Published June 3, 2023
| Version v1
Dataset
Open
Datasets for "Reconstructing Global High Quality 3–day Surface Soil Moisture from ESA CCI and SMAP product from 2015 to 2021 using Conditional Variational Auto-Encoder"
Authors/Creators
- 1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences
- 2. School of Geography, Development and Environment, The University of Arizona
- 3. Natural Resources Aero-geophysical and Remote Sensing Center of China Geological Survey
- 4. School of Software, Beihang University
- 5. Haofu Cryptography Testing CO., LTD
Description
These are supporting datasets for the paper titled, "Reconstructing Global High Quality 3–day Surface Soil Moisture from ESA CCI and SMAP product during 2015–2021 using Conditional Variational Auto-Encoder"
Files
reconstructed_global_3d_soil_moisture_2015.zip
Files
(8.6 GB)
| Name | Size | |
|---|---|---|
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md5:bfbf7fc2e2024956b7b592cac531ded6
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938.4 MB | Preview Download |
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md5:4834061efb028d0f518c4fc02dc668b3
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1.3 GB | Preview Download |
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md5:af47a2e190f2582d022c946bf99368f3
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1.3 GB | Preview Download |
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md5:1a69633465be9af0dedeb2b8dc4a7900
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1.3 GB | Preview Download |
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md5:65dd2497b6e74a53756e3409cb4bc771
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1.3 GB | Preview Download |
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md5:5ba1915a5c00de245e55b73fa699c3f5
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1.3 GB | Preview Download |
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md5:7046a706c58b115685a4679253f988e6
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1.3 GB | Preview Download |