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Convolutional neural networks for estimating spatially distributed evapotranspiration
Author(s): Angel M. García-Pedrero; Consuelo Gonzalo-Martín; Mario F. Lillo-Saavedra; Dionisio Rodriguéz-Esparragón; Ernestina Menasalvas
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Paper Abstract

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.

Paper Details

Date Published: 4 October 2017
PDF: 9 pages
Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270P (4 October 2017); doi: 10.1117/12.2278321
Show Author Affiliations
Angel M. García-Pedrero, Univ. Politécnica de Madrid (Spain)
Consuelo Gonzalo-Martín, Univ. Politécnica de Madrid (Spain)
Mario F. Lillo-Saavedra, Univ. de Concepción (Chile)
Dionisio Rodriguéz-Esparragón, Univ. de Las Palmas de Gran Canaria (Spain)
Ernestina Menasalvas, Univ. Politécnica de Madrid (Spain)


Published in SPIE Proceedings Vol. 10427:
Image and Signal Processing for Remote Sensing XXIII
Lorenzo Bruzzone, Editor(s)

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