Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms

Due to its worldwide coverage and high revisit time, satellite-based remote sensing provides the ability to monitor in-season crop state variables and yields globally. In this study, we presented a novel approach to training agronomic satellite retrieval algorithms by utilizing collocated crop growt...

Descripción completa

Detalles Bibliográficos
Autores principales: Levitan, Nathaniel, Gross, Barry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6349260/
https://www.ncbi.nlm.nih.gov/pubmed/30701108
http://dx.doi.org/10.3390/rs10121968

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6349260/
https://www.ncbi.nlm.nih.gov/pubmed/30701108
http://dx.doi.org/10.3390/rs10121968

Ejemplares similares