Published December 27, 2022
| Version v1
Dataset
Open
MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2018
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 2018. Please click here to download the MUSES LAI product in 2017, and click here to download the MUSES LAI product in 2019.
Dataset Characteristics:
- Spatial Coverage: Global
- Temporal Coverage: 2018
- 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):
- 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, 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. (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
(7.2 GB)
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md5:46e0859e8ffe3633048de9e80a84daeb
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56.3 MB | Download |
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md5:856568e1aa9c0db620b97c56ffc9cbba
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47.3 MB | Download |
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md5:5a1ac810dcb7ae66bd0fd8fbe42b4560
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17.7 MB | Download |
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md5:1001f6e3b97cbae84e2a70fb57900ca8
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3.2 MB | Download |
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md5:6ceea7e1f8acab181e4ba5531839ab1c
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187.9 kB | Download |
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md5:7018f4b957e25ff1482e01218158a07c
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161.1 kB | Download |
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md5:64a5f1f990c02069f0618803741d44c1
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123.0 kB | Download |
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md5:23e7706b8d114bf80e307c667629cfca
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566.0 kB | Download |
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md5:e34f1627cd9c5e2a6e23d4ffdada4ddf
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55.9 MB | Download |
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md5:e6949674241bab6a14b396c462421f9d
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68.3 MB | Download |
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md5:66a6f0fd132c2fc59f4ced4dc873b99b
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44.0 MB | Download |
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md5:48680c6825f9c6314f279ca653dadfd3
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46.5 MB | Download |
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md5:0f1a2f6e564f5bb94fa5cb94ad84901c
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52.3 MB | Download |
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md5:f2c4951981898d229482096ee5cb56e2
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2.5 MB | Download |
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md5:7f61de2384afb1a9e55f15894f906037
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96.6 kB | Download |
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md5:b638b7137039b06a82c673498ca355dc
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32.1 MB | Download |
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md5:16cdcb71f3afb819ae3007de61bccbe5
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56.1 MB | Download |
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md5:ac0e7e334dfa2c0bc999513f19476705
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37.1 MB | Download |
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md5:58ba08c3558496e31c1a9a5a5f71472f
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45.3 MB | Download |
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md5:62d8344fc244515279074060ea63e640
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75.7 MB | Download |
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md5:aff9d61c5c6bff2e66fd60ff5b9f29c1
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56.0 MB | Download |
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md5:61a55ed253ee4bcb0b2aa1a68d8dae94
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6.8 MB | Download |
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md5:e67e8c62a6c499770bf3c7bfc225dac1
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122.4 kB | Download |
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md5:5c2e91acb067be49e0ba0c4df6852017
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7.3 MB | Download |
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md5:71376ccfbc145b95166eb5122f094690
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33.7 MB | Download |
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md5:6e20a7a6809ddb380c2be616503f0fbc
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61.0 MB | Download |
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md5:867062cbb96e95b77d586df866413505
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66.5 MB | Download |
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md5:1b7759d795e84601b31e0adb86d2d9e2
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79.4 MB | Download |
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md5:8f4dcd06f4d0210bf5c77d3e07370077
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3.1 MB | Download |
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md5:153162caf2c2522d68ed6b57f4ac47d3
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2.9 MB | Download |
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md5:3bab5e9a05154d613c1cd1a7f4d383ab
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10.1 MB | Download |
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md5:56883e2fb670c26457c184475b5dbab4
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45.8 MB | Download |
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md5:20ada33b416b2d0f9b237a6241511f6e
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61.0 MB | Download |
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md5:9050b2bf3ac05cf292e4498bc0c5a58f
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86.4 MB | Download |
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md5:60d119b2a28dc26714f2f0c50701bdd9
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53.7 MB | Download |
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md5:003470e9adebe4c07d3f39ca5d550567
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16.0 MB | Download |
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md5:afdf941a7440f6621ce276812fb71b54
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859.3 kB | Download |
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md5:0c54cc2dde1f524bf8b065abba56cf24
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88.9 kB | Download |
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md5:4d4dd5b73689760e4094206b796ead40
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212.5 kB | Download |
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md5:3d14ac1cbf08e69176d7232418d9f64d
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10.2 MB | Download |
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md5:236b38c27cc48c07ad762f7e25e40caf
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392.3 kB | Download |
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md5:b68640386cc0b231779e43412a448459
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551.0 kB | Download |
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md5:50e890109f2b139aa5d2951dd1ce915a
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8.5 MB | Download |
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md5:b74d98c9a3e54bc4f1becd2188573e50
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25.0 MB | Download |
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md5:a5d147b617c25ed1b6a421e2c485b295
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55.1 MB | Download |
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md5:d9c312ecbbf4e26b3bb5d5b697f0b259
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38.7 MB | Download |
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md5:eaa09bd47741c7bad12bcef37103eb44
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21.5 MB | Download |
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md5:b4866d30e76468d39102c2cb3860c73c
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28.0 MB | Download |
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md5:e26f1c9421bfabf18e6550e69c085a72
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103.9 kB | Download |
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md5:56ef810b6fb5a70d2f1ae487e5533eff
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30.4 MB | Download |
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md5:2328fad6ce8e4cde24423a292d348c5d
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18.5 MB | Download |
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md5:6339b69ba6886b52a2976eb788b98488
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4.5 MB | Download |
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md5:875fb18a630fecfb0e40444efd0b9cf0
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496.0 kB | Download |
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md5:bb4a7d3843fab51afe530e10acd2e817
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15.3 MB | Download |
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md5:4601bbd62be987d968bb4c594c4bc559
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3.6 MB | Download |
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md5:3618da5917a53a56d4b9ccded898ea99
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11.4 MB | Download |
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md5:b625de40f547c3d257a890c677c57ad1
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32.7 MB | Download |
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md5:09a0e9ac2288a16cf347d6529dc374ff
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34.1 MB | Download |
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md5:db594a2424b9d1339731f7475bd91881
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10.0 MB | Download |
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md5:020996e444cb92ec19f47c07af139a68
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39.7 MB | Download |
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md5:bc9f58675001824bb633b7f725b17770
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42.3 MB | Download |
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md5:ab030e7ba77f3ddbc6b22d2383f9e6f2
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6.2 MB | Download |
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md5:f3194f8e7dab63c87f007ae7e10a7c6f
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77.0 kB | Download |
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md5:bfeb556b3fbedc6a3b51474fd40ebf3f
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115.9 kB | Download |
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md5:3f7e1950c7256b00d9f130534db37162
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5.0 MB | Download |
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md5:58acd318a1186e358b17261d5f6f65e0
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14.6 MB | Download |
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md5:b7108ba1c3f1b846ff62ca22c08bc11d
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15.1 MB | Download |
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md5:47f75026a3c6793a5d5b7c5124892ef4
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38.2 MB | Download |
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md5:4f9fd4dd136a853c3a877c1b7f5293b8
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40.7 MB | Download |
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md5:c6a7f158163353080ab9fa0c09727235
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29.7 MB | Download |
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md5:4aa783672b5a69311b59bc5fdfb55773
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7.9 MB | Download |
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md5:87da31bf2bfb0d1d3dfccd1250b5071c
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78.1 kB | Download |
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md5:c4ac82f902e952b7d2f3be731e86a549
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120.6 kB | Download |
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md5:7fd967df245160ed1d6bc0241dbad86e
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232.3 kB | Download |
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md5:5d34674d711c146218280a2ed7f35e5c
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31.4 MB | Download |
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md5:5dd0d1aca4548e598192d6350baea181
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39.1 MB | Download |
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md5:42758f59cab0fa1f6286ba1444e2170c
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51.0 MB | Download |
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md5:2f699182a5c58f40df517d11ae1737e0
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8.6 MB | Download |
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md5:a08253c4165cf98c1afa57d8bf33f9dc
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2.7 MB | Download |
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md5:3071ed60beb0938dcd87d6142608d6bd
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221.1 kB | Download |
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md5:1f90a034bdcce7d895445b6f47751e90
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93.5 kB | Download |
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md5:68a18f157a90176fc247a08fd737b5bc
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32.5 MB | Download |
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md5:f25d2359ac096bb1f8c853a7fe444158
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1.5 MB | Download |
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md5:a9963c49376f38eb3fa107404fa43c84
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86.8 kB | Download |
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md5:ba74cb85726accdaeea6d24161befa3a
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2.6 MB | Download |
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md5:1bfe0b020e3fe18a68b8605791e3e2d4
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84.3 kB | Download |
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md5:502bff31b7c2763f89edc771a327fe36
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184.0 kB | Download |
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md5:5702a76828f3fa1cf532c1db7dba582b
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5.9 MB | Download |
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md5:8df2e60068cfab72a85a1115c1730107
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829.6 kB | Download |
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md5:3a82025a2747ed2154044ca42a51221e
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2.2 MB | Download |
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md5:6710f3736c201c3f547e50c98ae6b2f2
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156.0 kB | Download |
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md5:c6d0f082bbc38235c0158977868147f3
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175.3 kB | Download |
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md5:08bf55ee382672f76b7dfad4b1ed86ea
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92.9 kB | Download |
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md5:95d175501b3b2e3becfbd573b0d575b4
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2.6 MB | Download |
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md5:e0334a5d09af66a97bf6d7550573c92a
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649.4 kB | Download |
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md5:4f1a68e551524295f23767da57b77f57
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668.6 kB | Download |
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md5:f2a01b539c5bfe178020142921e505fb
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1.5 MB | Download |