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
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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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
- Ereignis
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Veröffentlichung
- (wo)
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Hannover
- (wer)
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Gottfried Wilhelm Leibniz Universität Hannover
- (wann)
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2023
- Ereignis
-
Veröffentlichung
- (wo)
-
Hannover
- (wer)
-
Technische Informationsbibliothek (TIB)
- (wann)
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2023
- Urheber
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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
- Rechteinformation
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Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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25.03.2025, 13:56 MEZ
Datenpartner
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Beteiligte
- Fathi, Mahdiyeh
- Shah-Hosseini, Reza
- Moghimi, Armin
- Gottfried Wilhelm Leibniz Universität Hannover
- Technische Informationsbibliothek (TIB)
Entstanden
- 2023
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