MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2000
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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 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product 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. This dataset is the MUSES LAI product in 2000. <em>Please </em><em><strong>click here</strong> to download the MUSES LAI product <strong>in 2001</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2000 Spatial Resolution: 500m Temporal Resolution: 1 month Projection: Sinusoidal 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, <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, 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>. (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 (zhqxiao@bnu.edu.cn).
多尺度卫星遥感（MUltiscale Satellite remotE Sensing, MUSES）产品套件包含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）等参数的多时空分辨率产品。如需了解MUSES产品的更多信息，请访问官网（https://muses.bnu.edu.cn/）。本数据集为空间分辨率500米、时间分辨率为月度的MUSES全球叶面积指数产品。MUSES叶面积指数产品采用正弦网格（Sinusoidal grid）投影，时间跨度为2000年至2019年（持续更新中），其基于时间序列中等分辨率成像光谱辐射计（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<em>等</em>，2014；Xiao<em>等</em>，2016）。MUSES叶面积指数产品空间覆盖完整、时间序列连续。本数据集为2000年版MUSES叶面积指数产品。<em>请</em><em><strong>点击此处</strong>下载2001年版MUSES叶面积指数产品</em>。<strong>数据集特征：</strong>空间覆盖范围：全球 时间覆盖范围：2000年 空间分辨率：500米 时间分辨率：1个月 投影方式：正弦投影 数据格式：HDF 缩放系数：0.01 有效取值范围：0 – 1000<strong>引用说明（使用本数据集时请务必引用以下文献）：</strong>Xiao Zhiqiang, <em>等</em>.（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. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>等</em>.（2016）. 基于MODIS与AVHRR地表反射率的长时序全球地表卫星叶面积指数产品. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. 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. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>等</em>.（2017）. 四种长时序全球叶面积指数产品的评估. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. 如有任何疑问，请联系肖志强教授（zhqxiao@bnu.edu.cn）。
