Time-Resolved Sentinel-3 Vegetation Indices Via Inter-Sensor 3-D Convolutional Regression Networks
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Altres documents de l'autoria: Fernandez-Beltran, Ruben; Ibáñez Fernández, Damián; kang, jian; Pla, Filiberto
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Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
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comunitat-uji-handle3:10234/43643
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INVESTIGACIONMetadades
Títol
Time-Resolved Sentinel-3 Vegetation Indices Via Inter-Sensor 3-D Convolutional Regression NetworksData de publicació
2021-09-03Editor
Institute of Electrical and Electronics EngineersISSN
1545-598X; 1558-0571Cita bibliogràfica
R. Fernandez-Beltran, D. Ibañez, J. Kang and F. Pla, "Time-Resolved Sentinel-3 Vegetation Indices Via Inter-Sensor 3-D Convolutional Regression Networks," in IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2021.3108856.Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859Versió
info:eu-repo/semantics/acceptedVersionParaules clau / Matèries
Resum
Sentinel missions provide widespread opportunities of exploiting inter-sensor synergies to improve the operational monitoring of terrestrial photosynthetic activity and canopy structural variations using vegetation ... [+]
Sentinel missions provide widespread opportunities of exploiting inter-sensor synergies to improve the operational monitoring of terrestrial photosynthetic activity and canopy structural variations using vegetation indices (VI). In this context, continuous and consistent temporal data are logically required to rapidly detect vegetation changes across sensors. Nonetheless, the existing temporal limitations inherent to satellite orbits, cloud occlusions, data degradation, and many other factors may severely constrain the availability of data involving multiple satellites. In response, this letter proposes a novel deep 3-D convolutional regression network (3CRN) for temporally enhancing Sentinel-3 (S3) VI by taking advantage of inter-sensor Sentinel-2 (S2) observations. Unlike existing regression and deep learning-based methods, the proposed approach allows convolutional kernels to slide across the temporal dimension to exploit not only the higher spatial resolution of the S2 instrument but also its own temporal evolution to better estimate time-resolved VI in S3. To validate the proposed approach, we built a database made of multiple day-synchronized S2 and S3 operational products from a study area in Extremadura (Spain). The conducted experimental comparison, including multiple state-of-the-art regression and deep learning models, shows the statistically significant advantages of the presented framework. The codes of this work will be made available at https://github.com/rufernan/3CRN. [-]
Entitat finançadora
Ministerio de Ciencia, Innovación y Universidades (Spain) | Generalitat Valenciana | National Natural Science Foundation of China
Codi del projecte o subvenció
RTI2018-098651-B-C54 | GV/2020/167 | 62101371
Drets d'accés
1545-598X © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
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info:eu-repo/semantics/openAccess
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