Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Laura Martínez-Ferrer"'
Autor:
Laura Martínez-Ferrer, Álvaro Moreno-Martínez, Jordi Muñoz-Marí, Hanna Meyer, Marvin Ludwig, Gustau Camps-Valls
Machine learning algorithms have become widely used for geospatial applications, including spatial mapping and upscaling ecological variables and traits. Multivariate splines, random forests, and neural networks have been widely used to upscale a few
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f15f2c31cc2f4ed6505d76d236821ec8
https://doi.org/10.5194/egusphere-egu23-5331
https://doi.org/10.5194/egusphere-egu23-5331
Autor:
Álvaro Moreno-Martínez, Laura Martínez-Ferrer, Jordi Muñoz-Marí, Emma Izquierdo-Verdiguier, John S. Kimball, Steven W. Running, Nicholas Clinton, Gustau Camps-Valls
Carbon captured via photosynthesis by vegetation is known as gross primary production (GPP). It is an important variable related to climate regulation and determines ecosystem carbon sources and sinks. GPP is routinely estimated globally by operation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d21574c8836f6b65fd8e07a4ff6c294
https://doi.org/10.5194/egusphere-egu23-7439
https://doi.org/10.5194/egusphere-egu23-7439
Autor:
Laura Martínez-Ferrer, Álvaro Moreno-Martínez, John S. Kimball, Steven W. Running, Nicholas Clinton, Gustau Camps-Valls
Gross primary production (GPP) represents the amount of carbon captured via vegetation photosynthesis, being this process one of the main drivers of climate regulation. Due to its importance, GPP is routinely estimated at global scales using differen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea1312ca83238dedb5f1865c36305e4e
https://doi.org/10.5194/egusphere-egu22-10248
https://doi.org/10.5194/egusphere-egu22-10248
Autor:
Laura Martínez-Ferrer, Álvaro Moreno-Martínez, Manuel Campos-Taberner, Francisco Javier García-Haro, Jordi Muñoz-Marí, Steven W. Running, John Kimball, Nicholas Clinton, Gustau Camps-Valls
Publikováno v:
Martínez-Ferrer, L., Moreno-Martínez, Á., Campos-Taberner, M., García-Haro, F. J., Muñoz-Marí, J., Running, S. W., ... & Camps-Valls, G. (2022). Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning. Remote Sensing of Environment, 280, 113199.
The estimation of biophysical variables is at the core of remote sensing science, allowing a close monitoring of crops and forests. Deriving temporally resolved and spatially explicit maps of parameters of interest has been the subject of intense res
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14e9134b28b257882b9ef9bdbc34278f
https://doi.org/10.1016/j.rse.2022.113199
https://doi.org/10.1016/j.rse.2022.113199
