42 citations as recorded by crossref.

1. Multi-decadal temporal reconstruction of Sentinel-3 OLCI-based vegetation products with multi-output Gaussian process regression D. D.Kovács et al. https://doi.org/10.1016/j.ecoinf.2024.102816
2. A Two-Stage Reference-Guided Workflow for Improving VIIRS Leaf Area Index Retrieval over Mixed Pixels T. Yue et al. https://doi.org/10.3390/rs18132214
3. Large gains in leaf scale photosynthetic rates of sparsely vegetated arid and semi-arid lands J. Pu et al. https://doi.org/10.1038/s43247-025-03121-3
4. Retrieving forest LAI from Landsat via 3D look-up table generated by realistic LiDAR scenes J. Qi et al. https://doi.org/10.1016/j.rse.2025.115204
5. Spatially resolved analysis of speciated VOC emissions and their contributions to secondary pollutant formation: a comparative assessment of anthropogenic and biogenic sources in China Y. Wang et al. https://doi.org/10.1016/j.envint.2025.109627
6. An Insight Into the Internal Consistency of MODIS Global Leaf Area Index Products X. Zhang et al. https://doi.org/10.1109/TGRS.2024.3434366
7. Integrating GLASS LAI into the SWAT Model for Improved Hydrological Simulation in Semi-Arid Regions X. Zhang et al. https://doi.org/10.3390/agronomy16060639
8. Interactions and Conflicts between Urbanization and Greenness: A Case Study from Nanjing, China S. Yang et al. https://doi.org/10.3390/rs16132505
9. HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023 K. Yan et al. https://doi.org/10.1038/s41597-025-04391-4
10. Degradation and deforestation increase the sensitivity of the Amazon Forest to climate extremes M. Longo et al. https://doi.org/10.1088/1748-9326/adc58c
11. Enhancing digital mapping of soil organic carbon through spatial modeling and validation A. Jafari et al. https://doi.org/10.1038/s41598-026-39496-2
12. A dataset of MuSyQ 500 m and 4-day spatiotemporally continuous leaf area index product (2000–2020) Y. DONG et al. https://doi.org/10.11922/11-6035.csd.2025.0159.zh
13. A data-driven approach to estimate leaf area index for Landsat images over China H. Li et al. https://doi.org/10.1080/22797254.2026.2654470
14. Multi-layer model requires accurate information of vertical structure to realize its full potential in simulating gross primary production J. Xie et al. https://doi.org/10.1016/j.agrformet.2026.111036
15. Global dynamic habitat indices (DHIs) based on MODIS and VIIRS vegetation products D. Liu et al. https://doi.org/10.1016/j.rse.2025.115099
16. A dataset of continuous MuSyQ leaf chlorophyll content product with a 30 m spatial and 10-day temporal resolution in China(2021–2022) L. GUAN et al. https://doi.org/10.11922/11-6035.nesdc.2024.0186.zh
17. Modeling forest and rangeland ecosystem responses to drought across Hyrcanian bioclimatic zones of Iran using GLM and LAI analysis A. Bazrmanesh et al. https://doi.org/10.1038/s41598-025-09629-0
18. Ecosystem drought recovery and influencing factors in temperate China and the Qinghai-Tibet alpine region Y. Sun et al. https://doi.org/10.1016/j.catena.2025.109417
19. Global vegetation production may decrease in this century due to rising atmospheric dryness S. Lin et al. https://doi.org/10.1038/s41559-025-02885-3
20. Physics-Driven Machine-Learning Retrieval and Uncertainty Quantification of Crop Leaf Area Index W. Liu et al. https://doi.org/10.3390/rs17233924
21. Filling Gaps in Global Daily TROPOMI Solar-Induced Chlorophyll Fluorescence Data From 2018 to 2021 J. Li et al. https://doi.org/10.1109/TGRS.2025.3585237
22. A Comprehensive Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products of MODIS, GLASS, and GEO Over China L. Xia et al. https://doi.org/10.1109/JSTARS.2025.3575839
23. Seeking carbon reduction, pollution control, green expansion, and economic growth in urban China: The role of renewable energy policy attention C. Qin et al. https://doi.org/10.1016/j.uclim.2025.102616
24. Natural and anthropogenic influences on short-term forest growth status: Evidence and mechanisms from China Z. Tian et al. https://doi.org/10.1016/j.jenvman.2025.126084
25. Unveiling the influence pathways of the evolution of human-nature relationships in the Yangtze river delta urban agglomeration H. Zhu et al. https://doi.org/10.1016/j.isci.2026.114989
26. Multi-variable calibration of SWAT in a small experimental catchment area for afforestation scenario analyses E. Nervi et al. https://doi.org/10.1080/02626667.2025.2552349
27. QUADICA v2: extending the large-sample data set for water QUAlity, DIscharge and Catchment Attributes in Germany P. Ebeling et al. https://doi.org/10.5194/essd-18-691-2026
28. Pathway-specific responses of isoprene-derived secondary organic aerosol formation to anthropogenic emission reductions in a megacity in eastern China H. Hu et al. https://doi.org/10.5194/acp-25-17889-2025
29. Variability in quantifying leaf area index in humid, fire-affected ecosystems N. Corak et al. https://doi.org/10.1088/2515-7620/ae6396
30. Evaluation of vegetation drought resistance and resilience across central Asian drylands under water limitation and heat extremes S. Song et al. https://doi.org/10.1016/j.ejrh.2026.103726
31. Mapping global leaf inclination angle (LIA) based on field measurement data S. Li & H. Fang https://doi.org/10.5194/essd-17-1347-2025
32. National mapping of wetland vegetation leaf area index in China using hybrid model with Sentinel-2 and Landsat-8 data J. Zhen et al. https://doi.org/10.1016/j.isprsjprs.2025.11.031
33. The input-optimized and supplemented MEGAN improves biogenic volatile organic compounds estimation accuracy in China B. Cai et al. https://doi.org/10.1016/j.apr.2025.102650
34. From Leaves to Breezes: Machine learning based prediction of nitrogen dioxide concentration from surrounding urban greenery and meteorological, spatial, and traffic characteristics in Berlin, Germany R. Schmidt & L. Ong https://doi.org/10.1016/j.ecoinf.2025.103568
35. Aridity thresholds Reveal transitions in dominant drivers of partitioned Water–Carbon coupling across Central Asian drylands S. Song et al. https://doi.org/10.1016/j.jag.2026.105344
36. A Physics-Informed Spatiotemporal Deep Learning Algorithm for High-Resolution Leaf Area Index Retrieval D. Zhai et al. https://doi.org/10.1109/JSTARS.2026.3668316
37. Quantifying the ability of bidirectional reflectance distribution function (BRDF) model to Respond to soil moisture and the normalized difference vegetation index (NDVI) B. Gao et al. https://doi.org/10.1016/j.jag.2026.105209
38. Real-time generation of gap-free MODIS leaf area index product from 2000 to 2024 using a deep learning method G. Zhang et al. https://doi.org/10.1016/j.jag.2026.105240
39. GEOV2-AVHRR: Continuous and consistent time series of global leaf area index and fraction absorbed PAR from 1981 to 2022 A. Verger et al. https://doi.org/10.1016/j.rse.2025.115029
40. Remote Sensing-Based Leaf Area Index Estimation of Spring Wheat Through Machine-Learning Approaches P. Prakash et al. https://doi.org/10.1007/s12524-025-02347-0
41. Improving LAI retrieval in complex mountains with implicit mutual terrain irradiance C. Liu et al. https://doi.org/10.1016/j.tfp.2026.101151
42. Filling Gaps in Solar-induced Chlorophyll Fluorescence Data Using Deep Residual Neural Networks J. Li et al. https://doi.org/10.1088/1742-6596/3055/1/012026