MUSES Leaf Area Index (LAI) Derived from AVHRR Data 8-Day Global 0.05º Geographic Grid Since 1981

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from time-series Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. <strong>Dataset Characteristics:</strong> Spatial Coverage: 180º W – 180º E, 90º S – 90º N Temporal Coverage: 1981 – 2019 Spatial Resolution: 0.05º (approximately 5 km) Temporal Resolution: 8 days Projection: Geographic Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao ([e-mail]).多尺度卫星遥感（Multiscale Satellite Remote Sensing, MUSES）产品套件包含针对归一化植被指数（Normalized Difference Vegetation Index, NDVI）、植被近红外反射率（Near-Infrared Reflectance of Vegetation, NIRv）、叶面积指数（Leaf Area Index, LAI）、吸收光合有效辐射比例（Fraction of Absorbed Photosynthetically Active Radiation, FAPAR）、植被覆盖度（Fractional Vegetation Coverage, FVC）、总初级生产力（Gross Primary Production, GPP）、净初级生产力（Net Primary Production, NPP）等参数的不同时空分辨率产品。如需了解MUSES产品的更多信息，请访问官网（https://muses.bnu.edu.cn/）。 本数据集为时空分辨率分别为0.05°和8天的MUSES全球叶面积指数（LAI）产品。MUSES LAI产品采用地理网格投影，时间跨度为1981年至2019年（持续更新中）。该产品基于时间序列的陆地长期数据记录（Land Long-Term Data Record, LTDR）先进甚高分辨率辐射计（Advanced Very High Resolution Radiometer, AVHRR）每日地表反射率产品（第4版），通过广义回归神经网络（General Regression Neural Networks, GRNNs）生成（Xiao等人，2014；Xiao等人，2016）。MUSES LAI产品空间覆盖完整且时间序列连续。 **数据集特征：** 空间覆盖范围：西经180° – 东经180°，南纬90° – 北纬90° 时间覆盖范围：1981年 – 2019年 空间分辨率：0.05°（约5千米） 时间分辨率：8天 投影方式：地理投影 数据格式：HDF 缩放系数：0.01 有效取值范围：0 – 1000 **引用文献（使用本数据集时请引用以下文献）：** 1. 肖志强、宋金玲、杨华、孙锐、李娟. (2022). 基于MODIS地表反射率数据生成的250米分辨率全球叶面积指数产品. 《国际遥感学报》(International Journal of Remote Sensing), 43(4), 1199-1225. 2. Xiao Zhiqiang, 等. (2014). 利用广义回归神经网络从时间序列MODIS地表反射率生成GLASS叶面积指数产品. 《IEEE地球科学与遥感汇刊》(IEEE Transactions on Geoscience and Remote Sensing), 52, 209-223. 3. Xiao Zhiqiang, 等. (2016). 基于MODIS与AVHRR地表反射率的长时序全球陆面卫星叶面积指数产品. 《IEEE地球科学与遥感汇刊》, 54, 5301-5318. 4. Xiao Zhiqiang, 等. (2017). 四种长时序全球叶面积指数产品的评估. 《农业与森林气象学》(Agricultural and Forest Meteorology), 246, 218-230. 如有任何疑问，请联系肖志强教授（[e-mail]）。