Open BenjMy opened 1 year ago
C. Lauvernet et al. https://hal.inrae.fr/hal-02608128
Usually, remote sensing data and sequences are under-used, though their content in information is very high (shapes evolution, correlations, . . . )
HR Images would also help to identify the landscape elements (grass strips, hedges,. . . )
In classical approaches: uncorrelated noise, diagonal error covariance matrices
How to provide observation error covariance matrices adapted to spatially correlated errors? [2]
Focusing on the observations operator description, and distances definition in the DA scheme
https://www.frontiersin.org/articles/10.3389/frwa.2022.948832/full
[2] Chabot, V. et al., 2015. Accounting for observation errors in image data assimilation. Tellus A; Vol 67 (2015).
Data Assimilation of images
C. Lauvernet et al. https://hal.inrae.fr/hal-02608128
Usually, remote sensing data and sequences are under-used, though their content in information is very high (shapes evolution, correlations, . . . )
HR Images would also help to identify the landscape elements (grass strips, hedges,. . . )
In classical approaches: uncorrelated noise, diagonal error covariance matrices
How to provide observation error covariance matrices adapted to spatially correlated errors? [2]
Focusing on the observations operator description, and distances definition in the DA scheme
https://www.frontiersin.org/articles/10.3389/frwa.2022.948832/full
[2] Chabot, V. et al., 2015. Accounting for observation errors in image data assimilation. Tellus A; Vol 67 (2015).
TO-DO