MUSES Leaf Area Index (LAI) Derived from MODIS Data Monthly Global 0.05º Geographic Grid Since 2000

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 monthly temporal resolution. The MUSES LAI product was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao et al., 2014; Xiao et al., 2016). It is provided on Geographic grid and spans from 2000 to 2019 (continuously updated). The MUSES LAI product is spatially complete and temporally continuous. Dataset Characteristics: Spatial Coverage: 180º W – 180º E, 90º S – 90º N Temporal Coverage: 2000 – 2019 Spatial Resolution: 0.05º (approximately 5 km) Temporal Resolution: 1 month Projection: Geographic Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 Citation (Please cite this paper whenever these data are used): 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. International Journal of Remote Sensing, 43(4), 1199-1225. Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223. Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318. Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 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）产品套件包含针对多类遥感参数的不同时空分辨率产品，涵盖归一化差分植被指数（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）。如需了解MUSES产品的更多详情，请访问官网：https://muses.bnu.edu.cn/。 本数据集为MUSES全球叶面积指数（LAI）产品，空间分辨率为0.05°，时间分辨率为月度。MUSES LAI产品基于时间序列中分辨率成像光谱仪（Moderate Resolution Imaging Spectroradiometer, MODIS）地表反射率产品，通过广义回归神经网络（General Regression Neural Networks, GRNNs）生成（Xiao等，2014；Xiao等，2016）。该产品采用地理坐标系发布，时间跨度为2000年至2019年（持续更新中），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：2000年—2019年 空间分辨率：0.05°（约合5千米） 时间分辨率：1个月 投影方式：地理投影 数据格式：HDF 尺度因子：0.01 有效取值范围：0—1000 引用说明（使用本数据集时请务必引用以下文献）： Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). 基于MODIS地表反射率数据的250米分辨率全球叶面积指数产品. 《国际遥感学报》, 43(4), 1199-1225. Xiao Zhiqiang, et al. (2014). 利用广义回归神经网络从时间序列MODIS地表反射率数据生成GLASS叶面积指数产品. 《IEEE地球科学与遥感汇刊》, 52, 209-223. Xiao Zhiqiang, et al. (2016). 基于MODIS与先进甚高分辨率辐射计（Advanced Very High Resolution Radiometer, AVHRR）地表反射率数据的长时序全球陆表卫星叶面积指数产品. 《IEEE地球科学与遥感汇刊》, 54, 5301-5318. Xiao Zhiqiang, et al. (2017). 四款长时序全球叶面积指数产品的评估. 《农业与森林气象学》, 246, 218-230. 如有任何疑问，请联系肖志强教授（[e-mail]）。