概述

叶面积指数（leaf area index, LAI）是陆地生态系统中描述植被生物物理变化和冠层结构的重要变量，直接影响到植被的蒸腾作用效率、光合作用和能量平衡状态，其通常定义为单位地表面积上叶子表面积总和的一半。光合有效辐射吸收比率（Fraction of absorbed photosynthetically active radiation, FAPAR）定义为植被吸收光合有效辐射PAR（Photosynthetically Active Radiation）占总入射PAR的比例，是表征植被光合作用的关键参量之一。传统的地面实测方法虽然精度较高，但只能代表采样点或周围小区域的LAI/FAPAR值，难以满足大范围获取LAI/FAPAR的需求。遥感技术以其覆盖范围广、大尺度观测等优势，现已成为估算区域或全球尺度LAI/FAPAR的主要手段。

近年来，基于AVHRR、MODIS、MISR、VEGETATION、MERIS等传感器生产了多个全球或区域尺度LAI/FAPAR遥感产品(Baret et al. 2013; Knyazikhin et al. 1998; Ma and Liang 2022; Yan et al. 2018)，为监测LAI的时空变化以及陆面过程模拟、全球变化、气候分析等研究提供了很好的数据源。然而，全球尺度LAI产品通常为中低分辨率数据，产品仍存在不同程度的不确定性、产品之间不一致等现象，导致其在异质地表难以准确表达植被真实生长状况及物候变化特征，影响了LAI遥感产品的有效应用(Fang et al. 2019; Garrigues et al. 2008; Jiang et al. 2017)。现有的高分辨率卫星（如Sentinel-2）LAI/FAPAR产品(Weiss 2016)时间序列短、且产品精度有待提高，无法开展长时序植被动态变化监测。因此，构建高质量的长时序高空间分辨率LAI/FAPAR产品意义重大。

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参考文献：

1. Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P., & Smets, B. (2013). GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, 137, 299-309
2. Fang, H., Zhang, Y., Wei, S., Li, W., Ye, Y., Sun, T., & Liu, W. (2019). Validation of global moderate resolution leaf area index (LAI) products over croplands in northeastern China. Remote Sensing of Environment, 233, 111377
3. 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., & Yang, W. (2008). Validation and intercomparison of global Leaf Area Index products derived from remote sensing data. Journal of Geophysical Research-Biogeosciences, 113
4. Jiang, C., Ryu, Y., Fang, H., Myneni, R., Claverie, M., & Zhu, Z. (2017). Inconsistencies of interannual variability and trends in long‐term satellite leaf area index products. Global Change Biology
5. Knyazikhin, Y., Martonchik, J.V., Myneni, R.B., Diner, D.J., & Running, S.W. (1998). Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. Journal of Geophysical Research-Atmospheres, 103, 32257-32275
6. Ma, H., & Liang, S. (2022). Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model. Remote Sensing of Environment, 273, 112985
7. Weiss, M.B.F. (2016). S2ToolBox Level2 products: LAI, FAPAR, FCOVER. In
8. Yan, K., Park, T., Chen, C., Xu, B., Song, W., Yang, B., Zeng, Y., Liu, Z., Yan, G., Knyazikhin, Y., & Myneni, R.B. (2018). Generating Global Products of LAI and FPAR From SNPP-VIIRS Data: Theoretical Background and Implementation. IEEE Transactions on Geoscience and Remote Sensing, 56, 2119-2137

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