Publikationen von José Cortés

Zeitschriftenartikel (5)

1.
Zeitschriftenartikel
Panwar, A.; Migliavacca, M.; Nelson, J. A.; Cortés, J.; Bastos, A.; Forkel , M.; Winkler, A.: Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data. Scientific Reports 13, 13885 (2023)
2.
Zeitschriftenartikel
Cortés, J.; Mahecha, M. D.; Reichstein, M.; Myneni, R. B.; Chen, C.; Brenning, A.: Where are global vegetation greening and browning trends significant? Geophysical Research Letters 48 (6), e2020GL091496 (2021)
3.
Zeitschriftenartikel
Cortés, J.; Mahecha, M. D.; Reichstein, M.; Brenning, A.: Accounting for multiple testing in the analysis of spatio-temporal environmental data. Environmental and Ecological Statistics 27, S. 293 - 318 (2020)
4.
Zeitschriftenartikel
Kraft, B.; Jung, M.; Körner, M.; Requena Mesa, C.; Cortés, J.; Reichstein, M.: Identifying dynamic memory effects on vegetation state using recurrent neural networks. Frontiers in Big Data 2, 31 (2019)
5.
Zeitschriftenartikel
Zang, C. S.; Jochner-Oette, S.; Cortés, J.; Rammig, A.; Menzel, A.: Regional trend changes in recent surface warming. Climate Dynamics 52 (11), S. 6463 - 6473 (2019)

Konferenzbeitrag (1)

6.
Konferenzbeitrag
Benson, V.; Robin, C.; Requena Mesa, C.; Alonso, L.; Carvalhais, N.; Cortés, J.; Gao, Z.; Linscheid, N.; Weynants, M.; Reichstein, M.: Multi-modal learning for geospatial vegetation forecasting. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle , 17. Juni 2024 - 21. Juni 2024. (2024)

Hochschulschrift - Doktorarbeit (1)

7.
Hochschulschrift - Doktorarbeit
Cortés, J.: Novel approaches to spatio-temporal trend detection in environmental data. Dissertation, Friedrich Schiller University Jena, Jena (2024)

Preprint (1)

8.
Preprint
Benson, V.; Requena Mesa, C.; Robin, C.; Alonso, L.; Cortés, J.; Gao, Z.; Linscheid, N.; Weynants, M.; Reichstein, M.: Forecasting localized weather impacts on vegetation as seen from space with meteo-guided video prediction. arXiv (2023)
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