Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning
This study explores machine learning for estimating soil moisture at multiple depths (0–5 cm, 0–10 cm, 0–20 cm, 0–50 cm, and 0–100 cm) across the coterminous United States. A framework is developed that integrates soil moisture from Soil Moisture Active Passive (SMAP), precipitation from the Global...
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| Hlavní autoři: | , |
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| Médium: | Artigo |
| Jazyk: | Inglês |
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MDPI AG
2024-10-01
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| Edice: | Remote Sensing |
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| On-line přístup: | https://www.mdpi.com/2072-4292/16/19/3699 |
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