3D-ResNet-BiLSTM Model: A Deep Learning Model for County-Level Soybean Yield Prediction with Time-Series Sentinel-1, Sentinel-2 Imagery, and Daymet Data
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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In: Fathi, M.; Shah-Hosseini, R.; Moghimi, A.: 3D-ResNet-BiLSTM Model: A Deep Learning Model for County-Level Soybean Yield Prediction with Time-Series Sentinel-1, Sentinel-2 Imagery, and Daymet Data. In: Remote Sensing 15 (2023), Nr. 23, 5551. DOI: https://doi.org/10.3390/rs15235551
- Event
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Veröffentlichung
- (where)
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Hannover
- (who)
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Gottfried Wilhelm Leibniz Universität Hannover
- (when)
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2023
- Event
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Veröffentlichung
- (where)
-
Hannover
- (who)
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Technische Informationsbibliothek (TIB)
- (when)
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2023
- Creator
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Fathi, Mahdiyeh
Shah-Hosseini, Reza
Moghimi, Armin
- DOI
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10.15488/16899
- URN
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urn:nbn:de:101:1-2024041102252092299010
- Rights
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Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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25.03.2025, 1:56 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Fathi, Mahdiyeh
- Shah-Hosseini, Reza
- Moghimi, Armin
- Gottfried Wilhelm Leibniz Universität Hannover
- Technische Informationsbibliothek (TIB)
Time of origin
- 2023
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