MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2007

<p>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).&nbsp;For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global&nbsp;LAI product at 1km spatial&nbsp;resolution&nbsp;and 8-day temporal resolution.&nbsp;The MUSES LAI product is&nbsp;provided&nbsp;on a Sinusoidal grid&nbsp;and spans from 2000 to 2019 (continuously updated).&nbsp;It was generated from time-series&nbsp;Moderate Resolution Imaging Spectroradiometer (MODIS) surface&nbsp;reflectance product&nbsp;using general regression neural networks (GRNNs) &nbsp;(Xiao&nbsp;<em>et al</em>., 2014; Xiao&nbsp;<em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES&nbsp;LAI product in 2007.&nbsp;&nbsp;<em>Please&nbsp;<a href="https://zenodo.org/record/7583687#.Y9exQXZByUk"><strong>click here</strong></a>&nbsp;to download the&nbsp;MUSES&nbsp;LAI product<strong>&nbsp;in 2006</strong></em>, and&nbsp;<em><strong><a href="https://zenodo.org/record/7582426#.Y9c8nXZByUk">click here</a></strong>&nbsp;to download the&nbsp;MUSES&nbsp;LAI product&nbsp;<strong>in 2008</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2007</li> <li>Spatial Resolution: 1km</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 &ndash; 1000</li> </ul> <p><strong>Citation&nbsp;</strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang,&nbsp;<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.&nbsp;<em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang,&nbsp;<em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance.&nbsp;<em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>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.&nbsp;<em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang,&nbsp;<em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products.&nbsp;<em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>