Articles | Volume 18, issue 16
https://doi.org/10.5194/gmd-18-5115-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-5115-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
22 Aug 2025
Model description paper |
| 22 Aug 2025
| 22 Aug 2025
FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites
CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
University of Chinese Academy of Sciences, Beijing 101408, China
Yuan Zhang
CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
Anzhi Wang
CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
Wenli Fei
CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
Yiwei Diao
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration (ECSS-CMA), Wuxi University, Wuxi 214105, China
Rongping Li
Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110016, China
Jiabing Wu
CORRESPONDING AUTHOR
CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
Related authors
Jie Lai, Anzhi Wang, Yage Liu, Lidu Shen, Yuan Zhang, Yiwei Diao, Rongrong Cai, Rongping Li, Wenli Fei, and Jiabing Wu
EGUsphere, https://doi.org/10.5194/egusphere-2026-103, https://doi.org/10.5194/egusphere-2026-103, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study evaluated Noah-MP performance in simulating gross primary productivity (GPP) across China and analyzed the sensitivity of key parameterization schemes. The modified two-stream radiation scheme (RAD01) shows superior performance, especially in grassland and shrubland ecosystems, while the BTR03 β-factor performs better in croplands. Surface exchange and runoff schemes systematically overestimate GPP, indicating structural biases in energy–carbon and hydro–vegetation coupling.
Jie Lai, Anzhi Wang, Yage Liu, Lidu Shen, Yuan Zhang, Yiwei Diao, Rongrong Cai, Rongping Li, Wenli Fei, and Jiabing Wu
EGUsphere, https://doi.org/10.5194/egusphere-2026-103, https://doi.org/10.5194/egusphere-2026-103, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study evaluated Noah-MP performance in simulating gross primary productivity (GPP) across China and analyzed the sensitivity of key parameterization schemes. The modified two-stream radiation scheme (RAD01) shows superior performance, especially in grassland and shrubland ecosystems, while the BTR03 β-factor performs better in croplands. Surface exchange and runoff schemes systematically overestimate GPP, indicating structural biases in energy–carbon and hydro–vegetation coupling.
Yike Wang, Yueming Cheng, Boru Mai, Tie Dai, Xuejiao Deng, Tao Deng, Xiaoli Zhao, Yiwei Diao, Feng Xia, Miao Liang, Ying Li, and Yixiao Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2025-6272, https://doi.org/10.5194/egusphere-2025-6272, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We developed a carbon assimilation system to construct a near-real-time 4-km anthropogenic inventory in the Pearl River Delta. We analyze spatiotemporal emission distributions, compare the inversion inventories against statistical emissions, and elucidate their relationship with ambient CO2 concentrations. The results of this study provide a robust scientific basis for advancing the dynamic updating and quantitative evaluation of anthropogenic emissions across meso- to microscale domains.
Wei Zhang, Xunhua Zheng, Siqi Li, Shenghui Han, Chunyan Liu, Zhisheng Yao, Rui Wang, Kai Wang, Xiao Chen, Guirui Yu, Zhi Chen, Jiabing Wu, Huimin Wang, Junhua Yan, and Yong Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-141, https://doi.org/10.5194/gmd-2024-141, 2024
Revised manuscript not accepted
Short summary
Short summary
Process-oriented biogeochemical models are promising tools for estimating the carbon fluxes of forest ecosystems. In this study, the hydro-biogeochemical model of CNMM-DNDC was improved by incorporating a new forest growth module derived from the Biome-BGC. The updated model was validated using the multiple-year observed carbon fluxes and showed better performance in capturing the daily dynamics and annual variations. The sensitive eco-physiological parameters were also identified.
Cited articles
Adams, M. D., Massey, F., Chastko, K., and Cupini, C.: Spatial modelling of particulate matter air pollution sensor measurements collected by community scientists while cycling, land use regression with spatial cross-validation, and applications of machine learning for data correction, Atmos. Environ., 230, 117479, https://doi.org/10.1016/j.atmosenv.2020.117479, 2020.
Alemohammad, S. H., Fang, B., Konings, A. G., Aires, F., Green, J. K., Kolassa, J., Miralles, D., Prigent, C., and Gentine, P.: Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence, Biogeosciences, 14, 4101–4124, https://doi.org/10.5194/bg-14-4101-2017, 2017.
Anderson, G. B. and Bell, M. L.: Heat Waves in the United States: Mortality Risk during Heat Waves and Effect Modification by Heat Wave Characteristics in 43 U. S. Communities, Environ. Health Persp., 119, 210, https://doi.org/10.1289/ehp.1002313, 2010.
Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A., and Kustas, W. P.: A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology, J. Geophys. Res.-Atmos., 112, D11112, https://doi.org/10.1029/2006JD007507, 2007.
Ayantobo, O. O., Li, Y., and Song, S.: Multivariate Drought Frequency Analysis using Four-Variate Symmetric and Asymmetric Archimedean Copula Functions, Water Resour. Manag., 33, 103–127, https://doi.org/10.1007/s11269-018-2090-6, 2019.
Barbour, M. T.: Estimating Organic Carbon Burial in Freshwater Impoundments with a Rapid-Assessment Model and Geospatial Analysis, MS thesis, University of Wisconsin-La Crosse, https://minds.wisconsin.edu/handle/1793/82454 (last access: 7 May 2025), 2021.
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rödenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F. I., and Papale, D.: Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate, Science, 329, 834–838, https://doi.org/10.1126/science.1184984, 2010.
Bhattacharyya, P., Neogi, S., Singha Roy, K., and Rao, K. S.: Gross primary production, ecosystem respiration and net ecosystem exchange in Asian rice paddy: An eddy covariance-based approach, Curr. Sci. India, 104, 67–75, 2013.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Brownlee, J.: Histogram-Based Gradient Boosting Ensembles in Python, MachineLearningMastery.com, https://www.machinelearningmastery.com/histogram-based-gradient-boosting-ensembles/ (last access: 7 May 2025), 2020.
Cai, W., Yuan, W., Liang, S., Liu, S., Dong, W., Chen, Y., Liu, D., and Zhang, H.: Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models, Remote Sens.-Basel, 6, 8945–8965, https://doi.org/10.3390/rs6098945, 2014.
Cai, W., Ullah, S., Yan, L., and Lin, Y.: Remote Sensing of Ecosystem Water Use Efficiency: A Review of Direct and Indirect Estimation Methods, Remote Sens.-Basel, 13, 2393, https://doi.org/10.3390/rs13122393, 2021.
Chaney, N. W., Herman, J. D., Ek, M. B., and Wood, E. F.: Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning, J. Geophys. Res.-Atmos., 121, 13218–13235, https://doi.org/10.1002/2016JD024821, 2016.
Chang, X., Xing, Y., Gong, W., Yang, C., Guo, Z., Wang, D., Wang, J., Yang, H., Xue, G., and Yang, S.: Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data, Sci. Total Environ., 875, 162601, https://doi.org/10.1016/j.scitotenv.2023.162601, 2023.
Chen, A., Huang, L., Liu, Q., and Piao, S.: Optimal temperature of vegetation productivity and its linkage with climate and elevation on the Tibetan Plateau, Glob. Change Biol., 27, 1942–1951, https://doi.org/10.1111/gcb.15542, 2021a.
Chen, S.-P., Cui-Hai, Y. O. U., Zhong-Min, H. U., Zhi, C., Lei-Ming, Z., and Qiu-Feng, W.: Eddy covariance technique and its applications in flux observations of terrestrial ecosystems, Chin. J. Plant Ecol., 44, 291, https://doi.org/10.17521/cjpe.2019.0351, 2020.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, Association for Computing Machinery, San Francisco, CA, USA, 13–17 August 2016, 785–794, https://doi.org/10.1145/2939672.2939785, 2016.
Chen, Y., Feng, X., Fu, B., Wu, X., and Gao, Z.: Improved Global Maps of the Optimum Growth Temperature, Maximum Light Use Efficiency, and Gross Primary Production for Vegetation, J. Geophys. Res.-Biogeo., 126, e2020JG005651, https://doi.org/10.1029/2020JG005651, 2021b.
Coops, N. C. and Waring, R. H.: The use of multiscale remote sensing imagery to derive regional estimates of forest growth capacity using 3-PGS, Remote Sens. Environ., 75, 324–334, https://doi.org/10.1016/S0034-4257(00)00176-0, 2001.
Cover, T. and Hart, P.: Nearest neighbor pattern classification, IEEE T. Inform. Theory, 13, 21–27, https://doi.org/10.1109/TIT.1967.1053964, 1967.
Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., and Totterdell, I. J.: Erratum: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408, 750–750, https://doi.org/10.1038/35047138, 2000.
Ercoli, L.: Relationship between nitrogen and chlorophyll content and spectral properties in maize leaves, Eur. J. Agron., 2, 113–117, https://doi.org/10.1016/S1161-0301(14)80141-X, 1993.
Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A.: AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data, arXiv [preprint], https://doi.org/10.48550/arXiv.2003.06505, 2020.
Frank, D., Reichstein, M., Bahn, M., Thonicke, K., Frank, David, Mahecha, M. D., Smith, P., van der Velde, M., Vicca, S., Babst, F., Beer, C., Buchmann, N., Canadell, J. G., Ciais, P., Cramer, W., Ibrom, A., Miglietta, F., Poulter, B., Rammig, A., Seneviratne, S. I., Walz, A., Wattenbach, M., Zavala, M. A., and Zscheischler, J.: Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts, Glob. Change Biol., 21, 2861–2880, https://doi.org/10.1111/gcb.12916, 2015.
Geurts, P., Ernst, D., and Wehenkel, L.: Extremely randomized trees, Mach. Learn., 63, 3–42, https://doi.org/10.1007/s10994-006-6226-1, 2006.
Gherardi, L. A. and Sala, O. E.: Global patterns and climatic controls of belowground net carbon fixation, P. Natl. Acad. Sci. USA, 117, 20038–20043, https://doi.org/10.1073/pnas.2006715117, 2020.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Gumus, V.: Evaluating the effect of the SPI and SPEI methods on drought monitoring over Turkey, J. Hydrol., 626, 130386, https://doi.org/10.1016/j.jhydrol.2023.130386, 2023.
Guo, Q., Hu, Z., Li, S., Yu, G., Sun, X., Zhang, L., Mu, S., Zhu, X., Wang, Y., Li, Y., and Zhao, W.: Contrasting responses of gross primary productivity to precipitation events in a water-limited and a temperature-limited grassland ecosystem, Agr. Forest Meteorol., 214–215, 169–177, https://doi.org/10.1016/j.agrformet.2015.08.251, 2015.
Harris, N. L., Gibbs, D. A., Baccini, A., Birdsey, R. A., de Bruin, S., Farina, M., Fatoyinbo, L., Hansen, M. C., Herold, M., Houghton, R. A., Potapov, P. V., Suarez, D. R., Roman-Cuesta, R. M., Saatchi, S. S., Slay, C. M., Turubanova, S. A., and Tyukavina, A.: Global maps of twenty-first century forest carbon fluxes, Nat. Clim. Change, 11, 234–240, https://doi.org/10.1038/s41558-020-00976-6, 2021.
He, X., Zhao, K., and Chu, X.: AutoML: A survey of the state-of-the-art, Knowl.-Based Syst., 212, 106622, https://doi.org/10.1016/j.knosys.2020.106622, 2021.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Jiang, G., Sun, R., Zhang, L., Liu, S., Xu, Z., and Qiao, C.: Analysis of light use efficiency and gross primary productivity based on remote sensing data over a phragmites-dominated wetland in Zhangye, China, in: Land Surface Remote Sensing II, Presented at the Land Surface Remote Sensing II, SPIE, Beijing, China, 13–17 October 2014, 571–578, https://doi.org/10.1117/12.2068840, 2014.
Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D., Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D., Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B. E., Lindroth, A., Merbold, L., Montagnani, L., Moors, E. J., Papale, D., Sottocornola, M., Vaccari, F., and Williams, C.: Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations, J. Geophys. Res.-Biogeo., 116, G00J07, https://doi.org/10.1029/2010JG001566, 2011.
Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U., and Reichstein, M.: Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach, Biogeosciences, 17, 1343–1365, https://doi.org/10.5194/bg-17-1343-2020, 2020.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree, in: Advances in Neural Information Processing Systems 30 (NIPS 2017), Advances in Neural Information Processing Systems, edited by: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., Presented at the 31st Annual Conference on Neural Information Processing Systems (NIPS), Neural Information Processing Systems (nips), La Jolla, 4–9 December 2017, WOS:000452649403021, https://www.webofscience.com/wos/alldb/full-record/WOS:000452649403021 (last access: 7 May 2025), 2017.
Kong, D., Yuan, D., Li, H., Zhang, J., Yang, S., Li, Y., Bai, Y., and Zhang, S.: Improving the Estimation of Gross Primary Productivity across Global Biomes by Modeling Light Use Efficiency through Machine Learning, Remote Sens.-Basel, 15, 2086, https://doi.org/10.3390/rs15082086, 2023.
Laijie: FLAML_LUE data and code, Zenodo [code and data set], https://doi.org/10.5281/zenodo.15477703, 2025a.
Laijie: FLAML-main [Data set], Zenodo [data set], https://doi.org/10.5281/zenodo.14874754, 2025b.
Landry, J.-S. and Matthews, H. D.: Non-deforestation fire vs. fossil fuel combustion: the source of CO2 emissions affects the global carbon cycle and climate responses, Biogeosciences, 13, 2137–2149, https://doi.org/10.5194/bg-13-2137-2016, 2016.
LeDell, E. and Poirier, S.: H2O AutoML: Scalable Automatic Machine Learning, in: Proceedings of the AutoML Workshop at ICML (Vol. 2020) ICML,7th ICML Workshop on Automated Machine Learning, AutoML 2020, Vienna, Austria, 17–18 July 2020, https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf (last access: 12 August 2025), 2020.
Li, H., Zhang, F., Li, Y., Wang, J., Zhang, L., Zhao, L., Cao, G., Zhao, X., and Du, M.: Seasonal and inter-annual variations in CO2 fluxes over 10 years in an alpine shrubland on the Qinghai-Tibetan Plateau, China, Agr. Forest Meteorol., 228–229, 95–103, https://doi.org/10.1016/j.agrformet.2016.06.020, 2016.
Lloyd, J. and Taylor, J. A.: On the Temperature Dependence of Soil Respiration, Funct. Ecol., 8, 315–323, https://doi.org/10.2307/2389824, 1994.
Mahadevan, P., Wofsy, S. C., Matross, D. M., Xiao, X., Dunn, A. L., Lin, J. C., Gerbig, C., Munger, J. W., Chow, V. Y., and Gottlieb, E. W.: A satellite-based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM), Global Biogeochem. Cy., 22, GB2005, https://doi.org/10.1029/2006GB002735, 2008.
McDowell, N., Pockman, W. T., Allen, C. D., Breshears, D. D., Cobb, N., Kolb, T., Plaut, J., Sperry, J., West, A., Williams, D. G., and Yepez, E. A.: Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought, New Phytol., 178, 719–739, https://doi.org/10.1111/j.1469-8137.2008.02436.x, 2008.
Melanie: TPOT: All about this Machine Learning Python library, Data Sci. Courses DataScientest, https://datascientest.com/en/tpot-all-about-this-machine-learning-python-library (last access: 7 May 2025), 2023.
Menefee, D., Lee, T. O., Flynn, K. C., Chen, J., Abraha, M., Baker, J., and Suyker, A.: Machine learning algorithms improve MODIS GPP estimates in United States croplands, Front. Remote Sens., 4, 1240895, https://doi.org/10.3389/frsen.2023.1240895, 2023.
Metin, A. and Bilgin, T. T.: Automated machine learning for fabric quality prediction: a comparative analysis, PeerJ Comput. Sci., 10, e2188, https://doi.org/10.7717/peerj-cs.2188, 2024.
Nakano, S. and Liu, Y.: Interpreting Temporal Shifts in Global Annual Data Using Local Surrogate Models, Mathematics, 13, 626, https://doi.org/10.3390/math13040626, 2025.
Novick, K. A., Ficklin, D. L., Stoy, P. C., Williams, C. A., Bohrer, G., Oishi, A. C., Papuga, S. A., Blanken, P. D., Noormets, A., Sulman, B. N., Scott, R. L., Wang, L., and Phillips, R. P.: The increasing importance of atmospheric demand for ecosystem water and carbon fluxes, Nat. Clim. Change, 6, 1023–1027, https://doi.org/10.1038/nclimate3114, 2016.
Pagán, B. R., Maes, W. H., Gentine, P., Martens, B., and Miralles, D. G.: Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates, Remote Sens.-Basel, 11, 413, https://doi.org/10.3390/rs11040413, 2019.
Pei, Y., Dong, J., Zhang, Y., Yuan, W., Doughty, R., Yang, J., Zhou, D., Zhang, L., and Xiao, X.: Evolution of light use efficiency models: Improvement, uncertainties, and implications, Agr. Forest Meteorol., 317, 108905, https://doi.org/10.1016/j.agrformet.2022.108905, 2022.
Peltoniemi, M., Pulkkinen, M., Kolari, P., Duursma, R. A., Montagnani, L., Wharton, S., Lagergren, F., Takagi, K., Verbeeck, H., Christensen, T., Vesala, T., Falk, M., Loustau, D., and Mäkelä, A.: Does canopy mean nitrogen concentration explain variation in canopy light use efficiency across 14 contrasting forest sites, Tree Physiol., 32, 200–218, https://doi.org/10.1093/treephys/tpr140, 2012.
Potter, C. S., Randerson, J. T., Field, C. B., Matson, P. A., Vitousek, P. M., Mooney, H. A., and Klooster, S. A.: Terrestrial ecosystem production: A process model based on global satellite and surface data, Global Biogeochem. Cy., 7, 811–841, https://doi.org/10.1029/93GB02725, 1993.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A.: CatBoost: unbiased boosting with categorical features, in: Advances in Neural Information Processing Systems 31 (NIPS 2018), Advances in Neural Information Processing Systems, edited by: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., and Garnett, R., Presented at the 32nd Conference on Neural Information Processing Systems (NIPS), Neural Information Processing Systems (nips), La Jolla, 2–8 December 2018, https://dl.acm.org/doi/10.5555/3327757.3327770 (last access: 12 August 2025), 2018.
Qian, L., Zhang, Z., Wu, L., Fan, S., Yu, X., Liu, X., Ba, Y., Ma, H., and Wang, Y.: High uncertainty of evapotranspiration products under extreme climatic conditions, J. Hydrol., 626, 130332, https://doi.org/10.1016/j.jhydrol.2023.130332, 2023.
Qian, L., Yu, X., Zhang, Z., Wu, L., Fan, J., Xiang, Y., Chen, J., and Liu, X.: Assessing and improving the high uncertainty of global gross primary productivity products based on deep learning under extreme climatic conditions, Sci. Total Environ., 957, 177344, https://doi.org/10.1016/j.scitotenv.2024.177344, 2024.
Qu, L., De Boeck, H. J., Fan, H., Dong, G., Chen, J., Xu, W., Ge, Z., Huang, Z., Shao, C., and Hu, Y.: Diverging Responses of Two Subtropical Tree Species (Schima superba and Cunninghamia lanceolata) to Heat Waves, Forests, 11, 513, https://doi.org/10.3390/f11050513, 2020.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald, T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Glob. Change Biol., 11, 1424–1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005.
Reichstein, M., Ciais, P., Papale, D., Valentini, R., Running, S., Viovy, N., Cramer, W., Granier, A., Ogee, J., Allard, V., Aubinet, M., Bernhofer, C., Buchmann, N., Carrara, A., Grunwald, T., Heimann, M., Heinesch, B., Knohl, A., Kutsch, W., Loustau, D., Manca, G., Matteucci, G., Miglietta, F., Ourcival, J., Pilegaard, K., Pumpanen, J., Rambal, S., Schaphoff, S., Seufert, G., Soussana, J.-F., Sanz, M.-J., Vesala, T., and Zhao, M.: Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: a joint flux tower, remote sensing and modelling analysis, Glob. Change Biol., 13, 634–651, https://doi.org/10.1111/j.1365-2486.2006.01224.x, 2007.
Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M. D., Seneviratne, S. I., Zscheischler, J., Beer, C., Buchmann, N., Frank, D. C., Papale, D., Rammig, A., Smith, P., Thonicke, K., van der Velde, M., Vicca, S., Walz, A., and Wattenbach, M.: Climate extremes and the carbon cycle, Nature, 500, 287–295, https://doi.org/10.1038/nature12350, 2013.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N., and Huybers, P.: Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields, Nat. Food, 1, 127–133, https://doi.org/10.1038/s43016-020-0028-7, 2020.
Rosebrock, A.: Auto-Keras and AutoML: A Getting Started Guide, PyImageSearch, https://pyimagesearch.com/2019/01/07/auto-keras-and-automl-a-getting-started-guide (last access: 7 May 2025), 2019.
Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., and Hashimoto, H.: A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production, BioScience, 54, 547–560, https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2, 2004.
Schmid, H. P.: Footprint modeling for vegetation atmosphere exchange studies: a review and perspective, Agr. Forest Meteorol., 113, 159–183, https://doi.org/10.1016/S0168-1923(02)00107-7, 2002.
Sellers, P. J., Schimel, D. S., Moore, B., Liu, J., and Eldering, A.: Observing carbon cycle–climate feedbacks from space, P. Natl. Acad. Sci. USA, 115, 7860–7868, https://doi.org/10.1073/pnas.1716613115, 2018.
Stefanon, M., D'Andrea, F., and Drobinski, P.: Heatwave classification over Europe and the Mediterranean region, Environ. Res. Lett, 7, 014023, https://doi.org/10.1088/1748-9326/7/1/014023, 2012.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001.
Thornton, C., Hutter, F., Hoos, H. H., and Leyton-Brown, K.: Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms, arXiv [preprint], https://doi.org/10.48550/arXiv.1208.3719, 2013.
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I.: Attention Is All You Need, arXiv [preprint], https://doi.org/10.48550/arXiv.1706.03762, 2023.
Venturini, V., Bisht, G., Islam, S., and Jiang, L.: Comparison of evaporative fractions estimated from AVHRR and MODIS sensors over South Florida, Remote Sens. Environ., 93, 77–86, https://doi.org/10.1016/j.rse.2004.06.020, 2004.
Vicca, S., Bahn, M., Estiarte, M., van Loon, E. E., Vargas, R., Alberti, G., Ambus, P., Arain, M. A., Beier, C., Bentley, L. P., Borken, W., Buchmann, N., Collins, S. L., de Dato, G., Dukes, J. S., Escolar, C., Fay, P., Guidolotti, G., Hanson, P. J., Kahmen, A., Kröel-Dulay, G., Ladreiter-Knauss, T., Larsen, K. S., Lellei-Kovacs, E., Lebrija-Trejos, E., Maestre, F. T., Marhan, S., Marshall, M., Meir, P., Miao, Y., Muhr, J., Niklaus, P. A., Ogaya, R., Peñuelas, J., Poll, C., Rustad, L. E., Savage, K., Schindlbacher, A., Schmidt, I. K., Smith, A. R., Sotta, E. D., Suseela, V., Tietema, A., van Gestel, N., van Straaten, O., Wan, S., Weber, U., and Janssens, I. A.: Can current moisture responses predict soil CO2 efflux under altered precipitation regimes? A synthesis of manipulation experiments, Biogeosciences, 11, 2991–3013, https://doi.org/10.5194/bg-11-2991-2014, 2014.
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, J. Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2010.
Wang, C., Wu, Q., Weimer, M., and Zhu, E.: FLAML: A Fast and Lightweight AutoML Library, arXiv [preprint], https://doi.org/10.48550/arXiv.1911.04706, 2021a.
Wang, C., Wu, Q., Liu, X., and Quintanilla, L.: Automated Machine Learning & Tuning with FLAML, in: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '22, Association for Computing Machinery, New York, NY, USA, 4828–4829, https://doi.org/10.1145/3534678.3542636, 2022.
Wang, H., He, B., Zhang, Y., Huang, L., Chen, Z., and Liu, J.: Response of ecosystem productivity to dry/wet conditions indicated by different drought indices, Sci. Total Environ., 612, 347–357, https://doi.org/10.1016/j.scitotenv.2017.08.212, 2018.
Wang, H., Guan, H., Liu, B., and Chen, X.: Impacts of climate extremes on vegetation dynamics in a transect along the Hu Line of China, Ecol. Indic., 155, 111043, https://doi.org/10.1016/j.ecolind.2023.111043, 2023.
Wang, J., Liu, J., Cao, M., Liu, Y., Yu, G., Li, G., Qi, S., and Li, K.: Modelling carbon fluxes of different forests by coupling a remote-sensing model with an ecosystem process model, Int. J. Remote Sens., 32, 6539–6567, https://doi.org/10.1080/01431161.2010.512933, 2011.
Wang, Y., Li, R., Hu, J., Fu, Y., Duan, J., and Cheng, Y.: Daily estimation of gross primary production under all sky using a light use efficiency model coupled with satellite passive microwave measurements, Remote Sens. Environ., 267, 112721, https://doi.org/10.1016/j.rse.2021.112721, 2021b.
Xiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J. A., Huete, A. R., Ichii, K., Ni, W., Pang, Y., Rahman, A. F., Sun, G., Yuan, W., Zhang, L., and Zhang, X.: Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years, Remote Sens. Environ., 233, 111383, https://doi.org/10.1016/j.rse.2019.111383, 2019.
Xiao, X., Braswell, B., Zhang, Q., Boles, S., Frolking, S., and Moore, B.: Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia, Remote Sens. Environ., 84, 385–392, https://doi.org/10.1016/S0034-4257(02)00129-3, 2003.
Xiao, X., Hollinger, D., Aber, J., Goltz, M., Davidson, E. A., Zhang, Q., and Moore, B.: Satellite-based modeling of gross primary production in an evergreen needleleaf forest, Remote Sens. Environ., 89, 519–534, https://doi.org/10.1016/j.rse.2003.11.008, 2004.
Xie, M., Ma, X., Wang, Y., Li, C., Shi, H., Yuan, X., Hellwich, O., Chen, C., Zhang, W., Zhang, C., Ling, Q., Gao, R., Zhang, Y., Ochege, F. U., Frankl, A., De Maeyer, P., Buchmann, N., Feigenwinter, I., Olesen, J. E., Juszczak, R., Jacotot, A., Korrensalo, A., Pitacco, A., Varlagin, A., Shekhar, A., Lohila, A., Carrara, A., Brut, A., Kruijt, B., Loubet, B., Heinesch, B., Chojnicki, B., Helfter, C., Vincke, C., Shao, C., Bernhofer, C., Brümmer, C., Wille, C., Tuittila, E.-S., Nemitz, E., Meggio, F., Dong, G., Lanigan, G., Niedrist, G., Wohlfahrt, G., Zhou, G., Goded, I., Gruenwald, T., Olejnik, J., Jansen, J., Neirynck, J., Tuovinen, J.-P., Zhang, J., Klumpp, K., Pilegaard, K., Šigut, L., Klemedtsson, L., Tezza, L., Hörtnagl, L., Urbaniak, M., Roland, M., Schmidt, M., Sutton, M. A., Hehn, M., Saunders, M., Mauder, M., Aurela, M., Korkiakoski, M., Du, M., Vendrame, N., Kowalska, N., Leahy, P. G., Alekseychik, P., Shi, P., Weslien, P., Chen, S., Fares, S., Friborg, T., Tallec, T., Kato, T., Sachs, T., Maximov, T., di Cella, U. M., Moderow, U., Li, Y., He, Y., Kosugi, Y., and Luo, G.: Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing, Sci. Data, 10, 587, https://doi.org/10.1038/s41597-023-02473-9, 2023.
Yi, K., Li, R., Scanlon, T. M., Lerdau, M. T., Berry, J. A., and Yang, X.: Impact of atmospheric dryness on solar-induced chlorophyll fluorescence: Tower-based observations at a temperate forest, Remote Sens. Environ., 306, 114106, https://doi.org/10.1016/j.rse.2024.114106, 2024.
Yu, G., Ren, W., Chen, Z., Zhang, L., Wang, Q., Wen, X., He, N., Zhang, L., Fang, H., Zhu, X., Gao, Y., and Sun, X.: Construction and progress of Chinese terrestrial ecosystem carbon, nitrogen and water fluxes coordinated observation, J. Geogr. Sci., 26, 803–826, https://doi.org/10.1007/s11442-016-1300-5, 2016.
Yuan, W., Liu, S., Zhou, G., Zhou, G., Tieszen, L. L., Baldocchi, D., Bernhofer, C., Gholz, H., Goldstein, A. H., Goulden, M. L., Hollinger, D. Y., Hu, Y., Law, B. E., Stoy, P. C., Vesala, T., and Wofsy, S. C.: Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes, Agr. Forest Meteorol., 143, 189–207, https://doi.org/10.1016/j.agrformet.2006.12.001, 2007.
Yuan, W., Liu, S., Yu, G., Bonnefond, J.-M., Chen, J., Davis, K., Desai, A. R., Goldstein, A. H., Gianelle, D., Rossi, F., Suyker, A. E., and Verma, S. B.: Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data, Remote Sens. Environ., 114, 1416–1431, https://doi.org/10.1016/j.rse.2010.01.022, 2010.
Yuan, W., Cai, W., Xia, J., Chen, J., Liu, S., Dong, W., Merbold, L., Law, B., Arain, A., Beringer, J., Bernhofer, C., Black, A., Blanken, P. D., Cescatti, A., Chen, Y., Francois, L., Gianelle, D., Janssens, I. A., Jung, M., Kato, T., Kiely, G., Liu, D., Marcolla, B., Montagnani, L., Raschi, A., Roupsard, O., Varlagin, A., and Wohlfahrt, G.: Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database, Agr. Forest Meteorol., 192–193, 108–120, https://doi.org/10.1016/j.agrformet.2014.03.007, 2014.
Zhang, C., Tian, X., Zhao, Y., and Lu, J.: Automated machine learning-based building energy load prediction method, J. Build. Eng., 80, 108071, https://doi.org/10.1016/j.jobe.2023.108071, 2023a.
Zhang, W. L., Chen, S. P., Chen, J., Wei, L., Han, X. G., and Lin, G. H.: Biophysical regulations of carbon fluxes of a steppe and a cultivated cropland in semiarid Inner Mongolia, Agr. Forest Meteorol., 146, 216–229, https://doi.org/10.1016/j.agrformet.2007.06.002, 2007.
Zhang, Y., Song, C., Sun, G., Band, L. E., Noormets, A., and Zhang, Q.: Understanding moisture stress on light use efficiency across terrestrial ecosystems based on global flux and remote-sensing data, J. Geophys. Res.-Biogeo., 120, 2053–2066, https://doi.org/10.1002/2015JG003023, 2015.
Zhang, Z., Guo, J., Jin, S., and Han, S.: Improving the ability of PRI in light use efficiency estimation by distinguishing sunlit and shaded leaves in rice canopy, Int. J. Remote Sens., 44, 5755–5767, https://doi.org/10.1080/01431161.2023.2252165, 2023b.
Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin, C., Li, X., and Qiu, G. Y.: Physics-Constrained Machine Learning of Evapotranspiration, Geophys. Res. Lett., 46, 14496–14507, https://doi.org/10.1029/2019GL085291, 2019.
Zheng, Z., Fiore, A. M., Westervelt, D. M., Milly, G. P., Goldsmith, J., Karambelas, A., Curci, G., Randles, C. A., Paiva, A. R., Wang, C., Wu, Q., and Dey, S.: Automated Machine Learning to Evaluate the Information Content of Tropospheric Trace Gas Columns for Fine Particle Estimates Over India: A Modeling Testbed, J. Adv. Model. Earth Sy., 15, e2022MS003099, https://doi.org/10.1029/2022MS003099, 2023.
Zhou, S.-X., Prentice, I. C., and Medlyn, B. E.: Bridging Drought Experiment and Modeling: Representing the Differential Sensitivities of Leaf Gas Exchange to Drought, Front. Plant Sci., 9, 1965, https://doi.org/10.3389/fpls.2018.01965, 2019.
Short summary
In this study, a new model called FLAML-LUE was created by combining the Fast Lightweight Automated Machine Learning (FLAML) model with light use efficiency (LUE) models; the latter provides the key variables of vegetation growth for modeling. Such knowledge- and data-driven models aim to reduce the large uncertainty in estimating gross primary productivity (GPP).
In this study, a new model called FLAML-LUE was created by combining the Fast Lightweight...
