Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring.
Brown LAI
Gaussian processes regression (GPR)
Green LAI
Machine learning
Photosynthetic and non-photosynthetic vegetation
Sentinel-2
Journal
Remote sensing of environment
ISSN: 0034-4257
Titre abrégé: Remote Sens Environ
Pays: United States
ID NLM: 101572538
Informations de publication
Date de publication:
15 Mar 2021
15 Mar 2021
Historique:
entrez:
5
9
2022
pubmed:
21
11
2020
medline:
21
11
2020
Statut:
epublish
Résumé
For agricultural applications, identification of non-photosynthetic above-ground vegetation is of great interest as it contributes to assess harvest practices, detecting crop residues or drought events, as well as to better predict the carbon, water and nutrients uptake. While the mapping of green Leaf Area Index (LAI) is well established, current operational retrieval models are not calibrated for LAI estimation over senescent, brown vegetation. This not only leads to an underestimation of LAI when crops are ripening, but is also a missed monitoring opportunity. The high spatial and temporal resolution of Sentinel-2 (S2) satellites constellation offers the possibility to estimate brown LAI (LAI
Identifiants
pubmed: 36060228
doi: 10.1016/j.rse.2020.112168
pmc: PMC7613486
mid: EMS152655
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : European Research Council
ID : 755617
Pays : International
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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