Primary Production (GPP).” Global Change Biology 16 (1): 171–186. https://doi.org/10.1111/j.1365-2486.2009.01908.x.
  • Damm, A., L. Guanter, E. Paul-Limoges, C. van der Tol, A. Hueni, N. Buchmann, W. Eugster, et al. 2015. “Far-Red Sun-Induced Chlorophyll Fluorescence Shows Ecosystem-Specific Relationships to Gross Primary Production: An Assessment Based on Observational and Modeling Approaches.” Remote Sensing of Environment 166:91–105. https://doi.org/10.1016/j.rse.2015.06.004.
  • Dash, J., and P. J. Curran. 2007. “Evaluation of the MERIS Terrestrial Chlorophyll Index (MTCI).” Advances in Space Research 39 (1): 100–104. https://doi.org/10.1016/j.asr.2006.02.034.
  • Dechant, B., Y. Ryu, G. Badgley, P. Köhler, U. Rascher, M. Migliavacca, Y. Zhang, et al. 2022. “NIRVP: A Robust Structural Proxy for Sun-Induced Chlorophyll Fluorescence and Photosynthesis Across Scales.” Remote Sensing of Environment 268:112763. https://doi.org/10.1016/j.rse.2021.112763.
  • Dechant, B., Y. Ryu, and M. Kang. 2019. “Making Full Use of Hyperspectral Data for Gross Primary Productivity Estimation with Multivariate Regression: Mechanistic Insights from Observations and Process-Based Simulations.” Remote Sensing of Environment 234:111435. https://doi.org/10.1016/j.rse.2019.111435.
  • Dillen, S. Y., M. O. de Beeck, K. Hufkens, M. Buonanduci, and N. G. Phillips. 2012. “Seasonal Patterns of Foliar Reflectance in Relation to Photosynthetic Capacity and Color Index in Two Co-Occurring Tree Species, Quercus Rubra and Betula Papyrifera.” Agricultural and Forest Meteorology 160:60–68. https://doi.org/10.1016/j.agrformet.2012.03.001.
  • Dobrowski, S. Z., J. C. Pushnik, P. J. Zarco-Tejada, and S. USTIN. 2005. “Simple Reflectance Indices Track Heat and Water Stress-Induced Changes in Steady-State Chlorophyll Fluorescence at the Canopy Scale.” Remote Sensing of Environment 97 (3): 403–414. https://doi.org/10.1016/j.rse.2005.05.006.
  • Domingues, T. F., P. Meir, T. R. Feldpausch, G. Saiz, E. M. Veenendaal, F. Schrodt, M. Bird, et al. 2010. “Co-Limitation of Photosynthetic Capacity by Nitrogen and Phosphorus in West Africa Woodlands.” Plant, Cell & Environment 33 (6): 959–980. https://doi.org/10.1111/j.1365-3040.2010.02119.x.
  • Dou, Y., F. Tian, J.-P. Wigneron, T. Tagesson, J. Du, M. Brandt, Y. Liu, et al. 2023. “Reliability of Using Vegetation Optical Depth for Estimating Decadal and Interannual Carbon Dynamics.” Remote Sensing of Environment 285:113390. https://doi.org/10.1016/j.rse.2022.113390.
  • Drolet, G. G., K. F. Huemmrich, F. G. Hall, E. M. Middleton, T. A. Black, A. G. Barr, H. A. Margolis, et al. 2005. “A MODIS-Derived Photochemical Reflectance Index to Detect Inter-Annual Variations in the Photosynthetic Light-Use Efficiency of a Boreal Deciduous Forest.” Remote Sensing of Environment 98 (2): 212–224. https://doi.org/10.1016/j.rse.2005.07.006.
  • Drolet, G. G., E. M. Middleton, K. F. Huemmrich, F. G. Hall, B. D. Amiro, A. G. Barr, T. A. Black, et al. 2008. “Regional Mapping of Gross Light-Use Efficiency Using MODIS Spectral Indices.” Remote Sensing of Environment 112 (6): 3064–3078. https://doi.org/10.1016/j.rse.2008.03.002.
  • Drusch, M., J. Moreno, U. D. Bello, R. Franco, Y. Goulas, A. Huth, S. Kraft, et al. 2017. “The FLuorescence EXplorer Mission Concept—ESA’s Earth Explorer 8.” IEEE Transactions on Geoscience and Remote Sensing 55 (3): 1273–1284. https://doi.org/10.1109/TGRS.2016.2621820.
  • Du, J., J. S. Kimball, R. H. Reichle, L. Jones, J. Watts, and Y. Kim. 2018. “Global Satellite Retrievals of the Near-Surface Atmospheric Vapor Pressure Deficit from AMSR-E and AMSR2.” Remote Sensing 10 (8): 1175. https://doi.org/10.3390/rs10081175.
  • Du, S., L. Liu, X. Liu, X. Zhang, X. Zhang, Y. Bi, L. Zhang, et al. 2018. “Retrieval Of Global Terrestrial Solar-induced Chlorophyll Fluorescence From Tansat Satellite.” Science Bulletin 63 (22): 1502–1512. https://doi.org/10.1016/j.scib.2018.10.003.
  • Evans, J. R. 1989. “Photosynthesis and Nitrogen Relationships in Leaves of C3 Plants.” Oecologia 78 (1): 9–19. https://doi.org/10.1007/BF00377192.
  • Fang, H. 2021. “Canopy Clumping Index (CI): A Review of Methods, Characteristics, and Applications.” Agricultural and Forest Meteorology 303:108374. https://doi.org/10.1016/j.agrformet.2021.108374.
  • Fang, H., F. Baret, S. Plummer, and G. Schaepman‐Strub. 2019. “An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications.” Reviews of Geophysics 57 (3): 739–799. https://doi.org/10.1029/2018RG000608.
  • Fang, M., W. Ju, W. Zhan, T. Cheng, F. Qiu, and J. Wang. 2017. “A New Spectral Similarity Water Index for the Estimation of Leaf Water Content from Hyperspectral Data of Leaves.” Remote Sensing of Environment 196:13–27. https://doi.org/10.1016/j.rse.2017.04.029.
  • Farquhar, G. D., S. von Caemmerer, and J. A. Berry. 1980. “A Biochemical Model of Photosynthetic CO2 Assimilation in Leaves of C3 Species.” Planta 149 (1): 78–90. https://doi.org/10.1007/BF00386231.
  • Ferchichi, A., A. B. Abbes, V. Barra, and I. R. Farah. 2022. “Forecasting Vegetation Indices from Spatio-Temporal Remotely Sensed Data Using Deep Learning-Based Approaches: A Systematic Literature Review.” Ecological Informatics 68:101552. https://doi.org/10.1016/j.ecoinf.2022.101552.
  • Filella, I., T. Amaro, J. L. Araus, and J. Peñuelas. 1996. “Relationship Between Photosynthetic Radiation-Use Efficiency of Barley Canopies and the Photochemical Reflectance Index (PRI).” Physiologia Plantarum 96 (2): 211–216. https://doi.org/10.1111/j.1399-3054.1996.tb00204.x.
  • Finegan, B., M. Peña-Claros, A. de Oliveira, N. Ascarrunz, M. S. Bret‐Harte, G. Carreño‐Rocabado, F. Casanoves, et al. 2015. “Does Functional Trait Diversity Predict Above-Ground Biomass and Productivity of Tropical Forests? Testing Three Alternative Hypotheses.” Journal of Ecology 103 (1): 191–201. https://doi.org/10.1111/1365-2745.12346.
  • Frankenberg, C., C. O’Dell, J. Berry, L. Guanter, J. Joiner, P. Köhler, R. Pollock, et al. 2014. “Prospects for Chlorophyll Fluorescence Remote Sensing from the Orbiting Carbon Observatory-2.” Remote Sensing of Environment 147:1–12. https://doi.org/10.1016/j.rse.2014.02.007.
  • Frappart, F., J.-P. Wigneron, X. Li, X. Liu, A. Al-Yaari, L. Fan, M. Wang, et al. 2020. “Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review, Remote Sensing.” Remote Sensing 12 (18): 2915. https://doi.org/10.3390/rs12182915.
  • Fu, P., K. Meacham-Hensold, K. Guan, J. Wu, and C. Bernacchi. 2020. “Estimating Photosynthetic Traits from Reflectance Spectra: A Synthesis of Spectral Indices, Numerical Inversion, and Partial Least Square Regression.” Plant, Cell & Environment 43 (5): 1241–1258. https://doi.org/10.1111/pce.13718.
  • Gamon, J. A. 2015. “Reviews and Syntheses: Optical Sampling of the Flux Tower Footprint.” Biogeosciences 12 (14): 4509–4523. https://doi.org/10.5194/bg-12-4509-2015.
  • Gamon, J. A., J. Peñuelas, and C. B. Field. 1992. “A Narrow-Waveband Spectral Index That Tracks Diurnal Changes in Photosynthetic Efficiency.” Remote Sensing of Environment 41 (1): 35–44. https://doi.org/10.1016/0034-4257(92)90059-S.
  • Gamon, J. A., A. F. Rahman, J. L. Dungan, M. Schildhauer, and K. Huemmrich. 2006. “Spectral Network (SpecNet)—What is It and Why Do We Need It?” Remote Sensing of Environment 103 (3): 227–235. https://doi.org/10.1016/j.rse.2006.04.003.
  • Gamon, J. A., L. Serrano, and J. S. Surfus. 1997. “The Photochemical Reflectance Index: An Optical Indicator of Photosynthetic Radiation Use Efficiency Across Species, Functional Types, and Nutrient Levels.” Oecologia 112 (4): 492–501. https://doi.org/10.1007/s004420050337.
  • Gao, B.-C. 1996. “NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” Remote Sensing of Environment 58 (3): 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3.
  • Gao, L., X. Wang, B. A. Johnson, Q. Tian, Y. Wang, J. Verrelst, X. Mu, et al. 2020. “Remote Sensing Algorithms for Estimation of Fractional Vegetation Cover Using Pure Vegetation Index Values: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 159:364–377. https://doi.org/10.1016/j.isprsjprs.2019.11.018.
  • Garbulsky, M. F., J. Peñuelas, J. Gamon, Y. Inoue, and I. Filella. 2011. “The Photochemical Reflectance Index (PRI) and the Remote Sensing of Leaf, Canopy and Ecosystem Radiation Use Efficiencies: A Review and Meta-Analysis.” Remote Sensing of Environment 115 (2): 281–297. https://doi.org/10.1016/j.rse.2010.08.023.
  • Gitelson, A. A., Y. Peng, T. J. Arkebauer, and J. Schepers. 2014. “Relationships Between Gross Primary Production, Green LAI, and Canopy Chlorophyll Content in Maize: Implications for Remote Sensing of Primary Production.” Remote Sensing of Environment 144:65–72. https://doi.org/10.1016/j.rse.2014.01.004.
  • Gitelson, A. A., A. Viña, S. B. Verma, D. C. Rundquist, T. J. Arkebauer, G. Keydan, B. Leavitt, et al. 2006. “Relationship Between Gross Primary Production and Chlorophyll Content in Crops: Implications for the Synoptic Monitoring of Vegetation Productivity.” Journal of Geophysical Research: Atmospheres 111 (D8): D08S11. https://doi.org/10.1029/2005JD006017.
  • Goerner, A., M. Reichstein, and S. Rambal. 2009. “Tracking Seasonal Drought Effects on Ecosystem Light Use Efficiency with Satellite-Based PRI in a Mediterranean Forest.” Remote Sensing of Environment 113 (5): 1101–1111. https://doi.org/10.1016/j.rse.2009.02.001.
  • GomezChova, L., L. AlonsoChorda, J. Amoros Lopez, Vila Frances, J., del ValleTascon, S., Calpe, J. and Moreno, J., et al. 2006. “Solar Induced Fluorescence Measurements Using A Field Spectroradiometer.” AIP Conference Proceedings 852 (1): 274–281.
  • Goward, S. N., and K. F. Huemmrich. 1992. “Vegetation Canopy PAR Absorptance and the Normalized Difference Vegetation Index: An Assessment Using the SAIL Model.” Remote Sensing of Environment 39 (2): 119–140. https://doi.org/10.1016/0034-4257(92)90131-3.
  • Goward, S. N., C. J. Tucker, and D. G. Dye. 1985. “North American Vegetation Patterns Observed with the NOAA-7 Advanced Very High Resolution Radiometer.” Vegetatio 64 (1): 3–14. https://doi.org/10.1007/BF00033449.
  • Guanter, L., C. Frankenberg, A. Dudhia, P. E. Lewis, J. Gómez-Dans, A. Kuze, H. Suto, et al. 2012. “Retrieval and Global Assessment of Terrestrial Chlorophyll Fluorescence from GOSAT Space Measurements.” Remote Sensing of Environment 121:236–251. https://doi.org/10.1016/j.rse.2012.02.006.
  • Guanter, L., H. Kaufmann, K. Segl, S. Foerster, C. Rogass, S. Chabrillat, T. Kuester, et al. 2015. “The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation.” Remote Sensing 7 (7): 8830–8857. https://doi.org/10.3390/rs70708830.
  • Guanter, L., M. Rossini, R. Colombo, M. Meroni, C. Frankenberg, J.-E. Lee, J. Joiner, et al. 2013. “Using Field Spectroscopy to Assess the Potential of Statistical Approaches for the Retrieval of Sun-Induced Chlorophyll Fluorescence from Ground and Space.” Remote Sensing of Environment 133:52–61. https://doi.org/10.1016/j.rse.2013.01.017.
  • Guanter, L., Y. Zhang, M. Jung, J. Joiner, M. Voigt, J. A. Berry, C. Frankenberg, et al. 2014. “Global and Time-Resolved Monitoring of Crop Photosynthesis with Chlorophyll Fluorescence.” Proceedings of the National Academy of Sciences 111 (14): E1327–E1333. https://doi.org/10.1073/pnas.1320008111.
  • Guarini, R., C. Nichol, R. Clement, R. Loizzo, J. Grace, and M. Borghetti. 2014. “The Utility of MODIS-sPRI for Investigating the Photosynthetic Light-Use Efficiency in a Mediterranean Deciduous Forest.” International Journal of Remote Sensing 35 (16): 6157–6172. https://doi.org/10.1080/01431161.2014.950762.
  • Gu, L., J. Han, J. D. Wood, C.-Y.-Y. Chang, and Y. Sun. 2019. “Sun-Induced Chl Fluorescence and Its Importance for Biophysical Modeling of Photosynthesis Based on Light Reactions.” New Phytologist 223 (3): 1179–1191. https://doi.org/10.1111/nph.15796.
  • Hall, F. G., T. Hilker, and N. C. Coops. 2011. “PHOTOSYNSAT, Photosynthesis from Space: Theoretical Foundations of a Satellite Concept and Validation from Tower and Spaceborne Data.” Remote Sensing of Environment 115 (8): 1918–1925. https://doi.org/10.1016/j.rse.2011.03.014.
  • Hall, F. G., K. F. Huemmrich, S. J. Goetz, P. J. Sellers, and J. E. Nickeson. 1992. “Satellite Remote Sensing of Surface Energy Balance: Success, Failures, and Unresolved Issues in FIFE.” Journal of Geophysical Research: Atmospheres 97 (D17): 19061–19089. https://doi.org/10.1029/92JD02189.
  • Harris, A., and J. Dash. 2010. “The Potential of the MERIS Terrestrial Chlorophyll Index for Carbon Flux Estimation.” Remote Sensing of Environment 114 (8): 1856–1862. https://doi.org/10.1016/j.rse.2010.03.010.
  • Hashimoto, H., J. L. Dungan, M. A. White, F. Yang, A. Michaelis, S. Running, R. Nemani, et al. 2008. “Satellite-Based Estimation of Surface Vapor Pressure Deficits Using MODIS Land Surface Temperature Data.” Remote Sensing of Environment 112 (1): 142–155. https://doi.org/10.1016/j.rse.2007.04.016.
  • Hashimoto, H., W. Wang, C. Milesi, M. A. White, S. Ganguly, M. Gamo, R. Hirata, et al. 2012. “Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data.” Remote Sensing 4 (1): 303–326. https://doi.org/10.3390/rs4010303.
  • He, L., J. M. Chen, J. Liu, T. Zheng, R. Wang, J. Joiner, S. Chou, et al. 2019. “Diverse Photosynthetic Capacity of Global Ecosystems Mapped by Satellite Chlorophyll Fluorescence Measurements.” Remote Sensing of Environment 232:111344. https://doi.org/10.1016/j.rse.2019.111344.
  • He, M., W. Ju, Y. Zhou, J. Chen, H. He, S. Wang, H. Wang, et al. 2013. “Development of a Two-Leaf Light Use Efficiency Model for Improving the Calculation of Terrestrial Gross Primary Productivity.” Agricultural and Forest Meteorology 173:28–39. https://doi.org/10.1016/j.agrformet.2013.01.003.
  • Hernández-Clemente, R., P. Kolari, A. Porcar-Castell, L. Korhonen, and M. Mottus. 2016. “Tracking the Seasonal Dynamics of Boreal Forest Photosynthesis Using EO-1 Hyperion Reflectance: Sensitivity to Structural and Illumination Effects.” IEEE Transactions on Geoscience and Remote Sensing 54 (9): 5105–5116. https://doi.org/10.1109/TGRS.2016.2554466.
  • Hilker, T., N. C. Coops, F. G. Hall, C. J. Nichol, A. Lyapustin, T. A. Black, M. A. Wulder, et al. 2011. “Inferring Terrestrial Photosynthetic Light Use Efficiency of Temperate Ecosystems from Space.” Journal of Geophysical Research: Biogeosciences 116 (G3). https://doi.org/10.1029/2011JG001692.
  • Hilker, T., N. C. Coops, M. A. Wulder, T. A. Black, and R. D. Guy. 2008. “The Use of Remote Sensing in Light Use Efficiency Based Models of Gross Primary Production: A Review of Current Status and Future Requirements.” Science of the Total Environment 404 (2–3): 411–423. https://doi.org/10.1016/j.scitotenv.2007.11.007.
  • Homolová, L., Z. Malenovský, J. G. P. W. Clevers, G. García-Santos, and M. E. Schaepman. 2013. “Review of Optical-Based Remote Sensing for Plant Trait Mapping.” Ecological Complexity 15:1–16. https://doi.org/10.1016/j.ecocom.2013.06.003.
  • Huang, G., Z. Li, X. Li, S. Liang, K. Yang, D. Wang, Y. Zhang, et al. 2019. “Estimating Surface Solar Irradiance From Satellites: Past, Present, And Future Perspectives.” Remote Sensing of Environment 233:111371. https://doi.org/10.1016/j.rse.2019.111371.
  • Huang, L., X. Lin, S. Jiang, M. Liu, Y. Jiang, Z.-L. Li, R. Tang, et al. 2022. “A Two-Stage Light Use Efficiency Model for Improving Gross Primary Production Estimation in Agroecosystems.” Environmental Research Letters 17 (10): 104021. https://doi.org/10.1088/1748-9326/ac8b98.
  • 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. https://doi.org/10.3390/rs11151823.
  • Huang, X., Y. Zheng, H. Zhang, S. Lin, S. Liang, X. Li, M. Ma, et al. 2022. “High Spatial Resolution Vegetation Gross Primary Production Product: Algorithm and Validation.” Science of Remote Sensing 5:100049. https://doi.org/10.1016/j.srs.2022.100049.
  • Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira. 2002. “Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices.” Remote Sensing of Environment 83 (1): 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2.
  • Huete, A. R., N. Restrepo-Coupe, P. Ratana, K. Didan, S. R. Saleska, K. Ichii, S. Panuthai, et al. 2008. “Multiple Site Tower Flux and Remote Sensing Comparisons of Tropical Forest Dynamics in Monsoon Asia.” Agricultural and Forest Meteorology 148 (5): 748–760. https://doi.org/10.1016/j.agrformet.2008.01.012.
  • Ichii, K., M. Ueyama, M. Kondo, N. Saigusa, J. Kim, M. C. Alberto, J. Ardö, et al. 2017. “New Data-driven Estimation Of Terrestrial CO2 Fluxes In Asia Using A Standardized Database Of Eddy Covariance Measurements, Remote Sensing Data, And Support Vector Regression.” Journal of Geophysical Research: Biogeosciences 122 (4): 767–795. https://doi.org/10.1002/2016JG003640.
  • Jackson, T. J., and T. J. Schmugge. 1991. “Vegetation Effects on the Microwave Emission of Soils.” Remote Sensing of Environment 36 (3): 203–212. https://doi.org/10.1016/0034-4257(91)90057-D.
  • Jiang, C., Y. Ryu, H. Wang, and T. F. Keenan. 2020. “An Optimality-Based Model Explains Seasonal Variation in C3 Plant Photosynthetic Capacity.” Global Change Biology 26 (11): 6493–6510. https://doi.org/10.1111/gcb.15276.
  • Joiner, J., L. Guanter, R. Lindstrot, M. Voigt, A. P. Vasilkov, E. M. Middleton, K. F. Huemmrich, et al. 2013. “Global Monitoring of Terrestrial Chlorophyll Fluorescence from Moderate-Spectral-Resolution Near-Infrared Satellite Measurements: Methodology, Simulations, and Application to GOME-2.” Atmospheric Measurement Techniques 6 (10): 2803–2823. https://doi.org/10.5194/amt-6-2803-2013.
  • Joiner, J., Y. Yoshida, L. Guanter, and E. M. Middleton. 2016. “New Methods for the Retrieval of Chlorophyll Red Fluorescence from Hyperspectral Satellite Instruments: Simulations And application to GOME-2 and SCIAMACHY.” Atmospheric Measurement Techniques 9 (8): 3939–3967. https://doi.org/10.5194/amt-9-3939-2016.
  • Joiner, J., Y. Yoshida, A. P. Vasilkov, E. M. Middleton, P. K. E. Campbell, Y. Yoshida, A. Kuze, et al. 2012. “Filling-In of Near-Infrared Solar Lines by Terrestrial Fluorescence and Other Geophysical Effects: Simulations and Space-Based Observations from SCIAMACHY and GOSAT.” Atmospheric Measurement Techniques 5 (4): 809–829. https://doi.org/10.5194/amt-5-809-2012.
  • Jones, H. G., ed. 2014. Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology. Cambridge: Cambridge University Press.
  • Jung, M., M. Reichstein, H. A. Margolis, A. Cescatti, A. D. Richardson, M. A. Arain, A. Arneth, et al. 2011. “Global Patterns of Land-Atmosphere Fluxes of Carbon Dioxide, Latent Heat, and Sensible Heat Derived from Eddy Covariance, Satellite, and Meteorological Observations.” Journal of Geophysical Research: Biogeosciences 116 (G3). https://doi.org/10.1029/2010JG001566.
  • Jung, M., C. Schwalm, M. Migliavacca, S. Walther, G. Camps-Valls, S. Koirala, P. Anthoni, et al. 2020. “Scaling Carbon Fluxes from Eddy Covariance Sites to Globe: Synthesis and Evaluation of the FLUXCOM Approach.” Biogeosciences 17 (5): 1343–1365. https://doi.org/10.5194/bg-17-1343-2020.
  • Jung, M., M. Verstraete, N. Gobron, M. Reichstein, D. Papale, A. Bondeau, M. Robustelli, et al. 2008. “Diagnostic Assessment of European Gross Primary Production.” Global Change Biology 14 (10): 2349–2364. https://doi.org/10.1111/j.1365-2486.2008.01647.x.
  • Kaiser, E., A. Morales, J. Harbinson, J. Kromdijk, E. Heuvelink, and L. F. M. Marcelis. 2015. “Dynamic Photosynthesis in Different Environmental Conditions.” Journal of Experimental Botany 66 (9): 2415–2426. https://doi.org/10.1093/jxb/eru406.
  • Khan, A. M., P. C. Stoy, J. Joiner, D. Baldocchi, J. Verfaillie, M. Chen, J. A. Otkin, et al. 2022. “The Diurnal Dynamics of Gross Primary Productivity Using Observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite-R Series at an Oak Savanna Ecosystem.” Journal of Geophysical Research: Biogeosciences 127 (3): e2021JG006701. https://doi.org/10.1029/2021JG006701.
  • Kiang, N. Y., J. Siefert, Govindjee, and R. E. Blankenship. 2007. “Spectral Signatures of Photosynthesis.” I Review of Earth Organisms Astrobiology 7 (1): 222–251. https://doi.org/10.1089/ast.2006.0105.
  • King, D. A., D. P. Turner, and W. D. Ritts. 2011. “Parameterization of a Diagnostic Carbon Cycle Model for Continental Scale Application.” Remote Sensing of Environment 115 (7): 1653–1664. https://doi.org/10.1016/j.rse.2011.02.024.
  • Köhler, P., C. Frankenberg, T. S. Magney, L. Guanter, J. Joiner, and J. Landgraf. 2018. “Global Retrievals of Solar-Induced Chlorophyll Fluorescence with TROPOMI: First Results and Intersensor Comparison to OCO-2.” Geophysical Research Letters 45 (19): ,10,456–10,463. https://doi.org/10.1029/2018GL079031.
  • Köhler, P., L. Guanter, and J. Joiner. 2015. “A Linear Method for the Retrieval of Sun-Induced Chlorophyll Fluorescence from GOME-2 and SCIAMACHY Data.” Atmospheric Measurement Techniques 8 (6): 2589–2608. https://doi.org/10.5194/amt-8-2589-2015.
  • Kooijmans, L. M. J., W. Sun, J. Aalto, K.-M. Erkkilä, K. Maseyk, U. Seibt, T. Vesala et al. 2019. “Influences of Light and Humidity on Carbonyl Sulfide-Based Estimates of Photosynthesis.” Proceedings of the National Academy of Sciences 116 (7): 2470–2475. https://doi.org/10.1073/pnas.1807600116.
  • Kováč, D., A. Ač, L. Šigut, J. Peñuelas, J. Grace, and O. Urban. 2022. “Combining NDVI, PRI and the Quantum Yield of Solar-Induced Fluorescence Improves Estimations of Carbon Fluxes in Deciduous and Evergreen Forests.” Science of the Total Environment 829:154681. https://doi.org/10.1016/j.scitotenv.2022.154681.
  • Lambers, H., F. S. Chapin, and T. L. Pons. 2008. “Photosynthesis.” In Plant Physiological Ecology, edited by H. Lambers, F. S. Chapin, and T. L. Pons, 11–99. New York, New York, NY: Springer.
  • LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep learning.” Nature 521 (7553): 436–444. https://doi.org/10.1038/nature14539.
  • Lee, J.-E., J. A. Berry, C. van der Tol, X. Yang, L. Guanter, A. Damm, I. Baker, et al. 2015. “Simulations of Chlorophyll Fluorescence Incorporated into the C Ommunity L and M Odel Version 4.” Global Change Biology 21 (9): 3469–3477. https://doi.org/10.1111/gcb.12948.
  • Lee, B., N. Kim, E.-S. Kim, K. Jang, M. Kang, J.-H. Lim, J. Cho, et al. 2020. “An Artificial Intelligence Approach to Predict Gross Primary Productivity in the Forests of South Korea Using Satellite Remote Sensing Data, Forests.” Forests 11 (9): 1000. https://doi.org/10.3390/f11091000.
  • Liang, S., B. Jiang, and T. He. 2020. “Chapter 18 – Soil Moisture contents.” In Advanced Remote Sensing edited by S. Liang and J. Wang, 685–711. 2nd ed. United States: Academic Press.
  • Liang, W., Y. Lü, W. Zhang, S. Li, Z. Jin, P. Ciais, B. Fu, et al. 2017. “Grassland Gross Carbon Dioxide Uptake Based on an Improved Model Tree Ensemble Approach Considering Human Interventions: Global Estimation and Covariation with Climate.” Global Change Biology 23 (7): 2720–2742. https://doi.org/10.1111/gcb.13592.
  • Liang, S., and J. Wang. 2020. “Chapter 15 – Estimate of Vegetation Production of Terrestrial Ecosystem.” In Advanced Remote Sensing, edited by S. Liang and J. Wang, 581–620. 2nd ed. United States: Academic Press.
  • Liang, S., D. Wang, T. He, and Y. Yu. 2019. “Remote Sensing of Earth’s Energy Budget: Synthesis and Review.” International Journal of Digital Earth 12 (7): 737–780. https://doi.org/10.1080/17538947.2019.1597189.
  • Liang, S., X. Zhao, S. Liu, W. Yuan, X. Cheng, Z. Xiao, X. Zhang, et al. 2013. “A long-term Global LAnd Surface Satellite (GLASS) Data-set For Environmental Studies.” International Journal of Digital Earth 6 (sup1): 5–33. https://doi.org/10.1080/17538947.2013.805262.
  • Liang, S., T. Zheng, R. Liu, H. Fang, S.-C. Tsay, and S. Running. 2006. “Estimation of Incident Photosynthetically Active Radiation from Moderate Resolution Imaging Spectrometer Data.” Journal of Geophysical Research: Atmospheres 111 (D15). https://doi.org/10.1029/2005JD006730.
  • Liao, Z., B. Zhou, J. Zhu, H. Jia, and X. Fei. 2023. “A Critical Review of Methods, Principles and Progress for Estimating the Gross Primary Productivity of Terrestrial Ecosystems.” Frontiers in Environmental Science 11:11. https://doi.org/10.3389/fenvs.2023.1093095.
  • Li, B., Y. Ryu, C. Jiang, B. Dechant, J. Liu, Y. Yan, X. Li, et al. 2023. “BESSv2.0: A Satellite-Based and Coupled-Process Model for Quantifying Long-Term Global Land–Atmosphere Fluxes.” Remote Sensing of Environment 295:113696. https://doi.org/10.1016/j.rse.2023.113696.
  • Li, X., Y. Ryu, J. Xiao, B. Dechant, J. Liu, B. Li, S. Jeong, et al. 2023. “New-Generation Geostationary Satellite Reveals Widespread Midday Depression in Dryland Photosynthesis During 2020 Western U.S. Heatwave.” Science Advances 9 (31): eadi0775. https://doi.org/10.1126/sciadv.adi0775.
  • Li, Z.-L., B.-H. Tang, H. Wu, H. Ren, G. Yan, Z. Wan, I. F. Trigo, et al. 2013. “Satellite-Derived Land Surface Temperature: Current Status and Perspectives.” Remote Sensing of Environment 131:14–37. https://doi.org/10.1016/j.rse.2012.12.008.
  • Liu, J., J. M. Chen, J. Cihlar, Park, W.M. 1997. “A Process-Based Boreal Ecosystem Productivity Simulator Using Remote Sensing Inputs.” Remote Sensing of Environment 62 (2): 158–175. https://doi.org/10.1016/S0034-4257(97)00089-8.
  • Liu, Y., J. M. Chen, L. He, Z. Zhang, R. Wang, C. Rogers, W. Fan, et al. 2022. “Non-Linearity Between Gross Primary Productivity and Far-Red Solar-Induced Chlorophyll Fluorescence Emitted from Canopies of Major Biomes.” Remote Sensing of Environment 271:112896. https://doi.org/10.1016/j.rse.2022.112896.
  • Liu, L., L. Guan, and X. Liu. 2017. “Directly Estimating Diurnal Changes in GPP for C3 and C4 Crops Using Far-Red Sun-Induced Chlorophyll Fluorescence.” Agricultural and Forest Meteorology 232:1–9. https://doi.org/10.1016/j.agrformet.2016.06.014.
  • Liu, X., and L. Liu. 2015. “Improving Chlorophyll Fluorescence Retrieval Using Reflectance Reconstruction Based on Principal Components Analysis.” IEEE Geoscience and Remote Sensing Letters 12 (8): 1645–1649. https://doi.org/10.1109/LGRS.2015.2417857.
  • Liu, L., X. Liu, J. Chen, S. Du, Y. Ma, X. Qian, S. Chen, et al. 2020. “Estimating Maize GPP Using Near-Infrared Radiance of Vegetation.” Science of Remote Sensing 2:100009. https://doi.org/10.1016/j.srs.2020.100009.
  • Liu, X., L. Liu, S. Zhang, and X. Zhou. 2015. “New Spectral Fitting Method for Full-Spectrum Solar-Induced Chlorophyll Fluorescence Retrieval Based on Principal Components Analysis.” Remote Sensing 7 (8): 10626–10645. https://doi.org/10.3390/rs70810626.
  • Liu, Z., C. Wu, D. Peng, S. Wang, A. Gonsamo, B. Fang, W. Yuan, et al. 2017. “Improved Modeling of Gross Primary Production from a Better Representation of Photosynthetic Components in Vegetation Canopy.” Agricultural and Forest Meteorology 233:222–234. https://doi.org/10.1016/j.agrformet.2016.12.001.
  • Li, X., and J. Xiao. 2019. “A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data.” Remote Sensing 11 (5): 2563. https://doi.org/10.3390/rs11212563.
  • Li, X., J. Xiao, and B. He. 2018. “Chlorophyll Fluorescence Observed by OCO-2 is Strongly Related to Gross Primary Productivity Estimated from Flux Towers in Temperate Forests.” Remote Sensing of Environment 204:659–671. https://doi.org/10.1016/j.rse.2017.09.034.
  • Li, X., J. Xiao, B. He, M. Altaf Arain, J. Beringer, A. R. Desai, C. Emmel, et al. 2018. “Solar-Induced Chlorophyll Fluorescence is Strongly Correlated with Terrestrial Photosynthesis for a Wide Variety of Biomes: First Global Analysis Based on OCO-2 and Flux Tower Observations.” Global Change Biology 24 (9): 3990–4008. https://doi.org/10.1111/gcb.14297.
  • Long, S. P., and C. J. Bernacchi. 2003. “Gas Exchange Measurements, What Can They Tell Us About the Underlying Limitations to Photosynthesis? Procedures and Sources of Error.” Journal of Experimental Botany 54 (392): 2393–2401. https://doi.org/10.1093/jxb/erg262.
  • Lu, J., G. Wang, D. Feng, and I. K. Nooni. 2023. “Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning, Forests.” Forests 14 (6): 1201. https://doi.org/10.3390/f14061201.
  • Madani, N., J. S. Kimball, D. L. R. Affleck, J. Kattge, J. Graham, P. M. van Bodegom, P. B. Reich, et al. 2014. “Improving Ecosystem Productivity Modeling Through Spatially Explicit Estimation of Optimal Light Use Efficiency.” Journal of Geophysical Research: Biogeosciences 119 (9): 1755–1769. https://doi.org/10.1002/2014JG002709.
  • Mahadevan, P., S. C. Wofsy, D. M. Matross, X. Xiao, A. L. Dunn, J. C. Lin, C. Gerbig, et al. 2008. “A Satellite-Based Biosphere Parameterization for Net Ecosystem CO2 Exchange: Vegetation Photosynthesis and Respiration Model (VPRM).” Global Biogeochemical Cycles 22 (2): GB2005. https://doi.org/10.1029/2006GB002735.
  • Maseyk, K., J. A. Berry, D. Billesbach, J. E. Campbell, M. S. Torn, M. Zahniser, and U. Seibt. 2014. “Sources and Sinks of Carbonyl Sulfide in an Agricultural Field in the Southern Great Plains.” Proceedings of the National Academy of Sciences 111 (25): 9064–9069. https://doi.org/10.1073/pnas.1319132111.
  • Ma, Y., H. Wu, L. Wang, B. Huang, R. Ranjan, A. Zomaya, W. Jie, et al. 2015. “Remote Sensing Big Data Computing: Challenges And Opportunities.” Future Generation Computer Systems 51:47–60. https://doi.org/10.1016/j.future.2014.10.029.
  • Maxwell, K., and G. N. Johnson. 2000. “Chlorophyll fluorescence—a practical guide.” Journal of Experimental Botany 51 (345): 659–668. https://doi.org/10.1093/jexbot/51.345.659.
  • McCree, K. J. 1971. “The Action Spectrum, Absorptance and Quantum Yield of Photosynthesis in Crop Plants.” Agricultural Meteorology 9:191–216. https://doi.org/10.1016/0002-1571(71)90022-7.
  • Meroni, M., L. Busetto, R. Colombo, L. Guanter, J. Moreno, and W. Verhoef. 2010. “Performance of Spectral Fitting Methods for Vegetation Fluorescence Quantification.” Remote Sensing of Environment 114 (2): 363–374. https://doi.org/10.1016/j.rse.2009.09.010.
  • Meroni, M., and R. Colombo. 2006. “Leaf Level Detection of Solar Induced Chlorophyll Fluorescence by Means of a Subnanometer Resolution Spectroradiometer.” Remote Sensing of Environment 103 (4): 438–448. https://doi.org/10.1016/j.rse.2006.03.016.
  • Meroni, M., M. Rossini, L. Guanter, L. Alonso, U. Rascher, R. Colombo, J. Moreno, et al. 2009. “Remote Sensing of Solar-Induced Chlorophyll Fluorescence: Review of Methods and Applications.” Remote Sensing of Environment 113 (10): 2037–2051. https://doi.org/10.1016/j.rse.2009.05.003.
  • Middleton, E. M., K. F. Huemmrich, D. R. Landis, T. A. Black, A. G. Barr, and J. H. McCaughey. 2016. “Photosynthetic Efficiency of Northern Forest Ecosystems Using a MODIS-Derived Photochemical Reflectance Index (PRI).” Remote Sensing of Environment 187:345–366. https://doi.org/10.1016/j.rse.2016.10.021.
  • Mohammad Mahdi, N., and P. Babak. 2012. “Photosynthesis: How and Why?.” In Advances in Photosynthesis, edited by N. M. Mahdi, Ch. 1. Rijeka: IntechOpen.
  • Mohammed, G. H., R. Colombo, E. M. Middleton, U. Rascher, C. van der Tol, L. Nedbal, Y. Goulas, et al. 2019. “Remote Sensing of Solar-Induced Chlorophyll Fluorescence (SIF) in Vegetation: 50 years of Progress.” Remote Sensing of Environment 231:111177. https://doi.org/10.1016/j.rse.2019.04.030.
  • Monteith, J. L. 1972. “Solar Radiation and Productivity in Tropical Ecosystems.” The Journal of Applied Ecology 9 (3): 747–766. https://doi.org/10.2307/2401901.
  • Moreno, A., F. Maselli, M. A. Gilabert, M. Chiesi, B. Martínez, and G. Seufert. 2012. “Assessment of MODIS Imagery to Track Light-Use Efficiency in a Water-Limited Mediterranean Pine Forest.” Remote Sensing of Environment 123:359–367. https://doi.org/10.1016/j.rse.2012.04.003.
  • Muraoka, H., H. M. Noda, S. Nagai, T. Motohka, T. M. Saitoh, K. N. Nasahara, N. Saigusa, et al. 2013. “Spectral Vegetation Indices as the Indicator of Canopy Photosynthetic Productivity in a Deciduous Broadleaf Forest.” Journal of Plant Ecology 6 (5): 393–407. https://doi.org/10.1093/jpe/rts037.
  • Nakaji, T., R. Ide, H. Oguma, N. Saigusa, and Y. Fujinuma. 2007. “Utility of Spectral Vegetation Index for Estimation of Gross CO2 Flux Under Varied Sky Conditions.” Remote Sensing of Environment 109 (3): 274–284. https://doi.org/10.1016/j.rse.2007.01.006.
  • Nilson, T. 1971. “A Theoretical Analysis of the Frequency of Gaps in Plant Stands.” Agricultural Meteorology 8:25–38. https://doi.org/10.1016/0002-1571(71)90092-6.
  • Papale, D., and R. Valentini. 2003. “A New Assessment of European Forests Carbon Exchanges by Eddy Fluxes and Artificial Neural Network Spatialization.” Global Change Biology 9 (4): 525–535. https://doi.org/10.1046/j.1365-2486.2003.00609.x.
  • Parazoo, N. C., K. Bowman, J. B. Fisher, C. Frankenberg, D. B. A. Jones, A. Cescatti, Ó. Pérez‐Priego, et al. 2014. “Terrestrial Gross Primary Production Inferred from Satellite Fluorescence and Vegetation Models.” Global Change Biology 20 (10): 3103–3121. https://doi.org/10.1111/gcb.12652.
  • Paruelo, J. M., H. E. Epstein, W. K. Lauenroth, and I. C. Burke. 1997. “ANPP Estimates from NDVI for the Central Grassland Region of the United States.” Ecology 78 (3): 953–958. https://doi.org/10.1890/0012-9658(1997)078[0953:AEFNFT]2.0.CO;2.
  • Paruelo, J. M., M. Oesterheld, C. M. Di Bella, M. Arzadum, J. Lafontaine, M. Cahuepé, C. M. Rebella, et al. 2000. “Estimation of Primary Production of Subhumid Rangelands from Remote Sensing Data.” Applied Vegetation Science 3 (2): 189–195. https://doi.org/10.2307/1478997.
  • Pastorello, G., C. Trotta, E. Canfora, H. Chu, D. Christianson, Y.-W. Cheah, C. Poindexter, et al. 2020. “The FLUXNET2015 Dataset and the ONEFlux Processing Pipeline for Eddy Covariance Data.” Scientific Data 7 (1): 225. https://doi.org/10.1038/s41597-020-0534-3.
  • Patel, N. R. P., H. Devadas, R. Huete, A. Kumar, A. SenthilMurthy, and Y. V. N. Krishna. 2018. “Estimating Net Primary Productivity of Croplands in Indo-Gangetic Plains Using GOME-2 Sun-Induced Fluorescence and MODIS NDVI.” Current Science: A Fortnightly Journal of Research 114 (6): 5. https://doi.org/10.18520/cs/v114/i06/1333-1337.
  • Pei, Y., J. Dong, Y. Zhang, W. Yuan, R. Doughty, J. Yang, D. Zhou, et al. 2022. “Evolution of Light Use Efficiency Models: Improvement, Uncertainties, and Implications.” Agricultural and Forest Meteorology 317:108905. https://doi.org/10.1016/j.agrformet.2022.108905.
  • Peng, Y., A. A. Gitelson, and T. Sakamoto. 2013. “Remote Estimation of Gross Primary Productivity in Crops Using MODIS 250m Data.” Remote Sensing of Environment 128:186–196. https://doi.org/10.1016/j.rse.2012.10.005.
  • Perez-Priego, O., P. J. Zarco-Tejada, J. R. Miller, G. Sepulcre-Canto, and E. Fereres. 2005. “Detection of Water Stress in Orchard Trees with a High-Resolution Spectrometer Through Chlorophyll Fluorescence In-Filling of the O/Sub 2/-A Band.” IEEE Transactions on Geoscience and Remote Sensing 43 (12): 2860–2869. https://doi.org/10.1109/TGRS.2005.857906.
  • Piao, S., S. Sitch, P. Ciais, P. Friedlingstein, P. Peylin, X. Wang, A. Ahlström, et al. 2013. “Evaluation of Terrestrial Carbon Cycle Models for Their Response to Climate Variability and to CO2 Trends.” Global Change Biology 19 (7): 2117–2132. https://doi.org/10.1111/gcb.12187.
  • Plascyk, J. A., and F. C. Gabriel. 1975. “The Fraunhofer Line Discriminator MKII-An Airborne Instrument for Precise and Standardized Ecological Luminescence Measurement.” IEEE Transactions on Instrumentation and Measurement 24 (4): 306–313. https://doi.org/10.1109/TIM.1975.4314448.
  • Porcar-Castell, A., E. Tyystjärvi, J. Atherton, C. van der Tol, J. Flexas, E. E. Pfündel, J. Moreno, et al. 2014. “Linking Chlorophyll a Fluorescence to Photosynthesis for Remote Sensing Applications: Mechanisms and Challenges.” Journal of Experimental Botany 65 (15): 4065–4095. https://doi.org/10.1093/jxb/eru191.
  • Potter, C. S., J. T. Randerson, C. B. Field, P. A. Matson, P. M. Vitousek, H. A. Mooney, S. A. Klooster, et al. 1993. “Terrestrial Ecosystem Production: A Process Model Based on Global Satellite and Surface Data.” Global Biogeochemical Cycles 7 (4): 811–841. https://doi.org/10.1029/93GB02725.
  • Prince, S. D., S. J. Goetz, R. O. Dubayah, K. P. Czajkowski, and M. Thawley. 1998. “Inference of Surface and Air Temperature, Atmospheric Precipitable Water and Vapor Pressure Deficit Using Advanced Very High-Resolution Radiometer Satellite Observations: Comparison with Field Observations.” Journal of Hydrology 212-213:230–249. https://doi.org/10.1016/S0022-1694(98)00210-8.
  • Prince, S. D., and S. N. Goward. 1995. “Global Primary Production: A Remote Sensing Approach.” Journal of Biogeography 22 (4/5): 815–835. https://doi.org/10.2307/2845983.
  • Qiao, S., H. Wang, I. C. Prentice, and S. P. Harrison. 2020. “Extending a First-Principles Primary Production Model to Predict Wheat Yields.” Agricultural and Forest Meteorology 287:107932. https://doi.org/10.1016/j.agrformet.2020.107932.
  • Qiu, B., Y. Xue, J. B. Fisher, W. Guo, J. A. Berry, and Y. Zhang. 2018. “Satellite Chlorophyll Fluorescence and Soil Moisture Observations Lead to Advances in the Predictive Understanding of Global Terrestrial Coupled Carbon-Water Cycles.” Global Biogeochemical Cycles 32 (3): 360–375. https://doi.org/10.1002/2017GB005744.
  • Rahman, A. F., V. D. Cordova, J. A. Gamon, H. P. Schmid, and D. A. Sims. 2004. “Potential of MODIS Ocean Bands for Estimating CO2 Flux from Terrestrial Vegetation: A Novel Approach.” Geophysical Research Letters 31 (10). https://doi.org/10.1029/2004GL019778.
  • 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): L19404. https://doi.org/10.1029/2005GL024127.
  • Reichstein, M., G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, et al. 2019. “Deep Learning and Process Understanding for Data-Driven Earth System Science.” Nature 566 (7743): 195–204. https://doi.org/10.1038/s41586-019-0912-1.
  • 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. https://doi.org/10.1111/j.1365-2486.2005.001002.x.
  • Ronggao, L., L. Shunlin, H. Honglin, J. Liu, and T. Zheng. 2008. “Mapping Incident Photosynthetically Active Radiation from MODIS Data Over China.” Remote Sensing of Environment 112 (3): 998–1009. https://doi.org/10.1016/j.rse.2007.07.021.
  • Rossini, M., M. Meroni, M. Migliavacca, G. Manca, S. Cogliati, L. Busetto, V. Picchi, et al. 2010. “High Resolution Field Spectroscopy Measurements for Estimating Gross Ecosystem Production in a Rice Field.” Agricultural and Forest Meteorology 150 (9): 1283–1296. https://doi.org/10.1016/j.agrformet.2010.05.011.
  • Rouse, J. W. 1974. “Monitoring the Vernal Advancements and Retrogradation of Natural Vegetation.” NASA-CR-139243 PR-7 E74-10676: NASA/GSFC .
  • Roy, D. P., H. Huang, R. Houborg, and V. S. Martins. 2021. “A Global Analysis of the Temporal Availability of PlanetScope High Spatial Resolution Multi-Spectral Imagery.” Remote Sensing of Environment 264:112586. https://doi.org/10.1016/j.rse.2021.112586.
  • Ruimy, A., G. Dedieu, and B. Saugier. 1996. “TURC: A Diagnostic Model of Continental Gross Primary Productivity and Net Primary Productivity.” Global Biogeochemical Cycles 10 (2): 269–285. https://doi.org/10.1029/96GB00349.
  • Running, S. W., and J. C. Coughlan. 1988. “A General Model of Forest Ecosystem Processes for Regional Applications I. Hydrologic Balance, Canopy Gas Exchange and Primary Production Processes.” Ecological Modelling 42 (2): 125–154. https://doi.org/10.1016/0304-3800(88)90112-3.
  • Running, S. W., and S. T. Gower. 1991. “FOREST-BGC, a General Model of Forest Ecosystem Processes for Regional Applications. II. Dynamic Carbon Allocation and Nitrogen Budgets.” Tree Physiology 9 (1–2): 147–160. https://doi.org/10.1093/treephys/9.1-2.147.
  • Running, S. W., and E. R. Hunt Jr. 1993. “Generalization of a Forest Ecosystem Process Model for Other Biomes, BIOME-BGC, and an Application for Global-Scale Models.” In Scaling Physiological Processes: Leaf to Globe, edited by J. R. Ehleringer and C. B. Field, 141–158. San Diego: Academic Press.
  • Running, S. W., C. O. Justice, V. Salomonson, D. Hall, J. Barker, Y. J. Kaufmann, A. H. Strahler, et al. 1994. “Terrestrial Remote Sensing Science and Algorithms Planned for EOS/MODIS.” International Journal of Remote Sensing 15 (17): 3587–3620. https://doi.org/10.1080/01431169408954346.
  • Running, S., and M. Zhao. 2015. “User’s Guide Daily GPP and Annual NPP (MOD17A2/A3) Products NASA Earth Observing System MODIS Land Algorithm.”
  • Ryu, Y., D. D. Baldocchi, H. Kobayashi, C. van Ingen, J. Li, T. A. Black, J. Beringer, et al. 2011. “Integration of MODIS Land and Atmosphere Products with a Coupled-Process Model to Estimate Gross Primary Productivity and Evapotranspiration from 1 Km to Global Scales.” Global Biogeochemical Cycles 25 (4): GB4017. https://doi.org/10.1029/2011GB004053.
  • 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. https://doi.org/10.1016/j.rse.2019.01.016.
  • Schaefer, K., C. R. Schwalm, C. Williams, M. A. Arain, A. Barr, J. M. Chen, K. J. Davis, et al. 2012. “A Model-Data Comparison of Gross Primary Productivity: Results from the North American Carbon Program Site Synthesis.” Journal of Geophysical Research: Biogeosciences 117 (G3). https://doi.org/10.1029/2012JG001960.
  • Schimel, D., F. D. Schneider , and JPL Carbon and Ecosystem Participants. 2019. “Flux Towers in the Sky: Global Ecology from Space.” New Phytologist 224 (2): 570–584. https://doi.org/10.1111/nph.15934.
  • Schmid, H. P. 2002. “Footprint Modeling for Vegetation Atmosphere Exchange Studies: A Review and Perspective.” Agricultural and Forest Meteorology 113 (1): 159–183. https://doi.org/10.1016/S0168-1923(02)00107-7.
  • Scholze, M., M. Buchwitz, W. Dorigo, L. Guanter, and S. Quegan. 2017. “Reviews and Syntheses: Systematic Earth Observations for Use in Terrestrial Carbon Cycle Data Assimilation Systems.” Biogeosciences 14 (14): 3401–3429. https://doi.org/10.5194/bg-14-3401-2017.
  • Sellers, P. J., R. E. Dickinson, D. A. Randall, A. K. Betts, F. G. Hall, J. A. Berry, G. J. Collatz, et al. 1997. “Modeling the Exchanges of Energy, Water, and Carbon Between Continents and the Atmosphere.” Science 275 (5299): 502–509. https://doi.org/10.1126/science.275.5299.502.
  • Sellers, P. J., Y. Mintz, Y. C. Sud, and A. Dalcher. 1986. “A Simple Biosphere Model (SIB) for Use within General Circulation Models.” Journal of the Atmospheric Sciences 43 (6): 505–531. https://doi.org/10.1175/1520-0469(1986)043<0505:ASBMFU>2.0.CO;2.
  • Sellers, P. J., D. A. Randall, G. J. Collatz, J. A. Berry, C. B. Field, D. A. Dazlich, C. Zhang, et al. 1996. “A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation.” Journal of Climate 9 (4): 676–705. https://doi.org/10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2.
  • Sellers, P. J., C. J. Tucker, G. J. Collatz, S. O. Los, C. O. Justice, D. A. Dazlich, D. A. Randall, et al. 1996. “A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data.” Journal of Climate 9 (4): 706–737. https://doi.org/10.1175/1520-0442(1996)009<0706:ARLSPF>2.0.CO;2.
  • Serbin, S. P., D. N. Dillaway, E. L. Kruger, and P. A. Townsend. 2012. “Leaf Optical Properties Reflect Variation in Photosynthetic Metabolism and Its Sensitivity to Temperature.” Journal of Experimental Botany 63 (1): 489–502. https://doi.org/10.1093/jxb/err294.
  • Serbin, S. P., A. Singh, A. R. Desai, S. G. Dubois, A. D. Jablonski, C. C. Kingdon, E. L. Kruger, et al. 2015. “Remotely Estimating Photosynthetic Capacity, and Its Response to Temperature, in Vegetation Canopies Using Imaging Spectroscopy.” Remote Sensing of Environment 167:78–87. https://doi.org/10.1016/j.rse.2015.05.024.
  • Shi, H., L. Li, D. Eamus, A. Huete, J. Cleverly, X. Tian, Q. Yu, et al. 2017. “Assessing the Ability of MODIS EVI to Estimate Terrestrial Ecosystem Gross Primary Production of Multiple Land Cover Types.” Ecological Indicators 72:153–164. https://doi.org/10.1016/j.ecolind.2016.08.022.
  • Shoshany, M., T. Svoray, P. J. Curran, G. M. Foody, and A. Perevolotsky. 2000. “The Relationship Between ERS-2 SAR Backscatter and Soil Moisture: Generalization from a Humid to Semi-Arid Transect.” International Journal of Remote Sensing 21 (11): 2337–2343. https://doi.org/10.1080/01431160050029620.
  • Siebers, M. H., N. Gomez-Casanovas, P. Fu, K. Meacham-Hensold, C. E. Moore, C. J. Bernacchi, et al. 2021. “Emerging Approaches to Measure Photosynthesis from the Leaf to the Ecosystem.” Emerging Topics in Life Sciences 5 (2): 261–274. https://doi.org/10.1042/ETLS20200292.
  • Sims, D. A., and J. A. Gamon. 2003. “Estimation of Vegetation Water Content and Photosynthetic Tissue Area from Spectral Reflectance: A Comparison of Indices Based on Liquid Water and Chlorophyll Absorption Features.” Remote Sensing of Environment 84 (4): 526–537. https://doi.org/10.1016/S0034-4257(02)00151-7.
  • Sims, D. A., A. F. Rahman, V. D. Cordova, B. Elmasri, D. Baldocchi, P. Bolstad, L. 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. https://doi.org/10.1016/j.rse.2007.08.004.
  • 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. https://doi.org/10.1177/0309133313507944.
  • Song, Y., L. Wang, and J. Wang. 2021. “Improved Understanding of the Spatially-Heterogeneous Relationship Between Satellite Solar-Induced Chlorophyll Fluorescence and Ecosystem Productivity.” Ecological Indicators 129:107949. https://doi.org/10.1016/j.ecolind.2021.107949.
  • Srivastava, P. K. 2017. “Satellite Soil Moisture: Review of Theory and Applications in Water Resources.” Water Resources Management 31 (10): 3161–3176. https://doi.org/10.1007/s11269-017-1722-6.
  • Stagakis, S., N. Markos, O. Sykioti, and A. Kyparissis. 2014. “Tracking Seasonal Changes of Leaf and Canopy Light Use Efficiency in a Phlomis Fruticosa Mediterranean Ecosystem Using Field Measurements and Multi-Angular Satellite Hyperspectral Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 97:138–151. https://doi.org/10.1016/j.isprsjprs.2014.08.012.
  • Stimler, K., S. A. Montzka, J. A. Berry, Y. Rudich, and D. Yakir. 2010. “Relationships Between Carbonyl Sulfide (COS) and CO2 During Leaf Gas Exchange.” New Phytologist 186 (4): 869–878. https://doi.org/10.1111/j.1469-8137.2010.03218.x.
  • Stocker, B. D., H. Wang, N. G. Smith, S. P. Harrison, T. F. Keenan, D. Sandoval, T. Davis, et al. 2020. “P-Model V1.0: An Optimality-Based Light Use Efficiency Model for Simulating Ecosystem Gross Primary Production.” Geoscientific Model Development 13 (3): 1545–1581. https://doi.org/10.5194/gmd-13-1545-2020.
  • Street, L. E., G. R. Shaver, M. Williams, and M. T. Van wijk. 2007. “What is the Relationship Between Changes in Canopy Leaf Area and Changes in Photosynthetic CO2 Flux in Arctic Ecosystems?” Journal of Ecology 95 (1): 139–150. https://doi.org/10.1111/j.1365-2745.2006.01187.x.
  • Sun, Y., C. Frankenberg, J. D. Wood, D. S. Schimel, M. Jung, L. Guanter, D. T. Drewry, et al. 2017. “OCO-2 Advances Photosynthesis Observation from Space via Solar-Induced Chlorophyll Fluorescence.” Science 358 (6360): eaam5747. https://doi.org/10.1126/science.aam5747.
  • Sun, Z., X. Wang, X. Zhang, H. Tani, E. Guo, S. Yin, T. Zhang, et al. 2019. “Evaluating and Comparing Remote Sensing Terrestrial GPP Models for Their Response to Climate Variability and CO2 Trends.” Science of the Total Environment 668:696–713. https://doi.org/10.1016/j.scitotenv.2019.03.025.
  • Tan, C., A. Samanta, X. Jin, L. Tong, C. Ma, W. Guo, Y. Knyazikhin, et al. 2013. “Using Hyperspectral Vegetation Indices to Estimate the Fraction of Photosynthetically Active Radiation Absorbed by Corn Canopies.” International Journal of Remote Sensing 34 (24): 8789–8802. https://doi.org/10.1080/01431161.2013.853143.
  • Tao, X., Z. Xiao, and W. Fan. 2020. “Chapter 11 - Fraction of Absorbed Photosynthetically Active Radiation.” In Advanced Remote Sensing, edited by S. Liang and J. Wang, 447–476. 2nd ed. United States: Academic Press.
  • Teubner, I. E., M. Forkel, G. Camps-Valls, M. Jung, D. G. Miralles, G. Tramontana, R. van der Schalie, et al. 2019. “A Carbon Sink-Driven Approach to Estimate Gross Primary Production from Microwave Satellite Observations.” Remote Sensing of Environment 229:100–113. https://doi.org/10.1016/j.rse.2019.04.022.
  • Thanyapraneedkul, J., K. Muramatsu, M. Daigo, S. Furumi, N. Soyama, K. Nasahara, H. Muraoka, et al. 2012. “A Vegetation Index to Estimate Terrestrial Gross Primary Production Capacity for the Global Change Observation Mission-Climate (GCOM-C)/second-Generation Global Imager (SGLI) Satellite Sensor.” Remote Sensing 4 (12): 3689–3720. https://doi.org/10.3390/rs4123689.
  • Thenkabail, P. S., J. G. Lyon, and A. Huete. 2011. Hyperspectral Remote Sensing of Vegetation. Boca Raton, FL: CRC Press.
  • Tramontana, G., K. Ichii, G. Camps-Valls, E. Tomelleri, and D. Papale. 2015. “Uncertainty Analysis of Gross Primary Production Upscaling Using Random Forests, Remote Sensing and Eddy Covariance Data.” Remote Sensing of Environment 168:360–373. https://doi.org/10.1016/j.rse.2015.07.015.
  • Tramontana, G., M. Jung, C. R. Schwalm, K. Ichii, G. Camps-Valls, B. Ráduly, M. Reichstein, et al. 2016. “Predicting Carbon Dioxide and Energy Fluxes Across Global FLUXNET Sites with Regression Algorithms.” Biogeosciences 13 (14): 4291–4313. https://doi.org/10.5194/bg-13-4291-2016.
  • Turner, D. P., S. T. Gower, W. B. Cohen, M. Gregory, and T. K. Maiersperger. 2002. “Effects of Spatial Variability in Light Use Efficiency on Satellite-Based NPP Monitoring.” Remote Sensing of Environment 80 (3): 397–405. https://doi.org/10.1016/S0034-4257(01)00319-4.
  • Turner, D. P., W. D. Ritts, W. B. Cohen, T. K. Maeirsperger, S. T. Gower, A. A. Kirschbaum, S. W. Running, et al. 2005. “Site-Level Evaluation of Satellite-Based Global Terrestrial Gross Primary Production and Net Primary Production Monitoring.” Global Change Biology 11 (4): 666–684. https://doi.org/10.1111/j.1365-2486.2005.00936.x.
  • Ueyama, M., K. Ichii, H. Iwata, E. S. Euskirchen, D. Zona, A. V. Rocha, Y. Harazono, et al. 2013. “Upscaling Terrestrial Carbon Dioxide Fluxes in Alaska with Satellite Remote Sensing and Support Vector Regression.” Journal of Geophysical Research: Biogeosciences 118 (3): 1266–1281. https://doi.org/10.1002/jgrg.20095.
  • Ulsig, L., C. J. Nichol, K. F. Huemmrich, D. Landis, E. Middleton, A. Lyapustin, I. Mammarella, et al. 2017. “Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series.” Remote Sensing 9 (1): 49. https://doi.org/10.3390/rs9010049.
  • Ustin, S. L., and E. M. Middleton. 2021. “Current and Near-Term Advances in Earth Observation for Ecological Applications.” Ecological Processes 10 (1): 1. https://doi.org/10.1186/s13717-020-00255-4.
  • van der Tol, C., M. Rossini, S. Cogliati, W. Verhoef, R. Colombo, U. Rascher, G. Mohammed, et al. 2016. “A Model and Measurement Comparison of Diurnal Cycles of Sun-Induced Chlorophyll Fluorescence of Crops.” Remote Sensing of Environment 186:663–677. https://doi.org/10.1016/j.rse.2016.09.021.
  • van der Tol, C., W. Verhoef, J. Timmermans, A. Verhoef, and Z. Su. 2009. “An Integrated Model of Soil-Canopy Spectral Radiances, Photosynthesis, Fluorescence, Temperature and Energy Balance.” Biogeosciences 6 (12): 3109–3129. https://doi.org/10.5194/bg-6-3109-2009.
  • van der Tol, C., N. Vilfan, D. Dauwe, M. P. Cendrero-Mateo, and P. Yang. 2019. “The Scattering and Re-Absorption of Red and Near-Infrared Chlorophyll Fluorescence in the Models Fluspect and SCOPE.” Remote Sensing of Environment 232:111292. https://doi.org/10.1016/j.rse.2019.111292.
  • Van Laake, P. E., and G. A. Sanchez-Azofeifa. 2004. “Simplified Atmospheric Radiative Transfer Modelling for Estimating Incident PAR Using MODIS Atmosphere Products.” Remote Sensing of Environment 91 (1): 98–113. https://doi.org/10.1016/j.rse.2004.03.002.
  • Verma, M., D. Schimel, B. Evans, C. Frankenberg, J. Beringer, D. T. Drewry, T. Magney, et al. 2017. “Effect of Environmental Conditions on the Relationship Between Solar-Induced Fluorescence and Gross Primary Productivity at an OzFlux Grassland Site.” Journal of Geophysical Research: Biogeosciences 122 (3): 716–733. https://doi.org/10.1002/2016JG003580.
  • Veroustraete, F., H. Sabbe, and H. Eerens. 2002. “Estimation of Carbon Mass Fluxes Over Europe Using the C-Fix Model and Euroflux Data.” Remote Sensing of Environment 83 (3): 376–399. https://doi.org/10.1016/S0034-4257(02)00043-3.
  • Wagle, P., Y. Zhang, C. Jin, and X. Xiao. 2016. “Comparison of Solar-Induced Chlorophyll Fluorescence, Light-Use Efficiency, and Process-Based GPP Models in Maize.” Ecological Applications 26 (4): 1211–1222. https://doi.org/10.1890/15-1434.
  • Walker, A. P., A. P. Beckerman, L. Gu, J. Kattge, L. A. Cernusak, T. F. Domingues, J. C. Scales, et al. 2014. “The Relationship of Leaf Photosynthetic Traits – Vcmax and Jmax – to Leaf Nitrogen, Leaf Phosphorus, and Specific Leaf Area: A Meta-Analysis and Modeling Study.” Ecology and Evolution 4 (16): 3218–3235. https://doi.org/10.1002/ece3.1173.
  • Walker, M. D., C. H. Wahren, R. D. Hollister, G. H. R. Henry, L. E. Ahlquist, J. M. Alatalo, M. S. Bret-Harte, et al. 2006. “Plant Community Responses to Experimental Warming Across the Tundra Biome.” Proceedings of the National Academy of Sciences 103 (5): 1342–1346. https://doi.org/10.1073/pnas.0503198103.
  • Wang, S., K. Huang, H. Yan, H. Yan, L. Zhou, H. Wang, J. Zhang, et al. 2015. “Improving the Light Use Efficiency Model for Simulating Terrestrial Vegetation Gross Primary Production by the Inclusion of Diffuse Radiation Across Ecosystems in China.” Ecological Complexity 23:1–13. https://doi.org/10.1016/j.ecocom.2015.04.004.
  • Wang, H., G. Jia, C. Fu, J. Feng, T. Zhao, and Z. Ma. 2010. “Deriving Maximal Light Use Efficiency from Coordinated Flux Measurements and Satellite Data for Regional Gross Primary Production Modeling.” Remote Sensing of Environment 114 (10): 2248–2258. https://doi.org/10.1016/j.rse.2010.05.001.
  • Wang, Y. P., and R. Leuning. 1998. “A Two-Leaf Model for Canopy Conductance, Photosynthesis and Partitioning of Available Energy I: Model Description and Comparison with a Multi-Layered Model.” Agricultural and Forest Meteorology 91 (1): 89–111. https://doi.org/10.1016/S0168-1923(98)00061-6.
  • Wang, D., S. Liang, Y. Zhang, X. Gao, M. G. L. Brown, and A. Jia. 2020. “A New Set of MODIS Land Products (MCD18): Downward Shortwave Radiation and Photosynthetically Active Radiation.” Remote Sensing 12 (1): 168. https://doi.org/10.3390/rs12010168.
  • Wang, Y., R. Li, J. Hu, Y. Fu, J. Duan, and Y. Cheng. 2021. “Daily Estimation of Gross Primary Production Under All Sky Using a Light Use Efficiency Model Coupled with Satellite Passive Microwave Measurements.” Remote Sensing of Environment 267:112721. https://doi.org/10.1016/j.rse.2021.112721.
  • Wang, H., and J. Xiao. 2021. “Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers.” Remote Sensing 13 (4): 794. https://doi.org/10.3390/rs13040794.
  • Wang, S., Y. Zhang, W. Ju, B. Qiu, and Z. Zhang. 2021. “Tracking the Seasonal and Inter-Annual Variations of Global Gross Primary Production During Last Four Decades Using Satellite Near-Infrared Reflectance Data.” Science of the Total Environment 755:142569. https://doi.org/10.1016/j.scitotenv.2020.142569.
  • Wei, S., C. Yi, W. Fang, and G. Hendrey. 2017. “A Global Study of GPP Focusing on Light-Use Efficiency in a Random Forest Regression Model.” Ecosphere 8 (5): e01724. https://doi.org/10.1002/ecs2.1724.
  • Welp, L. R., R. F. Keeling, H. A. J. Meijer, A. F. Bollenbacher, S. C. Piper, K. Yoshimura, R. J. Francey, et al. 2011. “Interannual Variability in the Oxygen Isotopes of Atmospheric CO2 Driven by El Niño.” Nature 477 (7366): 579–582. https://doi.org/10.1038/nature10421.
  • Wolanin, A., G. Camps-Valls, L. Gómez-Chova, G. Mateo-García, C. van der Tol, Y. Zhang, L. Guanter, et al. 2019. “Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 Using Machine Learning Methods Trained with Radiative Transfer Simulations.” Remote Sensing of Environment 225:441–457. https://doi.org/10.1016/j.rse.2019.03.002.
  • Wu, C., J. M. 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. https://doi.org/10.1016/j.rse.2011.08.006.
  • Wu, G., K. Guan, C. Jiang, H. Kimm, G. Miao, C. J. Bernacchi, C. E. Moore, et al. 2022. “Attributing Differences of Solar-Induced Chlorophyll Fluorescence (SIF)-Gross Primary Production (GPP) Relationships Between Two C4 Crops: Corn and Miscanthus.” Agricultural and Forest Meteorology 323:109046. https://doi.org/10.1016/j.agrformet.2022.109046.
  • Wylie, B. K., E. A. Fosnight, T. G. Gilmanov, A. B. Frank, J. A. Morgan, M. R. Haferkamp, T. P. Meyers, et al. 2007. “Adaptive Data-Driven Models for Estimating Carbon Fluxes in the Northern Great Plains.” Remote Sensing of Environment 106 (4): 399–413. https://doi.org/10.1016/j.rse.2006.09.017.
  • Xia, J., S. Niu, P. Ciais, I. A. Janssens, J. Chen, C. Ammann, A. Arain, et al. 2015. “Joint Control of Terrestrial Gross Primary Productivity by Plant Phenology and Physiology.” Proceedings of the National Academy of Sciences of the United States of America 112 (9): 2788. https://doi.org/10.1073/pnas.1413090112.
  • Xiao, J., F. Chevallier, C. Gomez, L. Guanter, J. A. Hicke, A. R. Huete, K. Ichii, et al. 2019. “Remote Sensing of the Terrestrial Carbon Cycle: A Review of Advances Over 50 Years.” Remote Sensing of Environment 233:111383. https://doi.org/10.1016/j.rse.2019.111383.
  • Xiao, X., D. Hollinger, J. Aber, M. Goltz, E. A. Davidson, Q. Zhang, B. Moore, et al. 2004. “Satellite-Based Modeling of Gross Primary Production in an Evergreen Needleleaf Forest.” Remote Sensing of Environment 89 (4): 519–534. https://doi.org/10.1016/j.rse.2003.11.008.
  • Xiao, X., C. Jin, and J. Dong. 2014. “Gross Primary Production of Terrestrial Vegetation.” In Biophysical Applications of Satellite Remote Sensing, edited by J. M. Hanes, 127–148. Berlin Heidelberg, Berlin, Heidelberg: Springer.
  • Xiao, X., Q. Zhang, B. Braswell,Urbanski, S., Boles, S., Wofsy, S., Moore III, B. and Ojima, D. et al. 2004. “Modeling Gross Primary Production of Temperate Deciduous Broadleaf Forest Using Satellite Images and Climate Data.” Remote Sensing of Environment 91 (2): 256–270. https://doi.org/10.1016/j.rse.2004.03.010.
  • Xiao, J., Q. Zhuang, D. D. Baldocchi, B. E. Law, A. D. Richardson, J. Chen, R. Oren, et al. 2008. “Estimation of Net Ecosystem Carbon Exchange for the Conterminous United States by Combining MODIS and AmeriFlux Data.” Agricultural and Forest Meteorology 148 (11): 1827–1847. https://doi.org/10.1016/j.agrformet.2008.06.015.
  • Xie, X., A. Li, H. Jin, G. Yin, and X. Nan. 2018. “Derivation of Temporally Continuous Leaf Maximum Carboxylation Rate (V) from the Sunlit Leaf Gross Photosynthesis Productivity Through Combining BEPS Model with Light Response Curve at Tower Flux Sites.” Agricultural and Forest Meteorology 259:82–94. https://doi.org/10.1016/j.agrformet.2018.04.017.
  • Xie, Z., C. Zhao, W. Zhu, H. Zhang, and Y. H. Fu. 2023. “A Radiation-Regulated Dynamic Maximum Light Use Efficiency for Improving Gross Primary Productivity Estimation.” Remote Sensing 15 (5): 1176. https://doi.org/10.3390/rs15051176.
  • Yang, F., K. Ichii, M. A. White, H. Hashimoto, A. R. Michaelis, P. Votava, A.-X. Zhu, et al. 2007. “Developing a Continental-Scale Measure of Gross Primary Production by Combining MODIS and AmeriFlux Data Through Support Vector Machine Approach.” Remote Sensing of Environment 110 (1): 109–122. https://doi.org/10.1016/j.rse.2007.02.016.
  • Yang, P., E. Prikaziuk, W. Verhoef, and C. van der Tol. 2021. “SCOPE 2.0: A Model To Simulate Vegetated Land Surface Fluxes And Satellite Signals.” Geoscientific Model Development 14 (7): 4697–4712. https://doi.org/10.5194/gmd-14-4697-2021.
  • Yan, H., S. Q. Wang, D. Billesbach, W. Oechel, J. H. Zhang, T. Meyers, T. A. Martin, et al. 2012. “Global Estimation of Evapotranspiration Using a Leaf Area Index-Based Surface Energy and Water Balance Model.” Remote Sensing of Environment 124:581–595. https://doi.org/10.1016/j.rse.2012.06.004.
  • Yebra, M., A. Van Dijk, R. Leuning, A. Huete, and J. P. Guerschman. 2013. “Evaluation Of Optical Remote Sensing To Estimate Actual Evapotranspiration And Canopy Conductance.” Remote Sensing of Environment 129:250–261. https://doi.org/10.1016/j.rse.2012.11.004.
  • Yilmaz, M. T., E. R. Hunt, and T. J. Jackson. 2008. “Remote Sensing of Vegetation Water Content from Equivalent Water Thickness Using Satellite Imagery.” Remote Sensing of Environment 112 (5): 2514–2522. https://doi.org/10.1016/j.rse.2007.11.014.
  • You, Y., S. Wang, Y. Ma, X. Wang, and W. Liu. 2019. “Improved Modeling of Gross Primary Productivity of Alpine Grasslands on the Tibetan Plateau Using the Biome-BGC Model.” Remote Sensing 11 (11): 1287. https://doi.org/10.3390/rs11111287.
  • Yuan, W., W. Cai, J. Xia, J. Chen, S. Liu, W. Dong, L. Merbold, et al. 2014. “Global Comparison of Light Use Efficiency Models for Simulating Terrestrial Vegetation Gross Primary Production Based on the LaThuile Database.” Agricultural and Forest Meteorology 192-193:108–120. https://doi.org/10.1016/j.agrformet.2014.03.007.
  • Yuan, W., S. Liu, G. Zhou, G. Zhou, L. L. Tieszen, D. Baldocchi, C. Bernhofer, et al. 2007. “Deriving a Light Use Efficiency Model from Eddy Covariance Flux Data for Predicting Daily Gross Primary Production Across Biomes.” Agricultural and Forest Meteorology 143 (3): 189–207. https://doi.org/10.1016/j.agrformet.2006.12.001.
  • Yuan, D., S. Zhang, H. Li, J. Zhang, S. Yang, and Y. Bai. 2022. “Improving the Gross Primary Productivity Estimate by Simulating the Maximum Carboxylation Rate of the Crop Using Machine Learning Algorithms.” IEEE Transactions on Geoscience and Remote Sensing 60:1–15. https://doi.org/10.1109/TGRS.2022.3200988.
  • Yu, T., Q. Zhang, and R. Sun. 2021. “Comparison of Machine Learning Methods to Up-Scale Gross Primary Production.” Remote Sensing 13 (13): 2448. https://doi.org/10.3390/rs13132448.
  • Zarco-Tejada, P. J., A. Morales, L. Testi, and F. J. Villalobos. 2013. “Spatio-Temporal Patterns of Chlorophyll Fluorescence and Physiological and Structural Indices Acquired from Hyperspectral Imagery as Compared with Carbon Fluxes Measured with Eddy Covariance.” Remote Sensing of Environment 133:102–115. https://doi.org/10.1016/j.rse.2013.02.003.
  • Zarco-Tejada, P. J., J. C. Pushnik, S. Dobrowski, and S. L. Ustin. 2003. “Steady-State Chlorophyll a Fluorescence Detection from Canopy Derivative Reflectance and Double-Peak Red-Edge Effects.” Remote Sensing of Environment 84 (2): 283–294. https://doi.org/10.1016/S0034-4257(02)00113-X.
  • Zhang, Q., Y.-B. Cheng, A. I. Lyapustin, Y. Wang, F. Gao, A. Suyker, S. Verma, et al. 2014. “Estimation of Crop Gross Primary Production (GPP): fAparchl versus MOD15A2 FPAR.” Remote Sensing of Environment 153:1–6. https://doi.org/10.1016/j.rse.2014.07.012.
  • Zhang, B., Z. Chen, D. Peng, J. A. Benediktsson, B. Liu, L. Zou, J. Li, and A. Plaza. 2019. “Remotely Sensed Big Data: Evolution in Model Development for Information Extraction [Point of View].” Proceedings of the IEEE 107 (12): 2294–2301. https://doi.org/10.1109/JPROC.2019.2948454.
  • Zhang, Y., L. Guanter, J. A. Berry, J. Joiner, C. van der Tol, A. Huete, A. Gitelson, et al. 2014. “Estimation of Vegetation Photosynthetic Capacity from Space-Based Measurements of Chlorophyll Fluorescence for Terrestrial Biosphere Models.” Global Change Biology 20 (12): 3727–3742. https://doi.org/10.1111/gcb.12664.
  • Zhang, Y., L. Guanter, J. A. Berry, C. van der Tol, X. Yang, J. Tang, F. Zhang, et al. 2016. “Model-Based Analysis of the Relationship Between Sun-Induced Chlorophyll Fluorescence and Gross Primary Production for Remote Sensing Applications.” Remote Sensing of Environment 187:145–155. https://doi.org/10.1016/j.rse.2016.10.016.
  • Zhang, Y., D. Kong, R. Gan, F. H. S. Chiew, T. R. McVicar, Q. Zhang, Y. Yang, et al. 2019. “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. https://doi.org/10.1016/j.rse.2018.12.031.
  • Zhang, X., and S. Liang. 2020. “Chapter 5 – Solar Radiation.” In Advanced Remote Sensing, edited by S. Liang and J. Wang, 157–191. 2nd ed. United States: Academic Press.
  • Zhang, Z., X. Li, W. Ju, Y. Zhou, and X. Cheng. 2022. “Improved Estimation of Global Gross Primary Productivity During 1981–2020 Using the Optimized P Model.” Science of the Total Environment 838:156172. https://doi.org/10.1016/j.scitotenv.2022.156172.
  • Zhang, H., J. Li, Q. Liu, S. Lin, A. Huete, L. Liu, H. Croft, et al. 2022. “A Novel Red-Edge Spectral Index for Retrieving the Leaf Chlorophyll Content.” Methods in Ecology and Evolution 13 (12): 2771–2787. https://doi.org/10.1111/2041-210X.13994.
  • Zhang, Q., E. M. Middleton, H. A. Margolis, G. G. Drolet, A. A. Barr, and T. A. Black. 2009. “Can a Satellite-Derived Estimate of the Fraction of PAR Absorbed by Chlorophyll (FAPARchl) Improve Predictions of Light-Use Efficiency and Ecosystem Photosynthesis for a Boreal Aspen Forest?” Remote Sensing of Environment 113 (4): 880–888. https://doi.org/10.1016/j.rse.2009.01.002.
  • Zhang, H., B. Wu, N. Yan, W. Zhu, and X. Feng. 2014. “An Improved Satellite-based Approach For Estimating Vapor Pressure Deficit From MODIS Data.” Journal of Geophysical Research Atmospheres 119 (21): ,12,256–12,271. https://doi.org/10.1002/2014JD022118.
  • Zhang, L., B. Wylie, T. Loveland, E. Fosnight, L. L. Tieszen, L. Ji, T. Gilmanov, et al. 2007. “Evaluation and Comparison of Gross Primary Production Estimates for the Northern Great Plains Grasslands.” Remote Sensing of Environment 106 (2): 173–189. https://doi.org/10.1016/j.rse.2006.08.012.
  • Zhang, Q., X. Xiao, B. Braswell, E. Linder, F. Baret, and B. Mooreiii. 2005. “Estimating Light Absorption by Chlorophyll, Leaf and Canopy in a Deciduous Broadleaf Forest Using MODIS Data and a Radiative Transfer Model.” Remote Sensing of Environment 99 (3): 357–371. https://doi.org/10.1016/j.rse.2005.09.009.
  • Zhang, Y., and A. Ye. 2021. “Would the Obtainable Gross Primary Productivity (GPP) Products Stand Up? A Critical Assessment of 45 Global GPP Products.” Science of the Total Environment 783:146965. https://doi.org/10.1016/j.scitotenv.2021.146965.
  • Zhang, Y., Q. Zhang, L. Liu, Y. Zhang, S. Wang, W. Ju, G. Zhou, et al. 2021. “ChinaSpec: A Network for Long-Term Ground-Based Measurements of Solar-Induced Fluorescence in China.” Journal of Geophysical Research: Biogeosciences 126 (3): e2020JG006042. https://doi.org/10.1029/2020JG006042.
  • Zhang, Z., Y. Zhang, A. Porcar-Castell, J. Joiner, L. Guanter, X. Yang, M. Migliavacca, et al. 2020. “Reduction of Structural Impacts and Distinction of Photosynthetic Pathways in a Global Estimation of GPP from Space-Borne Solar-Induced Chlorophyll Fluorescence.” Remote Sensing of Environment 240:111722. https://doi.org/10.1016/j.rse.2020.111722.
  • Zhang, Z., Y. Zhang, Y. Zhang, N. Gobron, C. Frankenberg, S. Wang, Z. Li, et al. 2020. “The Potential of Satellite FPAR Product for GPP Estimation: An Indirect Evaluation Using Solar-Induced Chlorophyll Fluorescence.” Remote Sensing of Environment 240:111686. https://doi.org/10.1016/j.rse.2020.111686.
  • 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). https://doi.org/10.1029/2004JG000004.
  • Zheng, T., J. Chen, L. He, M. A. Arain, S. C. Thomas, J. G. Murphy, J. A. Geddes, et al. 2017. “Inverting the Maximum Carboxylation Rate (Vcmax) from the Sunlit Leaf Photosynthesis Rate Derived from Measured Light Response Curves at Tower Flux Sites.” Agricultural and Forest Meteorology 236:48–66. https://doi.org/10.1016/j.agrformet.2017.01.008.
  • Zheng, Y., R. Shen, Y. Wang, X. Li, S. Liu, S. Liang, J. M. Chen, et al. 2020. “Improved Estimate of Global Gross Primary Production for Reproducing Its Long-Term Variation, 1982–2017.” Earth System Science Data 12 (4): 2725–2746. https://doi.org/10.5194/essd-12-2725-2020.
  • Zheng, Y., L. Zhang, J. Xiao, W. Yuan, M. Yan, T. Li, Z. Zhang, et al. 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. https://doi.org/10.1016/j.agrformet.2018.08.003.
  • Zhou, Y., W. Ju, X. Sun, Z. Hu, S. Han, T. A. Black, R. S. Jassal, et al. 2014. “Close Relationship Between Spectral Vegetation Indices and Vcmax in Deciduous and Mixed Forests.” Tellus B: Chemical and Physical Meteorology 66 (1): 23279. https://doi.org/10.3402/tellusb.v66.23279.
  • Zhu, Z., J. Bi, Y. Pan, Anav, A., Xu, L., Samanta, A., Piao, S. et al. 2013. “Global Data Sets of Vegetation Leaf Area Index (LAI) 3g and Fraction of Photosynthetically Active Radiation (FPAR) 3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011.” Remote Sensing 5 (2): 927–948. https://doi.org/10.3390/rs5020927.
  • Zhu, W., C. Zhao, and Z. Xie. 2023. “An end-to-end satellite-based GPP estimation model devoid of meteorological and land cover data.” Agricultural and Forest Meteorology 331:109337. https://doi.org/10.1016/j.agrformet.2023.109337.