Info: Zenodo’s user support line is staffed on regular business days between Dec 23 and Jan 5. Response times may be slightly longer than normal.

Published December 25, 2022 | Version v1
Dataset Open

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

Creators

  • 1. Beijing Normal University

Description

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 et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous.

Dataset Characteristics:

  • 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

Citation (Please cite this paper whenever these data are used):

  1. 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.
  2. 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.
  3. 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.
  4. 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 (zhqxiao@bnu.edu.cn).

Files

Files (12.3 GB)

Name Size Download all
md5:2f3d37ed4e3aca578151bcb955d37f21
310.5 MB Download
md5:7a701ae8de04a7756baf663e3478958f
312.2 MB Download
md5:b9c1f6739d42ce12089cb1b33a8c2acd
313.6 MB Download
md5:8c148c62b888f4584bb111e82c06b9c2
317.4 MB Download
md5:15443909f2bdbd7b9a5f6e9cfd0aeaf7
312.5 MB Download
md5:b428ee99ad2998fe002d2c1b5c862fad
312.0 MB Download
md5:f5f042b48901e64c0644aec568f32774
315.5 MB Download
md5:0dbe23645029b1fa9f9d1a79a24ce29c
316.7 MB Download
md5:4b76d2430426451a809c312ac814f88b
308.7 MB Download
md5:b255b084a2c2f78465a3e0ad89694965
311.5 MB Download
md5:9b02b0728e5eb287829cdba5cddb218a
313.3 MB Download
md5:da81375ed700d3c42f80065e72eea70f
313.9 MB Download
md5:e153ee8e841797a783acbf27a1656a41
321.2 MB Download
md5:2ccdc8b6d32979c3faa1aca18f583afd
322.3 MB Download
md5:31a13f400589660ae5cc75adf75befc8
313.0 MB Download
md5:6f11d52c937b6b8860e4c5670fa6894d
312.7 MB Download
md5:fbddddc2a2940e6a1823b62a945f9e3c
315.1 MB Download
md5:a43bf06b0ab8471d8021a7de24631feb
316.9 MB Download
md5:7a5944bce011a654bc6317852507c9b5
318.5 MB Download
md5:15cba90429aaf859891cb88aebd81ea0
314.9 MB Download
md5:407d7a7066c52ac5ac175062fb2fb602
314.9 MB Download
md5:c40837816b9a566590711b234edb50e9
313.0 MB Download
md5:480f303e7b2ebe788c6bfff603a01bb1
314.1 MB Download
md5:0866a74b72ce31faa86cc0a4eceea302
315.6 MB Download
md5:bdfca2ec220c8103490859f55f8bce26
316.3 MB Download
md5:5777dba18668ed776fba419fdac7b217
315.3 MB Download
md5:52f18e585fd783310170e831e433b879
314.2 MB Download
md5:a9d7572321cfb8cceaac004c06253652
315.7 MB Download
md5:fc45c7a108564c4f4aa7dc0e1621b50c
316.0 MB Download
md5:e5bababa5af7378cac014a36dcce0166
315.9 MB Download
md5:78eb5d59afb5f255ebd8aa0421d0d9ea
315.0 MB Download
md5:a95930786fdac7d64bdff29a81af79d6
316.6 MB Download
md5:b624383f11bf009f30dedbfa4d0948da
319.3 MB Download
md5:42c5fc7df8ac7ddf1b0957295954868b
314.5 MB Download
md5:5f2f7435cb828fa3a540f2c7d5861891
313.5 MB Download
md5:832fb851387655b84d44e65147f82b1b
316.4 MB Download
md5:d97dff90dd9564c97ca59677aba470c9
319.6 MB Download
md5:81292b6f2cccbd3825121d3ac567accc
326.9 MB Download
md5:aea189be6e90ede60c4ebc18f8b50703
332.1 MB Download