Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/47347
Título: Convolutional Neural Networks for estimating spatially-distributed evapotranspiration
Autores/as: García-Pedrero, Angel
Lillo-Saavedra, Mario F.
Rodríguez-Esparragón, Dionisio 
Menasalvas, Ernestina
Gonzalo-Martin, Consuelo 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Energy-Balance
Remote
Model
Ndvi
Convolutional Neural Network, et al.
Fecha de publicación: 2017
Publicación seriada: Proceedings of SPIE - The International Society for Optical Engineering 
Conferencia: Conference on Image and Signal Processing for Remote Sensing XXIII 
Resumen: Efficient water management in agriculture requires an accurate estimation of evapotranspiration (ET). There are available several balance energy surface models that provide a daily ET estimation (ETd) spatially and temporarily distributed for different crops over wide areas. These models need infrared thermal spectral band (gathered from remotely sensors) to estimate sensible heat flux from the surface temperature. However, this spectral band is not available for most current operational remote sensors. Even though the good results provided by machine learning (ML) methods in many different areas, few works have applied these approaches for forecasting distributed ETd on space and time when aforementioned information is missing. However, these methods do not exploit the land surface characteristics and the relationships among land covers producing estimation errors. In this work, we have developed and evaluated a methodology that provides spatial distributed estimates of ETd without thermal information by means of Convolutional Neural Networks.
URI: http://hdl.handle.net/10553/47347
ISBN: 9781510613188
ISSN: 0277-786X
DOI: 10.1117/12.2278321
Fuente: Proceedings of SPIE - The International Society for Optical Engineering[ISSN 0277-786X],v. 10427 (2278321)
Colección:Actas de congresos
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