Published January 31, 2023 | Version v1
Dataset Open

MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2002

Creators

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 1km spatial resolution and 8-day 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 et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous.

This dataset is the MUSES LAI product in 2002.  Please click here to download the MUSES LAI product in 2001, and click here to download the MUSES LAI product in 2003.

Dataset Characteristics:

  • Spatial Coverage: Global
  • Temporal Coverage: 2002
  • Spatial Resolution: 1km
  • Temporal Resolution: 8 days
  • Projection: Sinusoidal
  • Data Format: HDF
  • Scale: 0.01
  • Valid Range: 0 – 1000

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

  1. 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.
  2. 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.
  3. 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.
  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 (7.1 GB)

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md5:adaa9874b101a468634854a938615a1b
38.5 MB Download
md5:ef6ab6aecf3efd82467766f51ecaa82e
21.3 MB Download
md5:58cfe127516c6ff366e0e69e524aa101
27.8 MB Download
md5:fbcd746d32a9387395b3b4d4f169af8c
104.0 kB Download
md5:edc7ba2512586723092416ea8afd05e0
29.6 MB Download
md5:8ca1e256fa1f4bc5aaabe420f85b6b49
17.5 MB Download
md5:5ab88a13555b39bbfd1a5408da1701cc
4.5 MB Download
md5:aaabcd8cdbf6f6659c901664d39dd1b9
496.0 kB Download
md5:caec3b49ff20618243d6379f741e2d2c
15.2 MB Download
md5:cd12c3dab03a8bb857f0ebc03dad651f
3.6 MB Download
md5:8c22c70f57ae804fb9dab75aacee985d
11.4 MB Download
md5:d750ff718e52978adced6f6512d50a62
32.4 MB Download
md5:82e1758e2a919d8d3908a3ddddf9eeef
33.6 MB Download
md5:eacb8b99e53e60053816feb7fe84c512
9.5 MB Download
md5:7abac820f785bac7c68596bbe279075d
40.5 MB Download
md5:1b95e97fbbd38544da3b441c8cc30f86
41.1 MB Download
md5:9717df89544725b68cbfd1c5491bd59d
6.1 MB Download
md5:94f5fdfe34c9f0be6fe7882417cd4a5d
76.5 kB Download
md5:48bb2eb026f57f3630370bf1ca9da92e
115.9 kB Download
md5:6a8274912bacef1baba494f9b679188e
5.0 MB Download
md5:88e252d1937682b0d12f7a34f9179218
14.5 MB Download
md5:28a3e9b27a38eb803c8492b3425991db
15.0 MB Download
md5:728a7189d9bb00559466e65046efd574
37.8 MB Download
md5:e1e89a1b333997c7d74b3545beacfa8e
43.0 MB Download
md5:ac0011e9e020c4e6bb20b1c2660ecc02
29.2 MB Download
md5:8d50bd9d1f056e922a8901e480efd17b
7.9 MB Download
md5:d73591aab97ffc1462ee279d3753a757
78.0 kB Download
md5:5a566c0ad6fd80d15792a5053389c3e7
118.9 kB Download
md5:b6b565f24a44335af3faa22fd51c0267
231.9 kB Download
md5:27b25314525bf8a2cd7fe7883921b456
31.2 MB Download
md5:190cde2cb5f8176a2be864c6ea16b57a
37.6 MB Download
md5:107c36dde8e97df1cf7d2e5e18455dc5
49.3 MB Download
md5:93e53c2cdf723e4f546cac97e930f8d5
8.6 MB Download
md5:e8c480fd8d83fee7d2c1da5b72565fc0
2.6 MB Download
md5:7870a2934cd4799a908c824eb3e5713a
68 Bytes Download
md5:ff6ae4ff90a1d861d6182c2315877b1e
222.3 kB Download
md5:d3897f8116a8ba2b40ccde42dc59b2fa
94.7 kB Download
md5:bc826a28493c75dde5f1cc17caf7656c
32.2 MB Download
md5:104960438be292fe2111a6311d1f4dd2
1.5 MB Download
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86.4 kB Download
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2.6 MB Download
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85.1 kB Download
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185.5 kB Download
md5:33dcd89f58929a554b169b9f6a709de4
5.9 MB Download
md5:29aa2ebb3a90f4861c79c2e7c74c2f49
826.5 kB Download
md5:39273bdb3f62661a8813b6e18817b642
2.2 MB Download
md5:1702cbaa464714f65aea1a126be37b39
155.2 kB Download
md5:c9efc788a7b7669f183dbf86c71e2a00
174.8 kB Download
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93.0 kB Download
md5:52306e817bab9e0fa6b91ed9e8793102
2.6 MB Download
md5:28bc3aa54085993bd93eaeee1d3e8995
643.8 kB Download
md5:a04592a25dc0125d8b3b455fd88ebf42
673.7 kB Download
md5:835ee8c502237c1038352f391ee63a10
1.5 MB Download