Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-4877-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-4877-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
01 Nov 2023
Data description paper |
| 01 Nov 2023
| 01 Nov 2023
Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Key Laboratory of Earth Surface System and Human–Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Muyi Li
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Key Laboratory of Earth Surface System and Human–Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Key Laboratory of Earth Surface System and Human–Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Zhe Wang
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Key Laboratory of Earth Surface System and Human–Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Junjun Zha
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Key Laboratory of Earth Surface System and Human–Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Weiqing Zhao
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Key Laboratory of Earth Surface System and Human–Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Zeyu Duanmu
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Key Laboratory of Earth Surface System and Human–Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Jiana Chen
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Key Laboratory of Earth Surface System and Human–Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Yaoyao Zheng
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Key Laboratory of Earth Surface System and Human–Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Yue Chen
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Key Laboratory of Earth Surface System and Human–Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Ranga B. Myneni
Department of Earth & Environment, Boston University, Boston, MA 02215, USA
Shilong Piao
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
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Cited articles
Alkama, R., Forzieri, G., Duveiller, G., Grassi, G., Liang, S., and Cescatti, A.: Vegetation-based climate mitigation in a warmer and greener World, Nat. Commun., 13, 606, https://doi.org/10.1038/s41467-022-28305-9, 2022.
Baret, F., Morissette, J. T., Fernandes, R. A., Champeaux, J. L., Myneni, R. B., Chen, J., Plummer, S., Weiss, M., Bacour, C., Garrigues, S., and Nickeso, J. E.: Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: proposition of the CEOS-BELMANIP, IEEE T. Geosci. Remote, 44, 1794–1803, https://doi.org/10.1109/TGRS.2006.876030, 2006.
Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Niño, F., Weiss, M., Samain, O., Roujean, J. L., and Leroy, M.: LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm, Remote Sens. Environ., 110, 275–286, https://doi.org/10.1016/j.rse.2007.02.018, 2007.
Basheer, I. A. and Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application, J. Microbiol. Meth., 43, 3–31, https://doi.org/10.1016/s0167-7012(00)00201-3, 2000.
Berner, L. T., Massey, R., Jantz, P., Forbes, B. C., Macias-Fauria, M., Myers-Smith, I., Kumpula, T., Gauthier, G., Andreu-Hayles, L., Gaglioti, B. V., Burns, P., Zetterberg, P., D'Arrigo, R., and Goetz, S. J.: Summer warming explains widespread but not uniform greening in the Arctic tundra biome, Nat. Commun., 11, 1–12, https://doi.org/10.1038/s41467-020-18479-5, 2020.
Boussetta, S., Balsamo, G., Beljaars, A., Kral, T., and Jarlan, L.: Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model, Int. J. Remote Sens., 34, 3520–3542, https://doi.org/10.1080/01431161.2012.716543, 2013.
Boussetta, S., Balsamo, G., Dutra, E., Beljaars, A., and Albergel, C.: Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction, Remote Sens. Environ., 163, 111–126, https://doi.org/10.1016/j.rse.2015.03.009, 2015.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Broge, N. H. and Leblanc, E.: Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density, Remote Sens. Environ., 76, 156–172, https://doi.org/10.1016/s0034-4257(00)00197-8, 2001.
Ciais, Ph., Reichstein, M., Viovy, N., Granier, A., Ogée, J., Allard, V., Aubinet, M., Buchmann, N., Bernhofer, Chr., Carrara, A., Chevallier, F., De Noblet, N., Friend, A.D., Friedlingstein, P., Grünwald, T., Heinesch, B., Keronen, P., Knohl, A., Krinner, G., Loustau, D., Manca, G., Matteucci, G., Miglietta, F., Ourcival, J.M., Papale, D., Pilegaard, K., Rambal, S., Seufert, G., Soussana, J.F., Sanz, M.J., Schulze, E.D., Vesala, T., and Valentini, R.: Europe-wide reduction in primary productivity caused by the heat and drought in 2003, Nature, 437, 529–533, https://doi.org/10.1038/nature03972, 2005.
Cao, S., Li, M., Zhu, Z., Wang, Z., Zha, J., Zhao, W., Duanmu, Z., Chen, J., Zheng, Y., Chen, Y., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020 (V1.2), Zenodo [data set], https://doi.org/10.5281/zenodo.7649107, 2023.
Chen, C., Park, T., Wang, X. H., Piao, S. L., Xu, B. D., Chaturvedi, R. K., Fuchs, R., Brovkin, V., Ciais, P., Fensholt, R., Tommervik, H., Bala, G., Zhu, Z. C., Nemani, R. R., and Myneni, R. B.: China and India lead in greening of the world through land-use management, Nat. Sustain., 2, 122–129, https://doi.org/10.1038/s41893-019-0220-7, 2019a.
Chen, J. M., Ju, W., Ciais, P., Viovy, N., Liu, R., Liu, Y., and Lu, X.: Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink, Nat. Commun., 10, 4259, https://doi.org/10.1038/s41467-019-12257-8, 2019b.
Chen, M., Willgoose, G. R., and Saco, P. M.: Investigating the impact of leaf area index temporal variability on soil moisture predictions using remote sensing vegetation data, J. Hydrol., 522, 274–284, https://doi.org/10.1016/j.jhydrol.2014.12.027, 2015.
Claverie, M., Matthews, J. L., Vermote, E. F., and Justice, C. O.: A 30+ year AVHRR LAI and FAPAR climate data record: algorithm description and validation, Remote Sens., 8, 263, https://doi.org/10.3390/rs8030263, 2016.
de Wit, A., Duveiller, G., and Defourny, P.: Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations, Agric. Forest Meteorol., 164, 39–52, https://doi.org/10.1016/j.agrformet.2012.04.011, 2012.
Dente, L., Satalino, G., Mattia, F., and Rinaldi, M.: Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield, Remote Sens. Environ., 112, 1395–1407, https://doi.org/10.1016/j.rse.2007.05.023, 2008.
Eyring, V., Gillett, N. P., Achutarao, K., Barimalala, R., Barreiro Parrillo, M., Bellouin, N., Cassou, C., Durack, P., Kosaka, Y., McGregor, S., Min, S.-K., Morgenstern, O., and Sun, Y.: Human Influence on the Climate System, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T.K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 423–552, https://doi.org/10.1017/9781009157896.005, 2021.
Fang, H., Baret, F., Plummer, S., and Schaepman-Strub, G.: An overview of global Leaf Area Index (LAI): methods, products, validation, and applications, Rev. Geophys., 57, 739–799, https://doi.org/10.1029/2018RG000608, 2019.
Friedl, M. and Sulla-Menashe, D.: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061 (V061), NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12Q1.061, 2022.
Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C.: Global land cover mapping from MODIS: algorithms and early results, Remote Sens. Environ., 83, 287–302, https://doi.org/10.1016/s0034-4257(02)00078-0, 2002.
Garrigues, S., Lacaze, R., Baret, F., Morisette, J. T., Weiss, M., Nickeson, J. E., Fernandes, R., Plummer, S., Shabanov, N. V., Myneni, R. B., Knyazikhin, Y., and Yang, W.: Validation and intercomparison of global Leaf Area Index products derived from remote sensing data: GLOBAL LAI PRODUCTS INTERCOMPARISON, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008.
WMO, UNESCO, IOC, UNEP, and ISC: The 2022 GCOS ECVs Requirements, WMO, Geneva, https://library.wmo.int/idurl/4/58111 (last access: 16 September 2023), 2022.
Helder, D., Thome, K. J., Mishra, N., Chander, G., Xiong, X. X., Angal, A., and Choi, T.: Absolute radiometric calibration of Landsat using a pseudo invariant calibration site, IEEE T. Geosci. Remote, 51, 1360–1369, https://doi.org/10.1109/tgrs.2013.2243738, 2013.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. L. C., Shih, H. H., Zheng, Q. N., Yen, N. C., Tung, C. C., and Liu, H. H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, P. R. Soc. A, 454, 903–995, https://doi.org/10.1098/rspa.1998.0193, 1998.
Jahan, N. and Gan, T. Y.: Modelling the vegetation-climate relationship in a boreal mixedwood forest of Alberta using normalized difference and enhanced vegetation indices, Int. J. Remote Sens., 32, 313–335, https://doi.org/10.1080/01431160903464146, 2011.
Jiang, C., Ryu, Y., Fang, H., Myneni, R., Claverie, M., and Zhu, Z.: Inconsistencies of interannual variability and trends in long-term satellite leaf area index products, Glob. Change Biol., 23, 4133–4146, https://doi.org/10.1111/gcb.13787, 2017.
Kang, Y., Ozdogan, M., Gao, F., Anderson, M. C., White, W. A., Yang, Y., Yang, Y., and Erickson, T. A.: A data-driven approach to estimate leaf area index for Landsat images over the contiguous US, Remote Sens. Environ., 258, 112383, https://doi.org/10.1016/j.rse.2021.112383, 2021.
Kimura, R., Okada, S., Miura, H., and Kamichika, M.: Relationships among the leaf area index, moisture availability, and spectral reflectance in an upland rice field, Agr. Water Manage., 69, 83–100, https://doi.org/10.1016/j.agwat.2004.04.009, 2004.
Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, 2023a.
Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2), Zenodo [data set], https://doi.org/10.5281/zenodo.8253971, 2023b.
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan, W., Zhang, X., Zhao, X., and Zhou, J.: The Global Land Surface Satellite (GLASS) Product Suite, B. Am. Meteorol., 102, E323–E337, https://doi.org/10.1175/BAMS-D-18-0341.1, 2021.
Liu, R., Liu, Y., and Chen, J.: GLOBMAP global Leaf Area Index since 1981 (Version 3.0), Zenodo [data set], https://doi.org/10.5281/zenodo.4700264, 2021.
Liu, Y., Liu, R., and Chen, J. M.: Retrospective retrieval of long-term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data, J. Geophys. Res.-Biogeosci., 117, G04003, https://doi.org/10.1029/2012jg002084, 2012.
Ma, H. and Liang, S.: Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model, Remote Sens. Environ., 273, 112985, https://doi.org/10.1016/j.rse.2022.112985, 2022.
Mahowald, N., Lo, F., Zheng, Y., Harrison, L., Funk, C., Lombardozzi, D., and Goodale, C.: Projections of leaf area index in earth system models, Earth Syst. Dynam., 7, 211–229, https://doi.org/10.5194/esd-7-211-2016, 2016.
Mao, D., Wang, Z., Luo, L., and Ren, C.: Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China, Int. J. Appl. Earth Obs., 18, 528–536, https://doi.org/10.1016/j.jag.2011.10.007, 2012.
Myneni, R., Knyazikhin, Y., and Park, T: MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], 10.5067/MODIS/MOD15A2H.006, 2015a.
Myneni, R., Knyazikhin, Y., and Park, T.: MCD15A2H MODIS/Terra+Aqua Leaf Area Index/FPAR 8-day L4 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], 10.5067/MODIS/MCD15A2H.006, 2015b.
Myneni, R. B., Hoffman, S., Knyazikhin, Y., Privette, J. L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G. R., Lotsch, A., Friedl, M., Morisette, J. T., Votava, P., Nemani, R. R., and Running, S. W.: Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data, Remote Sens. Environ., 83, 214–231, https://doi.org/10.1016/S0034-4257(02)00074-3, 2002.
Panda, S. S., Ames, D. P., and Panigrahi, S.: Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques, Remote Sens., 2, 673–696, https://doi.org/10.3390/rs2030673, 2010.
Piao, S., Yin, G., Tan, J., Cheng, L., Huang, M., Li, Y., Liu, R., Mao, J., Myneni, R. B., Peng, S., Poulter, B., Shi, X., Xiao, Z., Zeng, N., Zeng, Z., and Wang, Y.: Detection and attribution of vegetation greening trend in China over the last 30 years, Glob. Change Biol., 21, 1601–1609, https://doi.org/10.1111/gcb.12795, 2015.
Pierce, L. L., Running, S. W., and Walker, J.: Regional-scale relationships of leaf area index to specific leaf area and leaf nitrogen content, Ecol. Appl., 4, 313–321, https://doi.org/10.2307/1941936, 1994.
Pinzon, J. E. and Tucker, C. J.: A non-stationary 1981-2012 AVHRR NDVI3g yime series, Remote Sens., 6, 6929–6960, https://doi.org/10.3390/rs6086929, 2014.
Scurlock, J. M. O., Asner, G. P., and Gower, S. T.: Global Leaf Area Index from Field Measurements, 1932–2000, Oak Ridge National Laboratory Distributed Active Archive Center [data set], Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/584, 2001.
Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., Vermote, E. F., and El Saleous, N.: An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data, Int. J. Remote Sens., 26, 4485–4498, https://doi.org/10.1080/01431160500168686, 2005.
Valderrama-Landeros, L. H., España-Boquera, M. L., and Baret, F.: Deforestation in Michoacan, Mexico, from CYCLOPES-LAI time series (2000–2006), IEEE J. Sel. Top. Appl., 9, 5398–5405, https://doi.org/10.1109/JSTARS.2016.2597742, 2016.
Verger, A., Filella, I., Baret, F., and Penuelas, J.: Vegetation baseline phenology from kilometric global LAI satellite products, Remote Sens. Environ., 178, 1–14, https://doi.org/10.1016/j.rse.2016.02.057, 2016.
Wang, L., Good, S. P., and Caylor, K. K.: Global synthesis of vegetation control on evapotranspiration partitioning, Geophys. Res. Lett., 41, 6753–6757, https://doi.org/10.1002/2014GL061439, 2014.
Wang, W., Ciais, P., Nemani, R. R., Canadell, J. G., Piao, S., Sitch, S., White, M. A., Hashimoto, H., Milesi, C., and Myneni, R. B.: Variations in atmospheric CO2 growth rates coupled with tropical temperature, P. Natl. Acad. Sci. USA, 110, 13061–13066, https://doi.org/10.1073/pnas.1219683110, 2013.
Wang, Z., Wang, H., Wang, T., Wang, L., Liu, X., Zheng, K., and Huang, X.: Large discrepancies of global greening: Indication of multi-source remote sensing data, Global Ecol. Conserv., 34, e02016, https://doi.org/10.1016/j.gecco.2022.e02016, 2022.
Xiao, Z., Liang, S., and Jiang, B.: Evaluation of four long time-series global leaf area index products, Agr. Forest Meteorol., 246, 218–230, https://doi.org/10.1016/j.agrformet.2017.06.016, 2017.
Xiao, Z. Q., Liang, S. L., Wang, J. D., Chen, P., Yin, X. J., Zhang, L. Q., and Song, J. L.: Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance, IEEE T. Geosci. Remote, 52, 209–223, https://doi.org/10.1109/tgrs.2013.2237780, 2014.
Xiao, Z. Q., Liang, S. L., Wang, J. D., Xiang, Y., Zhao, X., and Song, J. L.: Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance, IEEE T. Geosci. Remote, 54, 5301–5318, https://doi.org/10.1109/tgrs.2016.2560522, 2016.
Yan, K., Park, T., Yan, G., Liu, Z., Yang, B., Chen, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B.: Evaluation of MODIS LAI/FPAR product collection 6. Part 2: validation and intercomparison, Remote Sens., 8, 460, https://doi.org/10.3390/rs8060460, 2016.
Yan, K., Park, T., Chen, C., Xu, B., Song, W., Yang, B., Zeng, Y., Liu, Z., Yan, G., Knyazikhin, Y., and Myneni, R. B.: Generating global products of LAI and FPAR from SNPP-VIIRS data: theoretical background and implementation, IEEE T. Geosci. Remote, 56, 2119–2137, https://doi.org/10.1109/TGRS.2017.2775247, 2018.
Yuan, H., Dai, Y., Xiao, Z., Ji, D., and Shangguan, W.: Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling, Remote Sens. Environ., 115, 1171–1187, https://doi.org/10.1016/j.rse.2011.01.001, 2011.
Zeng, Y. L., Hao, D. L., Huete, A., Dechant, B., Berry, J., Chen, J. M., Joiner, J., Frankenberg, C., Bond-Lamberty, B., Ryu, Y., Xiao, J. F., Asrar, G. R., and Chen, M.: Optical vegetation indices for monitoring terrestrial ecosystems globally, Nat. Rev. Earth Environ., 3, 477–493, https://doi.org/10.1038/s43017-022-00298-5, 2022.
Zha, J., Li, M., Zhu, Z., Cao, S., Zhang, Y., Zhao, W., and Chen, Y.: A direct evaluation of long-term global Leaf Area Index (LAI) products using massive high-quality LAI validation samples derived from Landsat archive, EarthArXiv [preprint], https://doi.org/10.31223/X58T05, 13 September 2023.
Zhang, G. Q., Patuwo, B. E., and Hu, M. Y.: Forecasting with artificial neural networks: The state of the art, Int. J. Remote Sens., 14, 35–62, https://doi.org/10.1016/s0169-2070(97)00044-7, 1998.
Zhang, P., Anderson, B., and Barlow, M.: Climate-related vegetation characteristics derived from Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index and normalized difference vegetation index, J. Geophys. Res.-Atmos., 109, D20105, https://doi.org/10.1029/2004jd004720, 2004.
Zhou, L., Tian, Y., Myneni, R. B., Ciais, P., Saatchi, S., Liu, Y. Y., Piao, S., Chen, H., Vermote, E. F., Song, C., and Hwang, T.: Widespread decline of Congo rainforest greenness in the past decade, Nature, 509, 86–90, https://doi.org/10.1038/nature13265, 2014.
Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., Samanta, A., Piao, S., Nemani, R. R., and Myneni, R. B.: 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 Sens., 5, 927–948, https://doi.org/10.3390/rs5020927, 2013.
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., Pan, Y., Peng, S., Peñuelas, J., Poulter, B., Pugh, T. A. M., Stocker, B. D., Viovy, N., Wang, X., Wang, Y., Xiao, Z., Yang, H., Zaehle, S., and Zeng, N.: Greening of the Earth and its drivers, Nat. Clim. Change, 6, 791–795, https://doi.org/10.1038/nclimate3004, 2016.
Short summary
The long-term global leaf area index (LAI) products are critical for characterizing vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It has better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
The long-term global leaf area index (LAI) products are critical for characterizing vegetation...
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