Publications 2023

  1. Zuo et al., 2023. Simulating Potential Tree Height for Beech–Maple–Birch Forests in Northeastern United States on Google Earth Engine, J. Remote Sens., 2023;3:Article 0084.
  2. Cao et al., 2023. Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020, Earth Syst. Sci. Data, 15, 4877–4899, 2023,
  3. Pan et al., 2023. Climate-driven land surface phenology advance is overestimated due to ignoring land cover changes, Env. Res. Lett., 18 044045,
  4. Li et al., 2023. 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,, 2023.
  5. Zeng et al., 2023. Structural complexity biases vegetation greenness measures. Nat Ecol Evol (2023).
  6. Gao et al., 2023. Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations. Remote Sens. Environ., doi: 10.1016/j.rse.2023.113665
  7. Zhang et al., 2023. Autumn canopy senescence has slowed down with global warming since the 1980s in the Northern Hemisphere. Comm. Earth and Environ., doi: 10.1038/s43247-023-00835-0
  8. Wang et al., 2023. Improving the Quality of MODIS LAI Products by Exploiting Spatiotemporal Correlation Information. IEEE Trans. Geosci. Remote Sens., doi: 10.1109/TGRS.2023.3264280
  9. Meng et al., 2023. Climate change increases carbon allocation to leaves in early leaf green-up. Ecol. Lett., doi: 10.1111/ele.14205
  10. Tucker et al., 2023. Sub-continental-scale carbon stocks of individual trees in African drylands. Nature, doi: 10.110.1038/s41586-022-05653-6
  11. Pu et al., 2023. Improving the MODIS LAI compositing using prior time-series information. Remote Sens.Env.,doi: 10.1016/j.rse.2023.113493
  12. Dong et al., 2023. A method for retrieving coarse-resolution leaf area index for mixed biomes using a mixed-pixel correction factor. IEEE Trans. Geosci. Remote Sens.,doi: 10.1109/TGRS.2023.3235949
  13. Li et al., 2023.A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels. J. Remote Sens., doi: 10.34133/remotesensing.0038